Government policies and climate change

Climate change has over the past few decades emerged as a major global issue. This has been due to its effects, which have become more visible with the increase in extreme weather events and patterns. This has forced governments globally to get involved in fighting climate change.

Governments are charged with making policies that govern the various aspects of the lives of the citizens. The policies include manufacturing and processing policies that applied during the Industrial Revolution to generate wealth. These policies have proven to contribute to a surge in carbon emissions globally since the Industrial Revolution (Ecolife, 2011).

In order to combat climate change, presently, governments have been forced to develop and implement policies that would assist in reducing carbon emissions (Oba, 2014). The policies can only be successful if they are integrated into the business strategies of the various companies and organizations in the country in question.

This paper focuses on establishing whether the policies developed by the various governments globally have had an impact on mitigation of climate change. This will be achieved by observing whether climate change has been integrated into the business strategies of companies and organization as well as observing the respective carbon emission percentages by each company or organization.  

Today, we live in a culture that has been significantly shaped by the forces of industry. We live in a society that has come to place an extreme value on the consumption of goods and services. However, the environmental cost of this kind of culture is not always immediately visible. To produce any single good or service, there is a long chain of processes that should also be accounted for when considering the environmental cost of anything. (Greenberg, 2014)

Organisations are using resources at a very high rate which results into increase in carbon emissions into the environment. Carbon Dioxide Emissions means the release of greenhouse gases and/or their precursors into the atmosphere over a specified area and period of time.  (OECD, 2005). Since the industrial revolution the burning of fossil fuels has increased, which directly correlates to the increase of co2 emissions level in our atmosphere and thus the rapid increase of global warming. (Ecolife, 2011). Co2 emissions have a very negative impact on the environment such as causing global warming, ocean acidification, smog pollution, ozone depletion as well as changes to plant growth and nutrition levels.

Now a days, most of the organisations consider corporate social responsibility in their business strategies and plans. Corporate social responsibility goes a long way in for creating a positive word of mouth for the organization on the whole. Doing something for your environment, society, stake holders, customers would not only take your business to a higher level but also ensure long term growth and success. 

Organizational impact on the environment

There are number of theories such as legitimacy, agency and stakeholder but in my perspective stakeholder theory is best. So in order to develop a hypothesis I am utilizing stakeholder theory. According to Edward Freeman, stakeholder theory holds that a company’s stakeholders include just about anyone affected by the company and its workings. Freeman suggests that a company’s stakeholders are “those groups without whose support the organization would cease to exist.”  These groups would include customers, employees, suppliers, society, managers, owners, government, creditors, shareholders and more (Freeman, 1984).

Stakeholder theory of research has two branches:-

1. Managerial Branch.
2. Normative Branch.

1. Managerial Branch-Under this branch, organization will not respond to all stakeholders equally, but to the most powerful. Stakeholder power is a function of the stakeholder’s degree of control over resources required by the organization.  (Hero, 2018)

1. Normative Branch-This branch hold that managers ought to pay attention to key stakeholder relationships. According to this perspective, managerial relationships with stakeholders are based on normative, moral commitments. Rather than on a desire to use those stakeholders solely to maximize profits.  (Zawaideh, 2006)

In order to consider environmental impact of the organization, out of these two branches managerial branch of stakeholder theory is more effective and useful.

In order to identify the influence of stakeholders, organization can use stakeholder salience. The Salience Model uses three parameters to categorize stakeholders: Power, Legitimacy and Urgency.  (Sharma, 2010)

1. The Stakeholders’ Powerto influence the firm.

2   Legitimacy of the stakeholders’ relationships with the firm

3 The urgency of the stakeholders claim on the firm.  (Morphy, 2008).

In my perspective, power is the best parameter to check the influence of the different stakeholders on the firm. Out of the different stakeholders, government is the most important and powerful stakeholder. There is a high degree of impact of government on the organisations. This is because the government has many powers that could be implemented on organisations depending on their environmental performance and corporate social responsibility. Government can implement many policies such as make new rules and regulations, impose taxes. Furthermore government can impose penalties on the firms which are not considering their operational activities impact on the environment.

H₁: There is a positive relationship between government influence on industries and reduction of carbon emissions in climate.

H₂: There is a no relationship between government influence on industries and reduction of carbon emissions in climate.

The sample data was collected from firms that are more directly involved in carbon emissions. These firms were prioritized over other firms that are mostly consumers of the products from firms that are directly involved in carbon emissions.  The excluded firms, which can be described as secondary contributors to carbon emissions, include the firms in the financial industry. The firms considered for the samples were also taken from four countries; Canada, United Kingdom, Brazil and United States of America.

The sample data has three variables; Countries (those selected for observation and research from which the firms originates), Government Role (in account of reduction of CO2 emissions in the climate) and Emission Reduction Rate (in the rate of CO2 emissions by different companies).

Corporate social responsibility

The Countries variable is the Control Variable (CV) with four different categories. These categories are the countries of Canada, United Kingdom, Brazil and United States of America. This data variable is Nominal in nature.

The Government Role variable is the Independent Variable (IV) with two different categories. Similar to the Countries variable, the Government Role is Nominal in nature. The categories are 1 and 2; 1 represents “Yes” (the firm has climate change integrated into its Business Strategy) while 2 represents “No” (the firm does not have climate change integrated into its Business Strategy).

The Emission Reduction Rate is the dependent variable (DV). This data variable is Ratio/Scale in nature representing the percentage change observed in 2013 compared to previous year’s data.

The sample data contains firms from various sectors or industries that can be considered as primary contributors to carbon emissions. This therefore implies that the analysis and findings from this research can be generalized for all sectors or industries of the primary contributors.

However, the sample data only contains data collected from four countries; Canada, United Kingdom, Brazil and United States of America. Whereas the list of countries does include moderately industrialized to industrialized countries which are the significant contributors to carbon emission, the list is not geographically balanced. The countries come from predominantly the Americas and Europe. Including countries from the Middle East and Asia would have made the analysis and findings from this research more generalizable in terms of region

The sample data contained missing values or entries. Below is a summary of the missing values or entries in the sample data from SPSS.

From the analysis results represented in Figure 1: Missing Values Analysis (Source SPSS) above, we observe that only one of the three data variables has missing entries. 23.75% of the cases or observation are affected, this represents a total of 38 of the 160 observations. The percentage of entered values (entries) affected is 7.917%, which represents a total of 38 of the 442 entries.

Variable Summarya,b
	Missing	Valid N	Mean	Std. Deviation
N	Percent
Percentage Reduction in Carbon Emissions in 2013	38	23.8%	122	569.0540	517.15598
a. Maximum number of variables shown: 25
b. Minimum percentage of missing values for variable to be included: 10.0%

Table 1: Missing Values Analysis (Source SPSS)

Table 1: Missing Values Analysis (Source SPSS) above represents the variable summary for missing values analysis. From Table 1: Missing Values Analysis (Source SPSS), the only variable with missing values is the Percentage Reduction in Carbon Emissions in 2013. The missing values represent 23.8% of the entries for this variable. This represents a total of 38 of the 160 entries for the variable. The remaining valid entries are 122 entries.

Stakeholder theory and research question

Since the percentage of the missing values is more than 5%, to deal with the empty entries we apply the Multiple Imputation Technique in SPSS. This technique will impute 23.8 (Rounded to 24) datasets.  However, for efficiency, we will use the default imputation number of 5 imputations for 5 datasets. Analysis on the resultant dataset will produce a pooled outcome.

The independent variable (IV) is the Government Role Variable, described as Climate Change Integration into Business Strategy. The analysis of the Independent Variable produced the outputs below:

Statistics
Integration of Climate Change into Business Strategy
Original data	N	Valid	160
Missing	0
1	N	Valid	160
Missing	0
2	N	Valid	160
Missing	0
3	N	Valid	160
Missing	0
4	N	Valid	160
Missing	0
5	N	Valid	160
Missing	0
Pooled	N	Valid	160
Missing	0

Table 2: General Statistics for Independent Variable

Integration of Climate Change into Business Strategy
Imputation Number	Frequency	Percent	Valid Percent	Cumulative Percent
Original data	Valid	Yes	80	50.0	50.0	50.0
No	80	50.0	50.0	100.0
Total	160	100.0	100.0
1	Valid	Yes	80	50.0	50.0	50.0
No	80	50.0	50.0	100.0
Total	160	100.0	100.0
2	Valid	Yes	80	50.0	50.0	50.0
No	80	50.0	50.0	100.0
Total	160	100.0	100.0
3	Valid	Yes	80	50.0	50.0	50.0
No	80	50.0	50.0	100.0
Total	160	100.0	100.0
4	Valid	Yes	80	50.0	50.0	50.0
No	80	50.0	50.0	100.0
Total	160	100.0	100.0
5	Valid	Yes	80	50.0	50.0	50.0
No	80	50.0	50.0	100.0
Total	160	100.0	100.0
Pooled	Valid	Yes	80
No	80
Total	160

Table 3: Detailed Statistics for Independent Variable

From Table 2: General Statistics for Independent Variable we observe that the Independent Variable had no missing values in all the imputations with a total of 160 entries in each imputation. The pooled results show no missing values and a total of 160 entries as well.

From Table 3: Detailed Statistics for Independent Variable we observe 80 “Yes” responses, representing 50% of the cases, and the remaining 80 responses being “No”, equally representing 50%. This represents the results of the analysis in each of the imputation as well as the pooled results.

Descriptives
Imputation Number	Statistic	Std. Error
Original data	Integration of Climate Change into Business Strategy	Mean	1.50	.040
95% Confidence Interval for Mean	Lower Bound	1.42
Upper Bound	1.58
5% Trimmed Mean	1.50
Median	1.50
Variance	.252
Std. Deviation	.502
Minimum	1
Maximum	2
Range	1
Interquartile Range	1
Skewness	.000	.192
Kurtosis	-2.025	.381
1	Integration of Climate Change into Business Strategy	Mean	1.50	.040
95% Confidence Interval for Mean	Lower Bound	1.42
Upper Bound	1.58
5% Trimmed Mean	1.50
Median	1.50
Variance	.252
Std. Deviation	.502
Minimum	1
Maximum	2
Range	1
Interquartile Range	1
Skewness	.000	.192
Kurtosis	-2.025	.381
2	Integration of Climate Change into Business Strategy	Mean	1.50	.040
95% Confidence Interval for Mean	Lower Bound	1.42
Upper Bound	1.58
5% Trimmed Mean	1.50
Median	1.50
Variance	.252
Std. Deviation	.502
Minimum	1
Maximum	2
Range	1
Interquartile Range	1
Skewness	.000	.192
Kurtosis	-2.025	.381
3	Integration of Climate Change into Business Strategy	Mean	1.50	.040
95% Confidence Interval for Mean	Lower Bound	1.42
Upper Bound	1.58
5% Trimmed Mean	1.50
Median	1.50
Variance	.252
Std. Deviation	.502
Minimum	1
Maximum	2
Range	1
Interquartile Range	1
Skewness	.000	.192
Kurtosis	-2.025	.381
4	Integration of Climate Change into Business Strategy	Mean	1.50	.040
95% Confidence Interval for Mean	Lower Bound	1.42
Upper Bound	1.58
5% Trimmed Mean	1.50
Median	1.50
Variance	.252
Std. Deviation	.502
Minimum	1
Maximum	2
Range	1
Interquartile Range	1
Skewness	.000	.192
Kurtosis	-2.025	.381
5	Integration of Climate Change into Business Strategy	Mean	1.50	.040
95% Confidence Interval for Mean	Lower Bound	1.42
Upper Bound	1.58
5% Trimmed Mean	1.50
Median	1.50
Variance	.252
Std. Deviation	.502
Minimum	1
Maximum	2
Range	1
Interquartile Range	1
Skewness	.000	.192
Kurtosis	-2.025	.381

Table 4: Normality Analysis of Independent Variable

From Table 4: Normality Analysis of Independent Variable we observe that the statistics for the normality are identical for all the imputations. Hence, the Independent Variable can be said to have a Range = 2, Interquartile Range = 1, Skewness = 0, Kurtosis = -2.025.

The dependent variable (DV) is the Emission Reduction Rate, described as Percentage Reduction in Carbon Emission in 2013. The analysis of dependent variable produced the outputs below:

 

Descriptive Statistics
Imputation Number	N	Minimum	Maximum	Mean	Std. Deviation
Original data	Percentage Reduction in Carbon Emissions in 2013	122	-999.00	999.00	569.0540	517.15598
Valid N (listwise)	122
1	Percentage Reduction in Carbon Emissions in 2013	160	-999.00	1787.28	532.8050	532.98531
Valid N (listwise)	160
2	Percentage Reduction in Carbon Emissions in 2013	160	-999.00	1752.81	536.9166	529.10505
Valid N (listwise)	160
3	Percentage Reduction in Carbon Emissions in 2013	160	-999.00	1806.71	530.1496	529.24333
Valid N (listwise)	160
4	Percentage Reduction in Carbon Emissions in 2013	160	-999.00	1662.83	538.9357	504.17060
Valid N (listwise)	160
5	Percentage Reduction in Carbon Emissions in 2013	160	-999.00	1389.87	535.9507	495.34613
Valid N (listwise)	160
Pooled	Percentage Reduction in Carbon Emissions in 2013	160			534.9515
Valid N (listwise)	160

Table 5: Detailed Statistics for the Dependent Variable

The analysis for this description produced the output below:

(The analysis will focus on the first and second imputations for pooled analysis)

Descriptives
Imputation Number	Integration of Climate Change into Business Strategy	Statistic	Std. Error
Original data	Percentage Reduction in Carbon Emissions in 2013	Yes	Mean	286.9461	70.77607
95% Confidence Interval for Mean	Lower Bound	144.7881
Upper Bound	429.1040
5% Trimmed Mean	286.3246
Median	-4.0000
Variance	255471.890
Std. Deviation	505.44227
Minimum	-999.00
Maximum	999.00
Range	1998.00
Interquartile Range	1010.60
Skewness	.444	.333
Kurtosis	-.730	.656
No	Mean	771.6949	50.37798
95% Confidence Interval for Mean	Lower Bound	671.2192
Upper Bound	872.1706
5% Trimmed Mean	803.7858
Median	999.0000
Variance	180193.799
Std. Deviation	424.49240
Minimum	-52.00
Maximum	999.00
Range	1051.00
Interquartile Range	.00
Skewness	-1.344	.285
Kurtosis	-.198	.563
1	Percentage Reduction in Carbon Emissions in 2013	Yes	Mean	284.7057	56.04703
95% Confidence Interval for Mean	Lower Bound	173.1468
Upper Bound	396.2645
5% Trimmed Mean	297.3186
Median	.0000
Variance	251301.556
Std. Deviation	501.29987
Minimum	-999.00
Maximum	1308.77
Range	2307.77
Interquartile Range	823.33
Skewness	.177	.269
Kurtosis	-.481	.532
No	Mean	780.9043	49.46920
95% Confidence Interval for Mean	Lower Bound	682.4383
Upper Bound	879.3702
5% Trimmed Mean	794.7040
Median	999.0000
Variance	195776.160
Std. Deviation	442.46600
Minimum	-54.96
Maximum	1787.28
Range	1842.24
Interquartile Range	189.20
Skewness	-1.015	.269
Kurtosis	-.144	.532

Table 6

The data points identified outside of the right hand side boxplot in Figure 10 represent the outlier values. To deal with the outliers, we conduct a transformation. It can either be a log transformation or a square root transformation. The alternative option to remove or drop the outliers would significantly reduce the observations, making the sample data insufficient for analysis.

Considering that the levels of measurements for the IV and DV are Nominal and Scale respectively, the appropriate statistical test would be a One-Way ANOVA F-Test.

The Assumptions of the test are as follows:

1. The level of measurement of the dependent variable is assumed to be at the ratio or interval level.
2. The independent variable is assumed to have two or more independent categories or groups.
3. The observations are assumed to be independent of each other.
4. Significant outliers are assumed to be absent in the sample data.
5. For each category of the independent variable, the dependent variable is assumed to be normally distributed.
6. The variances are assumed to be homogenous in nature.

The only assumption that the sample data does not satisfy is the assumption number four. The sample data does contain significant outliers. Therefore, we can conclude that after the transformation is carried out to deal with the outliers, the One-way ANOVA F-Test can then be comfortably applied to the resultant dataset.

References

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