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www.pknstan.ac.id

KEMENTERIAN KEUANGAN REPUBLIK INDONESIA
BADAN PENDIDIKAN DAN PELATIHAN KEUANGAN
SEKOLAH TINGGI AKUNTANSI NEGARA
TANGERANG SELATAN
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THESIS
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AN EMPIRICAL ANALYSIS OF THE DETERMINANTS OF CORPORATE
VOLUNTARY CARBON EMISSION DISCLOSURE IN INDONESIA
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Proposed by:
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NPM 134060018072
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NICZEN HENRY LOLOWANG
AJUN AKUNTAN
Sekolah Tinggi Akuntansi Negara
2009
In Partial Fulfillment of the Requirements
for the Degree of Sarjana Sains Terapan at Sekolah Tinggi Akuntansi Negara
2015
TANGERANG SELATAN
DECLARATION OF AUTHENTICITY
: NICZEN HENRY LOLOWANG
NPM
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NAME
: 134060018072
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FIELD OF THESIS
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: AN EMPIRICAL ANALYSIS OF THE
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THESIS TITLE
: ACCOUNTING
DETERMINANTS
VOLUNTARY
OF
CORPORATE
CARBON
EMISSION
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DISCLOSURE IN INDONESIA
I hereby declare that this thesis is indeed my original piece of work, completed
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on my own or fully and specifically acknowledged wherever adapted from other
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sources. If I am proven doing plagiarism, any degree or credits awarded to me on the
basis of that material may be revoked.
Tangerang Selatan,
April 2015
Declared by,
Niczen Henry Lolowang
NPM 134060018072
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TANGERANG SELATAN
THESIS APPROVAL FORM
NAME
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: 134060018072
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NPM
FIELD OF THESIS
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THESIS TITLE
: ACCOUNTING
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DETERMINANTS
VOLUNTARY
OF
CORPORATE
CARBON
EMISSION
DISCLOSURE IN INDONESIA
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Acknowledged by,
Approved by,
Director,
Thesis Advisor
Kusmanadji, Ak.., M.B.A.
Fadlil Usman, Ak., M.Acc.
NIP 196009151981121001
NIP 196210101983021001
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TANGERANG SELATAN
THESIS ENDORSEMENT
NAME
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NPM
: 134060018072
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FIELD OF THESIS
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THESIS TITLE
: ACCOUNTING
DETERMINANTS
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VOLUNTARY
OF
CORPORATE
CARBON
EMISSION
DISCLOSURE IN INDONESIA.
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Tangerang Selatan, April 2015
Budiasih Widiastuti, Ak., M.Si.
NIP 196912201990032001
2.
Fadlil Usman, Ak., M.Acc.
NIP 196210101983021001
(Member/Advisor)
3.
Dyah Purwanti, Ak., CA., M.Si.
NIP 197505111994032001
(Member)
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1.
(Head of Examiner)
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ACKNOWLEDGMENT
Foremost, I would like to express my sincere gratitude to the almighty GOD, the
cause prima, worshiped in different ways, for His perseverance in making everything
possible by giving me strength and courage to conduct this research. During the process
of this research there are parties I would like to thank. Among them are:
1. All of my family for the continuous support, love, and prayers.
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2. Mr. Kusmanadji, Ak., M.B.A., the Director of STAN, for his approval to every
green-pass documents I requested to obtain the observation data.
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3. My thesis advisor, Mr. Fadlil Usman, Ak., M.Acc. and my technical supervisor,
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Tjahjo Winarto, Ak., M.B.A. for giving the hints in making deeper analyses and for
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proofreading the script.
4. My thesis examiners, Mrs. Budiasih, Ak., M.Si. and Dyah Purwanti, Ak., CA.,
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M.Si. for the recommendations and for approving this thesis..
5. My thesis proposal examiners, Mr. Muhadi Prabowo, Ak., M.B.A. and Mr. Pratin,
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S.E., M.M. for the recommendations and approval in order to advance the proposal
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into thesis.
6. Mr. Chandra Kusuma, S.S.T, M.P.P. and Mrs. Dyah Purwanti, Ak., CA., M.Si. for
ideas on developing the proposal and general direction of the study.
7. Rocky Simamora and Fauziah Noor for the excellent support, caring, and assistance
in brainstorming ideas, proofreading the script, and in the completion of this thesis.
8. Dana, Fajar, and Ridhollah for the togetherness in finishing the research. It would
have been a lonely research without them. Many thanks to Taufik, Renosa, Rino,
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Sudramono, Marina, Grace, Defita, Heni, Luvvy, Arief, Fadli, and all my college
friends who directly or indirectly have lent their hands in this research. Thank you
for such support and togetherness.
I realize that this thesis still has flaws because of the limited knowledge and
experience of the researcher. Therefore, suggestions and feedback in improving the
quality of this thesis will be highly appreciated. Lastly, I hope this thesis will be
beneficial for any readers and for academic proliferation.
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April 2015
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Tangerang Selatan,
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Niczen Henry Lolowang
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TABLE OF CONTENTS
DECLARATION OF AUTHENTICITY ............................................................................ ii
THESIS APPROVAL FORM ............................................................................................. iii
THESIS ENDORSEMENT ..................................................................................................iv
ACKNOWLEDGMENT ........................................................................................................ v
TABLE OF CONTENTS .................................................................................................... vii
LIST OF TABLES .................................................................................................................ix
LIST OF FIGURES ................................................................................................................ x
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LIST OF APPENDIX ............................................................................................................xi
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CHAPTER I INTRODUCTION ........................................................................................... 1
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A. Research Background .....................................................................................1
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B. Delimitation Of Research ...............................................................................4
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C. Problem Formulation ......................................................................................4
D. Research Objective .........................................................................................5
Research Benefits............................................................................................5
F.
Systematics of Writing ....................................................................................6
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E.
CHAPTER II REVIEW OF LITERATURE ....................................................................... 7
A. Theoretical Framework ...................................................................................7
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B. Previous Research .........................................................................................12
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C. Research Hypotheses Development ..............................................................14
CHAPTER III METHODOLOGY ..................................................................................... 18
A. General Overview Of Research Object .........................................................18
B. Sampling Technique .....................................................................................18
C. Type and Source of Data...............................................................................20
D. Research Variables........................................................................................20
E.
Research Framework ....................................................................................24
F.
Research Model ............................................................................................25
G. Panel Data Processing Requirement Test .....................................................27
H. Hypothesis Test .............................................................................................29
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I.
Tools .............................................................................................................29
CHAPTER IV RESULT AND ANALYSIS ..................................................................... 30
A. Descriptive Statistics .....................................................................................30
B. Model Specification Test ..............................................................................33
C. Classical Assumptions Test ..........................................................................35
D. Hypothesis Test .............................................................................................39
E.
Result Analysis .............................................................................................43
CHAPTER V CONCLUSIONS, LIMITATIONS, AND RECOMMENDATIONS ... 47
A. Conclusions ...................................................................................................47
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B. Limitations ....................................................................................................48
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C. Recommendations .........................................................................................49
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APPENDIX
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REFERENCES ......................................................................................................................51
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viii
LIST OF TABLES
Table III.1
Sample Selection
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Table III.2
Independent Variables
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Table IV.1
Descriptive Statistics
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Table IV.2
The Score of Carbon Emission Disclosure
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Table IV.3
Pearson Product-Moment Correlation Coefficient
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Table IV.4
VIF
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T-Statistic and Probability
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Table IV.5
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LIST OF FIGURES
Figure III.1
Research Framework
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Figure III.2
Research Model
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Figure IV.1
The Average Score of the Extent of Carbon Emission
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Disclosure
Figure IV.2
Level of Carbon Emission Disclosure Based On Type of
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Industry
Figure IV.3
Histogram of Residual Data
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Shapiro Wilk and Shapiro Francia Test for Normal Data
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Figure IV.5
Wooldridge Test
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Figure IV.6
Heteroscedasticity Test
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Figure IV.7
Regression Output
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Figure IV.4
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LIST OF APPENDIX
Appendix 1
Related Research
Appendix 2
Carbon Emission Disclosure Checklist
Appendix 3
Research Sample
Appendix 4
Model Specification Test
Appendix 5
Normality Test
Appendix 6
Carbon Disclosure Checklist
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1
CHAPTER I
INTRODUCTION
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A. Research Background
We are living in the world where global communities are increasingly
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concerned about the sustainability of their planet due to global warming effects.
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Glaciers are melting, sea levels are rising, cloud forests are drying, and wildlife is
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scrambling to keep pace. Those are the effects of global warming which really affect
the existence of the earth. This issue has become the current trending topic discussed
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by world’s leaders. Recognizing the importance of responding to the climate change
challenge, countries all over the world are making tangible actions to combat global
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warming from its very root cause which is the greenhouse gas by reducing it. One of
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their actions is the existence of The United Nations Framework Convention on Climate
Change (UNFCCC) which is a form of concrete effort by countries around the world to
cooperatively consider what they could do to limit average global temperature increases
and the resulting climate change, and to cope with whatever impacts were, by then,
inevitable.
Indonesia is one of the countries which have strong commitment to support
global efforts to combat climate change. One of them is an effort to maintain the spirit
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of Bali Action Plan released in the 13th Conference of Parties (COP) 2007, Government
of Indonesia made a number of significant efforts to effectively implement the
UNFCCC post-conference. These include development of a number of policy
documents outlining Government of Indonesia’s efforts in integrating climate change
mitigation and adaptation activities into the National Development Plan for the LongTerm (RPJMP 2005-2035) as well as Medium-Term (RPJMN 2004-2009) and
preparation in the issuance of regulations that assist sectors and local governments in
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the implementation of climate change programs. In addition, Government of Indonesia
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has announced voluntary (non-binding) emission reduction in COP 15 with the target
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to achieve 26% GHG emission lower than the baseline using domestic budget and could
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be further increased to 41% with international support by the year of 2020 from the
condition without any action (business as usual/BAU).
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In order to meet the target, Government of Indonesia has prepared a Presidential
Regulation Number 61/2011 for a National Action Plan For Reducing Greenhouse Gas
Emissions (Rencana Nasional Penurunan Emisi Gas Rumah Kaca “RAN-GRK”) that
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will provide the basis for various related Ministries/Institutions as well as the Regional
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Governments to implement activities that will directly and indirectly reduce GHG
emissions. The RAN GRK is expected to become an integrated, concrete, measurable
and practical action plan for the period between 2010 and 2020. As one of the objectives
of National Action Plan in respect to mitigation is to design programs and activities in
order to reduce the GHG emissions, particularly in forestry and peat land, agriculture,
energy, industry and transportation, as well as waste sectors, it is can be inferred that
industries have important roles in reducing GHG or climate change mitigation. The
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success of Indonesian Government in achieving the target of GHG emission reduction
is also determined by businesspeople who run their industries or corporations through
their policies. That is why Indonesian Government encourages corporations to
cooperatively support them in reducing national GHG emissions as stipulated in article
4 of the regulation where RAN-GRK provides reference to the public and
businesspeople in the planning and in the reduction of GHG emissions.
In a harmonious way with government’s efforts in combating global warming,
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business practitioners are also concerned with this issue as many businesses have
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already taken actions that have had, or will have, the effect of lowering their GHG
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emissions. Many corporations show their supports by voluntarily disclose their carbon
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emissions and their effort in mitigating such emissions. This action can help Indonesian
Government in achieving the target of emission reduction as in every sector of the
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designated action plan, private corporations can play such a pivotal role in helping each
sector meeting its emission reduction target.
Unfortunately, because the disclosure of carbon emission conducted by
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corporations is in the form of voluntary report due to the absence of rules-based
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standards, the extent of such disclosure through corporate annual and/or sustainability
reports vary between corporations. Corporate carbon emission disclosure is really
important for Indonesian Government in having a broader view about the amount of
corporate carbon emission reduction and corporate mitigation actions as it can provide
the data needed to the development of National GHG inventory and database system.
The quality of data needed here lies in the extent of such disclosure. That’s why it is
important to conduct a research about the extent of corporate carbon emission
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disclosure in Indonesia as it is needed to explain also its determining factors causing
some corporations take greater strides in disclosing such information on their annual
and/or sustainability reports.
Based on the description above, to analyze the depth and breadth of corporate
voluntary carbon emission disclosure and its determining factors in the absence of
rules-based standards, the author is challenged to advance a research entitled “AN
EMPIRICAL ANALYSIS OF THE DETERMINANTS OF CORPORATE
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VOLUNTARY CARBON EMISSION DISCLOSURE IN INDONESIA.”
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B. Delimitation Of Research
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This research only studies the extent of corporate voluntary carbon emission
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disclosure and its determining factors of non-financial corporations in Indonesia. The
data obtained and processed in this research are the data of corporate annual and/or
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sustainability report for non-financial corporation in the period of 2011-2013. The
extent of corporate carbon emission disclosure is generated by scoring the information
disclosed using Carbon Disclosure Project (CDP) information request sheets.
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C. Problem Formulation
Based on the research background above, the questions which expected to be
answered through this study are:
1. Does the size of corporation influence the extent of voluntary carbon emission
disclosure?
2. Does the type of industry influence the extent of voluntary carbon emission
disclosure?
3. Does the leverage influence the extent of voluntary carbon emission disclosure?
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4. Does the profitability of corporation influence the extent of voluntary carbon
emission disclosure?
5. Does the listing age influence the extent of voluntary carbon emission disclosure?
D. Research Objective
The objectives of this research are as follows:
1. To empirically analyze whether or not corporation size influences the extent of
corporate voluntary carbon emission disclosure
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2. To empirically analyze whether or not industry type influences the extent of
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corporate voluntary carbon emission disclosure
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3. To empirically analyze whether or not leverage influences the extent of voluntary
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corporate carbon emission disclosure
4. To empirically analyze whether or not corporate profitability influences the extent
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of corporate voluntary carbon emission disclosure
5. To empirically analyze whether or not listing age influences the extent of corporate
voluntary carbon emission disclosure
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E. Research Benefits
This research gives an understanding about the current condition on the extent
of carbon emission disclosure which is reported voluntarily by corporations in
Indonesia, factors affecting the extent of such disclosure, and their significance. This
research can be beneficial for fellow researchers to conduct similar studies regarding
the topic, for Indonesian Government to consider these findings in the imminent
policies or regulation, for investors to manage their portfolio investment, and for
corporations for taking a chance to be more competitive.
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F. Systematics of Writing
A systematic sequence of this thesis is presented as follows:
BAB I
INTRODUCTION
This chapter provides research background, delimitation of research, problem
formulation, research objectives, research benefits, and systematics of
writing.
BAB II REVIEW OF LITERATURE
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This chapter contains relevant theoretical bases underlying this thesis,
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previous research, and research hypotheses development. The theory will
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theory.
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include agency theory, stakeholder theory, legitimacy theory, and signaling
BAB III METHODOLOGY
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This chapter contains general overview of research object, sampling
technique, type and source of data, research variables, research framework,
research model, panel data processing requirement test, hypotheses test, and
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BAB IV RESULT AND ANALYSIS
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tools.
This chapter analyzes the extent of corporate voluntary carbon emission
disclosure within the research period and its determining factors empirically.
BAB V CONCLUSIONS, LIMITATIONS, AND RECOMENDATIONS
This chapter provides the conclusion of this research, limitations, and some
recommendations for fellow researchers, Indonesian Government, investors
and corporations,
CHAPTER II
REVIEW OF LITERATURE
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A. Theoretical Framework
1. Agency theory.
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Jensen & Meckling (1976, 308) define the agency relationship as “a contract
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under which one or more persons (the principals) engage another person (the agent) to
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perform some service on their behalf which involves delegating some decision-making
authority to the agent.” From a corporation’s perspective, agents refer to managers,
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whereas principals refer to shareholders. This relationship creates a problem as stated
by Shehata (2014, 12) cited Jensen and Meckling that “the agency relationship leads to
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the information asymmetry problem due to the fact that managers can access
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information more than shareholders”. As the cornerstone of agency theory is the
assumption that the interest of principles and agents diverge, it creates agency costs
which are monitoring cost, bonding cost, and residual loss (Hill and Jones 1992, 132).
According to agency theory, monitoring costs are paid by the principals to limit
the agents’ aberrant activities. This is supported by Hill and Jones (1992, 132), they
state that “the principal can limit divergence from his/her interests by establishing
appropriate incentives for the agents, and by incurring monitoring costs designed to
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limit opportunistic action by agents”. Bonding costs are paid by the agents to guarantee
that no harm of the principal’s interests will result from their decisions and actions. The
agents pay to spend resources (bonding cost) to convince the principals that he/she will
not conduct such actions that will harm them (Hill and Jones 1992, 132). Despites the
incurring of these costs, it is recognized that some divergence between principals’
interest and agents’ actions may still remain. It happens when the decisions of the agents
diverge from decisions that would maximize the principal’s welfare. It is then called as
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residual loss as it reduces principals’ welfare. Accordingly, the agency cost is the
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summation of the monitoring cost, bonding cost, and the residual loss.
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As stated before that agency relationship leads to the information asymmetry
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problem, “optimal contract is one of the means of mitigating the agency problem
because it helps in bringing shareholders’ interests in line with managers’ interests”
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(Shehata 2014, 20, cited Healy and Palepu). “In addition, voluntary disclosure is
another means of mitigating the agency problem, where managers disclose more
voluntary information reducing the agency costs (Shehata 2014, 20 cited Barako et al.)
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and also to convince the external users that managers are acting in an optimal way”
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(Shehata 2014, 20 Watson et al.). Voluntary disclosure can reduce the conflict between
principals and agents and subsequently reduce the agency cost. This argument can also
be applied to carbon emission disclosure. Carbon emission voluntary disclosure may
therefore be used to reduce information asymmetry and the subsequent agency costs
in regards with strategies of corporation to tackle carbon issues (Ghomi and Leung
2013, 113).
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2. Legitimacy theory
Lindblom (1994, 2) defines legitimacy as “… a condition or status which exists
when an entity’s value system is congruent with the value system of the large social
system of which the entity is a part.” The definition of legitimacy is also stated by
Suchman (1995, 574) as “a generalized perception or assumption that the actions of an
entity are desirable, proper, or appropriate within some socially constructed system of
norms, values, beliefs, and definitions.” So based on those definitions, legitimacy
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theory is seen as interactions between corporations and society at large, demonstrating
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corporate activities to fit in the societal values. It can be linked to the concept of social
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contract (Choi, Lee, and Psaros 2013, 63). Social contract would exist between
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corporations and individual society members provided that society offers corporations
with legal rights and authority to access resources such as natural or human resources,
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thus corporations must continuously seek to comply with the expectations of the
community (consistent with the social contract) to ensure their operations remain
legitimate (Choi, Lee, and Psaros 2013, 63 cited Mathews). In summary, Tang and Luo
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(2011, 5) cited Suchmand, Milne and Paten, conclude legitimacy theory as follows:
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Therefore, legitimacy theory suggests that a company is a citizen of the society and
bound by social contracts in which the entity agrees to carry out various community
desired activities in return for approval of its objectives and other rewards, and this
ultimately guarantees its continued existence.
As noted by Choi, Lee, and Psaros (2013, 63) cited Lindblom, legitimacy is a
dynamic concept that always changes in time and place. Corporate activities which
were once viewed as acceptable may be no longer legitimate because of these changes
in community expectations. Thus, there can be a disparity (called the legitimacy gap)
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between the public expectations about how corporations should behave and the
perception on how corporations do act. To remain legitimate, corporations will adopt
strategies to eliminate the gap, for example, by changing the perceptions of the relevant
publics through social disclosures (Choi, Lee, and Psaros 2013, 63 cited Lindblom;
Gray et al.).
As mentioned before, one of the strategies conducted by corporations in
endeavor to eliminate the legitimacy gap is by providing disclosure as it provides more
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transparency on corporate activities. Corporations are disclosing more information to
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deal with societal perceptions. “Lack of transparency would be perceived as evidence
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of inadequate socially responsible mechanisms (e.g. environmental protection) of an
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entity that is thus more vulnerable to claims that the entity is negligent, irrational or
unnecessary” (Tang and Luo 2011, 6). There are studies as stated by Tang and Luo
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(2011, 6) that concluded corporations are providing environmental disclosures as there
are threats to their legitimacy. They also stated that the firm used social and
environmental disclosure strategically to legitimize a new production process through
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the manipulation of social perceptions and there is an interplay between firm
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legitimating strategies and state support for such strategies.
3. Stakeholder theory
The concept of stakeholder was first developed by Freeman (1984) to explain
corporate behavior and social performance (Ghomi and Leung 2013, 113). According
to stakeholder theory corporations are responsible to all stakeholders and their
responsibilities are not restricted to value creation for shareholders (Ghomi and Leung
2013, 113 cited Barsky, Hussein, and Joblonsky). Freeman (1984) as cited by Oliveira,
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Rodrigues, and Craig (2013, 76) defined a stakeholder to be “any group or individual
who can affect, or be affected by, the achievement of a firm’s objectives”. Thus, any
person/group which can affect/be affected by the actions of a business, including
shareholders, employees, customers, suppliers, competitors, lenders, government and
communities; and groups representing environmentalists, the media, and consumer
advocates, and even the wider community is the scope of stakeholder theory (Oliveira,
Rodrigues, and Craig 2013, 76, cited Clarkson).
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As there are many parties affected by corporate objectives, corporations conduct
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their activities in regard of their stakeholders. In order to maintain the relationship
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between corporations and their stakeholders, managers on behalf of corporations are
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doing their activities in responding to their diverse stakeholder interests. Relationships
between a company and its stakeholders are critical sources of wealth, as the ability to
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establish and maintain such relationships determines a corporation’s success (Oliveira,
Rodrigues, and Craig 2013, 76 cited Post et al.)
As the increasing awareness of today society towards climate change,
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stakeholders of corporations begin to ask corporations to act in regard of environmental
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issues. They pursue their needs through imposing pressure (directly or indirectly) on
corporations to release environmental information (Ghomi and Leung 2013, 113).
Stakeholders such as NGOs, employees, governments, consumers, etc want to secure a
commitment from the private sectors to keep them adequately informed through open,
transparent and honest carbon disclosure, even in the absence of state intervention
(Tang and Luo, 2011, 14).
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4. Signaling theory
Godfrey et al. (2010, 375) stated that according to signaling theory, managers
would try to signal the investors if they expected a high level of future growth of the
corporation. Bini et al. (2011, 2) cited Verrecchia stated that “the most profitable
companies signal their competitive strength by communicating more and better
information to the market.” Godfrey et al. (2010, 375) further stated that:
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Managers of other companies that are performing well would have the same
incentive, and managers of firms with neutral news would have incentives to report
positive news so that they were not suspected of having poor result. Managers of
firms with bad news would have incentives not to report. However, they would
also have the incentive to report their bad news, to maintain credibility in effective
markets where their shares are traded. Assuming these incentives to signal
information to capital markets, signaling theory predicts that firms will disclose
more information than is demanded.
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In responding to the increasing of global awareness towards environmental
problems, many corporations are trying to signal their great performance in mitigating
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these problems by providing a voluntary disclosure. As Shehata (2014, 20) cited
Campbell et al. stated that “voluntary disclosure is one of the signaling means, where
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companies would disclose more information than the mandatory ones required by laws
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and regulations in order to signal that they are better.” So, when corporations have great
performance in mitigating carbon emission, they will try to signal the investors their
competitive advantages by providing voluntary carbon emission disclosure.
B. Previous Research
Choi, Lee, and Psaros (2013) studies the extent of voluntary carbon emission
disclosures by major Australian companies during the years 2006 to 2008. Additionally,
the study aims to determine the variables that explain the extent of carbon disclosures.
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The carbon disclosure score is measured directly from individual companies’ annual
reports and sustainability reports. The regression results show that the level of carbon
emissions, firm size, and quality of corporate governance are key drivers for
determining the extent of voluntary carbon emission disclosures.
Ghomi and Leung (2013) investigate the determinants of greenhouse gas
emission voluntary disclosure of non GHG registered companies in Australia during
the years 2009 to 2011. The content analysis for the period 2009 to 2011 shows positive
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and significant association between the level of GHG voluntary disclosure in annual
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reports, corporate governance and firm size, which in general, support the application
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of agency theory and stakeholder theory. Further, firms with superior GHG
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performance are more likely to engage in discretionary disclosure, as predicted by
voluntary disclosure theory. Finally, in contrary to industry and leverage variables,
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listing status proxy appear to play a significant role in GHG disclosure decision. This
result can be explained by the fact that these firms voluntarily disclose information due
to their superior GHG performance though they are not subject to NGER Act 2007.
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They disclose voluntary GHG information to acquire the benefits of communicating
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good news.
Tang and Luo (2011) examine the determinants of transparency of the carbon
disclosure in the Global 500 firms in their carbon reports from Carbon Disclosure
Project (CDP) 2009 in which the firms voluntarily participated. Tang and Luo assess
the degree of transparency using the Carbon Disclosure Transparency Score (CDTS)
adopted from the CDP reports. The research is based on the academic literature on
motivations for sustainability and environmental reporting, together with an analysis of
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the carbon disclosures made by the Global 500 firms. They find firm size, leverage,
industry membership, emission trading scheme, stringency of environmental
regulation, as predicted, are significantly associated with carbon transparency. The
results imply that stakeholder theory and institutional theory provide different but
complementary explanations for the development of carbon disclosure practices. The
summary of related research used in this paper is presented in Appendix 1.
C. Research Hypotheses Development
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1. Relationship between corporate size with the extent of voluntary carbon
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emission disclosure
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The variable most consistently reported as significant in studies examining
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differences across corporations in their disclosure policy is corporate size. Agca and
Onder (2007, 244) stated that institutionalization would increase in corporations which
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have reached a certain size and that these corporations would likely disclose more
information to the public other than what is required by law. As stated by Choi, Lee,
and Psaros (2013, 65) cited Henriques and Sadorsky “The size of a firm is often used
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as a proxy for organizational visibility because it is thought that larger companies are
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exposed to greater pressure from environmental issues due to their high visibility and
thus are likely to show enhanced environmental responsiveness.”
Ghomi and Leung (2013, 113) cited Kolk et al. stated that preparation of carbon
disclosure reports needs resources allocation and this would require a high level of
technical skills and resources than preparation of any other social performance reports
which is more available in large corporations. Research also explains that large
corporations disclose more because they have more stakeholders demanding disclosure,
15
more incentives to reduce information asymmetries, and advantageous economies of
scale. Based on those reasons, the following hypothesis needs to be tested.
H1. The extent of voluntary carbon emission disclosures will be greater in corporations
with bigger size.
2. Relationship between type of industry with the extent of voluntary carbon
emission disclosure
From a legitimacy theory perspective, information disclosure is used as a tool
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for corporations to appear to be operating in accord with societal values to legitimize
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their activities due to political visibility (Ghomi and Leung 2002, 114 cited Patten).
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Legitimacy theory predicts that for the significance of carbon issues today, it is not
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inconceivable to propose that corporations tend to disclose GHG information to
legitimize their activities. There are studies as stated by Tang and Luo (2011, 6) which
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concluded that corporations used social and environmental disclosure strategically to
legitimize a new production process through the manipulation of social perceptions.
Thus, intensive emission industries are more likely to disclose carbon emissions to
c.
legitimize their activities. Therefore, the hypothesis needs to be tested will be:
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operating in emissions intensive industries.
3. Relationship between corporate leverage with the extent of voluntary carbon
emission disclosure
Leverage concerns the use of various financial instruments or borrowed capital
to increase the potential return of an investment. Leverage can be measured as the
amount of debt used to finance a firm's assets. A firm with significantly more debt than
16
equity is considered to be highly leveraged. Although empirical research shows
conflicting results on the association between leverage and disclosure, corporations in
good financial condition will be able to afford extra human or financial resources
required for voluntary reporting. Choi, Lee, and Psaros (2013, 66) stated that Cormier
and Magnan (1999) argue that corporations in good financial condition are more likely
to disclose environmental information voluntarily compared to those in poor financial
condition. For corporations with poor financial performance, the disclosure of future
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environmental liabilities or new regulations means extra costs, leading to concerns from
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their debt holders, suppliers and customers about corporation’s performance. Based on
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those reasons, the hypothesis is presented as follows:
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with low financial distress.
carbon emission disclosure
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4. Relationship between corporate profitability with the extent of voluntary
There is a general proposition that a company's willingness to disclose
c.
information is positively related to its profitability. One motive for this can be derived
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from agency theory. “It is suggested that managers of profitable corporation disclose
extensive information in order to show and explain to shareholders that they are acting
in their best interests and justify their compensation packages” (Soliman 2013, 73).
Stakeholder theory suggests that organizations have incentives to engage with
stakeholders. It can be expected that the more successful an organization functions, the
more it is inclined to communicate this success. In line with stakeholder theory, it can
be expected that organizations have incentives to express how corporate strategy pays
17
off, confirming the strength and direction of the organization in relation with
stakeholders. Based on this analysis, the hypothesis is presented as follows:
with high profitability.
5. Relationship between corporate listing age with the extent of voluntary carbon
emission disclosure
Uyar, Kilic, and Bayyurt (2013, 1088) cited Haniffa & Cooke stated that “listing
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age is the length of time a company has been listed on a capital market, and it may be
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relevant in explaining the voluntary disclosure level.” They investigated the association
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between listing age and the extent of voluntary disclosure because listing age has not
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been tested at all in earlier studies, and therefore, there is not much empirical evidence
pertaining to this variable. This approach has been adopted in this study as well. The
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longer the corporation is listed on a capital market, the more experienced it is in
gathering, processing and disseminating information to its stakeholders. Although
Owusu-Ansah (1998, 614) explains the age of corporation, not listing age, in finding
c.
its influence on the extent of a corporate information disclosure, the same points are
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applicable in terms of listing age.
This research used listing age as it is in line with stakeholder theory. It can be
expected that the longer a corporation is listed in a capital market, the more experienced
it is in accommodating its stakeholder’s needs of additional information. Based on this
analysis, the hypothesis is presented as follows:
with longer age of listing.
18
CHAPTER III
METHODOLOGY
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A. General Overview Of Research Object
The object in this research is corporations in Indonesia. The population of data
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used in this research is all corporations listed on Indonesia Stock Exchange. Sample of
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this research is non-financial corporations with purposive sampling method. The data
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used in this research are corporate annual report and/or sustainability report. The
research period is started in 2011 because in the year of 2011 Indonesian Government
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stipulated Presidential Regulation Number 61/2011 for a National Action Plan for
Reducing Greenhouse Gas Emissions (RAN-GRK). This research aims to know the
c.
response of corporations in Indonesia in relation to such regulation as in the article 4 of
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this regulation, corporations are also being considered as one of determining parties
included in the national plan for reducing carbon emission. The research period is ended
in 2013 because it is the closest feasible period in obtaining sufficient data to conduct
a research.
B. Sampling Technique
Researcher used purposive sampling method in selecting the sample of this
research. Purposive sampling, also known as selective or subjective sampling is one of
19
sampling method where the data are investigated based on the judgement of the
researcher. In purposive sampling there are criteria which need to be fulfilled by the
population in order to became sample in the research. The criteria used in selecting the
sample are:
1. Non-financial corporations listed on Indonesia Stock Exchange for the year 20112013
2. Provide annual report and/or sustainability report during period 2011-2013
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3. During period 2011-2013 consistently disclose corporate carbon emission through
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annual report and/or sustainability report or at least has an item requested in the
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Information Request Sheets by the CDP regarding carbon emission disclosure.
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The data analyzed in this research are the annual reports and/or sustainability
report of non-financial corporations listed on Indonesia Stock Exchange for the year
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2011-2013. Based on the criteria in purposive sampling method, there are only 32 nonfinancial corporations left which consistently reported their carbon emission disclosure
as shown by Table III.1. These corporations are then taken as sample of this research
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c.
for the period of 2011-2013 (Appendix 3).
The step of selecting the sample of this research is begun by excluding financial
corporations from the populations. Because the main criteria of this purposive sampling
method is corporations which consistently report their carbon emission from 20112013, sorting corporations from 2011 forward or from 2013 backward will get the same
result. The sorting begins from 2013 backwards resulting only 42 corporations
reporting their carbon emission through annual report and/or sustainability report. The
sorting is conducted by identifying items presented in the environmental section of
20
corporate annual report and/or sustainability report. The next step of sorting is
continued from the remaining 42 corporations in the 2012. The result shows that only
36 corporations left fulfilling the criteria. These remaining 36 corporations are then
looked up whether or not they are still fulfilling the criteria in 2011. The result is only
32 corporations from the population left fulfilling the criteria in purposive sampling
method.
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Table III.1. Sample Selection
Criteria
2011
2012
Corporations listed on Indonesia Stock exchange
460
478
Financial Corporations
72
74
Non-financial Corporations
388
404
Corporations which consistently report their carbon
32
32
emission from 2011-2013
Source: Developed from Indonesia Stock Exchange
2013
483
78
405
32
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C. Type and Source of Data
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This research only used secondary data. The secondary data are obtained from
corporate annual and/or sustainability reports. These reports can be accessed at
www.idx.co.id and/or corporate websites. All the reports are in the period of 2011-
c.
journals, books, and articles.
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2013. In addition, other secondary data used in this research are the data obtained from
D. Research Variables
1. Dependent variable
The dependent variable in this research is the extent of corporate voluntary
emission disclosure obtained from corporate annual report and/or sustainability report.
The extent of voluntary disclosure linked to carbon emission in these publicly available
report is determined by using checklist constructed based on the factors identified in
21
the Information Request Sheets by the CDP. The CDP is an independent non-profit
organization holding the largest volume of climate change information in the world,
from more than 3,000 organizations in 60 countries. The provision of data to the CDP
is made on a voluntary basis. There are five broad categories relevant to climate change
and carbon emissions and they are: climate change risks opportunities (CC), greenhouse
gas emissions accounting (GHG), energy consumption accounting (EC), greenhouse
gas reduction (RC), and cost and carbon emission accountability (ACC).
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Within these five categories there are 18 specific items. There are 2 items in
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climate change risks opportunities category, 7 items in the greenhouse gas emissions
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accounting category, 3 items in the energy consumption accounting category, 4 items
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in the greenhouse gas reduction category, and 2 items in the cost and carbon emission
accountability category. Each item is weighted equally, no attempt to assign relative
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weightings to the items. The categories and individual items listed in Table I are
adopted from Choi, Lee, and Psaros (2013). The maximum score a corporation can
achieve is 18 and is granted when a corporation discloses information relating to all 18
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2. Independent variables
c.
environmental items as indicated in Appendix 2.
There are 5 independent variables as presented in Table III.2 that have been
used in explaining their influence on the extent of corporate carbon emission disclosure
in Indonesia. Those variables are corporate size (SIZE), type of industry (IND),
leverage (LEV), profitability (PROF), and listing age (LIST). Each independent
variable, its expectation sign, notation, type, and proxy are defined and explained as in
the following table.
22
Table III.2. Independent Variables
Variable
Corporate
Size
Type of
Industry
Expected
Sign
+
SIZE
+
IND
+
LEV
PROF
+
LIST
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Leverage
Profitability
Listing age
Notation
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Variable
Variable Proxy
Type
Interval
Natural logarithm of
total assets
Dummy Takes a value of 1 if the
corporation
is
a
member of emission
intensive
industries,
otherwise 0
Ratio
Debt to Asset Ratio
Ratio
Net Profit to Equity
Ratio
Interval
Listing age since Initial
Public Offering (IPO)
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a. Corporate size (SIZE)
Corporate size is defined as how big is the corporation determined by its total
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assets. Corporate size is measured by a natural logarithm of total assets to simplify the
model. The sign of this variable is expected to be positive.
b. Type of industry (IND)
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c.
𝑆𝐼𝑍𝐸 = 𝐿𝑛 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
The definition type of industriy follows the definition from previous research
(Choi, Lee, and Psaros, 2013). Corporations are divided into two categories which are
emission intensive industries and non-emission intensive industries. Emission intensive
industries are steel, mineral, cement, and glass industries, pulp and paper
manufacturers, energy sectors including oil refineries and power generators, and
airlines and non-ferrous metal industries. A dummy variable (IND) takes a value of 1
23
if the corporation is a member of emission intensive industries which include energy,
transportation, materials, and utilities industries according to the Global Industry
Classification Standard (GICS), and takes value of 0 if it is not. The sign of this variable
is expected to be positive.
c. Leverage (LEV)
Leverage indicates the magnitude of the company's assets financed with debt.
In the same vein with previous research conducted by Choi, Lee, and Psaros (2013),
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the variables will be measured using a leverage ratio of total liabilities to total assets.
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The sign of this variable is expected to be negative.
𝐿𝐸𝑉 =
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d. Profitability (PROF)
𝑇𝑜𝑡𝑎𝑙 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡
Profitability describes the ability of a company to generate profit by using all
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capital owned. Profitability variable is measured using ROE (net profit after tax divided
by total equity). The sign of this variable is expected to be positive.
𝑛𝑒𝑡 𝑝𝑟𝑜𝑓𝑖𝑡
𝑡𝑜𝑡𝑎𝑙 𝑒𝑞𝑢𝑖𝑡𝑦
id
c.
𝑅𝑂𝐸 =
e. Listing age (LIST)
Listing age is defined as the length of time a corporation has been listed on
Indonesia Stock Exchange. Listing age is measured by the year of corporation listed
capital market or the year of its Initial Public Offering (IPO) in Indonesia up to the year
of this research is being conducted which is in 2015. The sign of this variable is
expected to be positive.
24
E. Research Framework
As the global concerns towards global warming are increasing, leaders around
the world are making commitment in reducing GHG emission within their countries.
Indonesian Government’s commitment in reducing GHG emission is shown by number
of instruments, which one of them is the RAN-GRK. As the attempts in combating
global warming cannot be done without supports by all parties, private sectors play such
an important role in helping government meeting the targets. Private sectors support
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government by adopting energy efficiency programs, using renewable energy, and
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conducting numbers of mitigation actions resulting in the reduction of their existing
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carbon emissions. Unfortunately, there are no specific standards promulgated on how
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such emissions should be reported by these corporations.
The disclosure of carbon emission conducted by corporations today is in the
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form of voluntary report. This then triggers curiosity on the reasons why these
corporations conduct such disclosure which is not obliged in the existing standards.
There are a number of theories (e.g., legitimacy theory, stakeholder theory, agency
c.
theory, and signaling theory) which have been used to empirically examine such an
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exposure of this information. The characteristics of corporations such as size, type of
industry, leverage, and profitability have become variables used in number of studies
to determine factors affecting the extent of such disclosures. Since the information of
corporate carbon emission and the characteristic of corporations are valuable data
needed by Indonesian Government in implementing current carbon emission-related
policies and in drafting specific standards, this research is important to be conducted.
This research framework is presented in Figure III.1.
25
Figure III.1. Research Framework
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c.
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F. Research Model
In order to examine the research question, here is the following regression
model as also presented in Figure III.2.
EXT= α + β1SIZE1,t + β2 IND2,t+ β3LEV3,t+ β4PROF4,t+ β5LIST5,t +εt
Where:
EXT
=
SIZE
IND
LEV
=
=
=
The extent of corporate voluntary carbon emission
disclosure
Corporate size
Type of industry
Leverage
26
PROF
LIST
α
β1,β2,β3,β4,β5,β6
ε
=
=
=
=
=
Profitability
Listing age
Constanta
Correlation coefficient
Error
Figure III.2. Research Model
Corporate Size
(+)
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Type of
Industry
(+)
The Extent of
Corporate Carbon
Emission
Disclosure
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Listing age (+)
Profitability
(+)
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Leverage
(-)
Source: Developed from Previous Research
c.
Figure III.2 depicts that the extent of corporate carbon emission disclosure is
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expected to be influenced by corporate size, type of industry, profitability, leverage,
and listing age. The expected sign indicates the directions. Positive sign indicates that
the increasing of the independent variable will be followed by the increasing of
dependent variable or the decreasing of the independent variable will be followed by
the decreasing of dependent variable. Negative sign indicates that increasing of the
independent variable will be followed by the decreasing of dependent variable and vice
versa.
27
G. Panel Data Processing Requirement Test
1. Model Specification Test
a.
Panel data are also called longitudinal data or cross-sectional time-series data.
These longitudinal data have observations on the same units in several different
periods of time. A panel data set has multiple entities, each of which has repeated
measurements at different periods of time. There are three models offered in panel
data, namely Pooled Ordinary Least Square (OLS), Fixed Effect Model (FEM),
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and Random Effect Model (REM)
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b. Pooled Ordinary Least Square (OLS)
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In this model, cross section and time series data are combined into a pool of
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data. Then the combined data is treated as a single entity observations used to estimate
the model with OLS. OLS assumes that both the intercept and the slope does not change
c. Fixed Effect Model (FEM)
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either between individuals or between times.
The existence of variables that are not all included in the model equations allow
c.
for an intercept that is not constant. Or in other words, this intercept may be changed
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for each individual and time.
d. Random Effect Model (REM)
In Fixed Effect Model, the differences between individuals and/or time reflected
through the intercept, but in Random Effect Model, the difference is accommodated by
error. This technique also takes into account that the error may be correlated throughout
the time series and cross section. Selection panel model will be determined through the
Chow test and Hausman test with the help of STATA.
28
2. Classical Assumptions Test
a. Normality Test
Normality test in STATA can be conducted by either graphical or numerical
methods. Graphical methods include drawing a stem-and-leaf plot, scatterplot, boxplot, histogram, probability-probability (P-P) plot, and quantile-quantile (Q-Q) plot.
Numeral methods involve computing the Shapiro-Wilk, Shapiro-Francia, and
Skewness/Kurtosis tests. If the significance value < 0.05 then the residual data are not
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normally distributed.
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b. Multicollinearity Test
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Multicollinearity is a symptom of a correlation between the independent
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variables that will lead to a bias in the calculation of the coefficient of variance of each
parameter. Multicollinearity can also be detected from the value of tolerance (TOL) and
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variance inflating factor (VIF). Commonly used cut-off to indicate multicollinearity is
tolerance which is less than 0.10 or VIF > 10 (Ghozali, 2009, 106).
c. Autocorrelation Test
c.
Autocorrelation test aims to test whether there is a correlation between members
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of a series of observation. Wooldridge test is used to detect autocorrelation problem in
panel data. The hypothesis regarding autocorrelation uses null hypothesis that there is
no autocorrelation problem in the model. If the significance value > 0.05 then there is
either positive or negative autocorrelation in the regression model.
d. Heteroscedasticity Test
Heteroscedasticity is an event which, despite the dependent variable rate rises
due to rising levels of independent, the variance of the dependent variable is not the
29
same at every level of the independent variable. Heteroscedasticity symptoms can be
detected by the scatter diagram (scatter plot) regression results. If the scatter diagram
forms a straight line pattern, the proposed model can be suspected of having symptoms
of heteroscedasticity. Heteroscedasticity problem can also be solved by standard error
cluster used to solve autocorrelation problem.
H. Hypothesis Test
a. F-Statistic Test
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This test is used to determine the significance level of influence of the
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independent variables simultaneously on the dependent variable. The testing is done by
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comparing F-statistic to the F table. F table is determined based on the 95% confidence
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level, the degree of freedom (df1) = k-1 and (df2) = n-k-1 (k is the number of
independent variables in regression model and n is the number of research data). If F
b. T-Statistic Test
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statistics > F table, it can be concluded that the regression model is fitted.
This test is conducted to demonstrate the effect of a variable independent
c.
individually on the dependent variable. This test is performed by determining t-statistics
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and t-table. Level of significance used to determine the t table is 5% with degree of
freedom (df) = n-k (k is the number of independent variables in the regression model
and n is the amount of research data).
I. Tools
In generating the result, researcher is assisted by computer programs. Computer
programs that are used to process statistical data in this study are Microsoft Excel
version 2007 and STATA/SE 13.
CHAPTER IV
RESULT AND ANALYSIS
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A. Descriptive Statistics
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The score of corporate voluntary carbon emission disclosure ranges from 1 to
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14 with the average score of 4.46. What can be inferred from these numbers is that the
extent of corporate voluntary carbon emission disclosure in Indonesia is relatively low.
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Despite the low extent of carbon emission disclosure, the trend of the extent of
corporate carbon emission disclosure in Indonesia is increasing from 2011-2013 as
shown by Figure IV.2. It indicates the good response from corporations in Indonesia in
c.
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mitigating environmental problems specifically caused by carbon emissions.
Figure IV.1. The Average Score of the Extent of Carbon Emission Disclosure
Source: Generated by Microsoft Excel
30
31
The value of corporate size presented in Table IV.1 is the logarithm of its total
assets. It ranges from 27.45 represented the total asset of 0.8 trillion rupiah to 33.00
represented the total assets of 2.797 trillion rupiah. The corporate size of the sample is
relatively big because the mean of this variable is 30.42.
Table IV.1. Descriptive Statistics
Variable
Mean
Standard
Min
Max
Deviation
IND
4.036457
1
14
30.42344
1.154586
27.45
32.99697
0.4866643
0
1
0.1912839
0.0976706
0.959945
st
kn
SIZE
4.458333
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DISC
0.625
LEV
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PROF
0.1295507
0.367477
-2.022695
1.258059
LIST
18.15625
8.244077
4
38
0.4777681
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Source: Output from STATA
The extent of carbon emission disclosure presented in Tabel IV.2 highlights the
c.
improving average score over the research period. The total carbon disclosure score in
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2011 is 129 (mean 4.03 per corporation); in 2012 it is 143 (mean 4.47 per corporation);
and in 2013 it is 156 (mean 4.88 per corporation). From the table, it shows that most of
corporations used as sample in this research are gaining score below 5 points and only
several corporations gain score above 10 points from 18 achievable points. It can be
concluded that corporations in Indonesia are still in the process of increasing their
extent of carbon emission disclosure.
The fact that there are corporations that could reach 14 points shows that the
32
level of carbon emission disclosure in Indonesia could have been better during the
research period if the rest of corporations used this particular corporation as a
benchmark. In the future it is expected that the extent of carbon emission disclosure in
Indonesia will increase.
Table IV.2. The Score of Carbon Emission Disclosure
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2012
11
3
4
4
0
2
1
1
1
0
1
2
1
1
0
0
0
0
32
143
4.47
%
0.34
0.09
0.13
0.13
0.00
0.06
0.03
0.03
0.03
0.00
0.03
0.06
0.03
0.03
0.00
0.00
0.00
0.00
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an
st
kn
%
0.38
0.13
0.16
0.06
0.00
0.03
0.03
0.03
0.00
0.09
0.03
0.06
0.00
0.00
0.00
0.00
0.00
0.00
2013
11
3
4
2
0
1
1
2
0
4
0
2
2
0
0
0
0
0
32
156
4.88
id
c.
2011
12
4
5
2
0
1
1
1
0
3
1
2
0
0
0
0
0
0
32
129
4.03
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Scores
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Total corporation
Total score
Average
%
0.34
0.09
0.13
0.06
0.00
0.03
0.03
0.06
0.00
0.13
0.00
0.06
0.06
0.00
0.00
0.00
0.00
0.00
Corporations used as sample of this research are divided into two categories
which are carbon emission intensive industries and those which are not. From 32
corporations used as sample through purposive sampling technique, there are 20
corporations categorized as emission intensive industries and 12 corporations as non-
33
emission intensive industries. Figure IV.2 explores voluntary carbon disclosures by
corporations in different industries. It shows the change in average disclosure scores
for both industries from 2011 to 2013. The average level of carbon emission disclosure
doesn’t always get higher in emission intensive industries. Although in 2011 and 2013
emission intensive industries show better level of disclosure than the non-emission
intensive industries, in 2012 they are below the average level of carbon emission
disclosure from non-emission intensive industries.
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Figure IV.2. Level of Carbon Emission Disclosure Based On Type of
Industry
7
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5
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an
4
st
kn
6
3
c.
2
id
1
0
2011
2012
2013
Non intensive 2.833333333 4.833333333 3.166666667
Intensive
4.75
4.25
5.9
Source: Generated by Microsoft Excel
B. Model Specification Test
In this test, we need to determine the best model for the estimation. There are
three models provided for data panel which are Common Effect Model, Fixed Effect
34
Model, and Random Effect Model.
1. Chow Test
In determining which model to be used in the regression, those models
mentioned before need to be tested. First we test which model is better between
Common Effect model and Fixed Effect Model using Chow Test.
H0 : Common Effect Model is better than Fixed Effect Model for model estimation
Ha: Fixed Effect Model is better than Common Effect Model for model estimation
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The result of command given in STATA concludes that H0 is rejected since
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Value (Prob>F) < Alpha 0,05. Therefore, Fixed effect Model is better than Common
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Effect for model estimation. The result of this test (before and after dropping outliers)
2. Hausman Test
st
kn
is presented in Appendix 4.
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After determining that Fixed Effect Model is better than Common Effect Model
for model estimation, the next step which needs to be conducted is Hausman Test.
Hausmen Test is used to determine which model between Fixed Effect Model and
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c.
Random Effect Model is better.
H0 : Random effect Model is better than Fixed Effect Model for model estimation
Ha: Fixed Effect Model is better than Random Effect Model for model estimation
The result of command given in STATA concludes that H0 is not rejected since
Value (Prob>chi2) > Alpha 0,05. Therefore, Random Effect Model is better than both
Ordinary Least Square Model and Fixed Effect Model for model specification in this
research. The result of this test (before and after dropping outliers) is presented in
Appendix 4.
35
C. Classical Assumptions Test
1. Normality test
In STATA, normality test can be conducted by either graphical or numerical
methods. The former include drawing a stem-and-leaf plot, scatterplot, box-plot,
histogram, probability-probability (P-P) plot, and quantile-quantile (Q-Q) plot. The
latter involve computing the Shapiro-Wilk, Shapiro-Francia, and Skewness/Kurtosis
tests. In this research, both methods are going to be used.
w
As shown in Appendix 5 through Histogram and Skewness/Kurtosis Tests,
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Shapiro-Wilk Test and Shapiro-Francia Test, the residual data are not normally
w
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distributed because Prob > chi2 is less than 0.05 and and Prob > z is less than 0.05,
st
kn
thus the data need to be fixed by excluding the outliers using command egen
zscore=std(residual). There are 8 observations data that are being dropped in order to
Figure IV.3 and Figure IV.4.
.a
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fit in the model. The result of normality after dropping the outliers test is shown by
Figure IV.3. Histogram of Residual Data
id
c.
36
Figure IV.4. Shapiro Wilk and Shapiro Francia Test for Normal Data
.p
w
w
w
st
kn
The results of normality test using Histogram as shown in Figure IV.3
concluded that the residual data are normally distributed. The value of Prob>z in both
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Shapior Wilk and Shapiro Francia test as shown in Figure IV.4 which is more than 0.05
indicates that the residual data are normally distributed.
c.
2. Multicollinearity Test
multicollinearity exists or not.
id
Pearson Product-Moment Correlation Coefficient is used to test whether
H0 : No multicollinearity exists
Ha: Mulitcollinearity exists
No multicollinearity exists within the model if coefficient value between
independent variables < 0.75. All coefficients between independent variables are < 0.40
which means that there is no multicollinearity assumption exists within the model.
37
Table IV.3. Pearson Product-Moment Correlation Coefficient
SIZE
IND
LEV
PROF
SIZE
1.0000
IND
-0.0465
1.0000
LEV
0.2522
-0.0035
1.0000
PROF
-0.0268
-0.1231
-0.3788
1.0000
LIST
-0.0098
-0.2171
-0.0620
0.2339
LIST
1.0000
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The other way to test multicollinearity within the model is by its VIF. The
hypothesis is presented as follows:
.p
H0 : No multicollinearity exists
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Ha: Mulitcollinearity exists
Table IV.4. VIF
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Variable
VIF
1/VIF
17.77
0.056270
IND
2.79
0.358410
LEV
8.82
0.113394
PROF
1.39
0.721355
LIST
6.39
0.156531
Mean VIF
7.43
id
c.
SIZE
Source: Output form STATA
If mean VIF < 10, no indication of high multicollinearity in the model. From
the output of command given in STATA presented by Table IV.4, the value of mean
VIF is 7.43. Thus, the model is free from high multicollinearity.
38
3. Autocorrelation Test
Wooldridge test is used for autocorrelation test in panel data. Below is the
hypothesis regarding autocorrelation with the null hypothesis that there is no
autocorrelation problem in the model.
H0 : No autocorrelation exists
Ha: Autocorrelation exists
The result of the test as presented in Figure IV.5 concluded that there is first-
w
order autocorrelation in the model, shown by Prob > F = 0.0064 which is less than
w
α=0.05. Regressing the data by using standard error cluster in the Random Effect Model
w
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can solve the problem autocorrelation and heteroscedasticity as shown also in Figure
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kn
IV.5 as a result of the regression.
Figure IV.5. Wooldridge Test
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c.
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4. Heteroscedasticity Test
Heteroscedasticity test for the model is also conducted as shown in Figure IV.6.
Although the result shows that there is problem of heteroscedasticity since the value
of Prob > chi2 is 0.0026 which is less than 0.05. Heteroscedasticity problem in this
model is also being solved by using standard error cluster in solving autocorrelation
problem.
39
Figure IV.6. Heteroscedasticity Test
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D. Hypothesis Test
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1. Regression Analysis
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The result of regression analysis is presented in Figure IV.7 as an output of data
is presented as follows:
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processed through STATA. Based on that result, the regression equation for the model
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DISC= -19.34 + 0.70 SIZE + 2.04 IND - 5.55 LEV - 2.16 PROF + 0.21 LIST + εt
The regression result above can interpret the relationship between each
dependent variable (the extent of carbon emission disclosure) and the independent
c.
variable (size, type of industry, leverage, profitability, and listing age). Positive or
id
negative relationship is determined by the sign + or - attached to the value of the
coefficient of each variable. A positive relationship means that in the condition when
other variables are assumed to be constant, if the independent variable increases by 1,
then the dependent variable will be increased by the value of the coefficient of the
independent variable. In contrast, the negative relationship means that if the
independent variable increases by 1, then the dependent variable will be decreased by
the value of the coefficient of the independent variable and vice versa.
40
Figure IV.7. Regression Output
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id
c.
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st
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2. Goodness of Fit
R-squared measures the proportion of the variation of dependent variable
explained by independent variables for a linear regression model. Random effect
estimator (GLS estimator) is a weighted average of between and within estimators. In
STATA, R-squared overall is used for GLS regression in the Random Effect Model.
The R-squared overall of the model in this research is 31%. The value 31% from
41
R-squared overall means that the extent of corporate voluntary carbon emission
disclosure can be explained 31% by the independent variables, the rest 69% is explained
by other variables which are not in the model.
3. F-test
In multiple linear regression (MLR), F-tests play a crucial role in testing
simultaneous hypotheses. This test is performed to determine the significance influence
of independent variables used simultaneously on the dependent variable. If the value of
w
Prob (F-Statistic) is smaller than α, then all independent variables together give a
w
significant influence on the dependent variable. From the output using STATA, the
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value of Prob > chi2 is 0.00 which is less than α= 0.05. It means that independent
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variables significantly influence the dependent variable altogether. So the
characteristics of corporations such as corporate size, type of industry, leverage,
emission disclosure in Indonesia.
4. T-test
c.
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profitability, and listing age simultaneously influence the extent of corporate carbon
id
T-test aims to determine the effect of each independent variable on the
dependent variable. It’s said to be a significant when the probability value is smaller
than the value of α=0.05.
From Table IV.5, there are three independent variables that have significant
effect on the dependent variable, which are leverage, profitability, and listing age. The
rest two variables which are corporate size and type of industry are statistically
insignificant in influencing the dependent variable. The t-test for each independent
variable is presented as follows:
42
Table IV.5. T-Statistic and Probability
Variables
Coefficient
Probability
SIZE
0.699
0.136
IND
2.043
0.072
LEV
-5.553
0.014
PROF
-2.159
0.000
LIST
0.214
0.000
w
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a. Hypothesis 1
From the result of t-test provided by Table IV.5 probability value is more than
w
α (0.14 > 0,05). This means that null hypothesis is not rejected. Thus, corporate size
.p
variable does not influence the extent of carbon emission disclosure. The extent of
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voluntary carbon emission disclosure will not be greater in corporations with bigger
size.
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b. Hypothesis 2
From the result of t-test provided by Table IV.5, probability value is more than
c.
α (0.07 > 0,05). This means that null hypothesis is not rejected. Thus, the type of
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industry variable, whether it is emission intensive or non-emission intensive, does not
influence the extent of corporate voluntary carbon emission disclosure. The extent of
voluntary carbon emission disclosures will not be greater in corporations operating in
emissions intensive industries.
c. Hypothesis 3
From the result of t-test provided by Table IV.5, probability value is more than
α (0.01 < 0,05). This means that null hypothesis is rejected. Thus, leverage variable
43
does influence the extent of corporate voluntary carbon emission disclosure. The extent
of voluntary carbon emission disclosures will be greater in corporations with low
financial distress.
d. Hypothesis 4
From the result of t-test provided by Table IV.5, probability value is less than α
(0.00 < 0,05). This means that null hypothesis is rejected. Thus, profitability variable
does influence the extent of voluntary carbon emission disclosures. The negative sign
w
shows negative direction. Thus, the extent of voluntary carbon emission disclosures
w
will be greater in corporations with low profitability.
.p
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e. Hypothesis 5
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From the result of t-test provided by Table IV.5, probability value is less than α
(0.00 < 0,05). This means that null hypothesis is rejected. Thus, corporate listing age
.a
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variable does influence the extent of corporate voluntary carbon emission disclosure.
This means that the longer a corporation is listed in Indonesia Stock Exchange, the more
extensive it is in disclosing its carbon emission information.
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c.
E. Result Analysis
1. Relationship between corporate size and corporate voluntary carbon emission
disclosure
The regression result shows the expected sign of coefficient which is positive,
but its influence is not statistically significant. It means that the bigger corporate size,
the more extensive it is in disclosing its carbon emission, however this relationship is
statistically proven insignificant. This finding is showing conflicting result from
previous studies, where the results indicate that corporate size is proven significant in
44
explain carbon emission disclosure. One interpretation of this finding is because all
corporations taken as sample in this research are relatively big with the total asset
ranges between of 0.8 trillion rupiah to 2.797 trillion rupiah. Because these corporation
are relatively big in size, their variations in the CDP score are not sufficient in
explaining the extent of carbon emission disclosure.
2. Relationship between type of industry and corporate voluntary carbon
emission disclosure
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The regression result shows that the dummy variable for a corporation operating
w
in emissions intensive industries also has a positive relationship with carbon emission
w
.p
disclosure scores, but its influence is not statistically significant, demonstrating that
st
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type of industry is not important explanatory factor in voluntary carbon disclosure. One
interpretation of this finding is because corporations which are not member of emission
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intensive industries are also disclosing their carbon emission in endeavor to gain the
benefit from informing good news. This is also supported by data presented in Figure
IV.2, where in 2012 non-emission intensive industries are showing higher average
c.
score of carbon emission disclosures than the emission intensive industries.
id
3. Relationship between leverage and corporate voluntary carbon emission
disclosure
The result of the regression shows the expected sign of coefficient which is
negative. The negative sign of the coefficient means that the more corporations finance
their activities through debts, the more extensive its carbon emission disclosure. The
influence of leverage is statistically proven significant, meaning that the extent of
corporate carbon emission disclosure will be greater in corporation with high financial
45
distress. One interpretation of this finding is that corporations which show high
financial distress to disclose additional information of their great performance in
environmental field by disclosing an extensive corporate carbon emission information
in endeavor to compensate its financially poor performance.
4. Relationship between profitability and corporate voluntary carbon emission
disclosure
The result of the regression doesn’t shows the expected sign of coefficient. The
w
sign of the coefficient is negative and it is statistically significant. The negative sign
w
indicates negative direction. Profitability variable has significant effect on the extent of
w
.p
corporate voluntary carbon emission disclosure but in negative direction. This means
st
kn
that the decreasing of corporate profitability will be followed by the more extensive of
corporate voluntary carbon emission disclosure, and vice versa. This finding is showing
.a
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conflicting result from previous study conducted by Choi, Lee, and Psaros (2013),
where the previous study shows positive sign which supports agency theory and
signaling theory. One interpretation of this finding is that corporations which show low
c.
profitability tend to disclose additional information in endeavor to compensate its
id
financially poor performance with the information of great performance in other aspect
such as performance in environmental field by disclosing an extensive corporate carbon
emission information.
5. Relationship between corporate listing age and corporate voluntary carbon
emission disclosure positively significant
The regression result shows the expected sign of the independent variable
coefficient which is positive, and its coefficient is statistically significant. This means
46
that the longer a corporation listed in Indonesia Stock Exchange, the more extensive its
voluntary carbon emission disclosure. Listing age plays a significant role in voluntary
carbon emission disclosure which suggests that the longer a corporation is listed in a
capital market, the more experienced it is in accommodating its stakeholder’s needs of
additional information whereas younger corporations are having less competitive
advantages, less source in disclosing information, and lack of track record in disclosing
such information.
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CHAPTER V
CONCLUSIONS, LIMITATIONS, AND RECOMMENDATIONS
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A. Conclusions
This research aims to understand the current condition on how extensive the
w
carbon emission disclosure voluntarily reported by corporations in Indonesia and
.p
factors affecting it. The determining factors studied in this research are corporate size,
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type of industry, leverage, profitability, and listing age. This research is carried out to
study 32 non-financial corporations fulfilling the criteria in purposive sampling. Based
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on the explanation from previous chapters, here are the conclusions:
1. The equation resulted from regression analysis for the extent of corporate voluntary
c.
carbon emission disclosure and its determinants is:
id
DISC= -19.34 + 0.70 SIZE + 2.04 IND - 5.55 LEV - 2.16 PROF + 0.21 LIST + εt
This result equation means that the increasing of one basis point in independent
variable will increase the extent of corporate voluntary carbon emission disclosure
as much as the coefficient of the particular independent variable.
2.
The corporate size, type of industry, leverage, profitability, corporate media
exposure, and listing age as determinants studied in this research can explain 31%
variations in the extent of corporate voluntary carbon emission disclosure.
47
48
3. All independent variables simultaniously have significant effect towards the extent
of corporate voluntary carbon emission disclosure.
4. Partially, independent variables are being tested to see their individual effect on the
extent of corporate voluntary carbon emission disclosure. The results are as follows:
a. Corporate size and type of industry do not have influence on the extent of
corporate carbon emission disclosure.
b. Leverage does have significant influence on the extent of corporate carbon
w
emission disclosure in negative direction.
w
c. Profitability does have significant influence on the extent of corporate carbon
w
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emission disclosure in negative direction.
st
kn
d. Listing age does have significant influence on the extent of corporate carbon
emission disclosure in positive direction
.a
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The extent of voluntary carbon emission disclosures will be greater in corporations
with high financial distress, low profitability, and longer time listed on Indonesia
Stock Exchange.
c.
5. Based on the comparison of these results with the results of previous studies, there
id
are conflicting results on the effect of several variables on the extent of corporate
carbon emission disclosure. This is likely due to differences in the object, sample,
place and time of research.
B. Limitations
The limitations in this research are:
1. The sample studied in this research is limited to the criteria established in purposive
sampling technique. The sample is limited to non-financial corporations which
49
consistently disclose their carbon emission in three consecutive years from 2011 to
2013.
2. The measure of carbon emission disclosure is based on 18 individual items. Though
researcher believes that these 18 items are consistent with good carbon emission
disclosure, there are likely other relevant factors that are not considered.
3. In assessing the voluntary carbon emission disclosures, researcher examines
disclosures made either in the annual report or the sustainability report, so there is
w
a risk that corporation may have made public disclosures in other forms of which
w
researcher is not aware.
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4. This research is not free from researcher’s subjectivity since the scoring for the
C. Recommendations
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extent of corporate carbon emission disclosure lies on researcher’s subjectivity.
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There are several recommendations for several parties as mentioned below:
1. Since the sample of this research is limited to non-financial corporations
consistently disclosed their carbon emission in three consecutive years, the next
c.
similar research could extend the sample to population which includes the financial
id
corporations as they could be regarded in scope 2 of carbon emission to get a better
capture of corporate voluntary carbon emission disclosure in Indonesia.
2. Seeing the condition of the level of carbon emission disclosure in Indonesia which
is relatively low, corporations can see this as a chance to be more competitive by
extending their carbon emission disclosure to signal investor that they have better
performance in dealing with environmental issues as investors right now are
becoming more concern towards these problems.
50
3. The results of this research can be used as considerations for investor in managing
their portfolio investment towards corporation which are more adaptive to the
change of environmental paradigm in relation to the sustainability and going
concern of such corporations.
4. As the result of this research showing that corporations with high financial distress,
low profitability, longer listing age are having greater carbon emission disclosure
while corporate size and type of industry have no significant effect in the extent of
w
carbon emission disclosure, Indonesian Government shall consider these findings
w
in giving incentives for corporations as stated in the regulation or impose the
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imminent regulation or policy to increase carbon emission disclosure as the extent
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of corporate carbon emission in Indonesia is still low.
51
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Clarkson, P. M., Li, Y., Richardson, G. D., & Vasvari, F. P. 2008. Revisiting the
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Coebergh, Henricus Petrus Theodorus. 2011. Voluntary Disclosure of Corporate
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Cormier, D. and Magnan, M. 1999. Determinants, Costs and Benefits. Journal of
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Freeman, R.E., J.S. Harisson, A. C. Wicks, B. L. Parmar, S. de Colle. 2010. Stakeholder
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Ghomi, Zahra Borghei & Philomena Leung. 2013. An Empirical Analysis of the
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2. Guidelines and Regulations
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Peraturan Presiden Nomor 61 Tahun 2011 tentang Rencana Aksi Nasional Penurunan
Emisi Gas Rumah Kaca (RAN-GRK).
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Appendix 1
Related Research
Title
Research
Scope of
Research
1
An analysis of
Australian
company
carbon
emission
disclosures
(2013)
Bo Bae
Choi,
Doowon
Lee and
Jim Psaros
The largest 100
companies
listed on the
Australian
Securities
Exchange as of
June 2009
during the years
2006 to 2008
are selected
based on their
market
capitalization
.p
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w
No
All companies
listed in ASX
which publicly
disclose their
GHG
information,
which are not
subject to
NGER Act
2007 between
July 1, 2008
(Note 9) and
June 30, 2011
I: IND as industry
in which the firm
operates,
EMISSION as
level of carbon
emission, SIZE as
firm’s size, ROA
and LEV as
companies’
profitability, and
GOV as quality
of corporate
governance
D: GHG
voluntary
disclosure
I: firm size
(SIZE), age of
firm (AGE),
leverage (LEV),
listing status
(EXCH),
corporate
governance
(CORP), industry
(IND), and
ownership
concentration
(OWN).
id
c.
Zahra
Borghei
Ghomi
and
Philomena
Leung
D: The extent of
carbon emission
disclosure
.a
an
An Empirical
Analysis of
the
Determinants
of
Greenhouse
Gas
Voluntary
Disclosure in
Australia
(2013)
st
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2
Dependent and
Independent
Variables
Conclusions
All the
independent
variables are
significantly
associated with
the total score
except for
ROA and LEV.
There’s a
positive and
significant
association
between the
level of GHG
voluntary
disclosure,
corporate
governance,
firm size, and
listing status
except industry
and leverage
variables
3
Transparency
of Corporate
Carbon
Disclosure:
International
Evidence
(2011)
Qingliang
Tang and
Le Luo
All companies
listed in CDP
Global 500
report 2009
D: Transparency
of Corporate
Carbon
Disclosure
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I: shareholders’
interest
(EquityRaise),
leverage
(Leverage), firm
size (Size), type
of industries
(CarbonIntense),
emission trading
scheme (ETS),
industry
membership
(Protocol),
stringency of
environmental
regulation
(Stringency),
legal protection of
minority investors
(LegalSys),
carbon
transparency
(Beta, TobinQ,
ROA, CapSpend,
NewTech),
Environmental
Performace Index
(EPI)
Firm size,
leverage,
industry
membership,
emission
trading scheme
(hereafter
ETS),
stringency of
environmental
regulation, as
predicted, are
significantly
associated with
carbon
transparency.
Appendix 2
Carbon Emission Disclosure Checklist
1
Climate Change:
risk and
opportunities
2
3
4
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GHG Emission
5
6
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7
Assessment/description of the risks (regulatory,
physical or general) relating to climate change and
actions taken or to be taken to manage the risks
Assessment/description of current (and future)
financial implications, business implications and
opportunities of climate change
Description of the methodology used to calculate GHG
emissions (e.g. GHG protocol or ISO)
Existence external verification of quantity of GHG
emission- if so by whom and on what basis
Total GHG Emissions – metric tons CO2-e emitted
Disclosure of scopes 1 and 2, or scope direct GHG
emissions
Disclosure of GHG emissions by sources (e.g. coal,
electricity, etc.)
Disclosure of GHG emissions by facility or segment
level
Comparison of GHG emissions with previous years
Total energy consumed (e.g. tera-joules or peta-joules)
Quantification of energy used from renewable sources
Disclosure by type, facility or segment
Detail of plans or strategies to reduce GHG emissions
Specification of GHG emissions reduction target level
and target year
Emissions reductions and associated costs or savings
Cost of future emissions factored into capital
expenditure planning
Indication of which board committee (or other
executive body) has overall responsibility for actions
related to climate change
Description of the mechanism by which the board (or
other executive body) reviews the company’s progress
regarding climate change
Source: Choi, Lee, and Psaros (2013)
8
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Carbon Emission
Accountability
18
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c.
15
16
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GHG Reduction
and Cost
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Energy
Consumption
9
10
11
12
13
14
Appendix 3
Research Sample
No
1
2
3
Corporation
Year of
Listing
Agriculture
UNSP
Bakrie Sumatera Plantations
SGRO Sampoerna Agro
SMAR SMART
Mining
ADRO Adaro Energy
BRAU Berau Coal energy
ITMG
Indo Tambangraya Megah
PTBA
Tambang Batubara Bukit Asam
MEDC Medco Energi Internasional
ANTM Aneka Tambang (Persero)
TINS
Timah (Persero)
INCO
Vale Indonesia
Basic Industry and Chemicals
SMCB Holcim Indonesia
INTP
Indocement Tunggal Prakarsa
SMGR Semen Indonesia (Persero)
ARNA Arwana Citramulia
KRAS Krakatau Steel (Persero)
BUDI
Budi Starch & Sweetener
TPIA
Chandra Asri Petrochemical
FASW Fajar Surya Wisesa
INKP
Indah Kiat Pulp & Paper
TKIM
Pabrik Kertas Tjiwi Kimia
Miscellaneous Industry
ASII
Astra International
AUTO Astra Otoparts
Consumer Goods Industry
RMBA Bentoel Internasional Investama
KLBF
Kalbe Farma
TCID
Mandom Indonesia
UNVR Unilever Indonesia
Infrastructure, Utilities, & Transportation
PGAS
Perusahaan Gas Negara (Persero)
ISAT
Indosat Tbk.
TLKM Telekomunikasi Indonesia (Persero)
EXCL
XL Axiata
GIAA
Garuda Indonesia (Persero)
Source: Developed from Indonesia Stock Exchange
id
c.
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28
29
30
31
32
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24
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26
27
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12
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4
5
6
7
8
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10
11
Code
1990
2007
1992
2008
2010
2007
2002
1994
1997
1995
1990
1977
1994
1991
2001
2010
1995
1996
1994
1990
1990
1990
1998
1989
1991
1993
1981
2003
1994
1995
2005
2011
Appendix 4
Model Specification Test
1. Chow Test (before dropping outliers)
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2. Chow Test (after dropping outliers)
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.a
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3. Hausman Test (before dropping outliers)
.p
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.a
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4. Hausman Test (after dropping outliers)
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Appendix 5
Normality Test (before dropping outliers)
1. Histogram
.p
w
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w
2. Skewness/Kurtosis Tests, Shapiro-Wilk Test and Shapiro-Francia Test
id
c.
.a
an
st
kn
CURRICULUM VITAE
Name
: Niczen Henry Lolowang
Place, Date of Birth
: Tomohon, 4 April 1987
Address
: Jalan Delman Asri V Nomor 5, Tanah Kusir,
Kebayoran Lama, Jakarta Selatan
Religion
: Christian
w
:
1. SD GMIM VIII Tomohon (1993-1999)
st
kn
Formal Education
: [email protected]
.p
w
Email
: Single
w
Status
2. SD GMIM I Bitung (1998-1999)
.a
an
3. SMP Negeri 1 Tomohon (1999-2002)
4. SMA Kristen 1 Tomohon (2002-2003)
id
c.
5. SMA Kristen 2 (Binaan Khusus) Tomohon
(2003-2005)
6. Fakultas Ekonomi UNSRAT (2005-2006)
7. Diploma III Akuntansi STAN (2006-2009)
8. Diploma IV Akuntansi STAN (2013 – now)
Position at Work
: Staff at Secretariat General, Ministry of Finance
(2009 – now)

www.pknstan.ac.id

Transcription

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