The Role Of Management Responsibility In Reducing Carbon Emissions

Management Responsibility in Organizations

This paper talks about does higher influence of Management’s Responsibility for climate change within an Organization helps to reduce carbon emission. Management are ones who handle organizations. Managers and Board of Director (BOD) are expected to reduce emission in the organization and provide awareness of the physical, political and social risks originate from the impacts of climate change (Wittneben and Kiyar, 2018)

Carbon emissions are increasing rapidly in developing countries because of the international climate change mitigation treaty of Kyoto Protocol. There is increase from 33% to 40% in 1990 and 1997 respectively. Kyoto Protocol is negotiated in 1997 and it commits signatories to achieve specific greenhouse gas (GHG) or carbon emission reduction targets (Kalu, Buang and Aliagha, 2016). The four-determinant factor such as Social factor, economic factor, the financial market factor and the institutional factor are included for carbon emissions reduction. Because of the attendant implication of climate disasters developing countries like India, China, Colombia, Malaysia and others must face increasing rate of carbon emissions which can be move up to 70%. Due to this, developing country should make contribution in any form of mitigation program for the achievement of success (Kalu, Buang and Aliagha, 2016).

Management responsibilities are one due to which there will be reduction in carbon emission. Due to the proper management actions it helps to reduce carbon emission and helps to observe how company can respond pressures in a global organization (Lane, 2010). If there will be explore of management actions by the Board of Director or by the manager of the organization related with air travel of employees, by drawing how managerial practice can affect organization’s environmental impact (Fraser, 2012). In London, management introduce an internal EMCS to reduce carbon emission for Beta’s Company. In that analysis they also compare that whether management incentives influence travellers or not due to which there will be reduction in carbon emission. When management actions were taken in the Beta’s company by following the steps like understanding the significant clients, identifying travellers in terms of carbon impact, producing carbon impact statement with individual traveller, identifying significant travel routes in terms of carbon impact, to reduce journey work with top travellers, by introducing incentive scheme to encourage travellers, increasing the awareness as well as use of technology and lastly improving use of communication channels there was reduction in carbon emission. Therefore, due to management responsibility in an organization by following management incentive there will be reduction in carbon emission (Giacomo, Guthrie and Farneti, 2017).

Carbon Emissions and the Kyoto Protocol

Stakeholders are the individuals who can affect the firm or who the actions of the corporation can affect. Stakeholders comprise the workers, clients, competitors, and creditors. The stakeholder theory posits that the corporations are responsible to not only shareholders but to a group of stakeholders, which is broad (Bridoux and Stoelhorst, 2014). According to Bridoux and Stoelhorst (2014), Edward Freeman is the one who proposed the stakeholder theory. Edward Freeman acknowledged the theory as an essential component of Corporate Social Responsibility. Corporate social responsibility (CSR) identifies the companies’ responsibilities, which are philanthropic, ethical, and economic. The stakeholder theory posits that the managers of the companies are supposed to take care of all the stakeholders of the firm when making governance decisions. The managers should reduce conflict that exists between the interests of the stakeholders (Kuo, Yah and Yu, 2012)

Therefore, from this review we can conclude that there is relation between management responsibilities for climate change in an organization, relation with stakeholder to reduce carbon emission.

Higher the influence of Board Responsibility for the climate change in an organization higher will be carbon emission reduction.

This paper uses descriptive and inferential statistics to investigate the relationship between dependent and independent variables. The dependent variable is Carbon Emission Reduction and independent variable is Board Responsible. Management Incentive is moderating variable.

For the propose of this paper, only 5 most polluter’s countries China, India, Japan, Russia and USA are included and rest are excluded from the data. Among 55 countries only 5 countries are taken in this paper. From 2011-2017 data are eliminated from 2294 samples to 847. Data are selected from Carbon emission reduction for the year 2012. All the missing data, unappropriated data are also deleted from the excel sheet.

Table 1 represents the five countries frequency of 847 samples.  China, India, Japan, Russia and USA are five main polluter’s countries where carbon emission reduction is mostly important. China has frequency of 29 with 3.4%, India has frequency of 47 with 5.5%, Japan has frequency of 146 with 17.2%, Russia has frequency of 4 with 0.5% and USA has frequency of 621 with 73.3%. USA is main polluted country where Russia is least polluted country among the sample. USA is one where carbon emission reduction is important.

Country	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	China	29	3.4	3.4	3.4
India	47	5.5	5.5	9.0
Japan	146	17.2	17.2	26.2
Russia	4	.5	.5	26.7
USA	621	73.3	73.3	100.0
Total	847	100.0	100.0

Table 1 Frequency of Countries

Figure 1 shows pie chart of five countries. From the pie chart we can conclude that carbon emission should be reduced in USA as compare to other countries. USA has higher percentage of pollution than other countries so carbon emission reduction should be focus on USA where as Japan is also one where carbon emission should be reduced. Figure 1 also shows that Russia is one of the least polluted country where carbon emission reduction is not required mostly as compare to other country.

Stakeholder Theory and Corporate Social Responsibility

Figure 1 Pie chart- Frequency of countries

Table 2 shows about central tendency and dispersion of independent variable which is Board Responsible for carbon emission reduction. The mean of the Board Responsible is 1.90 and median is 2.00.  The minimum value for this data set is 1, where maximum is 2. This independent variable is spread out with a standard deviation of 0.299.

N	Valid	847
Missing	0
Mean	1.90
Median	2.00
Std. Deviation	.299
Minimum	1
Maximum	2

Table 2 Central Tendency and Dispersion of Board Responsible

Table 3 represents the frequency of Board Responsible for carbon emission reduction. From the table it is observed that 9.9% are in favour of board responsible for carbon emission reduction where 90.1% are in favour of individual, other manager and senior manager who are responsible for carbon emission reduction

Board Responsible Yes-1, N0-2
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1	84	9.9	9.9	9.9
2	763	90.1	90.1	100.0
Total	847	100.0	100.0

Table 3 Frequency of Board Responsible

Figure 2  represents pie chart of the frequency of Board Responsible for Carbon emission reduction. From the figure it can be concluded that Board responsible yes-1 has less percentage as compare to Board responsible No-2. The sample shows that less percentage are in favour of Board responsible for carbon emission reduction where higher percentage are in favour of other to reduce carbon emission

 Figure 2 Pie chart- Frequency of Board Responsible

Table 4 shows descriptive Statistics of Carbon Emission Reduction which is dependent variable. From the sample of 847, the mean of Carbon emission reduction is -2.3972 where the minimum statistics for this set of data is -113.40 and maximum is 527.00. From the table we can see carbon emission reduction has a skewness of 10.990 and Kurtosis of 190.449. The value of the skewness should be between 3 and -3 but in this data it exceed to 3 because of the higher sample. If there is exceed of 3 than kurtosis is known as leptokurtic. Figure 3 represents histogram of Carbon emission reduction. From figure it can be also observed that the tails are fatter than normal distribution. This refer that in this sample there are greater outlier potential

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation	Skewness	Kurtosis
Statistic	Statistic	Statistic	Statistic	Statistic	Statistic	Std. Error	Statistic	Std. Error
Carbon Emission Reduction	847	-113.40	527.00	-2.3972	27.45887	10.990	.084	190.449	.168
Valid N (list wise)	847

Table 4 Central Tendency, Skewness and Kurtosis of Carbon emission reduction

 

Figure 3 Histogram- Frequency of Carbon Emission Reduction

Table 5 represents about the normality test of Carbon emission reduction and Board responsible. From the Shapiro-Wilk test the significant of carbon emission reduction for board responsible 1-yes has 0.000 which is less than 0.05 so null hypothesis is rejected whereas for carbon emission reduction for board responsible 2-No has also 0.000 which is also less than 0.05 due to this null hypothesis is rejected.

Tests of Normality
	Board Responsible Yes-1, N0-2	Kolmogorov-Smirnova	Shapiro-Wilk
	Statistic	df	Sig.	Statistic	df	Sig.
Carbon Emission Reduction	1	.501	84	.000	.232	84	.000
2	.283	763	.000	.390	763	.000
a. Lilliefors Significance Correction

Case Study: Management Incentives and Carbon Emission Reduction

Table 5 Test of Normality of Carbon emission reduction for Board Responsible

Figure 4 shows about normal Q-Q plot of Carbon emission reduction for Board responsible 1-yes. From the figure it can be observed that the dots are not along the line but somehow close to the line. Due to this null hypothesis is rejected. It is close to be normal distribution.

Figure 4 Normal Q-Q Plot of Carbon emission reduction for Board Responsible 1-yes

Figure 5 represents normal Q-Q plot of Carbon emission reduction for Board responsible 2-No. The figure shows that the dot are not along the line which is not normally distributed. So, H0 is rejected.

Figure 5 Normal Q-Q Plot of Carbon emission reduction for Board Responsible 2-No

Table 7 represents frequency of management incentives of 847 sample. From the table we can observed that frequency of 480 with 56.7% are in favour of management incentives which helps to reduce carbon emission and 367 with 43.3% are not in favour. But, the tables shows that if there will be increase in management incentive than there will be reduction in carbon emission.

Management Incentives
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1	480	56.7	56.7	56.7
2	367	43.3	43.3	100.0
Total	847	100.0	100.0

Table 7 Frequency of management incentives

Figure 6 represents pie chart of management incentives. It shows that from 847 sample the response of yes-1 is in the favour of management incentives to reduce carbon emission reduction.

Figure 6 Pie chart- Frequency of Management Incentives

For the inferential analysis, the hypothesis testing will be used based on the dependent and independent variables for the study. Based on the inferential analysis the hypothesis propose will be tested as follows;

i. ANOVA

The analysis of variance (ANOVA) test is conducted to test whether there significant difference between the board responsibility for climate in organization and carbon emission reduction within the organization. The Analysis of Variance test is also like the t test. The only difference is that the ANOVA test can include more than two groups which is not the case for the t test.

The first hypothesis we sought to test is;

H₀: There is no significant difference in the carbon emission reduction for the companies in the five countries.

H₁: There is significant difference in the carbon emission reduction for the companies in the five countries.

To test this, a one-way Analysis of Variance (ANOVA) was used and tested at 5% level of the significance. The one-way Analysis of Variance was applied in order to test whether there is differences in means of the carbon emission reduction for the companies in the five countries or not. Any significant difference will thus lead to the acceptance of the null hypothesis.

Frequency of Carbon Emission Reduction in Major Polluter Nations

In order to carry out this test, the assumption of normality must be checked and for this case, normality test of carbon emission reduction using Shapiro test was done.

ANOVA
carbon emission reduction
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	2458070.402	2	1229035.201	.276	.007
Within Groups	431383697.384	812	3268058.314
Total	433841767.785	814

The p-value is given as 0.007 (a value that is lower than α = 0.05), this means that the null hypothesis is not rejected. By failing to reject the null hypothesis we conclude that there is no significant difference in the amount of carbon intensity for the companies in the five countries. That is, none of the five countries can be thought to produce more carbon emissions than the other in 2012.

The second hypothesis we sought to test is;

H₀: There is significant difference in the influence of board responsibility for climate in an organization for the companies in the five countries.

H₁: There is no significant difference in the influence of board responsibility for climate in an organization for the companies in the five countries.

To test this, a one-way Analysis of Variance (ANOVA) was used and tested at 5% level of significance. Results are given in the ANOVA table as shown below;

ANOVA
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	538.960	2	269.480	.302	.004
Within Groups	185687.185	808	892.727
Total	186226.145	810

Again, the p-value is given as 0.004 which is a value lower than alpha (α) = 0.05. This therefore implies that the null hypothesis is not rejected. That is, none of the five countries can be thought to have had a significant change in carbon emissions than the other in 2012.

T-test

The second inferential analysis that was conducted out abased on the hypothesis under consideration. The T- test is conducted to examine whether there is significance difference in the mean value between the two different groups. In the current research t-test will help to analyse the difference in the carbon emission reduction in different types of organization and for different countries.

The third hypothesis we wish to test is based on the moderating variable which is management incentive to the employees. The hypothesis to be tested is whether there is significant difference in the carbon emission reduction between the companies that provide incentives for the management of climate change issues and those that do not provide management incentives. The following hypothesis was tested at 5% level significance using an independent samples t-test.

H0: There is no significant difference in the carbon emissions for the companies that provide incentives for the management of climate change issues and those that do not provide.

H1: There is significant difference in the carbon emissions for the companies that provide incentives for the management of climate change issues and those that do not provide. The results are presented in the table below;

Group Statistics
	Management incentives	N	Mean	Std. Deviation	Std. Error Mean
	Yes	523	323.4760	1749.18341	194.35371
No	307	360.2330	1888.52836	256.99616

Independent Samples Test
	Levene’s Test for Equality of Variances	t-test for Equality of Means
F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
Lower	Upper
Intensity	Equal variances assumed	.084	.772	-.116	133	.908	-36.76	317	-664	591
Equal variances not assumed			-.114	107.635	.909	-36.76	322	-675	602

We performed an independent t-test in order to compare the average means of carbon emissions reduction for the companies that provide incentives for the management of climate change issues and those that do not provide. Results showed that the average carbon intensity emissions for the companies that provide incentives for the management of climate change issues and those that do not provide (Mean = 523.48, Standard Deviation = 1749.18, N = 847) was not significantly with the average carbon intensity emissions for the companies that did not provide incentives for the management of climate change issues (Mean = 465.43, Standard Deviation = 1888.53, N = 847), t (1.33) = -0.219, p > .05, two-tailed test.

ANOVA

The ANOVA test is also like the t test. The only difference is that the ANOVA test can include more than two groups which is not the case for the t test.

The first hypothesis we sought to test is;

H₀: There is no significant difference in the amount of carbon emission for the companies in the four countries.

H₁: There is significant difference in the amount of carbon emission for the companies in the four countries.

To test this, a one-way Analysis of Variance (ANOVA) was used and tested at 5% level of significance.

The one-way Analysis of Variance was applied in order to test whether there is differences in means of the carbon emission for the companies in the four countries

In order to carry out this test, the assumption of normality must be checked and for this case, normality test of carbon emission using Shapiro test was done.

ANOVA
Carbon emission reduction
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	2458070.402	2	1229035.201	.376	.687
Within Groups	431383697.384	412	3268058.314
Total	433841767.785	414

The p-value is given as 0.687 (a value greater than α = 0.05), this means that the null hypothesis is not rejected. By failing to reject the null hypothesis we conclude that there is no significant difference in the amount of carbon intensity for the companies in the five countries. That is, none of the three countries can be thought to produce more carbon emissions than the other in 2012.

The second hypothesis we sought to test is;

H₀: There is no significant difference in the percentage change in carbon emissions for the companies in the four countries.

H₁: There is significant difference in the percentage change in carbon emissions for the companies in the four countries.

To test this, a one-way Analysis of Variance (ANOVA) was used and tested at 5% level of significance. Results are given below;

ANOVA
Carbon reduction
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	538.960	2	269.480	.302	.740
Within Groups	185687.185	508	892.727
Total	186226.145	810

Again, the p-value is given as 0.740 (a value greater than α = 0.05), this means that the null hypothesis is not rejected. That is, none of the five countries can be thought to have had a significant change in carbon emissions than the other in 2012.

Based on the above analyses (hypothesis) conducted above, some tests confirmed significant relationship and association between the dependent variables and the independent variable for this study. For instance, the hypothesis test about board management responsibilities for climate change in organization, showed a positive significant results and thus suggest that proper board management of a firm is good determinant in reducing carbon emission from a firms to great levels. When the hypothesis about difference in the percentage change of the carbon emissions for the firms regarding highest level of board management, there was significant difference in the level of the carbon reduction for the companies based on highest level of board management for the climate change within the organization. This implies that the higher the influence of board responsibility for climate in an organization, the higher the reduction in carbon emission from the company.  

Also, while the previous researches have demonstrated that reduction of carbon emissions and exhibiting good board management environmental performance is very vital for the companies, none of the study has ever examined the mechanisms of how the environmental goals are achieved at smaller levels within the firm. We therefore can argue that adoption of the incentive schemes for the employees should exist in any company that emits carbon as this will tend to reduce the level and percentage of carbon into the atmosphere McNabb 2015). Out finding have important implications for the public and government suggesting that non-mandatory guidance or the consideration of a likely introduction of a requirement may result to higher level of carbon emission and subsequent sustainable actions. The measurement and the subsequent disclosure of emissions is a tool that can make management be aware of the need to improve on the terms of the sustainable developments against climate change.

The analysis of variance (ANOVA) indicates that none of the five countries that can be thought to have had a significant change in carbon emissions than the other in the year 2013. Also, from the ANOVA test, the p-value is given as 0.003 (a value greater than α = 0.05), this means that the null hypothesis is not rejected. That is, the board responsibility has great influence in either increasing or reducing carbon emission within an organization. In addition, with reference to the moderating variable, there was a positive significant value which implies that provision of management incentives to the employees also contributes positively to lower levels of carbon emission within an organization.  

When an independent sample t-test was performed in order to compare the average means of carbon emissions for the companies with higher level of board management for the climate change in an organization, the results showed that the average carbon emissions for the companies that have better and good level of management tend to have low emission of carbon from the firms. Moreover, based on the moderating variable, the management incentives for the employees of climate change issues and those that do not provide, there was significant difference with the average carbon emissions for the companies that did not provide incentives for the management of climate change issues based on the two tail test. The findings therefore suggests that the management incentives and policies enhances climate change mitigation in many countries across the world by reducing carbon emissions. Therefore, in this research paper, it can be asserted that the higher the influence of board responsibility for the climate change in an organization, the higher will be carbon emission reduction.

In relation to this research, a many caveats were recognized as well. First, our sample is comprised of only big predominantly multinational organizations from United States, China, Japan and Russia which can be considered to have achieved greater carbon reduction leaving out smaller and average firms which have not or are in the process of reducing carbon emission. It is thus possible that consequences dominated here do not claim for the smaller firms that are competing locally.

Another limitation to this study is that only five countries data have been examined and also the data examined is limited to a period of two years. It could be well and reasonable if the data is analysed over a more than five countries through a longer time frame. This may result to somewhat different and better findings and outcomes for the future study. All these will as well form the fruitful areas of the future work.

 It also appears that though a general methodology statement describes the figures were projected, none of the original reference models and data sources are publicly available for usage. Lastly, the study was only limited to among 847 companies which have achieved greater carbon reduction and thus may not give overall inference about whole carbon reduction among firms in different settings.

Lastly, in relation to the analysis conducted based on the dependent and the independent variable, the dataset used for the analysis had few outliers which were removed. This may have compromised the nature of the results in general as the data collected was not 100 % accurate for the analysis.

For the future studies, a relatively bigger sample size of the companies should be considered for the analysis as this would result to more concrete and reliable findings for better references. The outcomes in this paper cannot be fully relied upon for future references since only five world countries and sample of 847 companies from them were selected for the study and analysis. Moreover, future work should not only aim at bigger predominantly multinomial organizations which have achieved or have well integrated board management and business strategy in reducing carbon emission but also, focus on small organizations which have no or little strategies on carbon reduction. This will compromise the nature of the results and can lead to poor references about the existing variable and predictors hence bad results.  

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