Does the climate change integration into business strategy help in carbon emission reduction.
ChiSquare Tests
The table below shows the chisquare test between management incentive and carbon emission reduction. The Pearson ChiSquare value is 271.192^{a }with degree of freedom of 280 and pvalue 0.636. The Likelihood Ratio has a value of 260.832 with degree of freedom of 280 with pvalue 0.434. LinearbyLinear Association has a value of 0.613 with a degree of freedom of 1 and pvalue of 434.
Since from the table the pvalue is 0.636>0.05 at 95% confidence level it can be conclude that management incentive has no significant relationship with carbon emission reduction
ChiSquare Tests 

Value 
df 
Asymptotic Significance (2sided) 

Pearson ChiSquare 
271.192^{a} 
280 
.636 
Likelihood Ratio 
260.832 
280 
.788 
LinearbyLinear Association 
.613 
1 
.434 
N of Valid Cases 
387 

a. 558 cells (99.3%) have expected count less than 5. The minimum expected count is .17. 
The table below has the additional information on the Pearson correlation coefficient and Spearman correlation coefficient between management incentive and carbon emission reduction. The Pearson correlation coefficient has a value of 0.040 and asymptotic standardized error of 0.027. The Spearman’s correlation coefficient has a value of 0.27 and asymptotic standardized error of 0.054
Symmetric Measures 

Value 
Asymptotic Standardized Error^{a} 
Approximate T^{b} 
Approximate Significance 

Interval by Interval 
Pearson's R 
.040 
.027 
.783 
.434^{c} 
Ordinal by Ordinal 
Spearman Correlation 
.027 
.054 
.539 
.590^{c} 
N of Valid Cases 
387 

a. Not assuming the null hypothesis. 

b. Using the asymptotic standard error assuming the null hypothesis. 

c. Based on normal approximation. 
The table below shows the chisquare test between climate change integration and carbon emission reduction. The Pearson ChiSquare value is 260.168^{a} with degree of freedom of 280 and pvalue 0.797. The Likelihood Ratio has a value of 192.967with degree of freedom of 280 with pvalue 1.000. LinearbyLinear Association has a value of 0.445 with a degree of freedom of 1 and pvalue of 0.505.
Since from the table the pvalue is 0.797>0.05 at 95% confidence level it can be conclude that management incentive has no significant relationship with carbon emission reduction
ChiSquare Tests 

Value 
df 
Asymptotic Significance (2sided) 

Pearson ChiSquare 
260.168^{a} 
280 
.797 
Likelihood Ratio 
192.967 
280 
1.000 
LinearbyLinear Association 
.445 
1 
.505 
N of Valid Cases 
387 

a. 557 cells (99.1%) have expected count less than 5. The minimum expected count is .11. 
The table below has the additional information on the Pearson correlation coefficient and Spearman correlation coefficient between management incentive and carbon emission reduction. The Pearson correlation coefficient has a value of 0.034 and asymptotic standardized error of 0.019. The Spearman’s correlation coefficient has a value of 0.04 and asymptotic standardized error of 0.050
Symmetric Measures 

Value 
Asymptotic Standardized Error^{a} 
Approximate T^{b} 
Approximate Significance 

Interval by Interval 
Pearson's R 
.034 
.019 
.667 
.505^{c} 
Ordinal by Ordinal 
Spearman Correlation 
.004 
.050 
.079 
.937^{c} 
N of Valid Cases 
387 

a. Not assuming the null hypothesis. 

b. Using the asymptotic standard error assuming the null hypothesis. 

c. Based on normal approximation. 
ANOVA is the analysis of variance. ANOVA uses anova table to compare the means of two or more than two variables. In cases where we cannot use the Studentt distribution because we have more than two variables we apply the ANOVA test. Comparing for example the means of the countries, industries and carbon dioxide emissions
The regression analysis relate integration of climate change in business strategy and carbon emission
The table below shows the summary of ANOVA and regression test. Multiple R is 0.039849, R square is 0.001588, Adjusted R square is 0.00101 with a standard error of 56.37351.
SUMMARY OUTPUT 

Regression Statistics 

Multiple R 
0.039849 
R Square 
0.001588 
Adjusted R Square 
0.00101 
Standard Error 
56.37351 
Observations 
387 
The table below is an ANOVA table giving the columns for source of variation, degree of freedom, sum of squares, mean square F value. The regressed has degree of freedom of 1, sum of squares of 1945.922, mean square of 1945.922. The residual has a degree of freedom of 385, sum of squares of 1223519, mean square of 3177.972. The total sum of squares is 1225465 and degree of freedom of 386. The overall Fvalue is 0.612315.
Additional Information on Correlation Coefficient
Table 6. Analysis of variance table
ANOVA 

df 
SS 
MS 
F 
Significance F 

Regression 
1 
1945.922 
1945.922 
0.612315 
0.434398 

Residual 
385 
1223519 
3177.972 

Total 
386 
1225465 
The table below shows the intercept and coefficient of the regression model. The intercept is 7.15604 and coefficient is 5.9266. The intercept also has a standard error of 9.335796, t statistics of 0.766516 and pvalue of 0.443839. The x variable has a standard error of 7.573875, t statistics of 0.78251 and pvalue of 0.434398.
Table 7. Coefficient of Intercept and independent variable
Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 
Lower 95.0% 
Upper 95.0% 

Intercept 
7.15604 
9.335796 
0.766516 
0.443839 
11.1995 
25.51157 
11.1995 
25.51157 
X Variable 1 
5.9266 
7.573875 
0.78251 
0.434398 
20.8179 
8.964734 
20.8179 
8.964734 
The table below shows the ANOVA table. The table has on the column the source of variation, sum of square, degree of freedom, mean square, F value, pvalue and critical value of F. Rows has sum of squares of 612496.5, degree of freedom of 365, mean square of 1590.9, Fvalue of 0.999255 and pvalue of 0.502916 with critical value of F being 1.182778. The column has the sum of squares of 150.8793, degree of freedom of 1, mean square of 150.8793, Fvalue 0.094768 and pvalue of 0.758367 and the critical value of F to be 3.865725. The error or the residual has sum of squares of 612953.4, degree of freedom of 385, and mean square of 1592.087. The total sum of squares is 1225601 and total degree of freedom of 771.
The F value of the columns and rows are 0.999255 and 0.094768 respectively.
Table 8. Analysis of variance table
ANOVA 

Source of Variation 
SS 
df 
MS 
F 
Pvalue 
F crit 
Rows 
612496.5 
385 
1590.9 
0.999255 
0.502916 
1.182778 
Columns 
150.8793 
1 
150.8793 
0.094768 
0.758367 
3.865725 
Error 
612953.4 
385 
1592.087 

Total 
1225601 
771 
TTEST
Ttest is a statistical test used to compare means of the variables. It enable us to make conclusions based on the findings we get. For example, on the data in question about the percentage change of carbon dioxide emitted in metric tons, comparing the mean of the current emissions by the previous emissions can help us to establish if there is statistical difference in the means of the two seasons. Ttest uses the Studentt distribution table (Lyamin, 2011) (Dongming Zhu, 2010).
 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.
OneSample Statistics 

N 
Mean 
Std. Deviation 
Std. Error Mean 

Carbon emission reduction 
387 
.203385 
56.3451908 
2.8641870 
In the table below, the tvalue is 0.071, degree of freedom of 386, mean difference of 0.2033853 and confidence interval (5.427975, 5.83474) at 95% confidence interval
OneSample Test

From the above table of onesample statistics the mean is 0.203385
The pvalue on the second table is 0.943, this is higher than the reference confidence level of pvalue 0.05. We fail to reject the null hypothesis and conclude that Integration of climate change in business strategy has no significant relationship with carbon emission reduction
ANOVA Table
From the above findings it is shown that the highest number of factories emitting carbon dioxide in the atmosphere is from Japan at 51.9% with the least number of countries from Indonesia at only 0.5%. This is due to the fact that Japan is more developed than Indonesia and therefore, has more active industries.
From the industries observed, it be observed that many of them offer management incentives to the employees as 82.7% saying they offer incentives to the employees. It is also observed that majority of the industries agreeing by the fact that they integrate climate change in their business strategy as 89.4% agreeing that they integrate climate change in their business strategy.
From the analysis we observed that, there is no significant relationship between integrate climate change in the business strategy and the reduction of carbon dioxide emission. Many industries have adopted the integration but the implementation is lacking. This might be reason for the result of ttest analysis (Taeger, 2014) (Taeger, 2014) (Taeger, 2014).
 One of the major challenge of the research was finance. The research involved many countries which are far apart hence a lot of moving from one to another. This made the overreliance on secondary data.
 Calculate the size of the sample from such a huge data was not an easy task.
 The only countries whose effects have been observed here are only five. This cannot give a good representation of the contribution of countries to global climate change.
 Data cleaning from such big data posed a lot of challenges as it took time to finally come up with substantial data to analyze.
More research should be done on the same variables from other countries to observe the difference on how different countries handle the issue on emission. This can help to address some challenges other countries face in addressing the issue of carbon dioxide emission and how those challenges can be tackled (I., 2012) (MABE FRANKLIN NANTUI, 2012) (OGUNBAMERU B.O., 2013) (PANOV VIKTOR I., 2011)..
Further research should be conducted on the different kinds of knowledge and specifically engage on ecological, social, physical, and health science.
Research touching on the implications of certain choices across scales and sectors to optimize the benefits and bring understanding net effects on different scales of decision making.
Further researches can focus the mechanisms which are flexible in identifying and addresses new scientific challenges emerging in the daily basis. It should also focus on the interaction by the decision makers because of the dynamic needs. More on monitoring, projecting and assessing the change in climate (Andre J. C., 2005) (Brönnimann, 2015) (Brönnimann, 2015)
References
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.
Ballou,b, 2015. exploring the strategic integration of sustainbility. 26(sustanable development ), pp. 265288.
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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