Relationship Between Voluntary Disclosures Of Carbon Emissions And Business Strategies Towards Climate Change In Australian Firms

Data Collection and Preprocessing

The current research is aimed to examine the relationship between the voluntary disclosures of the carbon emission of the firms with the various business strategies taken by the firms towards the climate change. This relationship can be related to the agency theory. The original agent theory which is also known as the principle agent problem, states that due to the asymmetric information where the agents have more information than the principles, there is conflict of interests. In this case the agents are the managers in the firms whereas the shareholders are the principles. Similarly in the current research also the agency theory can be linked as the firms have more information related to its emission as compared to the regulators who have limited information. So, taking into account the agency theory, in the current research the impact of the various business strategies taken by the firms and its impact on the voluntary disclosure of the emission have been examined. This will not only help the firms to improve their strategies to address the climate change but also allows the public and the regulators to know about the current situation of emission in the firms.

For the current research paper, the data was collected for the Australian firms. The sample size of 60 firms has been taken into consideration and the firms were selected randomly. The data preprocessing was performed prior to analyzing the data in SPSS. The missing data were coded as -99, the variables which were not useful for the research were removed from the data set.  A separate excel sheet has been attached with the data set used for the current research.

Descriptive analysis is conducted before the inferential analysis in every research as the descriptive statistics helps the researcher to examine whether the data collected is appropriate for the research or not.  In this case also the inferential analysis is one of the important part of the research, so the descriptive statistics has been conducted.

The results from the descriptive statistics is shown in the table below. The first descriptive statistics shows the results for the disclosure score of the selected firms for the time period 2012 to 2015. Comparing the score for different time period will help to analyze the trend in the recent years. As the results shows the mean disclosure score in 2012 was 41. 9 which has increased to 42.88 in 2015. This indicates that the disclosure score has increased over the years, however the increase in not significant. In fact the disclosure score has declined in 2015 as compared to 2014 when the score was 44.53. The results from the mode shows that for most of the firms the disclosure score is 0.  Furthermore in case of the minimum and the maximum value, for all the years except the 2012, the maximum score is 100 whereas the minimum score is 0 for all years.

Descriptive Analysis

The descriptive results for the independent variables is shown in Table 2 below. As the results show the mean value for the first independent variable (intensity figure) is 9904 with the standard deviation of 49666.98. This indicates high variation in the intensity figure as the standard deviation is very high. Similarly the mean value for the IV3 (metric denominator) is 27554541. The Skewness for this variable is positive which indicates that the variable is right skewed or most of the data set are higher than the series average. Results for the IV5 is also shown in the table below, which shows the average value of 4.95 with standard deviation of 8.9.

Furthermore the histograms of the dependent variable has also been show, which shows the distribution of the variable. The dependent variable in the current research is the disclosure score and the histogram for all the years have been shown differently.

Except for the firms who have 0 score most of the data is right skewed. This shows that disclosure score is either 0, or higher than the average value. Similar trend is shown for the year 2014 also.

The distribution of the disclosure for the year 2013 also shows that disclosure is either 0, or the right skewed.

After the histograms of the dependent variable, the descriptive analysis for the categorical variables has been discussed. Since the measures of central tendency are not suitable for the categorical variable, the graphic representation has been used.

As shown in the figure below the sector wise distribution of the Australian firms used in the research shows that 27 % of the firms are in the financial industry followed by 18 % of the firms in the materials industry.

Similarly the results from the data which shows whether the disclosure score is published for public or not, results shows that 57 % of the firms make it public whereas the rest of firm do not make it public(Winn et al., 2011; Guo, 2014).

The descriptive results for the IV2 shows that for 46 % of the firm the metric denominator is full time employee whereas for 54 % of the firms there are other metric denominator.

Also, 81 % of the firms used the location based metric denominator whereas 19 % of the firms follow the location based measures.

Except for the firms who have 0 score most of the data is right skewed. This shows that disclosure score is either 0, or higher than the average value. Similar trend is shown for the year 2014 also.

Inferential Analysis

Inferential analysis for the current research are based on the chi square, correlation and the regression analysis.

The first chi square test is used to examine whether for different sectors the disclosure is different or not. As the results in the table below shows the chi square value is not statistically significant. This infer that there is no statistically difference in the mean disclosure value for different firms.

However on the other hand there is statistically significant difference in the mean value of the firm who make the disclosure public from those who do not make it public. This is because the chi square is significant at 5 % significance level(ACCA, 2009).

Results from the correlation analysis suggests that the disclosure score (dependent variable ) is negatively and significantly correlated with the first independent variable as the two tailed significance value is less than 0.05.  However the correlation with other independent variables is positive but not significant

The last inferential analysis is the regression analysis, which is one of the most popular techniques used to predict the dependent variable based on the independent variable.

The R squared is 0.511 indicating that more than 50 % variation in the response variable is being explained by the explanatory variables in the regression model. The R squared in this case is satisfactory, taking into consideration the number of independent variable.

The F statistics value is 2.69 with the significance value of 0.043. Since the significance value is less than 0.05, the cumulative effect of the independent variables on the dependent variable is significant.

Among the different independent variables, all of them shows positive impact on the dependent variable as the regression coefficients for all the variables is positive. However in terms of the significance only IV1 and IV4 shows statistically significant results(Gasbarro and Pinkse, 2015; Orsato, 2017).

References

ACCA (2009) High-impact Sectors: the Challenge of Reporting on Climate Change.

Gasbarro, F. and Pinkse, J. (2015) ‘Corporate Adaptation Behaviour to Deal With Climate Change: The Influence of Firm?Specific Interpretations of Physical Climate Impacts’, Corporate Social Responsibility and Environmental Management, 23(3).

Guo, Y. (2014) Climate Change Disclosure?: Determinants and impact. University of Hawai.

Orsato, R. J. (2017) ‘Organizational adaptation to climate change: learning to anticipate energy disruptions’, International Journal of Climate Change Strategies and Management, i(5), pp. 645–665.

Winn, M. et al. (2011) ‘Impacts from climate change on organizations: a conceptual foundation’, Business Strategy and the Environment, 20(3).

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