Business Strategy And Incentives For Climate Management On Carbon Emission Reduction

The Impact of Climate Change on Business and Opportunities

The inclusion of the considerations of climate change into the corporate strategies has begun giving a new shape to the manner in which enterprises perform the businesses. In their attempts to handles climate change, companies and firms come across new opportunities, among them creating new technologies as well as commodities and gaining access to new markets (Herold, Lee and Gunarathne, 2018). Through gaining higher energy efficiency, the companies are as well able to make significant savings.

However, companies still face greater and more significant risks when it comes to climate change among them increasing regulatory and legal pressure, enhanced costs resulting from internalization of carbon, risks associated with reputation as well as disruptions to the business activity inducted by changes in the climate (Ben?Amar and McIlkenny,2017). The end results is that the balance between the costs and the available opportunities will rely on a number of factors among them government policies, specificities as well as the ability of the companies to involve the consumers in handling the challenges of climate change (Andrew and  Cortese, 2011).

This aim of this paper is to establish if business strategy as well as incentives have a significant role to play when it to comes to checking the level of carbon emissions in a business organization with more focus being on those activities that are geared towards the establishment of a low carbon economy. The aim of this paper will be met by drawing mainly from the works of recent literature, adoption of the recommendations as outlines in the OEDCD Guidelines for Multinational Enterprises as well as from experimental studies that will be conducted (Freedman, 2009). 

A continuous audit by McKinsey(2015) on the manner in which associations take into consideration climate change, where more than 2000 authorities responded, revealed that 60% of chairmen see ecological change as an indispensable idea inside their general system of the organization, while 70% perceive it as a basic estimation for reputation and check. Elucidation into the activities of the organization remains at any rate compelled: to the tune of 4 out of 10 of the CEOs ecological change is certifiably not a basic thing on their arrangement, 70% uncovered that their association excludes ecological change centers in the implementation review of authorities as well as including the heads specifying that supervising characteristic issues was basic, 60% encompassed a niche with firms that had not portrayed surges diminish targets. In any case, 80% of chairmen foreseen that may be impacted by some kind of change in the environment in the next half a decade (Sullivan, 2017).

The Challenges of Climate Change for Businesses

Business state of mind towards environmental change is driven by an assortment of components, including government approaches also, control and weight from shoppers and different partners (Ben?Amar and McIlkenny, 2015). Lately, governments in OECD nations have actualized residential atmosphere strategy systems, including a blend of approach instruments went for moderating ozone harming substance (GHG) outflows. These arrangement blends incorporate market-based instruments, for example, duties and top and-exchange frameworks, and additionally direction and data battles (Herold, Lee and Gunarathn, 2018). Residential atmosphere arrangements are advancing, with key points of reference still to come, for example, the universal post-2012 system that nations are intending to concur at COP15 in December 2009, in Copenhagen. While organizations are confronting expanding government measures, a vital part of the business reply to environmental change is likewise determined by private activities to react to societal desires imparted by different channels than law (Bocken  et al, 2014 )(e.g., purchaser affiliations, the press, global associations, and so on.).

The goal of this examination is to decide the connection between carbon emanations decrease and two different elements business system (as far as environmental change coordination) and Incentives (as far as motivating force for administration of atmosphere) (Liao, Luo and Tang, 2015). The examination likewise attempts investigates the response to the two inquiries; first, why environmental change ought to be coordinated in a business technique and what influence does it have in decreasing the carbon outflows? Second, does motivating force for administration of environmental change affect decrease in carbon outflows? The CDP reactions will help both the organizations and every one of the clients of data, as it gives chances to the firm to distinguish the methodology for administration and decrease of emanations and the information ought to likewise profit the financial specialists and different partners by giving data about organization practices and activities in regards to environmental change (Andrew and Cortese, 2011).

What significance does integration of climate change in business strategy have on carbon emission reduction? 

Hypothesis 1: Business Strategy with regard to integration of climate change has a significant impact on the reduction of carbon emissions.

Hypothesis 2: Provision of incentives aimed at climate management has a relatively significant effect on the reduction of carbon emissions. 

The tables 2, 3 and 4 below are an illustration of the changes in the values for the independent variable. From the tables it can be observed that the descriptive statistics which for this case was also the independent variable i.e. the Business Strategy (Integration of Climate Change), there were 150 observations and not even a single missing observation.

Business Strategy (Climate Change Integration)
N	Valid	150
Missing	0

Research Objectives

Table 1: Descriptive Statistics (Business Strategy)Table 1: Descriptive Statistics (Business Strategy) above indicates that the data variable had 150 valid observations with no missing observations.

The histogram illustrates the frequency the reduction in the emission of carbon for the year 2014 from the 150 valid observations that were made. From the histogram it can be noticed that the highest reduction was experienced at a frequency of about 50 where the reduction was at 0.00. Note should take of the nature of the frequency polygon generate from the bars which demonstrated an exponential increase in the reduction which levels off at 100 (Liesen et al 2015).

The table 7 below is an illustration of the Normality of Carbon Emissions Reduction. Information regarding the skewness and kurtosis as well as the accompanying std. errors is shown. This data shows that the findings of the research illustrated relatively skewed results or data to a given direction (Chu, 2015)

Carbon Emissions Reduction (% Reduction in Carbon Emissions for 2014)
N	Valid	150
Missing	0
Skewness	1.387
Std. Error of Skewness	.198
Kurtosis	6.645
Std. Error of Kurtosis	.394
Range	195.13

Table 6: Normality of Carbon Emissions Reduction

From Table 6: Normality of Carbon Emissions Reduction above, we observe that the normality statistics for the carbon emissions reduction are: Range = 195.13, Skewness = 1.387, Kurtosis = 6.645. The data on the carbon emissions reduction can therefore be said to be slightly skewed to the right (Liesen et al, 2015).

Key

“No” Outliers:-3, 2, 13, 7 & 17

 “Yes”: -131, 102, 127, 118, 140, 128, 80, 93, 25, 106 & 100

Shown above is the graphical portrayal of the depiction of ward variable (Carbon Emissions Reduction) as far as the autonomous variable (Business Strategy). Figure 4: Carbon Emissions Reduction in Terms of Business Strategy appears there are 5 anomalies on the “No”: 3, 2, 13, 7 and 17, while on the “Yes” we have 11 exceptions: 131, 102, 127, 118, 140, 128, 80, 93, 25, 106 and 100. Of the aggregate 16 exceptions, 2 can be named extraordinary anomalies: 131 and 100. With the end goal to have proper information for the investigation, the outrageous exceptions ought to be evacuated (Ioannou, Li and Serafeim, 2015).

Regression analysis is a statistical analysis that explains the relationship between variables.  There are two types of variables in regression. The response and the explanatory variables. The explanatory are independent and are used to predict the response or independent variables. Linear Regression Analysis would be appropriate for the investigation because of the nature of variables. The Independent Variable (Business Strategy) is estimated on the ordinal scale, the Moderating Variable (Incentive) is estimated on the ordinal scale and the Dependent variable (Carbon Emissions Reduction) is estimated on the Ratio Scale

Literature Review

Regression analysis general equation take the form

The table1 and table 2 below shows the results of linear regression of the dependent variable (carbon emission reduction) and the independent variable (business strategy on climate change)

Table 1: Table of variables

Variables Entered/Removeda
Model	Variables Entered	Variables Removed	Method
1	business strategy, incentives	.	Enter
a. Dependent Variable: carbon emission reduction
b. All requested variables entered.

Table 2 model summary

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.151a	.023	.009	84.13181
a. Predictors: (Constant), business strategy, incentives

According to Table 1: Table of variables output the independent variables that were included in the model were climate change included in the business strategy and whether incentives were provided for climate management. The dependent variable that was included on the model for the regression analysis was the reduction in carbon emission

The R square was 0.23 and the adjusted R squared was 0.009. R squared in the model was very low. According to the R square statistic 0.9% of the independent variable (business strategy) and control variable (incentives) determined the response variable. (Carbon emission reduction.)

ANOVA is a statistical test that is used to determine the significance of a results of the analysis carried out on the study variable. Table 3 is an output of the analysis of variance conducted on the study’s regression model.

Table 3 ANOVA table

ANOVAa
Model	Sum of Squares	Df	Mean Square	F	Sig.
1	Regression	24229.939	2	12114.970	1.712	.184b
Residual	1040489.796	147	7078.162
Total	1064719.735	149
a. Dependent Variable: carbon emission reduction
b. Predictors: (Constant), business strategy, incentives

Significance level was at 0.184 for the combined independent variables. Significance of a model indicates whether the prediction made using the model are statistically significant. At 0.05 level of significance the model prediction is insignificant since 0.184 > 0.05. This means that predictor variables (business strategy and incentives.) were not statistically significant to predict dependent variable (reduction in carbon emission)

ANOVA univariate test is used to analyse the significance of each response variable used in the model at a given level of significance. The table 4 below is the output of the test.

Table 4 Table for the AVNOVA univariate test

Tests of Between-Subjects Effects
Dependent Variable:   carbon emission reduction
Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	27160.610a	3	9053.537	1.274	.286
Intercept	5369.498	1	5369.498	.756	.386
Incentives	7568.118	1	7568.118	1.065	.304
Business strategy	3812.449	1	3812.449	.536	.465
incentives, business strategy	2930.670	1	2930.670	.412	.522
Error	1037559.126	146	7106.569
Total	1070093.966	150
Corrected Total	1064719.735	149
a. R Squared = .026 (Adjusted R Squared = .005)

In the F-value for the intercept (point where the response variables were at zero) was 0.386. The F-value for the independent variables provision of incentives foe climate management and was climate integrated in business strategy were 0.304 and 0.465 respectively.  The F- value for the model and the combined effect of the independent variables were 0.286 and 0.522 respectively. All the F-value are insignificant at 0.05 level of significance since they are all greater than the F-value. This means that they are greater than the acceptable level

The table 5 Levene’s test is used to test whether the control variable and the independent variable are homoscedastic 

Table 5: Levenes test

Levene’s Test of Equality of Error Variancesa
Dependent Variable:   carbon emission reduction
F	df1	df2	Sig.
5.812	1	148	.017
Tests the null hypothesis that the error variance of the dependent variable is equal across groups.
a. Design: Intercept + incentives

 At the 0.05 level of significance we reject the null hypothesis since 5.82 > 0.05 and conclude the response and control variable has different variances or are heteroscedastic. 

Methodology

The coefficients offer the required information in the prediction of the reduction in the emission from business strategy as well as establishing if there is statistical significance to the model. From the analysis of the regression, the regression equation can be presented as

Reduction in carbon emission=-26.08+25.2 (Business Strategy)

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	-26.0889	20.7765221	-1.25569135	0.21120669	-67.14585	14.9681	-67.14585	14.968054
Business Strategy	25.18945	15.4005828	1.635616644	0.10404483	-5.243989	55.6229	-5.243989	55.622888

Table 12: Coefficients Analysis Table

 The null and alternative hypotheses for the research were:

H₀: Business Strategy with regard to integration of climate change has a significant impact on the reduction of carbon emissions

H₁: Provision of incentives aimed at climate management has a relatively significant effect on the reduction of carbon emissions.

Decision rule: To assess the truth value of the hypothesis we refer to Table3 (ANOVA table) and Table 4 (ANOVA univariate table). In Table 3 the F-value of the model is 1.84 > 0.05 thus insignificant at 0.05 level of significance. In Table 4 the F- value for two independent variables were 0.304 and 0.4645and their combined effect was 0.522 which were again insignificant at 0.05 level of significance. The model and the intercept F-values were 0.286 and0.386 respectively and are both insignificant at 0.05 level of significance. We hence reject the null hypothesis and conclude that provision of incentives aimed at climate management has a relatively significant effect on the reduction of carbon emissions

Carbon emission is an ideal case of externality. Most of companies do not realize the cost incurred by emission of carbon into the environment. However, as indicated in the research, carbon emission has adverse effect to businesses, community and investors at large (Fernando and Lawrence, 2014). Business stakeholders include management, shareholders, customers and government. All the shareholders have part to play in reduction of the company’s carbon emission levels.

The study was conducted to investigate whether integration of climate change in business strategy had and provision of incentives to management of climate had had impact on carbon emission rate. Integration of climate change in the business strategy means that the business takes active and conscious initiatives to reduce carbon emission into the environment (Fernando and Lawrence, 2014). Based on the results of the research, business strategy (independent variable) to did not have a significant effect on reduction in carbon emission on a significance level of 0.05.  Using a higher level of significance 20%, according to table 4, the independent variable is still insignificant as 0.465 > 0.20.

Incentives to may management of climate is the control variable in the study. Incentives are meant motivate companies to reduce carbon emission. Incentives come in different forms form different stakeholders. According to the research result the addition of incentives did not have a significant effect on the response variable. However, at 20% of significance the combined effect business strategy and incentives had significant effect on carbon emission. In table 3, 0.18 < 0.2.

Results

The implication of research results is that carbon emission can be reduced by integrating climate change the business strategy. The results of business strategy are mild. However, the use of incentives yields better results in the effort to reduce carbon emission. Business, especially those that are emission intensive, should make therefore use incentives boost their active efforts to curb carbon emission (Dou, Zhu and Sarkis, 2015).

Recommendations

• Governments should give incentives to companies that integrate climate change in their business strategy. This would serve to motivate companies to actively seek alternative environment friendly modes of operation (Chun, 2016).
• Business should give incentives to their staff that implement the business strategy in environment conservation. As indicated in the research incentives go a long way yielding the business strategy results.
• Governments and business should educated the masses and staff on the cost of carbon emission. This would create intrinsic motivation to actively make efforts to reduce carbon emission

• Inferences were drawn from companies with a small sample size. Only companies and countries from BRICS namely Brazil, Russia, India, China and South Africa were sampled thus the results may be not be applicable to other countries with different economic environments. (Herold, Lee and Gunarathne, 2018),
• There were numerous null entries in the data, the data may therefore be said to be incomplete. Null entries were replaced with the mean, this led to more inaccurate results.

The purpose of a further research would be to address the limitations of the study. Such a research would be done on a data set that does not contain missing entries. This would serve to reduce estimation and increase in data in the research’s accuracy. ( Graceffo, 2011).

The research would be carried out on a broader number of countries. In the research, only five countries were considered all members of BRICS. This countries formed the union since they were industrial countries at a certain stage of development (Herold, Lee and Gunarathne, 2018). The results of the study would therefore not suitable for extrapolation to estimate the carbon emission states in other countries with different economic environments.

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