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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; ttest and ANOVA.
ANOVA table can be used to compare means of more than two population compared to ttest.
 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 Fvalue of the test is 365.
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
1347.633 
1 
1347.633 
.365 
.546^{b} 
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.
Coefficients^{a} 

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.
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
6133.784 
2 
3066.892 
.830 
.436^{b} 
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
Coefficients^{a} 

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 
 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 Fvalue of the test is 0.470.
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
3476.894 
2 
1738.447 
.470 
.625^{b} 
Residual 
6284075.774 
1700 
3696.515 

Total 
6287552.668 
1702 

a. Dependent Variable: carbon emission reduction 

b. Predictors: (Constant), Board of directors, Board responsible 
Coefficients^{a} 

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 
 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 Fvalue of the test is 365.
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
1347.633 
1 
1347.633 
.365 
.546^{b} 
Residual 
6286205.036 
1701 
3695.594 

Total 
6287552.668 
1702 

a. Dependent Variable: carbon emission 

b. Predictors: (Constant), board responsible 
Coefficients^{a} 

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 
 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 Fvalue of the test is 1.570
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
5799.511 
1 
5799.511 
1.570 
.210^{b} 
Residual 
6281753.158 
1701 
3692.977 

Total 
6287552.668 
1702 

a. Dependent Variable: carbon emission 

b. Predictors: (Constant), Incentive targets 
Coefficients^{a} 

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 
 TTEST
Coefficients of the independent variables
Ttest 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
TTEST
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.
OneSample 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. (2tailed) which is the p value of 0.41406 and confidence of (2.4747, 3.3028).
OneSample Test 

Test Value = 0 

t 
df 
Sig. (2tailed) 
Mean Difference 
95% Confidence Interval of the Difference 

Lower 
Upper 

Carbon dioxide emission reduction 
.281 
1702 
.779 
.41406 
2.4747 
3.3028 
Hypothesis:
H_{0}: Integration of climate change in business strategy has no significant relationship with carbon emission reduction.
H_{1}: Integration of climate change in business strategy has significant relationship with carbon emission reduction.
From the table, the pvalue is 0.779 which is higher than the reference pvalue 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 ttest 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.
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