Data Analysis For Testing Hypothesis On Co2 Emission In Developed Countries

Data Collection

One of the most important part of any research is the data collection. The hypothesis proposed by the researcher are tested on the basis of the collected data. There are mainly two types of data sources which are used for research papers. The first one is the primary data, which is also called the fists hand data. This type of data is collected by the researchers as per the requirement of the research. The major techniques of collecting the primary data are the primary survey and the personal interview. If the researcher want to collect the quantitative data primary survey is the most appropriate technique. On the other hand if the researcher wants to collect the qualitative data, then conducting the personal interview is more appropriate as compared to the primary survey. One of the major advantage of primary data is that the researcher can collect whatever information is required for the research. However the primary survey is both time consuming and costly(VARGAS-HERNÁNDEZ, LEÓN and VALDÉZ, 2011; Rajasekar, Philominathan and Chinnathambi, 2013; Kumar, 2014).

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The second type of the data is the secondary data which is also called the second hand data. This data has been already collected by someone else (government, research organisations, international institutions) for their own purpose. The major source of secondary data includes published books, journals, government data bases, research papers, data base of international organisations such as World Bank, IMF, ILO etc. One of the major advantage of the secondary data is that it is easy to collect and are less costly as compared to the primary data. However, in some cases the appropriate data is not available which is one of the major drawback(Stewart and Kamins, 1993; Saunders, Lewis and Thornhill, 2007; Bryman, A. and Bell, 2011; Flick, 2011).

For the current search the secondary data has been used to test the proposed hypothesis in the previous section. The data has been used for both the descriptive and the inferential analysis and the results from the analysis have been discussed in the next section. However before analysing the data the basic data cleaning process has been followed. First the missing values in the data set were checked and also the outliers were tested. This is because the missing values and the outliers may affect the results and the robust results may not be obtained. The outliners are those data points which are far from the mean value of the series. Once the data was cleaned then it was exported to SPSS for further analysis.

Descriptive Data Analysis

In this case the data has been collected for United Kingdom and the United States of America, which are the most developed countries in the world. The data from the developed countries have been used because they are responsible for the majority of the historical carbon emission. However in the current context the developing countries are also contributing significantly in the carbon emission. Furthermore the government is also strict about the carbon emission in developed countries as compared to the developing ones. This is another reason to select these countries. 

In this section the results from the descriptive data analysis has been presented. The descriptive statistics are the most appropriate way of presenting the overview of the collected data. This will provide a clear picture to both the writer and the reader about the data being used for the research. Also the descriptive statistics set the base for the inferential analysis(Gaur and Gaur, 2006; Greenland et al., 2016).

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In the current case the descriptive statistics has been conducted using various measures of central tendencies. This includes mean, mode, median and other statistics such as skewness, kurtosis, maximum value and minimum value. The results from the descriptive statistics is shown in the table below. However one important thing to notice is that the descriptive statistic is appropriate only if the variable is continuous. In the current research only the emission of Co2 is the only continuous variable and the result for the same has been presented.

Statistics

Co2 emission  

N

Valid

120

Missing

0

Mean

-9.3728

Median

-7.0000

Mode

.00

Std. Deviation

9.83731

Variance

96.773

Skewness

-1.505

Std. Error of Skewness

.221

Kurtosis

2.832

Std. Error of Kurtosis

.438

Minimum

-49.24

Maximum

.00

Sum

-1124.73

Percentiles

25

-15.6750

50

-7.0000

75

-1.0000

Table 1 Results from the descriptive statistics

As shown in the table above, the mean change (5%) of the co2 emission as compared to the previous year is -9.3728. This indicates that on an average the emission of co2 has decreased as compared to the previous year. The standard deviation is 9.8 which is comparatively high. A high standard deviation indicates higher variation in the data set. On the other hand a low standard deviation indicates that there is less variation in the data set of most of the data points lies near the mean value of the series. Furthermore the median value is -7 which also indicates that there has been decline in the emission of co2 as compared to the previous year. Researchers sometimes argues that as compared to mean, median is more appropriate. This is because the mean value is highly influenced by the extreme values whereas median is not influenced. In addition the minimum value is -49 % which indicates there has been 49 % reduction in the emission of co2 as compared to the previous year. On the other hand maximum is 0, which shows that there are some firms whose carbon emission has neither increased nor decreased. One interesting thing to notice is that there is no single firm, where the carbon emission has increased(Burnett, 2009; Millimet and Roy, 2011; Zhang et al., 2016)

Inferential Data Analysis

Figure 1 Histogram of the carbon emission

Figure 2 QQ plot of the carbon emission

Figure 3 Distribution of the carbon emission

For the categorical variables the graphical representation has been used and the results are shown below.

Figure 4 Country wise distribution of the firms

The country wise distribution of the firms shows that there are 50% firms from the United Kingdom and other 50 % from the USA. It shows that all the firms in the current research are from the developed countries. In 2015 most of the countries around the world has signed the paris climate change agreement which was aimed to reduce the emission the carbon in the environment and it was a global effort towards the climate change. However in the recent time USA is showing the intention to withdraw from the agreement. However UK has been a great supporter of the Paris agreement.(Hai-BinZhang et al., 2017).

Figure 5 Highest level of responsibility

Another important variable in the research is to examine who is responsible for the emission of carbon by the firm. This is the indication for the seriousness of the firm towards the carbon emission. If the firm is serious about the carbon emission, the highest authority will take the responsibility and vice versa. As shown in the figure above in most of the firm the board has the highest level of responsibility. This indicates that most of the firms are serious so the highest responsibility has been given to the highest authority.

As shown in the above figure for most of the firms the board is the highest authority responsible for the climate changes issues. One of the major reason for the same is that pressure from the government to comply with the climate change regulations. Especially the government in the developed countries have strict regulations about the environment related regulations. So, the firms are pressurized to take the decision from the highest level. The board are the highest decision making body in every firm. Since climate change is the very sensitive issue, it is better to have the decision from the highest level which also includes the environment experts in most of the firm.

Figure 6 Incentive for achieving the climate change issues

 Results show that more than 70 % of the firm provides incentives to its employees or department who achieve the target for the carbon emission. Providing incentive may lead to less carbon emission which has been shown in the previous researches also.

Results and Discussion

Figure 7 Climate change taken into consideration while making the business strategies.

As shown in the figure above in more than 80 % of the firm the climate change is taken into consideration while making the business strategies. This also indicates that the firms are making efforts to reduce the carbon emission. However, the intention of USA from the Paris agreement may have negative effect on the efforts made by other countries as USA is one of the leading sources for carbon emission.

In this case the percentage of firms are higher who includes the climate change while taking into consideration the business strategies. One of the major reason for that is the pressure from the government about the carbon emission. As discussed earlier also the government in the developed countries are strict about the environmental issues. Almost all the projects are studies from the environmental prospective also then only the permission is allowed to start the project. The punishment for violation of the environment regulation is also very high. This has led to firms, especially in the manufacturing sector, to include the climate change in their business strategies. One of the implication of such provisions is that, now the manufacturing units are shifted to the developing countries where the environmental regulations are less strict. 

Apart from the descriptive statistics, inferential statistics will also be conducted using the same variables as discussed in the previous section. The inferential analysis will include the following:

T-Test

The t test is used to examine whether there is statistical difference in means of two samples which follows the normal distribution. If the variables do not follow the normal distribution then the t test cannot be conducted. The t test can only be conducted between the two groups which is one of its limitations.

ANOVA

ANOVA is similar to the t-test. The only difference is that the ANOVA test can be used when there are more than two groups for which the difference in the mean has to be calculated. It is one of the most popular inferential technique which has been used in wide range of subjects. 

For the inferential analysis the SPSS software will be used. The software allows the researcher to conduct various types of descriptive and the inferential analysis. The analysis can be conducted either by directly writing the syntax or the researcher can select the option from the drop down menu to conduct the test. 

References

Bryman, A. and Bell, E. (2011) Business Research Methods 3e. Oxford University Press.

Burnett, J. W. (2009) Economic Growth and Environmental Degradation. Athens.

Flick, U. (2011) Introducing Research Methodology: A Beginner’s Guide to Doing a Research Project. SAGE Publications.

Gaur, A. S. and Gaur, S. S. (2006) Statistical Methods for Practice and Research: A Guide to Data Analysis Using SPSS. Response Books, Sage Publications India, New Delhi.

Greenland, S. et al. (2016) ‘Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations’, European Journal of Epidemiology, 31, pp. 337 – 350.

Hai-BinZhang et al. (2017) ‘U.S. withdrawal from the Paris Agreement: Reasons, impacts, and China’s response’, Advances in Climate Change Research, 8(4), pp. 220–225.

Kumar, R. (2014) Research Methodology: A Step-by-Step Guide for Beginners. SAGE Publications.

Millimet, D. L. and Roy, J. (2011) Three New Empirical Tests of the Pollution Haven Hypothesis When Environmental Regulation is Endogenous. Bonn.

Rajasekar, S., Philominathan, P. and Chinnathambi, V. (2013) Research methodology. Tamilnadu India.

Saunders, M., Lewis, P. and Thornhill, A. (2007) Research methods for business students fifth edition. 3rd edn. Harlow: Prentice Hall. Available at: https://is.vsfs.cz/el/6410/leto2014/BA_BSeBM/um/Research_Methods_for_Business_Students__5th_Edition.pdf.

Stewart, D. W. and Kamins, M. A. (1993) Secondary Research: Information Sources and Methods. 2nd edn. Newbury Park, CA: SAGE.

VARGAS-HERNÁNDEZ, J. G., LEÓN, A. de and VALDÉZ, A. (2011) ‘RESEARCH METHODOLOGY STRATEGIES IN STRATEGIC MANAGEMENT’, African Journal of Social Sciences, 2.

Zhang, Z. et al. (2016) ‘The Decoupling of Resource Consumption and Environmental Impact from Economic Growth in China: Spatial Pattern and Temporal Trend’, pp. 1–13.