XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 279
Table 2. Descriptive Codes Related to Variables
Variables type Measures
Dependent
Variables
Return on Assets Net Profit/ Total assets
Tobin Q (Market value of common equity +preferred stock +total
debt) /Total assets
Independent
variables
Firm specific factors
Size Natural logarithm of total assets
Financial Leverage Total Liabilities/Total Assets
Energy Efficiency ((energy /revenue) -(energy[_n-1] /revenue[_n-1])) /
(energy[_n-1] /revenue[_n-1]) if _n 1
Firm specific sustainability behaviors
total_adoptofstandards_rules Sum of SDG1 to 17 Adoption of Standards &Rules
total_assess_measurement Sum of SDG1 to 17 Assessment&Measurement
total_assetmodifications Sum of SDG1 to 17 Assets Modification
total_associations Sum of SDG1 to 17 Association
total_communications Sum of SDG1 to 17 Communication
total_donationfundings Sum of SDG1 to 17 Donations &Funding
total_incentives Sum of SDG1 to 17 Incentives
total_modificationofprocedures Sum of SDG1 to 17 Modification of Procedures
total_newproducts Sum of SDG1 to 17 New product
total_orgstructuring Sum of SDG1 to 17 Organizational Structuring
total_pricing Sum of SDG1 to 17 Pricing
total_rdinvestments Sum of SDG1 to 17 R &D Investments
total_training Sum of SDG1 to 17 Training
total_volunteerism Sum of SDG1 to 17 Volunteerism
Number of
Companies
356
Data Analysis
Panel data analysis techniques were utilized for the data
analysis (Eccles et al., 2014). This approach incorporates
both the cross-sectional dimension (variation across enti-
ties) and the time-series dimension (variation over time)
of the data (Busch et al., 2020). By taking into account
these dimensions, panel data analysis permitted the control
of unobserved heterogeneity at the entity level, the capture
of time-varying effects, and the attainment of more robust
estimates for the examined relationship.
RESULTS
Descriptive Statistics
Table 3 summarizes descriptive statistics for the panel.
ROA shows the most variation (mean 5.141, SD 9.385,
range –15.381 to 43.046), highlighting diverse financial
performance. Conversely, Tobin’s Q exhibits greater stabil-
ity (mean 1.207, SD 0.429, range 0.404 to 2.465). Similar
trends are observed for financial leverage (mean 2.004,
SD 0.543, range 1.069 to 3.744) and size (mean 9.606,
SD 1.139). Sustainability behavior variables, including
total adoption of standards, associations, and communica-
tions, also display notable variation in engagement levels.
This dataset diversity suggests a linear regression approach
can provide valuable insights into the factors influencing
financial performance within the context of sustainability
behaviors.
Table 4s pairwise correlations offer key insights into
variable relationships. ROA’s positive correlation with
Tobin’s Q echoes expected links between financial perfor-
mance and market valuation (Busch et al., 2020). Financial
leverage and firm size exhibit weak correlations with most
variables. Energy efficiency shows mixed associations.
Notably, sustainability behaviors (adoption, associations,
communication) positively correlate, suggesting co-engage-
ment. Similarly, total donations and total assessments cor-
relate positively with various variables, indicating broader
engagement in sustainability practices. The diverse correla-
tions in Table 4 provide a valuable foundation for regres-
sion analysis.
Table 2. Descriptive Codes Related to Variables
Variables type Measures
Dependent
Variables
Return on Assets Net Profit/ Total assets
Tobin Q (Market value of common equity +preferred stock +total
debt) /Total assets
Independent
variables
Firm specific factors
Size Natural logarithm of total assets
Financial Leverage Total Liabilities/Total Assets
Energy Efficiency ((energy /revenue) -(energy[_n-1] /revenue[_n-1])) /
(energy[_n-1] /revenue[_n-1]) if _n 1
Firm specific sustainability behaviors
total_adoptofstandards_rules Sum of SDG1 to 17 Adoption of Standards &Rules
total_assess_measurement Sum of SDG1 to 17 Assessment&Measurement
total_assetmodifications Sum of SDG1 to 17 Assets Modification
total_associations Sum of SDG1 to 17 Association
total_communications Sum of SDG1 to 17 Communication
total_donationfundings Sum of SDG1 to 17 Donations &Funding
total_incentives Sum of SDG1 to 17 Incentives
total_modificationofprocedures Sum of SDG1 to 17 Modification of Procedures
total_newproducts Sum of SDG1 to 17 New product
total_orgstructuring Sum of SDG1 to 17 Organizational Structuring
total_pricing Sum of SDG1 to 17 Pricing
total_rdinvestments Sum of SDG1 to 17 R &D Investments
total_training Sum of SDG1 to 17 Training
total_volunteerism Sum of SDG1 to 17 Volunteerism
Number of
Companies
356
Data Analysis
Panel data analysis techniques were utilized for the data
analysis (Eccles et al., 2014). This approach incorporates
both the cross-sectional dimension (variation across enti-
ties) and the time-series dimension (variation over time)
of the data (Busch et al., 2020). By taking into account
these dimensions, panel data analysis permitted the control
of unobserved heterogeneity at the entity level, the capture
of time-varying effects, and the attainment of more robust
estimates for the examined relationship.
RESULTS
Descriptive Statistics
Table 3 summarizes descriptive statistics for the panel.
ROA shows the most variation (mean 5.141, SD 9.385,
range –15.381 to 43.046), highlighting diverse financial
performance. Conversely, Tobin’s Q exhibits greater stabil-
ity (mean 1.207, SD 0.429, range 0.404 to 2.465). Similar
trends are observed for financial leverage (mean 2.004,
SD 0.543, range 1.069 to 3.744) and size (mean 9.606,
SD 1.139). Sustainability behavior variables, including
total adoption of standards, associations, and communica-
tions, also display notable variation in engagement levels.
This dataset diversity suggests a linear regression approach
can provide valuable insights into the factors influencing
financial performance within the context of sustainability
behaviors.
Table 4s pairwise correlations offer key insights into
variable relationships. ROA’s positive correlation with
Tobin’s Q echoes expected links between financial perfor-
mance and market valuation (Busch et al., 2020). Financial
leverage and firm size exhibit weak correlations with most
variables. Energy efficiency shows mixed associations.
Notably, sustainability behaviors (adoption, associations,
communication) positively correlate, suggesting co-engage-
ment. Similarly, total donations and total assessments cor-
relate positively with various variables, indicating broader
engagement in sustainability practices. The diverse correla-
tions in Table 4 provide a valuable foundation for regres-
sion analysis.