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Journal Of Economics, Technology, and Business (JETBIS)

Volume 3, Number 6 June 2024

p-ISSN 2964-903X; e-ISSN 2962-9330

FACTORS AFFECTING CARBON EMISSION DISCLOSURE AND ITS IMPACT

ON COMPANY FINANCIAL PERFORMANCE (Study of Energy Sector Companies

Listed on the IDX in 2020-2022)

Rinda Lestari

, Mukhzarudfa

, Ratih Kusumastuti

Universitas Jambi, Indonesia

Email: [email protected]

, [email protected]

[email protected]

KEYWORDS:

carbon performance,

environmental costs, green

product innovation, financial

performance, and carbon

emissions disclosure.

ABSTRACT

This research aims to determine the influence of carbon performance,

environmental costs, and green product innovation on carbon

emission disclosure and its impact on financial performance (a case

study of Energy Sector Companies Listed on the IDX in 2020-2022).

The population in the research is energy sector companies listed on

the BEI in 2020-2022. The research sample was selected using a

purposive sampling technique, namely a sample determination

technique using predetermined criteria, so that a total sample of 54

research samples was obtained. This research method uses

quantitative methods. This research uses secondary data obtained

through the publication of financial reports, annual reports and

sustainability reports for each energy sector company listed on the

Indonesia Stock Exchange (BEI). The research results show that

carbon performance and green product innovation have a positive and

significant effect on carbon emissions disclosure. Environmental

costs have a negative but not significant effect on carbon emissions

disclosure. Carbon performance has a negative and significant effect

on financial performance. Environmental costs have a positive and

significant effect on financial performance. Green product innovation

has a negative but not significant effect on financial performance.

Disclosure of carbon emissions has a positive and significant effect

on financial performance. Carbon performance and green product

innovation have a positive and significant effect on financial

performance through carbon emission disclosure. Environmental

costs have a negative but insignificant effect on financial performance

through carbon emission disclosure.

INTRODUCTION

Climate change is an interesting issue and the center of world attention. It causes global

warming, which results in environmental damage and pollution (Nursulistyo et al., 2022).

Indonesia produces approximately 15-20 million tons of carbon emissions per day (Andrian &

Kevin, 2021). The Global Carbon Project shows that Indonesia ranks as the tenth largest carbon

emitting country in the world (Global Carbon Atlas, 2022).

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Figure 1

Ranking of Countries with Carbon Dioxide Emissions Production in the World in 2022

Source: Global Carbon Project, 2023

Figure 1 illustrates that Indonesia's carbon emissions production reached 616 million tons

of carbon dioxide. The main sources of these emissions are coal use, gas flaring, oil and gas

activities, and cement production. Although Indonesia is considered the lungs of the world, the

country plays a significant role in climate change. China, the United States and India are the

top three countries in terms of carbon production. More surprisingly, seven of the ten countries

with the highest carbon emissions are from Asia.

Figure 2

Indonesia's Carbon Dioxide Emissions Production

Source: Global Carbon Project, 2023

Figure 2 explains that the trend of Indonesia's CO2 emissions (metric tons per capita)

from 1989 to 2022 is increasing from year to year. The peak was in 2021 and decreased until

2022. This downward trend is likely due to the government's increasing pressure to reduce

carbon emissions by encouraging companies engaged in industries that contribute the highest

carbon emissions to intensify carbon emissions management practices and disclose carbon

emissions to the public (Nasih et al., 2019).

The Indonesian government encourages companies to pay attention to the impact of their

operational activities on sustainability reporting obligations through POJK Number

51/POJK.03/2017 Article 10 paragraph 1 which indicates that the obligation to prepare

sustainability reports applies to financial services institutions, issuers, and public companies.

Referring to POJK Number 51/POJK.03/2017, public companies are required to start

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publishing their sustainability reports for the reporting period starting January 1 to December

31, 2020. Furthermore, SEOJK Number 16 /SEOJK.04/2021 was issued to complement POJK

51/POJK.03/2017, which regulates the sustainability report of Financial Services Institutions,

Issuers, or Public Companies. A Sustainability Report is a publicly published document that

includes an evaluation of the financial, social, economic and environmental performance of an

entity such as financial institutions, listed companies and public companies, in the context of

conducting business in a sustainable manner.

Climate Reporting in ASEAN (2022) issued by GRI ASEAN, it is known that the

assessment of climate change-related reporting in Indonesian companies is only 44% where

the assessment is carried out on 7 aspects, namely the reporting framework, materiality, risks

and opportunities, governance, strategy, targets, and performance (Putri & Arieftiara, 2023).

Based on data released by Climate Transparency, the majority of greenhouse gas (GHG)

emissions in Indonesia come from burning fossil fuels, mainly in the form of carbon dioxide

(CO2) emissions.

Figure 3

graph of annual CO2 emissions in Indonesia by sector

Source: Climate Transparency (2023)

Figure 3 illustrates the upward trend of emissions in Indonesia from 1990 to its peak in

2018, reaching a level of 620 million tons of carbon dioxide (MtCO2). In 2020, the power

sector became the largest contributor to CO2 emissions with 35%, mainly due to the expansion

of coal power plant construction supported by government subsidies. The transportation and

industrial sectors each accounted for 27% of total emissions, with increased vehicle use and

industrial activity resulting in excessive use of fossil fuels. The low level of disclosure in

sustainability reports by companies leads to a mismatch of emissions generated with

sustainable practices.

Kalu et al. (2016) showed that there are several factors that can influence the level of

carbon emissions disclosure by companies. These factors include financial market dynamics,

social considerations, economic pressures, and corporate ownership structure.

Research on the effect of carbon performance on disclosure of carbon emissions has been

carried out in Indonesia, while the results of research by Ladista (2023) and Datt (2019) indicate

that carbon performance affects the disclosure of carbon emissions and is inversely

proportional to research conducted by Bui et al (2020).

Companies that implement green product innovation will reduce the amount of carbon

emissions generated during the production process (Wei et al., 2020). So that companies will

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tend to disclose carbon emissions. Research conducted by Li et al. (2016) revealed that green

product innovation affects the disclosure of carbon emissions which is inversely proportional

to research conducted by Ladista (Ladista et al., 2023).

A study on how carbon emissions disclosure affects financial performance, using Return

on Assets (ROA) as a measurement indicator conducted by Borghei et al. (2018) revealed that

disclosure of carbon emissions affects financial performance which is inversely proportional

to research conducted by Siddique et al. (2021) which reveals that disclosure of carbon

emissions has no effect on financial performance.

This study aims to determine how the influence of carbon performance, environmental

costs and green product innovation on financial performance through disclosure of carbon

emissions.

RESEARCH METHODS

The type of data used in this study is secondary data. The data source used is the

publication of financial statements, annual reports and sustainability reports of each energy

sector company listed on the Indonesia Stock Exchange (IDX). The data was obtained through

the Indonesia Stock Exchange website, namely www.idx.co.id. The data collection techniques

used in this research are the documentation method and the literature study method. The

population used in this study are energy sector companies listed on the Indonesia Stock

Exchange (IDX) in 2020-2022. The sampling used is purposive sampling, with the following

criteria:

a. Energy sector companies listed on the Indonesia Stock Exchange in 2020-2022

b. Companies that publish financial statements, annual reports and sustainability reports for

the reporting year from 2020-2022

c. The company presents complete data in accordance with the variables studied

The following are the final sample stages with predetermined criteria :

Table 1

Research sample criteria

Criteria

Total

Number of energy sector companies listed on the Indonesia Stock

Exchange 2020-2022

Number of Companies that do not publish financial statements,

annual reports, and sustainability reports for the reporting year from

2020-2022

(18)

Number of companies that do not present complete data by the

variables studied

(46)

Sample Quantity

Total Observations (Years)

Total number of observations during the study period

Source: Processed data, 2024

Research Variables

An exogenous variable is a variable that affects or has an impact on other variables,

usually based on the chronology of events first. The exogenous variables in this study are as

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follows:

a. Carbon Performance

Carbon performance refers to the ability of a company to reduce carbon emissions in

ways such as reducing carbon emissions per product, replacing or minimizing the use of

materials with high carbon content, and reducing energy consumption (Ladista et al., 2023).

This study uses a measurement for carbon performance, namely the natural logarithm of the

ratio between the value of carbon-producing assets and total carbon emissions. The first

carbon performance calculation adapts the research conducted by Ladista et al (2023) with

the following formula:

Carbon Performance =(Carbon Generating Assets)/(Total Carbon Emissions)

The second measurement for carbon performance is measured using the proxy carbon

emission intensity, which is the natural logarithm of the ratio of total carbon emissions

divided by total company sales. The second carbon performance calculation adapts the

research conducted by Sadira Ashia Priliana & Ermaya (2023) with the following formula:

Carbon Performance = (Total Carbon Emissions)/(Total Sales)

b. Environmental Costs

Environmental costs are economic expenditures incurred by companies to avoid

potential environmental degradation or repair environmental damage caused by their

activities (Ladista et al., 2023). Environmental cost data is obtained from the sustainability

report or annual report of each company. The first environmental cost calculation adapts to

research conducted by Ladista et al (2023) with the following formula:

Environmental Costs = (Environmental Costs x 100%)/(Total Company Operating

Costs) The second environmental cost calculation adapts to research conducted by Saputr

(2023) with the following formula:

Environmental Cost = (Environmental Cost x 100%)/(Net Profit After Tax)

c. Green Product Innovation

Green product innovation in this study follows the approach adopted from the research

of Ladista et al. (2023) by counting green patents containing keywords:

1) Energy-saving products

2) Raw material-saving products

3) Products are easy to recycle

Each element is worth 1 point if included in the company's sustainability report to achieve

a maximum green product innovation score of 3.

Intervening variables are intermediate variables used to mediate the relationship between

exogenous variables and endogenous variables. The intervening variable in this study is the

disclosure of Carbon Emissions. Disclosure of carbon emissions contains information related to

the measurement, recognition, and presentation of carbon emissions, which is the same as

research conducted by Ladista (2023) which uses the criteria for disclosing carbon emissions by

Choi (Choi et al., 2013).

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Table 2

Carbon Emissions Disclosure Criteria are as follows:

Category

Item

Description

Climate change: Risks and

opportunities

CC1

An evaluation or description of the risks

(whether regulatory, physical or general)

associated with climate change, and the steps

that have been or will be taken to manage these

risks.

CC2

An evaluation or description of the current (and

future) financial consequences, business

impacts, and opportunities associated with

climate change.

Carbon Emissions

GHG1

Explanation of the method used to calculate

GHG Emissions

GHG2

Whether there is an external check on the

amount of GHG emissions - if so, by which

party and on what basis.

GHG3

Total GHG emissions

GHG4

Presentation of scopes 1 and 2, or GHG

emissions

GHG5

Disclosure of GHG Emissions by source

GHG6

GHG emissions disclosure by facility or

segment level

GHG7

Comparison of GHG emissions with previous

years

Energy Consumption (EC)

EC1

Total energy consumed

EC2

Quantification of energy used from renewable

sources

EC3

Disclosure of energy consumption by type,

facility or segment

Greenhouse Gas Reduction

and Cost (RC/Reduction and

Cost)

RC1

Details of plans or strategies to reduce GHG

emissions

RC2

Specification of GHG emission reduction target

levels and target years

RC3

Emission reductions and associated costs or

savings achieved to date as a result of a

reduction plan

RC4

Projected future emission costs considered in

capital expenditure planning.

Akuntabilitas Emisi Karbon

(AEC/Accountability of

Emission Carbon)

ACC1

Indication of the board committee (or other

executive body) with overall responsibility for

actions related to climate change.

ACC2

A description of the way in which the board (or

other executive body) reviews the company's

progress on climate change.

Source: Choi et al. (2013)

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If all indicators have been disclosed, the maximum value obtained is 18. The scoring

approach is incremental and each object is equally weighted.

Carbon Emissions Disclosure=(Σn x 100%)/N

Description :

η = number of scores that fit the criteria

N = the number of criteria is 18 criteria

Endogenous variables are variables that are influenced or are the result of the existence

of these exogenous variables. The endogenous variable of this study is the company's financial

performance. One method of evaluating financial performance is to use financial ratios such as

profitability. It shows how far companies can go if they manage their operations properly and

in accordance with financial regulations (Fahmi., 2017).

The ratios used in this study are as follows:

ROA=( Net profit after tax)/( Total Assets)

ROE=( Net profit after tax)/( Total Equity)

NPM=( Net profit after tax)/( Net Sales)

Data Analysis Method

a. Descriptive Analysis

Ghozali (Ghozali, 2016) explains that descriptive statistics provide an overview or

description of data seen from Minimum, maximum, sum, average value (mean), standard

deviation, variance, range, kurtosis and skewness (distribution skewness).

b. Statistical Analysis of Data

The data in this study were processed using smartPLS SEM (Partial Least Square -

Structural Equation Modeling) software. PLS-SEM analysis generally consists of two

submodels, namely the measurement model which is often referred to as the outer model

and the structural model which is often referred to as the inner model.

In statistical analysis of data using the SEM PLS method. The following is the PLS

method analysis technique:

c. Outer Model Analysis

Hussein (2015) conducted an outer model analysis to verify that the measurements used

were feasible as indicators (valid and reliable). Some of the calculations performed in this

analysis include:

1. Convergent Validity: This is assessed by the factor loading on the latent variable with

its indicators. The expected value is greater than 0.7.

2. Discriminant Validity: This involves factor crossloading to ensure that the constructs

have adequate discriminants. This is done by comparing the value of the construct of

interest with the values of other constructs.

3. Composite Reliability: This measures the reliability of the construct by checking if its

reliability value is greater than 0.7, signaling high reliability.

4. Average Variance Extraction (AVE): This is the average of the variance explained by

the indicators that are part of the construct, which should be at least 0.5.

d. Inner Model Analysis

This model analysis is to test the relationship between latent constructs. The

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calculations in this analysis are as follows:

1. R Square is the coefficient of determination on endogenous constructs. Chin (1998) in

(Ghozali & Latan, 2015) explains “the criteria for limiting the R square value in three

classifications, namely 0.67 as substantial; 0.33 as moderate and 0.19 as weak”.

e. Hypothesis Testing

Husaein (2015) revealed that hypothesis testing involves evaluating the t-statistic value

and the probability value. In hypothesis testing using statistical values, for an alpha of 5%,

the relevant t-statistic value is 1.96. Therefore, the criterion for accepting or rejecting a

hypothesis is if the t-statistic value > 1.96. In the context of using probability to reject or

accept a hypothesis, the hypothesis will be rejected if the p value is <0.05.

RESULTS AND DISCUSSION

Table 3

Descriptive Statistical Analysis

Mean

Min

Max

Standard

Deviation

Excess

Kurtosis

Skewn

ess

Carbon Emissions

Disclosure

0.863

0.167

0.232

3.362

-2.084

Carbon Performance 1

7.933

-6.935

18.127

3.978

3.457

-0.185

Carbon Performance 2

-8.956

-19.245

-4.315

3.461

1.869

-1.561

Environmental Cost 1

0.057

0.442

0.099

6.052

2.517

Environmental Cost 2

-0.013

-1.414

0.245

0.198

47.958

-6.693

Green Product

Innovation

0.778

0.629

-0.563

0.216

ROA

0.097

-0.384

0.618

0.177

2.139

0.763

ROE

0.117

-2.543

1.249

0.597

9.585

-2.332

NPM

0.094

-1.63

0.813

0.343

11.664

-2.611

Table 2 of the descriptive analysis explains the results as follows:

a) Carbon Emissions Disclosure

The disclosure of carbon emissions obtained an average value of 0.863 with a standard

deviation of 0.232 (below average), which means that the disclosure of carbon emissions

has a low level of data variation. The highest value of the carbon emission disclosure

variable is 1 while the lowest value is 0.167.

b) Carbon Performance 1

The first carbon performance obtained an average value of 7.933 with a standard

deviation of 3.978 (below average), which means that the first carbon performance has a

low level of data variation. The highest value of the first carbon performance variable is

18,127 while the lowest value is -6,935.

c) Carbon 2 Performance

The second carbon performance obtained an average value of -8,956 with a standard

deviation of 3,461 (above average) which means that the second carbon performance has a

high level of data variation. The highest value of the second carbon performance variable is

-4,315 while the lowest value is -19,245.

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d) Environmental Cost 1

The first environmental cost obtained an average value of 0.057 with a standard

deviation of 0.099 (above average), which means that the first environmental cost has a high

level of data variation. The highest value of the first environmental cost variable is 0.442

while the lowest value is 0.

e) Environmental Cost 2

The second environmental cost obtained an average of -0.013 with a standard deviation

of 0.198 (above average), which means that the second environmental cost has a high level

of data variation. The highest value of the second environmental cost variable is 0.245 while

the lowest value is -1.414.

f) Green Product Innovation

Green Product Innovation obtained an average of 0.778 with a standard deviation of

0.629 (below average), which means that green product innovation has a low level of data

variation. The highest value of the green product innovation variable is 2 while the lowest

value is 0.

g) Return On Asset (ROA)

Return On Asset (ROA) obtained an average of 0.097 with a standard deviation of 0.177

(above average), which means that ROA has a high level of data variation. The highest value

of the ROA variable is 0.618 while the lowest value is -0.384.

h) Return On Equity (ROE)

Return On Equity (ROE) obtained an average of 0.117 with a standard deviation of

0.597 (above average), which means that ROE has a high level of data variation. The highest

value of the ROE variable is 1,249 while the lowest value is -2,543.

i) Net Profit Margin (NPM)

Net Profit Margin (NPM) obtained an average of 0.094 with a standard deviation of

0.343 (above average), which means that NPM has a high level of data variation. The highest

value of the NPM variable is 0.813 while the lowest value is -1.63.

Outer Model Analysis

The outer model analysis defines how each indicator relates to its latent variable.

a. Corvergent Validity Test

An indicator is considered valid if its loading factor exceeds 0.70, so if there is a loading

factor below 0.70, the indicator will be removed from the model. The validity of reflective

indicators is tested by examining the correlation between the item score and the construct

score. Measurement using reflective indicators indicates that there is a change in an indicator

in a construct if there is a change in another indicator in the same construct (or if the indicator

is removed from the model).

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Figure 4

Initial Structural Model

Table 4

Initial Outer Loading

Indicator/Variable

Factor loading

Description

X1 (Carbon Performance)

X1.1

-0,947

Invalid

X1.2

0,961

Valid

X2 (Environmental Cost)

X2.1

0,952

Valid

X2.2

0,315

Valid

X3 (Green Product Innovation)

X3.1

1,000

Valid

Y (Carbon Emissions Disclosure)

1,000

Valid

Z (Kinerja Keuangan)

0,962

Valid

0,903

Valid

0,842

Valid

Source: Data Processed with SmartPLS, 2024

Table 4 shows that there are several variables that have been valid because the loading

factor value is above 0.70. However, there are 2 invalid items because they have a loading

factor value below 0.70, namely the first carbon performance variable and the second

environmental cost variable. Furthermore, it is necessary to eliminate items to eliminate invalid

items.

Figure 5

Final Structural Model

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Table 5

Final Loading Factor

Indicator/Variable

Factor loading

Description

X1 (Carbon Performance)

X1.2

1,000

Valid

X2 (Environmental Costs)

X2.1

1,000

Valid

X3 (Green Product Innovation)

1,000

Valid

Y (Carbon Emissions Disclosure)

1,000

Valid

Z (Financial Performance)

0,959

Valid

0,904

Valid

0,850

Valid

Source: Data Processed with SmartPLS, 2024

Table 4 above shows the results after the elimination process, where all variable items

have proven valid. This is due to the loading factor value that exceeds 0.70. In addition to the

loading factor value, the validity analysis of the research data can also be carried out using the

Average Variance Extracted (AVE) value. The following are the results of the validity test

using the AVE value.

Table 6

AVE Testing Results

Average Variance Extracted (AVE)

X1 (Carbon Performance)

1,000

X2 (Environmental Cost)

1,000

X3 (Green Product Innovation)

1,000

Y (Carbon Emissions Disclosure)

1,000

Z (Financial Performance)

0,820

Source: Data Processed with SmartPLS, 2024

Figure 6

Average Variance Extracted (AVE)

Table 6 and Figure 6 above show that all research variables are valid. This is because the

AVE value is above the requirement of 0.50.

a. Discriminant Validity Test

Discriminant validity tests can be carried out using the Fornell-Lacker criteria. In the

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Fornell-Lacker criterion, discriminant validity is evaluated by comparing the correlation

between variables with the Average Variance Extracted (AVE) of these variables. The

discriminant validity measurement model is considered good if the AVE value of the

variable itself is greater than the correlation between the variable and other variables. The

overall AVE value can be found in the following table:

Table 7

Fornell Lacker

X1 (Carbon

Performance)

(Environm

ental Cost)

X3 (Green

Product

Innovation)

Y (Carbon

Emissions

Disclosure)

Z (Financial

Performance)

X1 (Carbon

Performance)

1,000

(Environmenta

l Cost)

0,006

1,000

X3 (Green

Product

Innovation)

-0,016

0,191

1,000

Y (Carbon

Emissions

Disclosure)

0,494

0,000

0,088

1,000

Z (Financial

Performance)

-0,221

0,342

0,051

0,034

0,905

Source: Data Processed with SmartPLS, 2024

Table 7 above shows that the variable correlation value is greater than the correlation

of other variables, therefore it is concluded that all variables are valid for use. In addition to

testing with the Fornell-Lacker criterion, discriminant validity can also be tested by looking

at the Cross Loading value. An indicator is considered to meet discriminant validity if the

cross loading value on its dimension on that variable is the highest compared to other

variables. The following are the results of the cross loading value.

Table 8

Results of Cross Loading Value

X1.2

1.000

0.006

-0.016

0.494

-0.221

X2.1

0.006

1.000

0.191

0.000

0.342

-0.016

0.191

1.000

0.088

0.051

0.494

0.000

0.088

1.000

0.034

Z.1

-0.240

0.470

0.037

0.021

0.959

Z.2

-0.168

0.134

-0.005

0.043

0.904

Z.3

-0.154

0.134

0.129

0.044

0.850

Source: Data Processed with SmartPLS, 2024

a. Reliability Test

Reliability reflects the level of accuracy, consistency, and reliability of the measuring

device in making measurements. If a study is considered reliable, then the research data has

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proven to be reliable and consistent. In Partial Least Squares (PLS) analysis, reliability

testing can be done using two methods, namely Cronbach's alpha and Composite reliability.

Table 9

Composite Reliability Test Results

Composite Reliability

X1 (Carbon Performance)

1,000

X2 (Environmental Cost)

1,000

X3 (Green Product Innovation)

1,000

Y (Carbon Emissions Disclosure)

1,000

Z (Financial Performance)

0,932

Source: Data Processed with SmartPLS, 2024

Figure 7

Composite Reliability

Table 8 and Figure 7 above show that all constructs in the study are declared reliable

because the Composite Reliability value for each construct exceeds 0.70.

Table 10

Cronbach Alpha Test Results

Cronbach's Alpha

X1 (Carbon Performance)

1,000

X2 (Environmental Cost)

1,000

X3 (Green Product Innovation)

1,000

Y (Carbon Emissions Disclosure)

1,000

Z (Financial Performance)

0,901

Source: Data Processed with SmartPLS, 2024

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Figure 8

Cronbach Alpha

Table 10 and figure 8 above show that all constructs in the study are declared reliable

because the Cronbach's Alpha value for each construct exceeds 0.70.

a. Inner Model Analysis

Test Coefficient of Determination (R2)

After the estimated model meets the requirements on the Outer Model, the next step

is testing the Structural Model (Inner Model) by the researcher, the following is the R-

Square (R2) value on the research construct:

Table 11

Determination Coefficient Test

R Square

R Square Adjusted

Y (Carbon Emissions Disclosure)

0,254

0,209

Z (Financial Performance)

0,196

0,130

Source: Data Processed with SmartPLS, 2024

Table 10 above shows that the R-Square value for the carbon emission disclosure

construct is 0.254, indicating that this model has a good level of goodness-fit model. This

also means that the variability of carbon emission disclosure can be explained by the three

variables in the model, namely carbon performance, environmental costs and green product

innovation by 25.4%.

The R-Square value for the carbon emission disclosure construct of 0.196 indicates

that this model has a good level of goodness-fit model. This also means that the variability

of financial performance can be explained by the four variables in the model, namely

carbon performance, environmental costs of green product innovation and disclosure of

carbon emissions by 19.6%.

Hypothesis Testing

a. Significance Test t

To see the significance results of the parameter coefficients, it can be calculated from

the valid variable dimensions. To determine whether there is a positive or negative effect as

well as statistical significance, it is measured through P Values that must be less than 0.05

and t-statistics that are at least greater than or equal to 1.96. If the t-statistic exceeds the

specified value (1.96), then the correlation between the two constructs is considered

significant; whereas if the t-statistic is lower than the specified value (1.96), the correlation

Factors Affecting Carbon Emission Disclosure And Its Impact

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is considered insignificant.

Table 12

Significance Test of Direct Effect

Original

Sample

(O)

Sample

Mean

(M)

Standard

Deviation

(STDEV)

T Statistics

(|O/STDEV|)

Value

X1 (Carbon

Performance) -> Y

(Carbon Emissions

Disclosure)

0,496

0,494

0,069

7,182

0,000

X2 (Environmental

Costs) -> Y (Carbon

Emissions Disclosure)

-0,022

-0,023

0,063

0,344

0,731

X3 (Green Product

Innovation) -> Y

(Carbon Emissions

Disclosure)

0,100

0,048

2,073

0,039

X1 (Carbon

Performance) -> Z

(Financial

Performance)

-0,321

-0,322

0,047

6,782

0,000

X2 (Environmental

Cost) -> Z (Financial

Performance)

0,351

0,358

0,072

4,903

0,000

X3 (Green Product

Innovation) -> Z

(Financial

Performance)

-0,038

0,060

0,640

0,522

Y (Carbon Emissions

Disclosure) -> Z

(Financial

Performance)

0,196

0,198

0,055

3,555

0,000

Source: Data Processed with SmartPLS, 2024

Table 12 above shows the results for the research hypothesis are as follows:

a. Carbon Performance on Carbon Emissions Disclosure

Table 11 shows that the original sample estimate value of the carbon performance

variable on the carbon emission disclosure variable is positive at 0.496. In addition, the t

statistic of 7.182 ≥ 1.96 and pValues of 0.000 < 0.05 so that it can be said to have a

significant effect. Thus, the hypothesis in this study is accepted. In conclusion, carbon

performance has a positive and significant effect on carbon emission disclosure.

The results showed that carbon performance has a positive effect on carbon emission

disclosure. This means that companies with good carbon performance are motivated to

disclose carbon emissions more widely.

Companies that demonstrate consistently good carbon performance have been shown

to increase their carbon emissions disclosure levels. This is due to their motivation to

communicate the steps they have taken to improve their carbon performance, as well as to

differentiate themselves from companies with low carbon performance.

b. Environmental Costs on Carbon Emissions Disclosure

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Table 11 shows that the original sample estimate value of the carbon performance

variable on the carbon emission disclosure variable is negative at -0.022. Then, it can be

seen that the t statistic is 0.344 < 1.96 and pValues of 0.731 > 0.05 so that it can be said that

it has no significant effect. Thus, the hypothesis in this study is rejected. In conclusion,

environmental costs have a negative but insignificant effect on the disclosure of carbon

emissions.

The amount of environmental costs incurred by companies has not yet had an impact

that encourages companies to disclose carbon emissions. This situation could be due to a

lack of resources for companies to integrate carbon emissions disclosures into their

sustainability reports. In addition, companies may already feel that the information on

environmental costs they convey is sufficient so that companies do not disclose carbon

emissions.

c. Green Product Innovation on Carbon Emissions Disclosure

Table 11 shows that the original sample estimate value of the green product innovation

variable on the carbon emission disclosure variable is positive at 0.100. Then, it can be seen

that the t statistic of 2.073 ≥ 1.96 and pValues of 0.039 < 0.05 so that it can be said to have

a significant effect. Thus, Hypothesis H3 in this study is accepted. The conclusion is that

green product innovation has a positive and significant effect on carbon emission disclosure.

The findings suggest that the adoption of green product innovations by companies can

be a motivation for companies to disclose carbon emissions. Green products are made with

attention to energy efficiency, efficient use of raw materials, and easy recycling. This more

efficient use of resources can reduce the costs that must be incurred by the company. The

adoption of green product innovation by companies not only aims to improve the efficiency

and financial performance of the company, but also gives serious attention to environmental

issues.

d. Carbon Performance on Financial Performance

Table 11 shows that the original sample estimate value of the carbon performance

variable on the financial performance variable is negative at -0.321. Then, it can be seen that

the t statistic is 6.782 ≥ 1.96 and pValues of 0.000 < 0.05 so that it can be said to have a

significant effect. Thus, Hypothesis H4 in this study is accepted. The conclusion is that

carbon performance has a negative and significant effect on financial performance.

Improved carbon performance contributes to improved financial performance of the

company. It shows that companies can achieve low levels of carbon emissions while still

maintaining high levels of sales. Companies that have low carbon emissions are considered

to be able to manage their assets efficiently and conduct business in an environmentally

friendly manner.

e. Environmental Costs on Financial Performance

Table 11 shows that the original sample estimate value of the carbon performance

variable on the financial performance variable is positive at 0.351. Then, it can be seen that

the t statistic is 4.903 ≥ 1.96 and pValues of 0.000 < 0.05 so that it can be said to have a

significant effect. Thus, Hypothesis H5 in this study is accepted. The conclusion is that

environmental costs have a positive and significant effect on financial performance.

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Allocating costs to environmental management shows the consistency of the company's

concern for the environment, thus strengthening public trust in corporate social

responsibility. These environmental costs can be considered as a long-term investment,

because the money spent now can provide a good reputation for the company, thus

increasing stakeholder confidence in the company.

f. Green Product Innovation on Financial Performance

Table 11 shows that the original sample estimate value of the green product innovation

variable on the financial performance variable is negative at -0.038. Then, it can be seen that

the t statistic is 0.640 < 1.96 and the pValues are 0.522> 0.05 so it can be said that it has no

significant effect. Thus, Hypothesis H6 in this study is accepted. The conclusion is that green

product innovation has a negative but insignificant effect on financial performance.

The findings suggest that firms' adoption of green product innovation does not result in

improved financial performance. This indicates that while firms may integrate green product

innovation into their products, it does not necessarily provide significant value in the eyes

of investors. This suggests that environmentally friendly products are not necessarily a

factor that drives wealth creation or increased profitability.

g. Disclosure of Carbon Emissions on Financial Performance

Table 11 shows that the original sample estimate value of the carbon emission

disclosure variable on the financial performance variable is positive at 0.196. Then, it can

be seen that the t statistic is 3.555 ≥ 1.96 and pValues of 0.000 < 0.05 so that it can be said

to have a significant effect. Thus, Hypothesis H7 in this study is accepted. In conclusion,

the disclosure of carbon emissions has a positive and significant effect on financial

performance.

This finding provides support for signaling theory, which states that disclosure of

carbon emissions can send a positive signal to consumers that the company is involved in

climate change mitigation efforts. This can be attractive to consumers and potentially

improve the company's financial performance. Therefore, companies have the opportunity

to strengthen stakeholder trust.

Table 13

Significance Test of Indirect Effect

Original

Sample

(O)

Sample

Mean

(M)

Standard

Deviation

(STDEV)

T Statistics

(|O/STDE

V|)

Values

X1 (Carbon Performance) -

> Y (Carbon Emissions

Disclosure) -> Z (Financial

Performance)

0,097

0,100

0,036

2,663

0,008

X2 (Environmental Costs) -

> Y (Carbon Emissions

Disclosure) -> Z (Financial

Performance)

-0,004

-0,006

0,013

0,317

0,751

X3 (Green Product

Innovation) -> Y (Carbon

Emissions Disclosure) -> Z

(Financial Performance)

0,020

0,019

0,010

1,976

0,049

Source: Data Processed with SmartPLS, 2024

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Table 12 above shows the results for the research hypothesis are as follows:

a. Carbon Performance on Financial Performance through Disclosure of Carbon Emissions

Table 12 shows that the original sample estimate value of the carbon performance

variable on the financial performance variable through the carbon emission disclosure

variable is positive at 0.097. Then, it can be seen that the t statistic of 2.663 ≥ 1.96 and

pValues of 0.008 < 0.05 so that it can be said to have a significant effect. Thus, Hypothesis

H8 in this study is accepted. The conclusion is that carbon performance has a positive and

significant effect on financial performance through the disclosure of carbon emissions.

The results indicate that the disclosure of carbon emissions is able to strengthen the

relationship between carbon performance and corporate financial performance. With the

disclosure of carbon emissions, the improvement of the company's carbon performance can

be accounted for so that it will improve the company's financial performance.

b. Environmental Costs on Financial Performance through Carbon Emissions Disclosure

Table 12 shows that the original sample estimate value of the environmental cost

variable on the financial performance variable through the carbon emission disclosure

variable is negative at -0.004. Then, it can be seen that the t statistic is 0.317 < 1.96 and

pValues of 0.751 > 0.05 so that it can be said that it has no significant effect. Thus,

Hypothesis H9 in this study is rejected. The conclusion is that environmental costs have a

negative but insignificant effect on financial performance through the disclosure of carbon

emissions.

The results indicate that the disclosure of carbon emissions is not able to strengthen the

relationship between environmental costs and corporate financial performance. This

suggests that environmental costs have a direct influence on financial performance.

c. Green Product Innovation on Financial Performance through Carbon Emissions Disclosure

Table 12 shows that the original sample estimate value of the green product innovation

variable on the financial performance variable through the carbon emission disclosure

variable is positive at 0.020. Then, it can be seen that the t statistic is 1.976 ≥ 1.96 and

pValues of 0.049> 0.05 so that it can be said to have a significant effect. Thus, Hypothesis

H10 in this study is accepted. The conclusion is that green product innovation has a positive

and significant effect on financial performance through disclosure of carbon emissions.

The results indicate that disclosure of carbon emissions can strengthen the relationship

between green product innovation and corporate financial performance. The implementation

of green product innovation can help companies improve their financial performance

through high product sales.

CONCLUSION

The results showed that carbon performance and green product innovation have a positive

and significant effect on carbon emission disclosure. Environmental costs have a negative but

insignificant effect on the disclosure of carbon emissions. Carbon performance has a negative

and significant effect on financial performance. Environmental costs have a positive and

significant effect on financial performance. Green product innovation has a negative but

insignificant effect on financial performance. Disclosure of carbon emissions has a positive

Factors Affecting Carbon Emission Disclosure And Its Impact

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Companies Listed On The IDX In 2020-2022)

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and significant effect on financial performance. Carbon performance and green product

innovation have a positive and significant effect on financial performance through disclosure

of carbon emissions. Environmental costs have a negative but insignificant effect on financial

performance through disclosure of carbon emissions.

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