Theoretical Framework

Discuss about the Eradicate Poverty and Transform Economies.

Climate change have become an issue of concern to both the developed and emerging economies. The economic growth is important in eradicating poverty but it comes with some challenges such as increased carbon emission from the companies (Panel, 2013). Development and expansion of companies in a country lead to creation of job opportunities to the local population (Katzenbach, and Smith, 2015). Carbon emission have led to great effect on the climate change such as the global warming. Countries and companies are bringing their efforts together to reduce the level of carbon emission to the environment (Hansen et al., 2013; Zhang, Wang and Song, 2013). Developed countries are the high emitters of carbon dioxide gas to the environment. For instance, the United States’ industrial and commercial sources were reported to have emitted carbon dioxide gas three times more than the residents in the year 2010 (Nordhaus, 2010). Continued increase of carbon emission led to the emergence of Carbon Disclosure Project that required the companies to be keeping records of carbon disclosure and reporting the records within a certain period of time (Ascui and Lovell, 2012; Andrew, and Cortese, 2013). Even though the companies are keeping records and reporting the carbon emission, they do little in minimizing the emissions. Reports addressing the climate change challenges considered the growing markets for products and services’ effects on the companies’ responses to the climate change (Saka and Oshika, 2014). Despite the efforts and pressure mounted by the non-governmental organization (CDP) on the companies’ report on carbon emissions, not all companies respond to the provided emissions and climate change questionnaire on their next course of action towards combating carbon emissions (Matsumura, Prakash and Vera-Muñoz, 2013). The rising concern from both private and public sectors help the CDP in mounting pressure and encouraging companies to adopt and mitigate carbon emissions that lead to climate change. This research is then aimed at assessing the effects of CDP towards minimizing carbon emissions.

This research adopted legitimacy theory in the examination of carbon disclosure by both private and public sectors. No theory has come clear in explaining corporate social responsibility disclosure practices until when legitimacy theory came about and had wide dependence by the researchers (Fernando, and Lawrence, 2014). Environmental and social disclosure can easily be explained currently by using legitimacy theory (Deegan, 2014). Due to legitimacy ability of providing disclosing strategies, legitimacy theory is preferred over other theories (Bakker, Raab, and Milward, 2012). The strategies identified by the legitimacy theory can be adopted by the organizations which might legitimize the existence of the organizations hence could be empirically tested (Maier, Meyer and Steinbereithner, 2016). Agency theory on the other hand provides the assumption details of the relationship between business agents and principals (Westley et al., 2013). Through the adopted legitimacy theory, the existing problems in the carbon disclosure can be somewhat resolved with ease and keeping all the set goals at check.

Literature Review

Business organizations and companies are the major sources of carbon emissions as in the previous reviews (Huisingh, Zhang, Moore, Qiao and Li, 2015; Böttcher, and Müller, 2015). The so referred companies are supposed to adopt new techniques that are aimed at minimizing carbon emissions and keep it at the lowest level possible. The climate change made the companies and other business organizations suffer some significant risks whose effects are felt in the value of the organizations’ investments of shareholders (Okereke, Wittneben and Bowen, 2012). The earlier researches show that there is existence of correlation between the share price and the environmental performance (Eccles, Ioannou and Serafeim, 2014; Edwards, 2014). Performance evaluation in conjunction with performance based incentives are used to protect shareholders’ interests. The levels of carbon emissions is almost unmanageable due to the companies’ managers’ lack of sufficient experience to manage emissions. As a result, the set target have been difficult to be achieved.

Further studies on financial performance exhibited targets and incentives effects without including the non-financial performance (Sierzchula, Bakker, Maat and van Wee, 2014.). Being that carbon emissions and climate change is a bother to all the sectors, non-financial performance and target operations was the identified gap. Furthermore, future research should be on the relationship between non-financial performance and the environmental performance. Strategic importance still remain hanging and sidelined in the firms’ management and operations since firms are not quite familiar with it. Challenging targets might lead to increase in the number of targets completed and less weight given to environment related targets.

Seminal articles have been covered in legitimacy theory on the control and separation of ownership. Society can operate in the funny manner by revoking the contract awarded to an organization if the society feel unsatisfied with organizations’ services (Marcuse, 2013). The behavior of the agents can be bonded with the guide of principals’ interest related to incentive performance and setting targets (Fayezi, O’Loughlin and Zutshi, 2012). Legitimacy theory plays a vital role in explaining environmental and social disclosures in regards to carbon emissions. In the hypothesis development, it is important to consider the advantages of legitimacy theory that is not in other theories. One such advantage is providing disclosure strategies that companies adopt to make their existence legitimate. Slacks are created due to available information asymmetry that is targeted by the agents. In regards to aforementioned theory in the operational proxies, slack formed the dependent variables (DV) while the incentives provided by the managers and budgetary process in setting easy targets are referred to in the past researches. In order to understand carbon emissions, there is dire need for incorporating slack literature in the operationalization and its application in carbon emissions. Independent variable in this research will be information asymmetry even though there is their decrease with time. The numbers in the organization for the past years in which no targets have been set are collected to measure the information asymmetry on carbon emissions. Greater performance have the effects of greater completion and the effects are moderated using the performance incentives. Managers in their sit in the management role of the organization know the potentials of the organizations than the shareholders or investors. Inverse proportionality exist between information asymmetry and the created slacks by the managers.

Methodology

In order to exhaust the companies and countries’ response to climate change due to carbon disclosure, electronic methods was applied to obtain CDP data from their database which covered the period of six years from 2009 to 2015. In that regards therefore, the data used in this research were secondary and covered a total population of 5,054 companies from various countries such as China, Colombia, France, Mexico, South Korea, United Kingdom, USA, Australia, Canada etc. the data consisted of bot categorical and numerical variables which led to both categorical and numerical data. It was in the aim of CDP for all the companies from all countries to comply and report their actions towards carbon disclosure. As a result therefore, this research took a sample of companies from three developed countries i.e. Belgium, UK and Germany. The sample size used was 30 companies from the three stated countries and have their efforts evaluated in the period of the past seven years from 2009 to 2015. From the entire data set, thirty samples were randomly selected basing particularly on the three developed countries which covered a relatively wider range of companies.

The retrieved data were in excel data files and were organized and prepared in the same software for analysis. Descriptive and inferential statistical analysis were conducted to leverage the characteristics and the companies’ response towards minimizing carbon disclosure in their daily activities in the covered period.

The percentage frequency in figure 1 shows the number of companies from the selected countries in their response to carbon disclosure emission. In response to that, the companies that did not make their disclosure score public was represented by 33.33% whereas those which made their disclosure score public represented by 66.67%. The results showed that despite the efforts and the debates on reporting carbon emission to the environment by the companies, some of the companies could still defy and not report their carbon emissions to the public.

Table 1: Descriptive statistics for Carbon Disclosure score

	Minimum	Maximum	Mean	Standard deviation	Skewness	Kurtosis	Count
2015 Disclosure score	0	100	52.2	44.46882	-0.16274	-1.87064	30
2014 Disclosure score	0	100	49.6667	41.81184	-0.17859	-1.79161	30
2013 Disclosure score	0	96	51.1333	38.73138	-0.31093	-1.62692	30
2012 Disclosure score	0	99	50.0667	35.00929	-0.36888	-1.48136	30
2011 Disclosure score	0	87	33.4	33.05335	0.244065	-1.61382	30
2010 Disclosure score	0	93	28.2	32.98213	0.531158	-1.41057	30
2009 Disclosure score	0	73	23	30.33036	0.775858	-1.24885	30

Carbon disclosure score was recorded from the year 2009 to the year 2015. From the scores, maximum score of 100 was recorded in 2015 with the score showing the mean of 52.2 and standard deviation of 44.47. In the year 2014, another maximum score of 100 was recorded with the mean of 49.67 and standard deviation of 41.81. The maximum score was low in the year 2013 where it was 96 with the mean of 51.13 and standard deviation of 38.73. In the previous year to that, the maximum score was 99 with the mean of 50.07 and standard deviation of 35.01. In the year 2011, maximum score of 87 was recorded with the mean of 33.4 and the standard deviation of 33.05. The maximum score was a bit higher in 2010 as compared to 2011 where it was 93 but with the lower mean of 28.2 and standard deviation of 32.98. The year 2009 recorded the lowest maximum of 73 as compared to all other years and the lowest mean too of 23 with the standard deviation of 30.33.

Results

For the normality of the data, skewness and kurtosis was used where data in some variables such as 2012 to 2015 disclosure score showed to be negatively skewed while the remaining ones 2009 to 2011 disclosure score showed positive skewness with all their kurtosis values negative showing that the tails were longer to the negative side than to the positive side. The skewness and Kurtosis values are as shown in table 1 above.

Table 2: Two sample T-test for comparing means between 2015 and 2014 disclosure score
	2015 Disclosure score	2014 Disclosure Score
Mean	52.2	49.66666667
Variance	1977.476	1748.229885
Observations	30	30
Hypothesized Mean Difference	0
df	58
t Stat	0.227326
P(T<=t) one-tail	0.410485
t Critical one-tail	1.671553
P(T<=t) two-tail	0.82097
t Critical two-tail	2.001717

Hypothesis

H₀: There is no mean difference between 2015 disclosure score and 2014 disclosure score

H₁: There is mean difference between 2015 disclosure score and 2014 disclosure score

From the test conducted, p=0.82097 which is less than significance level 0.05 in response to that therefore, we fail to reject the null hypothesis and conclude that there was no significant difference between the mean of 2015 disclosure score and 2014 disclosure score. On the same, the mean for 2015 disclosure score was 52.2 while that of 2014 disclosure score being 49.7. The difference between the two mean was small that could be ignored in relation to the set of data sample used.

	2015 Disclosure score	2014 Disclosure Score	2013 Disclosure Score	2012 Disclosure Score	2011 Disclosure Score	2010 Disclosure Score	2009 Disclosure Score
2015 Disclosure score	1
2014 Disclosure Score	0.950902	1
2013 Disclosure Score	0.813513	0.835209	1
2012 Disclosure Score	0.666335	0.714522	0.897285	1
2011 Disclosure Score	0.777648	0.842145	0.741596	0.69203	1
2010 Disclosure Score	0.592538	0.661478	0.598912	0.555926	0.806094	1
2009 Disclosure Score	0.212763	0.282514	0.179057	0.156949	0.412718	0.579136	1

Pearson’s correlation coefficient was used in the test of relationship between the disclosure scores for the years from 2009 to 2015. As a result, there was a strong positive correlation 2014 and 2015 disclosure score with (r=0.95). As well, the strong positive correlation was shown between 2015 and 2014 disclosure scores with (r=0.81). The disclosure score for the year 2009 showed weak positive correlation with the disclosure scores for all other years with only strong positive correlation shown between it and 2010 disclosure score i.e. (r=0.58). From the results, it is was clear that the disclosure scores were related positively since there was no negative correlation coefficient shown between any of the tested variables.

Most of the companies made their disclosure score public represented by 66.67% as from figure 1. This means that most of the companies complied with CDP greenhouse gas disclosure requirement of reporting carbon emissions. In relation to disclosing the carbon emissions as required by the CDP, a relatively large percentage (33.3%) of the companies were still not complying with the requirement. From the hypothesis tested for the significance in the mean difference between the carbon disclosure reported in the year 2015 against that which was reported in the year 2014, though there was a small difference between the actual means from the results but the difference was not statistically significant to reject the null hypothesis and thus the null hypothesis was supported. From the hypothesis test result in this report, it can be seen that the disclosure report was relatively the same across the years the reports were made. From the normality measure, the skewness scores showed that the data was positively skewed with most of the disclosure scores to the right hand side of the mean. That revealed that the data was not normally distributed and that the data points were more to the right side of the mean than to the left side of the mean hence it could be concluded that it was asymmetrical. From the correlation test results, the correlation coefficient between the 2015 carbon disclosure and 2014 carbon disclosure showed a strong positive correlation of (r=0.95). The correlation between disclosure score in 2015 and that of 2013 showed stronger positive correlation too but with the Pearson’s correlation coefficient (r=0.81) lower than that of disclosure score between 2015 and 2014. Almost all the disclosure scores showed to have strong positive correlation with only 2009 disclosure scores showing weak positive correlation with the score in other years like 2015 disclosure score, 2014 disclosure score, 2013 disclosure score and 2012 disclosure score with relatively strong positive correlation with 2011 disclosure score report and 2010 disclosure score report i.e. (r=0.41 and r=0.58) respectively. This could tell that the disclosure scores were improving over the latest years from the previous years as this can as well be supported by the mean disclosure results from the data e.g. 2013 mean disclosure score from the sampled companies was 51.13 and the disclosure score for the year 2009 had the mean of 23. Large difference (28.13) of the mean disclosure scores reported in the years showed improvement in the companies’ report on the carbon disclosure.

Discussion

This research only tested on the correlation and the mean difference between the carbon disclosure scores for the years 2009 to 2015. The root cause of the mean difference and the importance of the disclosure values have not been discussed thus remained unclear. In the future research, it would be important if they would carry out the research on the practical methods that should be applied by the companies both in public and private sectors to minimize or fully do away the carbon emissions and control the climate change. Furthermore, after the carbon disclosure reported by the companies, nothing much seem to be done due to lack of directions to take since there were no penalties imposed on the companies defaulting to give the carbon disclosure reports. The report did not cover what the CDP does to the companies that file their reports every required period whether or not they are rewarded to keep others dedicate their potential towards meeting the same goal of reducing keeping the carbon disclosure records on check and thus working towards minimizing the emissions by acquiring environment friendly sources of energy thus dealing with climate change. This study adopted legitimacy theory which mentioned of identifying the strategies for the companies to adopt in legitimizing their existence but did not explicate any of such strategies. Finally, this study identified that the existing theories had not in any way explained the relationship that exist between non-financial performance and the environmental performance. This is the gap of knowledge that be focused on for future research.

Referring to the limitation identified from this report, further research should address the importance of the companies’ disclosure score values for clarity. When this is done, the proportion of the companies that were was reported to have not been disclosing their annual carbon disclosure data might be drawn and convinced to comply with the CDP requirement by providing the disclosure reports. Further, future research should be conducted on the practical methods that should be applied by the companies in both public and private sectors to fully minimize the carbon emissions by the companies and have climate change under control. Researches to come in the future should be on encouraging the companies in complying with the disclosure report as outlined by the CDP. In that regard, researches should be conducted suggesting penalties that should be imposed on the companies that fail to provide the carbon emissions disclosure report to prohibit companies from defaulting to give their disclosure report.

Conclusion

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