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Data collection

Data collection is one of the most important part of the every research project. In this case the

data has been collected from 60 different firms which are based on different countries all over

the world. The data was extracted from the master data set using the random sampling method,

which is one of the most popular method for the sample selection. The data set has been provided

in separate excel sheet. The data cleaning process has been completed before importing the data

in the SPSS, such as the removal of the variable which are not required, addressing the missing

values and also coding the variables which are in string form. Results from the data analysis are

discussed in the next section.

Data analysis (Descriptive)

Results from the descriptive statistics helps the researcher to know more about the data in more

meaningful way. The most measures of the descriptive statistics are the median, mode, mean,

variance, standard deviation. Similarly the kurtosis and skewness helps to understand the

distribution of the data.

The results for the dependent variable (the disclosure score) shows that mean score is 93.31,

which is a very good score as the maximum value is 100. The median is much higher than the

mean value, similarly the mode value of 100 shows that 100 is the most frequent score in the

data set. The standard deviation of 14 suggests that the variation in the data set is not very high.

The minimum and maximum value are 0 and 100 respectively, which are also the two extreme

points.

Similarly the descriptive statistics for the independent variables, which are the factors related to

the decision board for the carbon disclosure, are also shown in the table below. The mean score

for IV1 is 270. 12 with standard deviation of 814. This suggests that there is high variation

among the firms for the second independent variable. Results for the other variables can also be

interpreted similarly.

Statistics

Disclosure

score

IV1 IV2 IV4

N Valid 60 54 54 54

Missing 0 6 6 6

Mean 93.3167 270.1290 1.1852 1.3519

Median 97.5000 11.5000 1.0000 1.0000

Mode 100.00 .00 1.00 2.00

Std. Deviation 14.83639 814.62752 .64644 .70463

Variance 220.118 663617.999 .418 .497

Data collection is one of the most important part of the every research project. In this case the

data has been collected from 60 different firms which are based on different countries all over

the world. The data was extracted from the master data set using the random sampling method,

which is one of the most popular method for the sample selection. The data set has been provided

in separate excel sheet. The data cleaning process has been completed before importing the data

in the SPSS, such as the removal of the variable which are not required, addressing the missing

values and also coding the variables which are in string form. Results from the data analysis are

discussed in the next section.

Data analysis (Descriptive)

Results from the descriptive statistics helps the researcher to know more about the data in more

meaningful way. The most measures of the descriptive statistics are the median, mode, mean,

variance, standard deviation. Similarly the kurtosis and skewness helps to understand the

distribution of the data.

The results for the dependent variable (the disclosure score) shows that mean score is 93.31,

which is a very good score as the maximum value is 100. The median is much higher than the

mean value, similarly the mode value of 100 shows that 100 is the most frequent score in the

data set. The standard deviation of 14 suggests that the variation in the data set is not very high.

The minimum and maximum value are 0 and 100 respectively, which are also the two extreme

points.

Similarly the descriptive statistics for the independent variables, which are the factors related to

the decision board for the carbon disclosure, are also shown in the table below. The mean score

for IV1 is 270. 12 with standard deviation of 814. This suggests that there is high variation

among the firms for the second independent variable. Results for the other variables can also be

interpreted similarly.

Statistics

Disclosure

score

IV1 IV2 IV4

N Valid 60 54 54 54

Missing 0 6 6 6

Mean 93.3167 270.1290 1.1852 1.3519

Median 97.5000 11.5000 1.0000 1.0000

Mode 100.00 .00 1.00 2.00

Std. Deviation 14.83639 814.62752 .64644 .70463

Variance 220.118 663617.999 .418 .497

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Skewness -4.820 3.913 -.192 -.623

Std. Error of

Skewness

.309 .325 .325 .325

Kurtosis 27.344 15.472 -.595 -.753

Std. Error of Kurtosis .608 .639 .639 .639

Minimum .00 .00 .00 .00

Maximum 100.00 3981.00 2.00 2.00

Percentiles

25 94.0000 .0293 1.0000 1.0000

50 97.5000 11.5000 1.0000 1.0000

75 99.7500 74.0750 2.0000 2.0000

Histograms

Std. Error of

Skewness

.309 .325 .325 .325

Kurtosis 27.344 15.472 -.595 -.753

Std. Error of Kurtosis .608 .639 .639 .639

Minimum .00 .00 .00 .00

Maximum 100.00 3981.00 2.00 2.00

Percentiles

25 94.0000 .0293 1.0000 1.0000

50 97.5000 11.5000 1.0000 1.0000

75 99.7500 74.0750 2.0000 2.0000

Histograms

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To examine the distribution and the skewness of the variables included in the data set. The

histogram of the dependent variable shows that the data is right skewed as most of the data lies

on the right side of the average value. Similarly the first independent variable is left skewed

indicating most of the data points are to the left of the mean value. However the second and the

third independent variable are normally distributed as the histogram are bell shaped. In case of

the normal distribution most of the data points lies around the mean value.

histogram of the dependent variable shows that the data is right skewed as most of the data lies

on the right side of the average value. Similarly the first independent variable is left skewed

indicating most of the data points are to the left of the mean value. However the second and the

third independent variable are normally distributed as the histogram are bell shaped. In case of

the normal distribution most of the data points lies around the mean value.

The tabular descriptive statistics was for the continuous variable. In case of the categorical

variable the pie chart has been used to show the descriptive results. As shown in the figure

below, most of the countries are from the United States. The proportion of firms from The

United Kingdom is 18 %. The proportion of firms from other countries are also given in the

figure.

32%

18%

8%

8%

33%

Country

USA

United Kingdom

Germany

Japan

others

The sector wise distribution of the firms show that the firms in the Information technology are of

20 % and also the consumer discretionary firms have 20 % presence in the data set. Similarly the

proportion for the other industry are also given in the figure below.

20%

20%

15%

12%

12%

22%

Sector

IT

Industrials

Materials

Health care

Consumer Discretionary

Others

variable the pie chart has been used to show the descriptive results. As shown in the figure

below, most of the countries are from the United States. The proportion of firms from The

United Kingdom is 18 %. The proportion of firms from other countries are also given in the

figure.

32%

18%

8%

8%

33%

Country

USA

United Kingdom

Germany

Japan

others

The sector wise distribution of the firms show that the firms in the Information technology are of

20 % and also the consumer discretionary firms have 20 % presence in the data set. Similarly the

proportion for the other industry are also given in the figure below.

20%

20%

15%

12%

12%

22%

Sector

IT

Industrials

Materials

Health care

Consumer Discretionary

Others

One of the categorical variable was to measure whether the disclosure was public or not. Results

from the analysis shows that the 98 % of the firms made their score public whereas only 2 % of

the firms do not make it public.

98%

2%

Disclosure public or not

Yes

No

Furthermore in case of the second independent variable the figure shows that the for 68 % of the

firm the metric denominator for the emission is full time whereas for other firms it is some other

form of measurement.

68%

32%

IV2

full time equivalent (FTE)

employee

Other

from the analysis shows that the 98 % of the firms made their score public whereas only 2 % of

the firms do not make it public.

98%

2%

Disclosure public or not

Yes

No

Furthermore in case of the second independent variable the figure shows that the for 68 % of the

firm the metric denominator for the emission is full time whereas for other firms it is some other

form of measurement.

68%

32%

IV2

full time equivalent (FTE)

employee

Other

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Data analysis (inferential)

This section is devoted to the inferential analysis for the current research paper. Inferential

analysis are used to test the hypothesis which are proposed by the researcher at the beginning of

the project. In this case also the inferential analysis has been conducted using the chi square test,

correlation analysis and the regression analysis.

Country

The chi square test is conducted to find if there is any statistically significant difference in the

mean value for the different categories. In this case the chi square has been conducted to

examine to test the mean score of the firms in different countries.

As shown in the table below the chi statistics is statistically significant at 5 % as the p value is

less than 0.05. So the null hypothesis can be rejected. So it can be concluded that there is

significant difference in the disclosure value for firms in different countries.

Chi-Square Tests

Value df Asymp. Sig.

(2-sided)

Pearson Chi-Square 78.222a 52 .011

Likelihood Ratio 71.091 52 .040

Linear-by-Linear

Association

.617 1 .432

N of Valid Cases 60

a. 69 cells (98.6%) have expected count less than 5. The

minimum expected count is .08.

Sector

The sector wise chi square value is also statistically significant as shown in the table below. On

the basis of the results it can be concluded that there is difference in the disclosure score for

firms in the different sector.

Chi-Square Tests

Value df Asymp. Sig.

(2-sided)

Pearson Chi-Square 105.108a 65 .001

This section is devoted to the inferential analysis for the current research paper. Inferential

analysis are used to test the hypothesis which are proposed by the researcher at the beginning of

the project. In this case also the inferential analysis has been conducted using the chi square test,

correlation analysis and the regression analysis.

Country

The chi square test is conducted to find if there is any statistically significant difference in the

mean value for the different categories. In this case the chi square has been conducted to

examine to test the mean score of the firms in different countries.

As shown in the table below the chi statistics is statistically significant at 5 % as the p value is

less than 0.05. So the null hypothesis can be rejected. So it can be concluded that there is

significant difference in the disclosure value for firms in different countries.

Chi-Square Tests

Value df Asymp. Sig.

(2-sided)

Pearson Chi-Square 78.222a 52 .011

Likelihood Ratio 71.091 52 .040

Linear-by-Linear

Association

.617 1 .432

N of Valid Cases 60

a. 69 cells (98.6%) have expected count less than 5. The

minimum expected count is .08.

Sector

The sector wise chi square value is also statistically significant as shown in the table below. On

the basis of the results it can be concluded that there is difference in the disclosure score for

firms in the different sector.

Chi-Square Tests

Value df Asymp. Sig.

(2-sided)

Pearson Chi-Square 105.108a 65 .001

Likelihood Ratio 98.513 65 .005

Linear-by-Linear

Association

1.545 1 .214

N of Valid Cases 60

a. 84 cells (100.0%) have expected count less than 5. The

minimum expected count is .12.

Correlation analysis

Results from the correlation analysis between the variables included in the data set shows that

the dependent variable is negatively correlated with the first independent variable. However for

all other independent variable the correlation is positive and for most of them the correlation is

statistically significant also. Since the correlation are significant, further analysis can be

conducted.

Correlations

Disclosure

score

IV1 IV2 IV3 IV4 IV5 I

Disclosure score

Pearson Correlation 1 -.015 .495** .154 .311* .272* .

Sig. (2-tailed) .912 .000 .266 .022 .046 .

N 60 54 54 54 54 54 5

IV1

Pearson Correlation -.015 1 .045 -.081 .293* -.064 -

Sig. (2-tailed) .912 .748 .560 .031 .644 .

N 54 54 54 54 54 54 5

IV2

Pearson Correlation .495** .045 1 .321* .600** .471** .

Sig. (2-tailed) .000 .748 .018 .000 .000 .

N 54 54 54 54 54 54 5

IV3

Pearson Correlation .154 -.081 .321* 1 .178 .298* -

Sig. (2-tailed) .266 .560 .018 .199 .029 .

N 54 54 54 54 54 54 5

IV4

Pearson Correlation .311* .293* .600** .178 1 .285* .

Sig. (2-tailed) .022 .031 .000 .199 .037 .

N 54 54 54 54 54 54 5

IV5

Pearson Correlation .272* -.064 .471** .298* .285* 1 .

Sig. (2-tailed) .046 .644 .000 .029 .037 .

N 54 54 54 54 54 54 5

IV6 Pearson Correlation .243 -.005 .350** -.048 .383** .064 1

Linear-by-Linear

Association

1.545 1 .214

N of Valid Cases 60

a. 84 cells (100.0%) have expected count less than 5. The

minimum expected count is .12.

Correlation analysis

Results from the correlation analysis between the variables included in the data set shows that

the dependent variable is negatively correlated with the first independent variable. However for

all other independent variable the correlation is positive and for most of them the correlation is

statistically significant also. Since the correlation are significant, further analysis can be

conducted.

Correlations

Disclosure

score

IV1 IV2 IV3 IV4 IV5 I

Disclosure score

Pearson Correlation 1 -.015 .495** .154 .311* .272* .

Sig. (2-tailed) .912 .000 .266 .022 .046 .

N 60 54 54 54 54 54 5

IV1

Pearson Correlation -.015 1 .045 -.081 .293* -.064 -

Sig. (2-tailed) .912 .748 .560 .031 .644 .

N 54 54 54 54 54 54 5

IV2

Pearson Correlation .495** .045 1 .321* .600** .471** .

Sig. (2-tailed) .000 .748 .018 .000 .000 .

N 54 54 54 54 54 54 5

IV3

Pearson Correlation .154 -.081 .321* 1 .178 .298* -

Sig. (2-tailed) .266 .560 .018 .199 .029 .

N 54 54 54 54 54 54 5

IV4

Pearson Correlation .311* .293* .600** .178 1 .285* .

Sig. (2-tailed) .022 .031 .000 .199 .037 .

N 54 54 54 54 54 54 5

IV5

Pearson Correlation .272* -.064 .471** .298* .285* 1 .

Sig. (2-tailed) .046 .644 .000 .029 .037 .

N 54 54 54 54 54 54 5

IV6 Pearson Correlation .243 -.005 .350** -.048 .383** .064 1

Sig. (2-tailed) .077 .972 .010 .731 .004 .644

N 54 54 54 54 54 54 5

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Regression

The impact of the independent variable on the dependent variable is measured by the regression

analysis. In this case the regression model has been performed using the dependent variable,

independent variable along with the control variable.

Model Summary

Model R R Square Adjusted R

Square

Std. Error of

the Estimate

1 .580a .336 .218 6.25358

a. Predictors: (Constant), IV6, Country, IV1, IV5, IV3,

Sector, IV4, IV2

The results from the model summary indicates that the 33 % of the variation in the disclosure

score is explained by the independent and the control variable. The adjusted R squared is less

than the R squared as expected.

ANOVAa

Model Sum of

Squares

df Mean Square F Sig.

1

Regression 891.210 8 111.401 2.849 .012b

Residual 1759.827 45 39.107

Total 2651.037 53

a. Dependent Variable: Disclosure score

b. Predictors: (Constant), IV6, Country, IV1, IV5, IV3, Sector, IV4, IV2

N 54 54 54 54 54 54 5

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Regression

The impact of the independent variable on the dependent variable is measured by the regression

analysis. In this case the regression model has been performed using the dependent variable,

independent variable along with the control variable.

Model Summary

Model R R Square Adjusted R

Square

Std. Error of

the Estimate

1 .580a .336 .218 6.25358

a. Predictors: (Constant), IV6, Country, IV1, IV5, IV3,

Sector, IV4, IV2

The results from the model summary indicates that the 33 % of the variation in the disclosure

score is explained by the independent and the control variable. The adjusted R squared is less

than the R squared as expected.

ANOVAa

Model Sum of

Squares

df Mean Square F Sig.

1

Regression 891.210 8 111.401 2.849 .012b

Residual 1759.827 45 39.107

Total 2651.037 53

a. Dependent Variable: Disclosure score

b. Predictors: (Constant), IV6, Country, IV1, IV5, IV3, Sector, IV4, IV2

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The ANOVA table indicates that the F statistics is also significant at 5 % indicating the

cumulative impact of the independent variables is also robust. In other words there is at least one

independent variable which has non zero regression coefficient.

Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) 90.883 2.781 32.675 .000

Country -1.388 .593 -.331 -2.341 .024

Sector .516 .589 .133 .876 .386

IV1 .000 .001 .054 .384 .702

IV2 3.200 2.005 .293 1.596 .117

IV3 1.275E-008 .000 .048 .336 .738

IV4 .860 1.705 .086 .505 .616

IV5 .137 .130 .155 1.051 .299

IV6 .502 1.465 .051 .343 .733

a. Dependent Variable: Disclosure score

Results from the coefficient table suggests that that the all the independent variables have

positive impact on the dependent variable as the regression coefficient for all the variables are

positive. In case of the control variable the coefficient of country is negative. The coefficient of

the first independent variable is also very low.

cumulative impact of the independent variables is also robust. In other words there is at least one

independent variable which has non zero regression coefficient.

Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) 90.883 2.781 32.675 .000

Country -1.388 .593 -.331 -2.341 .024

Sector .516 .589 .133 .876 .386

IV1 .000 .001 .054 .384 .702

IV2 3.200 2.005 .293 1.596 .117

IV3 1.275E-008 .000 .048 .336 .738

IV4 .860 1.705 .086 .505 .616

IV5 .137 .130 .155 1.051 .299

IV6 .502 1.465 .051 .343 .733

a. Dependent Variable: Disclosure score

Results from the coefficient table suggests that that the all the independent variables have

positive impact on the dependent variable as the regression coefficient for all the variables are

positive. In case of the control variable the coefficient of country is negative. The coefficient of

the first independent variable is also very low.

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