The Relationship Between Incentives and Reduction in Carbon Emissions: A Chi-Square Analysis

MGT723 Research Project
Semester 2 2018
Assessment Task 3: Data Analysis
Student Name: Latika Dalal
Title: The relationship between incentives provided to the employees for climate change and
the reduction in carbon dioxide (C02) emission
Submission Date: 03/10/2018
Acknowledgement:
I certify that I have carefully reviewed the university’s academic misconduct policy. I
understand that the source of ideas must be referenced and that quotation marks and a
reference are required when directly quoting anyone else’s words.

Data Analysis- Inferential
In this section, the relationship between provision of incentives by management and
reduction of carbon emissions will be checked for both the Forest and Paper Industry and also
for the Air Freight Transportation Industry. Chi- square analysis tests will be used in both
cases to check for the association between the independent and dependent variables. A chi-
square test is used to check whether there is a significant connection amid two variables
(Rana & Singhal, 2015).
Previous research studies have revealed that provision of incentives motivates employees to
adopt new proposed policies (Lazaroiu, 2015). Therefore in this case, provision of incentives
as a bid to push employees to accept climate change policies aimed at reducing carbon
emissions might work. To confirm this however, statistical tests will be conducted as shown
in the sections below.
Forest and Paper Products Industry: Relationship between Provision of Incentives and
Reduction of Carbon Emissions
The table below shows results from a chi- square test between the aforementioned study
variables.
Table 1: Chi-Square Tests on Forest and Paper Products Industry
Value df
Asymp. Sig. (2-
sided)
Pearson Chi-Square 28.718a 29 .480
Likelihood Ratio 28.112 29 .512
Linear-by-Linear Association 3.489 1 .062
N of Valid Cases 32
a. 60 cells (100.0%) have expected count less than 5. The minimum
expected count is .19.
The results are given under the Pearson chi- square test statistics. From the results, it is
evident that there was no significant connection between provision of incentives by the
management of companies and reduction in the rate of carbon emission in the Forest and
Paper Products Industry (χ2 = 28.718, p = 0.480). The obtained p- value is greater than the
0.05 level of significance, thus showing that there is no relationship between the independent
and dependent variables. In this case therefore, the null hypothesis that states there is no
relationship between provision of incentives and reduction of carbon emissions will be
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accepted. This provides a possibility of type 2 error, which is an error of accepting a null
hypothesis that is wrong (Hopkins, 2017). However, this error is small in this case, since the
sample size used is large enough to detect any relationships between provision of incentives
and reduction of carbon emissions.
Air Freight Industry: Relationship between Provision of Incentives and Reduction of
Carbon Emissions
The table below shows results from a chi- square test between the aforementioned study
variables.
Table 2: Chi-Square Tests on Air Freight Industry
Value df
Asymp. Sig. (2-
sided)
Pearson Chi-Square 32.000a 30 .368
Likelihood Ratio 27.738 30 .584
Linear-by-Linear Association .102 1 .749
N of Valid Cases 32
a. 62 cells (100.0%) have expected count less than 5. The minimum
expected count is .16.
The results are given under the Pearson chi- square test statistics. From the results, it is
evident that there was no significant connection between provision of incentives by the
management of companies and reduction in the rate of carbon emission in the Air Freight
Industry (χ2 = 32.000, p = 0.368). The obtained p- value is greater than the 0.05 level of
significance, thus showing that there is no relationship between the independent and
dependent variables. The null hypothesis that states no relationship exists between provision
pf incentives and reduction of carbon emissions is accepted. This, just like in the section
above, creates a chance of committing a type 2 error. But as stated before, the utilized sample
size is big enough to detect any significant connection between the independent variable and
the dependent variable.
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