Impact Of Voluntary Disclosure And Carbon Emission On Organizational Performance

Summary of Marks and Task Plan

This is a research project where the main interest of the study is assessing the impact of voluntary disclosure and carbon emission on the performance of the organization (Albertini 2013). This is a new concept and assessment of the actions to be taken in the future in order to manage the risks and the opportunities to the organization is done with the help of quantitative analysis (Saka & Oshika 2014). The data collection and the analysis and interpretation of the data is presented in this report. Analysis will be conducted with the help of SPSS. Analysis will be conducted based on the information collected from 306 organizations on 2015.

Conceptual FrameworkThe null and the alternate hypothesis on the basis of this research study can be stated as follows:

Null Hypothesis (H₀): No significant relationship exists between the industry performance based on voluntary disclosure and carbon emission

Alternate Hypothesis (H_A): Significant relationship exists between the industry performance based on voluntary disclosure and carbon emission

Data Collection

            The dataset contains information about 306 selected organizations that have participated in the Carbon Disclosure Project survey. These information was collected as a result of the CDP (Carbon Disclosure project) survey. The respective data have been collected from the companies in terms of disclosure scores, scopes of carbon emissions and initiatives taken by the organization. The results of the analysis have to be generalized as the collected information id for 2015 while the recorded disclosure scores are from 2012 – 2015 (Luo, Lan & Tang 2012).

            The whole dataset is available from the CDP survey and has been shortened to the required variables. There are three variables which are distinguished as the independent variable, the dependent variable and the control variable. Rest of the variables that were present in the dataset and not considered have been removed (Karanja, Zaveri & Ahmed 2013).

Sample Description

            The summary of the disclosure scores are shown with the help of the mean and the median. A median value of all the disclosure scored over the years have been allocated to the disclosure scores variable considered for the study. For the second variable carbon emissions, the figures on gross global emissions have been clubbed in to this variable (Pallant 2013). The organizational objective has been recoded as 1 which indicates “yes” and 0, which indicates “No”. Appropriate statistical analysis have been performed on these variables (Delvore 2011).

Proposed Data Analysis

Data Analysis – Descriptive

            The data that has been considered for the analysis have been described theoretically in the following table:

Table 1: Data Description

Theoretical Construct	Proxy Measure	Dependent (DV), Independent (IV) or Control Variable (CV)	Source
Disclosure Score (Ratio scale)	Carbon Disclosure score in CDP Survey from the year 2009 to 2015	Dependent  (DV)	CDP Survey – Disclosure Score
Scope 1 and 2 carbon emission (Ratio scale)	Gross Global Scope 1 and Score 2 emissions mentioned in CDP survey for all 1047 countries	Independent (IV)	CDP Survey – Gross Global Scope 1 and Score 2 emission figures in metric tonnes units
Organizational Initiatives (Nominal Scale)	All the initiative taken by the organization in categorical values (Yes = 2 and No = 1)	Control Variable (CV)	CDP Survey – Did you have emissions reduction initiatives that were active?

            The descriptive statistics for the data has been calculated according to the nature of the data that has been described in the above given table.

Descriptive Statistics

            It can be seen clearly from table 1 that the variable “organizational initiatives” has been considered as a nominal variable and hence only the frequency distribution table has been produced based on the responses recorded for the variable.

Table 2: Organizational_Initiatives
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Yes	299	97.7	97.7	97.7
No	7	2.3	2.3	100.0
Total	306	100.0	100.0

The frequency table clearly shows that 306 companies have opted for the initiatives that will be helpful in reducing the carbon emissions. 97.7 percent of the sample have taken interest in the initiatives for the reduction. This also indicates that the companies are known about the sustainability of the organizations and thus, they have taken approaches to satisfy the demands of the investors in their company. The responses are shown diagrammatically in figure 1. 

 The disclosure score and the scopes of carbon emissions are given in ratio scale and hence descriptive statistics can be evaluated from them. The descriptive statistics are given in the following table:

Table 3: Descriptive Statistics
	Disclosure_Scores	Carbon_Emission
N	Valid	306	306
Missing	0	0
Mean	77.1155	19910.9837
Std. Error of Mean	.90592	17982.17477
Median	80.7571	15.2584
Mode	.00a	.00a
Std. Deviation	15.84720	314559.58815
Variance	251.134	98947734494.412
Skewness	-2.401	17.450
Std. Error of Skewness	.139	.139
Kurtosis	8.454	304.984
Std. Error of Kurtosis	.278	.278
Range	98.67	5499961.49
Minimum	.00	.00
Maximum	98.67	5499961.49
Sum	23597.35	6092761.02
Percentiles	25	71.0000	4.6621
50	80.7571	15.2584
75	86.5714	86.3346
a. Multiple modes exist. The smallest value is shown

  The variable disclosure score is given out of 100 which indicates the position of the firm in the financial reports. It can be seen from the mean value that 77.12 percent of the companies have provided the disclosure scores. The median value of 80.76 shows that 50 percent of the companies have a voluntary disclosure score of above 80.76. The mode of the data has not been obtained. This indicates inequality in the mean, the median and the mode and thus violates normality (Blanca et al. 2013). Thus, the data is not distributed normally. A high standard deviation of 15.85 indicated variability in the scores. The negative value of skewness (-2.401) indicates that the data is negatively skewed (Park 2015). The histogram given below shows the shape of the data for the disclosure scores.                                                       Figure 2: Distribution of Carbon Disclosure Scores

            The carbon emissions of the company are given in metric tonnes and it can be seen from the analysis that the average emission is 19910.98 metric tonnes. The median emission is found to be 15.26 metric tonnes and there is no mode to the data. This indicates inequality in the mean, the median and the mode and thus violates normality. Thus, the data is not distributed normally. A high standard deviation of 314559.59 indicated variability in the scores. The positive value of skewness (17.450) indicates that the data is positively skewed (De Vaus 2013). The histogram given below shows the shape of the data for the carbon emission.                                                                Figure 3: Distribution of Carbon Emission

Spelling, Grammar and Referencing

Correlation Analysis

            Since, it has been already tested that the data does not follow normality, thus, the only test to show the relationship between the two ratio scale variables are correlation analysis. The spearman correlation coefficient will be appropriate in giving an idea about the relationship.

Table 4: Correlations
	Disclosure_Scores	Carbon_Emission
Disclosure_Scores	Pearson Correlation	1	-.023
Sig. (2-tailed)		.682
N	306	306
Carbon_Emission	Pearson Correlation	-.023	1
Sig. (2-tailed)	.682
N	306	306

 It can be seen from the correlation table that there is a weak negative relationship between the independent and the dependent variable. The correlation coefficient is not significant at α=0.01 as the p value is higher than α. Thus, there exists an insignificant relationship between Carbon emission and carbon disclosure scores.

Inferential Statistics

Mann-Whitney U Test

            To test the differences in the average values of a variable with respect to two different categories, an independent sample t-test is conducted. In case if an independent sample t-test, there is an assumption that the data is normally distributed. In this case, as already seen from the descriptive statistics that the data is not normally distributed. Thus, the non-parametric test which is used to test the difference in the average values of two groups is used. Thus is the Mann-Whitney U Test. The dependent variable considered here is in ratio scale whereas the control variable is categorical. Thus, Mann-Whitney test would be the most appropriate test in this case as the data also violates normality. However, when compared as the case of control variable then Mann Whitney Test is considered to be apt as it has two categories as Yes or No.

            The test statistics is based on two – tailed which has asymptotic significance. This data can be concluded that disclosure scores of the organization over the years is statistically significant in taking organization initiative to reduce carbon emission if the p-value obtained from conducting the test results to be more than 0.05.

References

Albertini, E., 2013. Does environmental management improve financial performance? A meta-analytical review. Organization & Environment, 26(4), pp.431-457.

Blanca, M.J., Arnau, J., López-Montiel, D., Bono, R. and Bendayan, R., 2013. Skewness and kurtosis in real data samples. Methodology.

De Vaus, D., 2013. Surveys in social research. Routledge.

Devore, J.L., 2011. Probability and Statistics for Engineering and the Sciences. Cengage learning.

Karanja, E., Zaveri, J. and Ahmed, A., 2013. How do MIS researchers handle missing data in survey-based research: A content analysis approach. International Journal of Information Management, 33(5), pp.734-751.

Luo, L., Lan, Y.C. and Tang, Q., 2012. Corporate incentives to disclose carbon information: Evidence from the CDP Global 500 report. Journal of International Financial Management & Accounting, 23(2), pp.93-120.

Pallant, J., 2013. SPSS survival manual. McGraw-Hill Education (UK).

Park, H.M., 2015. Univariate analysis and normality test using SAS, Stata, and SPSS.

Saka, C. and Oshika, T., 2014. Disclosure effects, carbon emissions and corporate value. Sustainability Accounting, Management and Policy Journal, 5(1), pp.22-45.