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institutional theory as the results seemed to show why companies institute integrated/ multifaceted programmes and activities in the attempt to enhancing their interaction with the environment and meeting stakeholder demands.
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corporate economic performance employing Bootstrap Panel dynamic threshold models.
Finally, review of extant literature showed that this is the first study in sustainability accounting research in South Africa to examine the carbon intensity effect on financial performance using a combination of carbon output intensity (EMSINT) and carbon input intensity (ENGINT), and four accounting control variables, which are: OPTINC, GROWTH, LEV and/or ASSET/ LNMVE. Therefore, the improved model employed in this research, which future researchers may replicate in other countries, is re-produced in the Table 5.I.
Table 5.I: Variables for OLS, Fixed Effects and Arellano-BOND Estimations
Dependent Independent variables
Return on Assets (y) EMISNT(x1),ENGINT(x2),OPTINC(x3),LEV(x4),LNASSET(x5), GROWTH(x6)
Return on Sales (y) EMISNT(x1),ENGINT(x2),OPTINC(x3),LEV(x4),ASSETS/S(x5), GROWTH(x6)
Equity returns (y) EMISNT(x1), ENGINT(x2), OPTINC(x3), LEV(x4), LNMVE(x5), GROWTH(x6)
Market value of equity
/Sales EMISNT(x1), ENGINT(x2), OPTINC(x3), LEV(x4), ASSETS/S(x5), GROWTH(x6)
Owing to the low power of OLS and Fixed Effects estimations, some recent studies in sustainability accounting research have resorted to more robust statistical techniques including; 2 Stage Least Squares and Partial Least Squares/ Structural Equation Models (SEMs). This study however applied different statistical tools including: Arellano-Bond DPD estimations, Impulse Response Function analysis in short panel vector auto regressions (SPVARs), and Bootstrap dynamic panel threshold models. Application of these estimators in this current study makes this
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study unique and distinct from previous studies in sustainability accounting research (see Table 5.II).
Table 5.II: Some Advanced Statistical Models used in related previous studies
Authors/ Study 2/ 3 Stage Least Squares
Partial Least Squares/
SEMs
Erhemjamts et al., (2013) X
Sambasivan et al., (2012); X
Boltcher & Muller, (2014) X
Klingenberg et al., (2013) X
Russo & Pogutz, (2009) X
Lee and Park, (2009) X
Salama, (2005) X
Mullin et al., (2014) X
Alzboun et al., (2016) x
Sen et al., (2015) X
Agan, et al., (2014) X
Table 5.III: Some Advanced Statistical Models used in this research but not in previous related studies
Authors/ Study Arellano-
Bond DPD Model
Impulse Response Function in
SPVARs
Bootstrap Dynamic Panel Threshold
Model
This study by Worae., (2016)
Previous studies:
Erhemjamts et al., (2013) X X X
Sambasivan et al., (2012) X X X
Boltcher and Muller, (2014) X X X
Klingenberg et al., (2013) X X X
Russo and Pogutz, (2009) X X X
Lee and Park, (2009) X X X
Salama, (2005) X X X
Mullin et al., (2014) X X X
Sen et al., (2015) X X X
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Alzboun et al.,( 2016) X X X
Agan, et al., (2014) X X X
Chart (figure 5.I) of impulse and threshold offers a further agenda for research. So far, no previous research has examined which of the two points may trigger better financial gains. Therefore it becomes pertinent for further research to examine the following relationships:
Figure 5.I: Suggested Framework for further analysis of Carbon Emissions & Financial Performance
i. The relationship between residuals from the threshold model and impulse response function on financial performance
y
= 0 + 1xit + 1xit + it,Where:
y
= the level of financial gainsXit = residuals from threshold estimation
Xit = residuals from impulse response analysis
ԑit = error term following a normal distribution with mean zero and variance 1 Impulse Response
Analysis in Short PVARs
Dynamic Panel Threshold Effect
Analysis
When changes in Emissions/ Energy Usage Intensity pay
Financially
The ''Tipping Point'' above which Financial
Gains could retard
Decision on Balanced Emission/ Energy Usage Reduction &
Financial Gains
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ii. The relationship between residuals from the threshold model and impulse response function on managerial decisions
y
= 0 + 1xit + 2xit + itWhere:
y
= level of managerial decisions on carbon output/ input reduction Xit = residuals from threshold estimationXit = residuals from impulse response analysis
ԑit = error term following normal distribution with mean zero and variance 1.
5.3.2 Contribution to Practice and Policy Practice
Conventional managerial performance evaluation is based on financial and non- financial measures which exclude environmental Greenhouse Gas variables. But from the results of this study, it becomes evident that managerial performance evaluation needs transformation to include environmental ratios such as, emissions intensity, energy intensity to the traditionally adopted internal managerial performance measures against divisional investment and/or earnings.
Policy
As climate change policies trigger unprecedented emergence in internal corporate carbon policies, companies are increasingly developing ambitious carbon reduction agendas in all activities. Yet, one of the setbacks amongst others is how to determine which of the corporate activities that have significant influence on corporate carbon levels (Kjaer, Høst-Madsen, Schmidt, and McAloone, 2015). This research has demonstrated the use of impulse response and threshold analysis in determining economic implications of carbon reduction. This research has contributed to internal corporate carbon policy through the application of impulse response and threshold effects to determine what level of carbon reduction might be economically feasible and/or worthwhile to maintain a permissible level of
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carbon at a tolerable economic level for a firm’s economic capacity. This threshold- impulse response assessment should be able to direct management as to when, and at what level they should swing into action regarding carbon abatement management. The assessment could also inform policy on carbon reduction investment commitments, and signal management as to where to stop or continue with carbon improvement activities and investments. This it is believe could enhance internal policy on carbon reduction in a more sustainable competitive manner.