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ANOVA table comparing board of directors and carbon emissions

Data analysis –inferential

At this section of the report we are going apply two methods of analysis and that is; t-test and ANOVA.

ANOVA table can be used to compare means of more than two population compared to t-test.

  1. Board of directors versus carbon emission reduction

The table below shows ANOVA table comparing board of director as independent variable and carbon emission on the environment as dependent variable.

The table gives the columns for source of variation, degree of freedom, sum of squares, mean square F value. The degree of freedom of regression is 1, sum of squares of regression is 1347.633, and mean square of regression is 1347.633. The degree of freedom of residuals or error is 1701, sum of squares of residual is 6286205.036 and the mean square is 3695.594. The total degree of freedom is 1702, total sum of squares is 6287552.668. The F-value of the test is 365.

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

1347.633

1

1347.633

.365

.546b

Residual

6286205.036

1701

3695.594

Total

6287552.668

1702

 

The table below shows the coefficients of the independent variables. The coefficient of board of directors is -5.375 and intercept is 11.012.

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

11.012

17.612

.625

.532

Board of directors

-5.375

8.901

-.015

-.604

.546

The table below shows the ANOVA table comparing the independent variables; board of directors and management incentives and dependent variable carbon emission.

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

6133.784

2

3066.892

.830

.436b

Residual

6281418.885

1700

3694.952

Total

6287552.668

1702

 

The table below shows the coefficients of the independent variables; board of directors and management incentives

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

.853

19.744

.043

.966

Board of directors

-2.764

9.191

-.008

-.301

.764

Management incentives

4.026

3.537

.028

1.138

.255

  1. Board responsible and board of directors versus carbon emission reduction

The table below shows ANOVA table comparing board of director and board responsible as independent variables and carbon emission on the environment as dependent variable.

The table gives the columns for source of variation, degree of freedom, sum of squares, mean square F value. The degree of freedom of regression is 2, sum of squares of regression is 3476.894, and mean square of regression is 1738.447. The degree of freedom of residuals or error is 1700, sum of squares of residual is 6284075.774 and the mean square is 3696.515. The total degree of freedom is 1702, total sum of squares is 6287552.668. The F-value of the test is 0.470.

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

3476.894

2

1738.447

.470

.625b

Residual

6284075.774

1700

3696.515

Total

6287552.668

1702

a. Dependent Variable: carbon emission reduction

b. Predictors: (Constant), Board of directors, Board responsible

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

-1.340

23.983

-.056

.955

Board responsible

-2.459

9.696

-.007

-.254

.800

Board of directors

2.359

3.108

.020

.759

.448

a. Dependent Variable: Carbon emission reduction

  1. Board responsible versus carbon emission

The table below shows ANOVA table comparing board responsible as independent variable and carbon emission on the environment as dependent variable.

The table gives the columns for source of variation, degree of freedom, sum of squares, mean square F value. The degree of freedom of regression is 1, sum of squares of regression is 1347.633, and mean square of regression is 1347.633. The degree of freedom of residuals or error is 1701, sum of squares of residual is 6286205.036 and the mean square is 3695.594. The total degree of freedom is 1702, total sum of squares is 6287552.668. The F-value of the test is 365.

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

1347.633

1

1347.633

.365

.546b

Residual

6286205.036

1701

3695.594

Total

6287552.668

1702

a. Dependent Variable: carbon emission

b. Predictors: (Constant), board responsible

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

11.012

17.612

.625

.532

Board responsible

-5.375

8.901

-.015

-.604

.546

a. Dependent Variable: Carbon emission reduction

  1. Incentive targets versus carbon emission reduction

The table below shows ANOVA table comparing incentive targets as independent variable and carbon emission on the environment as dependent variable.

The table gives the columns for source of variation, degree of freedom, sum of squares, mean square F value. The degree of freedom of regression is 1, sum of squares of regression is 5799.511, and mean square of regression is 5799.511. The degree of freedom of residuals or error is 1701, sum of squares of residual is 6281753.158 and the mean square is 3692.977. The total degree of freedom is 1702, total sum of squares is 6287552.668. The F-value of the test is 1.570

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

5799.511

1

5799.511

1.570

.210b

Residual

6281753.158

1701

3692.977

Total

6287552.668

1702

a. Dependent Variable: carbon emission

b. Predictors: (Constant), Incentive targets

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

-4.928

4.510

-1.093

.275

Incentives targets

4.292

3.425

.030

1.253

.210

a. Dependent Variable: Carbon emission

  1. T-TEST

Coefficients of the independent variables

T-test is a statistical test used to compare means of the variables. It enable us to make conclusions based on the findings we get.  (Lyamin, 2011) (Dongming Zhu, 2010).

Hypothesis testing

T-TEST

The table below gives the sample summary. Sample size is 1703, the mean of 0.4141 and standard deviation of 60.78005 and standard error mean 1.47283.

One-Sample Statistics

N

Mean

Std. Deviation

Std. Error Mean

Carbon dioxide emission reduction

1703

.4141

60.78005

1.47283

The table below shows t value of 0.281, degree of freedom of 1702, sig. (2-tailed) which is the p value of 0.41406 and confidence of (-2.4747, 3.3028).

One-Sample Test

Test Value = 0

t

df

Sig. (2-tailed)

Mean Difference

95% Confidence Interval of the Difference

Lower

Upper

Carbon dioxide emission reduction

.281

1702

.779

.41406

-2.4747

3.3028

Hypothesis:

H0: Integration of climate change in business strategy has no significant relationship with carbon emission reduction.

H1: Integration of climate change in business strategy has significant relationship with carbon emission reduction.

From the table, the p-value is 0.779 which is higher than the reference p-value of 0.05. We therefore, fail to reject the null hypothesis and conclude that board of directors has no significant relationship with carbon emission reduction.

Discussion

The main objective was to find if there is relationship between Board responsible and the carbon emission. There are also some other specific objects like if there is any relationship between board of directors and carbon emission and the relationship between incentive targets and carbon emission.  

From the findings indeed we find that there is relationships existing among those variables. For example, board of directors can influence the emission of carbon in many ways from the firms they board. Facilitating the adoption and the implementation of policies of a certain firm lies in the hand of board of directors. By studying on how different board of directors all over the world are influencing the reduction of carbon emission, some board of directors from other can countries can copy to achieve the global reduction of carbon emission. Apart from the board directors, we have the board responsible. These could be the people among the board of directors who are specifically have the role of ensuring that the release of carbon. They may come up with specific implementations which will ensure that carbon emission is reduced as much as possible. They are involved in the real action making the effective for the main goal. From our test statistics, it is realized that there is no significant relationship between board responsible and the carbon emission. This might have been contributed with some gaps which this particular study is trying to address. Some countries across the world might have adopted the issue of board responsible for carbon emission while some might have not, or they have adopt it in ways that it doesn’t work for them. The government or the firm’s management may take the initiative to copy from the ones who have adopted it and worked.

The incentives target, one of the policies adopted by some firms, encourage more the employees hence they become aggressive to achieve the goal of the firm. If climate change by reducing emission of carbon is one of the strategy, management incentive will help achieve this.

The standard deviation from the t-test analysis of carbon emission reduction we realized that the standard deviation of the mean is very large. This means from the data that as much as many firms trying to reduce the carbon emission, some are reducing it at a slower rate. The report can encourage the firms who have reduce to reduce even more to zero emission as other too to continue reducing to level zero too.

ANOVA table comparing board of directors, management incentives, and carbon emissions

Limitations to the study 

There are some challenges encountered during the collection and analysis of data. The data was collected from a secondary source since it would have been very expensive to get such kinds of data at a primary level. This made it very difficult to come up with the variables which specifically address our main objective. Developing a sample size which would represent the whole massive of data was very difficult to come up. Coming up with proper statistical tool for data analysis was not easy since the data we were using was mixture qualitative and quantitative data. Cleaning data wasted a lot of time as sometimes, it makes some vital values disappearing making the clerks start all over again.

Being limited by the number of countries to choose from did not offer the opportunity to study as much as possible many countries. Comparison from many countries could bring good conclusion on what to be done and what not to be done to reduce carbon emission 

Further Research

More research need to be done to address some of the challenges we encountered so that they don’t be the barriers to the future researchers. More research should be conducted frequently on the issue of climate change to see the progress and adoption of different types of carbon emission reduction strategies of different firms all over the world. This will enable evaluation of the progress of carbon emission reduction. To avoid struggling in choosing the proper tool for statistical analysis, the researcher can do research on climate change data which are quantitative to ease the choice of a statistical tool.

References

Adesiji G.B., B. S. T. I., 2013. Effects Of Climate Change On Poultry Production. p. 6.

Andre J. C., M. M. L. J. P., 2005. From GICC, the French research programme on management and impacts of climate change, to circle, a coordinated European initiative including Russia. p. 7.

Anon., n.d.

Ballou,b, 2015. exploring the strategic integration of sustainbility. 26(sustanable development ), pp. 265-288.

Brönnimann, S., 2015. [Advances in Global Change Research] Climatic Changes Since 1700 Volume 55 || Climatic Changes Since 1700. p. 155.

Brönnimann, S., 2015. [Advances in Global Change Research] Climatic Changes Since 1700 Volume 55 || The Machinery: Mechanisms Behind Climatic Changes. p. 96.

Dongming Zhu, J. W. G., 2010. A generalized asymmetric Student- distribution with application to financial econometrics.

I., D. L., 2012. Cr And Other Space Climate Factors Influenced On The Earth's Climate Change. DORMAN LEV I., p. 11.

Lyamin, O. O., 2011. On the rate of convergence of the distributions of certain statistics to the Laplace and student distributions.

Mabe Franklin Nantui, S. D. B. O.-A. Y., 2012. Adaptive Capacities Of Farmers To Climate Change Adaptation Strategies And Their Effects On Rice Production In The Northern Region Of Ghana. p. 9.

Ogunbameru B.O., M. S. I. Y., 2013. Capacity Building For Climate Change Adaptation: Modules For Agricultural Extension Curriculum Development. p. 6.

Panov Viktor I., K. S. R., 2011. Climate Change And The Ecological Psychology. p. 12.

Taeger, D. K. S., 2014. Statistical Hypothesis Testing with SAS and R (Taeger/Statistical Hypothesis Testing with SAS and R) || Statistical hypothesis testing. p. 14.

Taeger, D. K. S., 2014. Statistical Hypothesis Testing with SAS and R (Taeger/Statistical Hypothesis Testing with SAS and R) || Tests on the Mean. p. 17.

Taeger, D. K. S., 2014. Statistical Hypothesis Testing with SAS and R (Taeger/Statistical Hypothesis Testing with SAS and R) || Tests on the variance. p. 12.

Wiley, 2011. british journal of management. What Makes Better Boards? A Closer Look at Diversity and Ownership.

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