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KEMENTERIAN KEUANGAN REPUBLIK INDONESIA BADAN PENDIDIKAN DAN PELATIHAN KEUANGAN SEKOLAH TINGGI AKUNTANSI NEGARA TANGERANG SELATAN .p w w w THESIS st kn AN EMPIRICAL ANALYSIS OF THE DETERMINANTS OF CORPORATE VOLUNTARY CARBON EMISSION DISCLOSURE IN INDONESIA .a an Proposed by: c. NPM 134060018072 id 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 KEMENTERIAN KEUANGAN REPUBLIK INDONESIA BADAN PENDIDIKAN DAN PELATIHAN KEUANGAN SEKOLAH TINGGI AKUNTANSI NEGARA TANGERANG SELATAN DECLARATION OF AUTHENTICITY : NICZEN HENRY LOLOWANG NPM w w NAME : 134060018072 w FIELD OF THESIS .p : AN EMPIRICAL ANALYSIS OF THE st kn THESIS TITLE : ACCOUNTING DETERMINANTS VOLUNTARY OF CORPORATE CARBON EMISSION .a an DISCLOSURE IN INDONESIA I hereby declare that this thesis is indeed my original piece of work, completed c. on my own or fully and specifically acknowledged wherever adapted from other id 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 ii KEMENTERIAN KEUANGAN REPUBLIK INDONESIA BADAN PENDIDIKAN DAN PELATIHAN KEUANGAN SEKOLAH TINGGI AKUNTANSI NEGARA TANGERANG SELATAN THESIS APPROVAL FORM : NICZEN HENRY LOLOWANG NAME w : 134060018072 w w NPM FIELD OF THESIS .p THESIS TITLE : ACCOUNTING : AN EMPIRICAL ANALYSIS OF THE st kn DETERMINANTS VOLUNTARY OF CORPORATE CARBON EMISSION DISCLOSURE IN INDONESIA id c. .a an Acknowledged by, Approved by, Director, Thesis Advisor Kusmanadji, Ak.., M.B.A. Fadlil Usman, Ak., M.Acc. NIP 196009151981121001 NIP 196210101983021001 iii KEMENTERIAN KEUANGAN REPUBLIK INDONESIA BADAN PENDIDIKAN DAN PELATIHAN KEUANGAN SEKOLAH TINGGI AKUNTANSI NEGARA TANGERANG SELATAN THESIS ENDORSEMENT : NICZEN HENRY LOLOWANG NAME w NPM : 134060018072 w FIELD OF THESIS .p w THESIS TITLE : ACCOUNTING : AN EMPIRICAL ANALYSIS OF THE DETERMINANTS st kn VOLUNTARY OF CORPORATE CARBON EMISSION DISCLOSURE IN INDONESIA. c. .a an 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) id 1. (Head of Examiner) iv 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. w w 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. w 3. My thesis advisor, Mr. Fadlil Usman, Ak., M.Acc. and my technical supervisor, .p Tjahjo Winarto, Ak., M.B.A. for giving the hints in making deeper analyses and for st kn proofreading the script. 4. My thesis examiners, Mrs. Budiasih, Ak., M.Si. and Dyah Purwanti, Ak., CA., .a an 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, c. S.E., M.M. for the recommendations and approval in order to advance the proposal id 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, v 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. w April 2015 .p w w Tangerang Selatan, id c. .a an st kn Niczen Henry Lolowang vi 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 w LIST OF APPENDIX ............................................................................................................xi w CHAPTER I INTRODUCTION ........................................................................................... 1 w A. Research Background .....................................................................................1 .p B. Delimitation Of Research ...............................................................................4 st kn C. Problem Formulation ......................................................................................4 D. Research Objective .........................................................................................5 Research Benefits............................................................................................5 F. Systematics of Writing ....................................................................................6 .a an E. CHAPTER II REVIEW OF LITERATURE ....................................................................... 7 A. Theoretical Framework ...................................................................................7 c. B. Previous Research .........................................................................................12 id 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 vii 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 w B. Limitations ....................................................................................................48 w C. Recommendations .........................................................................................49 .p APPENDIX w REFERENCES ......................................................................................................................51 id c. .a an st kn viii LIST OF TABLES Table III.1 Sample Selection 20 Table III.2 Independent Variables 22 Table IV.1 Descriptive Statistics 31 Table IV.2 The Score of Carbon Emission Disclosure 32 Table IV.3 Pearson Product-Moment Correlation Coefficient 37 Table IV.4 VIF 37 w 42 T-Statistic and Probability .p w w Table IV.5 id c. .a an st kn ix LIST OF FIGURES Figure III.1 Research Framework 25 Figure III.2 Research Model 26 Figure IV.1 The Average Score of the Extent of Carbon Emission 30 Disclosure Figure IV.2 Level of Carbon Emission Disclosure Based On Type of 33 Industry Figure IV.3 Histogram of Residual Data 35 w Shapiro Wilk and Shapiro Francia Test for Normal Data 36 Figure IV.5 Wooldridge Test 38 Figure IV.6 Heteroscedasticity Test 39 Figure IV.7 Regression Output 40 .p w w Figure IV.4 id c. .a an st kn x 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 .p w w w id c. .a an st kn xi 1 CHAPTER I INTRODUCTION w w A. Research Background We are living in the world where global communities are increasingly w concerned about the sustainability of their planet due to global warming effects. .p Glaciers are melting, sea levels are rising, cloud forests are drying, and wildlife is st kn 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 .a an 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 c. warming from its very root cause which is the greenhouse gas by reducing it. One of id 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 2 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 w the implementation of climate change programs. In addition, Government of Indonesia w has announced voluntary (non-binding) emission reduction in COP 15 with the target w .p to achieve 26% GHG emission lower than the baseline using domestic budget and could st kn be further increased to 41% with international support by the year of 2020 from the condition without any action (business as usual/BAU). .a an 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 c. will provide the basis for various related Ministries/Institutions as well as the Regional id 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 3 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, w business practitioners are also concerned with this issue as many businesses have w already taken actions that have had, or will have, the effect of lowering their GHG w .p emissions. Many corporations show their supports by voluntarily disclose their carbon st kn 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 .a an 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 c. corporations is in the form of voluntary report due to the absence of rules-based id 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 4 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 w VOLUNTARY CARBON EMISSION DISCLOSURE IN INDONESIA.” w B. Delimitation Of Research w .p This research only studies the extent of corporate voluntary carbon emission st kn 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 .a an 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. id c. 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? 5 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 w 2. To empirically analyze whether or not industry type influences the extent of w corporate voluntary carbon emission disclosure w .p 3. To empirically analyze whether or not leverage influences the extent of voluntary st kn corporate carbon emission disclosure 4. To empirically analyze whether or not corporate profitability influences the extent .a an of corporate voluntary carbon emission disclosure 5. To empirically analyze whether or not listing age influences the extent of corporate voluntary carbon emission disclosure id c. 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. 6 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 w This chapter contains relevant theoretical bases underlying this thesis, w previous research, and research hypotheses development. The theory will w st kn theory. .p include agency theory, stakeholder theory, legitimacy theory, and signaling BAB III METHODOLOGY .a an 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 id BAB IV RESULT AND ANALYSIS c. 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 w w A. Theoretical Framework 1. Agency theory. w Jensen & Meckling (1976, 308) define the agency relationship as “a contract .p under which one or more persons (the principals) engage another person (the agent) to st kn 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, .a an 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 c. the information asymmetry problem due to the fact that managers can access id 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 7 8 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 w residual loss as it reduces principals’ welfare. Accordingly, the agency cost is the w summation of the monitoring cost, bonding cost, and the residual loss. w .p As stated before that agency relationship leads to the information asymmetry st kn 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” .a an (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.) c. and also to convince the external users that managers are acting in an optimal way” id (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). 9 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 w theory is seen as interactions between corporations and society at large, demonstrating w corporate activities to fit in the societal values. It can be linked to the concept of social w .p contract (Choi, Lee, and Psaros 2013, 63). Social contract would exist between st kn corporations and individual society members provided that society offers corporations with legal rights and authority to access resources such as natural or human resources, .a an 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 c. (2011, 5) cited Suchmand, Milne and Paten, conclude legitimacy theory as follows: id 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) 10 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 w transparency on corporate activities. Corporations are disclosing more information to w deal with societal perceptions. “Lack of transparency would be perceived as evidence w .p of inadequate socially responsible mechanisms (e.g. environmental protection) of an st kn 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 .a an (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 c. the manipulation of social perceptions and there is an interplay between firm id 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, 11 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). w As there are many parties affected by corporate objectives, corporations conduct w their activities in regard of their stakeholders. In order to maintain the relationship w .p between corporations and their stakeholders, managers on behalf of corporations are st kn 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 .a an 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, c. stakeholders of corporations begin to ask corporations to act in regard of environmental id 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). 12 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: .p w w w 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. st kn In responding to the increasing of global awareness towards environmental problems, many corporations are trying to signal their great performance in mitigating .a an 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 c. companies would disclose more information than the mandatory ones required by laws id 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. 13 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 w and significant association between the level of GHG voluntary disclosure in annual w reports, corporate governance and firm size, which in general, support the application w .p of agency theory and stakeholder theory. Further, firms with superior GHG st kn performance are more likely to engage in discretionary disclosure, as predicted by voluntary disclosure theory. Finally, in contrary to industry and leverage variables, .a an 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. c. They disclose voluntary GHG information to acquire the benefits of communicating id 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 14 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 w 1. Relationship between corporate size with the extent of voluntary carbon w emission disclosure w .p The variable most consistently reported as significant in studies examining st kn differences across corporations in their disclosure policy is corporate size. Agca and Onder (2007, 244) stated that institutionalization would increase in corporations which .a an 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 c. as a proxy for organizational visibility because it is thought that larger companies are id 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 w for corporations to appear to be operating in accord with societal values to legitimize w their activities due to political visibility (Ghomi and Leung 2002, 114 cited Patten). w .p Legitimacy theory predicts that for the significance of carbon issues today, it is not st kn 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 .a an 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: id H2. The extent of voluntary carbon emission disclosures will be greater in corporations 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 w environmental liabilities or new regulations means extra costs, leading to concerns from w their debt holders, suppliers and customers about corporation’s performance. Based on w .p those reasons, the hypothesis is presented as follows: st kn H3. The extent of voluntary carbon emission disclosures will be greater in corporations with low financial distress. carbon emission disclosure .a an 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 id 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: H4. The extent of voluntary carbon emission disclosures will be greater in corporations 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 w age is the length of time a company has been listed on a capital market, and it may be w relevant in explaining the voluntary disclosure level.” They investigated the association w .p between listing age and the extent of voluntary disclosure because listing age has not st kn 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 .a an 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 id 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: H5. The extent of voluntary carbon emission disclosures will be greater in corporations with longer age of listing. 18 CHAPTER III METHODOLOGY w w A. General Overview Of Research Object The object in this research is corporations in Indonesia. The population of data w used in this research is all corporations listed on Indonesia Stock Exchange. Sample of .p this research is non-financial corporations with purposive sampling method. The data st kn 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 .a an 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 id 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 w 3. During period 2011-2013 consistently disclose corporate carbon emission through w annual report and/or sustainability report or at least has an item requested in the w .p Information Request Sheets by the CDP regarding carbon emission disclosure. st kn 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 .a an 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 id 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. .p w w w 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 st kn C. Type and Source of Data .a an 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. id 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). w Within these five categories there are 18 specific items. There are 2 items in w climate change risks opportunities category, 7 items in the greenhouse gas emissions w .p accounting category, 3 items in the energy consumption accounting category, 4 items st kn 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 .a an 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 id 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 w w Leverage Profitability Listing age Notation .p w 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) st kn a. Corporate size (SIZE) Corporate size is defined as how big is the corporation determined by its total .a an 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) id 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), w w the variables will be measured using a leverage ratio of total liabilities to total assets. .p w The sign of this variable is expected to be negative. 𝐿𝐸𝑉 = st kn d. Profitability (PROF) 𝑇𝑜𝑡𝑎𝑙 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡 Profitability describes the ability of a company to generate profit by using all .a an 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 w government by adopting energy efficiency programs, using renewable energy, and w conducting numbers of mitigation actions resulting in the reduction of their existing w .p carbon emissions. Unfortunately, there are no specific standards promulgated on how st kn such emissions should be reported by these corporations. The disclosure of carbon emission conducted by corporations today is in the .a an 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 id 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 .p w w w c. .a an st kn id 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 (+) w w Type of Industry (+) The Extent of Corporate Carbon Emission Disclosure .p w Listing age (+) Profitability (+) .a an st kn Leverage (-) Source: Developed from Previous Research c. Figure III.2 depicts that the extent of corporate carbon emission disclosure is id 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), w and Random Effect Model (REM) w b. Pooled Ordinary Least Square (OLS) w .p In this model, cross section and time series data are combined into a pool of st kn 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) .a an 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 id 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 w normally distributed. w b. Multicollinearity Test w .p Multicollinearity is a symptom of a correlation between the independent st kn 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 .a an 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 id 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 w This test is used to determine the significance level of influence of the w independent variables simultaneously on the dependent variable. The testing is done by w .p comparing F-statistic to the F table. F table is determined based on the 95% confidence st kn 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 .a an 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 id 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 w w w A. Descriptive Statistics .p The score of corporate voluntary carbon emission disclosure ranges from 1 to st kn 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. .a an 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. id 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 w w w DISC 0.625 LEV .p PROF 0.1295507 0.367477 -2.022695 1.258059 LIST 18.15625 8.244077 4 38 0.4777681 .a an 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 id 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 .p 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 .a 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 w w w 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. w w w Figure IV.2. Level of Carbon Emission Disclosure Based On Type of Industry 7 .p 5 .a 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 w The result of command given in STATA concludes that H0 is rejected since w Value (Prob>F) < Alpha 0,05. Therefore, Fixed effect Model is better than Common w .p Effect for model estimation. The result of this test (before and after dropping outliers) 2. Hausman Test st kn is presented in Appendix 4. .a an 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 id 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, w Shapiro-Wilk Test and Shapiro-Francia Test, the residual data are not normally w .p 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 an 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. Source: Output from STATA 36 Figure IV.4. Shapiro Wilk and Shapiro Francia Test for Normal Data .p w w w Source: Output from STATA 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 .a an 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 Source: Output from STATA w w w The other way to test multicollinearity within the model is by its VIF. The hypothesis is presented as follows: .p H0 : No multicollinearity exists st kn Ha: Mulitcollinearity exists Table IV.4. VIF .a an 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 .p can solve the problem autocorrelation and heteroscedasticity as shown also in Figure st kn IV.5 as a result of the regression. Figure IV.5. Wooldridge Test id c. .a an Source: Output from STATA 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 Source: Output from STATA w D. Hypothesis Test w 1. Regression Analysis w .p The result of regression analysis is presented in Figure IV.7 as an output of data is presented as follows: st kn processed through STATA. Based on that result, the regression equation for the model .a an 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 .p w w w id c. .a an st kn Source: Output from STATA 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 w .p value of Prob > chi2 is 0.00 which is less than α= 0.05. It means that independent st kn 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. .a an 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 Source: Output from STATA w w 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 st kn voluntary carbon emission disclosure will not be greater in corporations with bigger size. .a an 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 id 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 w e. Hypothesis 5 st kn 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 an 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. id 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 w 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 kn 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 .a an 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 an 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. .p w w w id c. .a an st kn CHAPTER V CONCLUSIONS, LIMITATIONS, AND RECOMMENDATIONS w w 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, st kn 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 .a an 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 .p 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 an 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. w .p 4. This research is not free from researcher’s subjectivity since the scoring for the C. Recommendations st kn extent of corporate carbon emission disclosure lies on researcher’s subjectivity. .a an 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 w .p imminent regulation or policy to increase carbon emission disclosure as the extent id c. .a an st kn of corporate carbon emission in Indonesia is still low. 51 REFERENCES 1. Literature Books and Journals Agca, Ahmed and Uerife Önder, 2007. Voluntary Disclosure in Turkey: A Study on Firms Listed in Istanbul Stock Exchange (ISE). The Journal of Accounting and Finance. Volume 5, Issue 3:241-251. Choi, Bo Bae, Doowon Lee, Jim Psaros. 2013. An analysis of Australian company carbon emission disclosures. Pacific Accounting Review. Vol. 25 Iss 1:58 – 79 w w w Clarkson, P. 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Guidelines and Regulations st kn Peraturan Presiden Nomor 61 Tahun 2011 tentang Rencana Aksi Nasional Penurunan Emisi Gas Rumah Kaca (RAN-GRK). id c. .a an 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 w w 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 kn 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 .p w w w id c. .a an st kn 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 w GHG Emission 5 6 w w 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 .p Carbon Emission Accountability 18 id 17 c. 15 16 .a an GHG Reduction and Cost st kn 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. .a an 28 29 30 31 32 st kn 24 25 26 27 .p 22 23 w 12 13 14 15 16 17 18 19 20 21 w w 4 5 6 7 8 9 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) .p w w w id c. .a an st kn 2. Chow Test (after dropping outliers) .p w w w id c. .a an st kn 3. Hausman Test (before dropping outliers) .p w w w id c. .a an st kn 4. Hausman Test (after dropping outliers) .p w w w id c. .a an st kn Appendix 5 Normality Test (before dropping outliers) 1. Histogram .p w w 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)