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Assessment of Different Methods for Measuring the Sustainability of Forest Management

Retno Kuswandari March, 2004

Assessment of Different Methods for Measuring the Sustainability of Forest Management by Retno Kuswandari

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation Planning and Coordination in Natural Resources Management

Degree Assessment Board Prof. Dr. W.H. van den Toorn (Chairman) PGM Department, ITC Dr. M.A. Sharifi (First Supervisor) PGM Department, ITC Drs. E.J.M. Dopheide (Second Supervisor) PGM Department, ITC Dr. L. de Boer (External Examiner) University of Twente Ir. B.G.C.M. Krol (Internal Examiner) ESA Department, ITC

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

ABSTRACT Sustainability of Forest Management (SFM) is a vague concept and complex system. There is no yardstick to measure it. SFM rests on three pillars, Production Sustainability, Ecological Sustainability and Social Sustainability. Criteria and Indicator (C & I) and forest certification are instruments used to measure SFM. The Sustainable Natural Production Forest Management (SNPFM) is a certification system applied in Indonesia. The decision making process in the current system uses the Analytical Hierarchy Process (AHP), which is criticised as a compensatory approach using linear additive utility model to integrate crisp values. The aim of the research is to explore different methods as alternatives to assess the SFM. The Fuzzy AHP and the Rule Based (Fuzzy Reasoning Method) are explored to overcome the compensatory approach and the inability of the AHP in proper handling linguistic variables. The Cognitive Mapping technique is used to show the causal relationship between verifiers and indicators and to determine the order of importance, which represents the roles of verifiers and indicators in achieving SFM. The result of Cognitive Mapping analysis is used to derive and to check the consistency of the rules in the Rule Based Method. The data of real certification for Labanan Forest Management Unit (FMU), East Kalimantan Province, Indonesia is used to simulate SFM assessment using three different methods. The results of the SFM assessment for Production Principle using the three methods give the certification grade “Bronze”, the same as the result from the real certification. It means that the FMU pass the certification process and four times surveillance will be done within 5 years period. Both of Actual Performance and Passing Performance in the Fuzzy AHP has the highest number (0.6414 and 0.5384), the second is Normal AHP (0.5241 and 0.3805) and the Rule Based assessment has the lowest performance (0.4760 and 0.2500). The Fuzzy AHP result clarifies the result from the normal AHP and already takes account of the uncertainty of experts’ judgment that is not accommodated in the normal AHP. The Rule Based method, which relies on logical approach derived from experts’ knowledge and experience, give more insight meaning and more transparent way in assessment, than both the Normal AHP and the Fuzzy AHP, which rely on mathematical approach. On the other hand it is perceived more subjective and more complex. Since the application of Rule Based in environmental system including SFM assessment still in exploration stage, it remains difficult to convince the Decision Maker for its implementation. Therefore if the method will be implemented it needs to build Decision Support System (DSS) with friendly user interface, so the user only needs to give the input in linguistic values and the final result will be directly in certification grade. Key words: SFM assessment, Forest Certification, Fuzzy Decision Making, AHP, Fuzzy AHP, Fuzzy Rule Base

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ACKNOWLEDGEMENTS Firstly I would like to express my thanks and gratitude to The Netherlands Educational Centre (NEC) for the STUNED Fellowship that financially supported the 18 months of my study, especially to Mrs. Monique Soesman, the Head of the Scholarship Department, and also Ms. Siska Aprilianti and Ms. Desi Amelia Dimas, the staffs of NEC for their kind help. I have a deep gratitude to my first supervisor, Dr. M. Ali Sharifi, for giving me the great opportunity to do research in a challenging subject, Fuzzy decision making in the Sustainable Forest Management assessment, since my background is neither a forester nor a mathematician. My sincere thanks for his advice, kind help, caring attention and support during my research period. I will never forget his way to encourage and convince me that I can do this research. My grateful thanks to my second supervisor, Drs. E.J.M. Dopheide, for his kind guidance and his patience, and he always very thorough in reading my thesis draft. Many thanks to Dr. Michael Weir, the Programme Director of Natural Resource Management, and Dr. Dick van der Zee, the Student Advisor of Planning and Coordination in NRM specialisation for their encouragement for me to finish my thesis in a better shape. My truthful thanks to Dr. Y.A. Hussin for his kind help during fieldwork in Labanan FMU area, East Kalimantan, Indonesia. I would like also to thank to Dr. D.G. Rossiter for allowing me to borrow some reference books which useful for my thesis. Many thanks also for all ITC staffs that I could not mention their name one by one. My truthful thanks to Dr. M. Buce Saleh and Dr. Haryanto R. Putro, Faculty of Forestry at Bogor Agricultural University (IPB), and Ir. Alan Purbawiyatna, MSc from Indonesian Ecolabel Institute (LEI), who gave the great contribution in my research by giving their knowledge and experience in SFM assessment that become the core information for my work. Thanks also for LEI staffs, especially to Ir. Daru Arcahya, for their kindly support. I would extend my appreciation to the Director of PT. Hutan Sanggam Labanan Lestari (Labanan FMU) in Berau and to the Director of PT Inhutani I in Jakarta, who gave me permission to carry out my research in Labanan. My truthful thanks to PT. Hutan Sanggam Labanan Lestari officials in Berau, especially to Ir. Doddy Herika, for their kindly support during my fieldwork. My sincere thanks to PT. Inhutani I officials, especially for my friends Anas Fauzi and Haniasti, who help me during fieldwork and their suggestion and support me to do MSc. Course. Many thanks also for my group in the fieldwork : Budi, Anita, Virginia, Humphrey and Brando. I would like to express my thanks and gratitude to the Head of Forestry Planning Agency, Ministry of Forestry Republic of Indonesia (my employer), the Head of Center for Forest Management Area Establishment and Forest Area Changing; the former head of my division, Ir. Kris Heryanto; the head of my division Ir. Chaerudin Mangkudisastra; the head of my section, Ir. Bowo H. Satmoko for allowing II

me to do my study in ITC. I also would like to thank my colleagues, especially to Herban, who take over my duties during I am away in Netherlands. I would like to thank Mr. Ard Blenke who help me to get licence for Mathlab software, Mr. C. Jeganathan for his former research that help me a lot in my research, Prakash TN from India and Zulkifly from University of Twente who help to manage Mathlab software, and also for Tatang who give me some supporting material. My sincere thanks to “my family” in Enschede: Budi, who always help me whenever I need; Ismail, who always give me nice suggestions; and Ida, who always support me. Also for all of my nice Indonesian friends: Ina, Dessi, Syarif, Anita, Anggoro, Muhy, Ary, Arief, Indra, Marisa, Doddy, Nia, and Trias family (Diana and Tara), also the ones who have already left: Yanti, Bobby, Hartanto, Arif, Arlan, Aris, Ninik, and Dian, many thanks for their help, support and friendship that makes me not missing Indonesia and my family so much. I would like to thank also to the Indonesian Student Association in Enschede (PPIE) and the Indonesian Moslem Community in Enschede (IMEA) for their friendship. My truthful thanks to all of my best friends in Indonesia that gives me support and inspiration to study: Prita, Sari, Yuli, Retno Narsuko, Naning, Ayu, Evi, Eka, Ninuk, Wiwiet, Eka J. Singka, and especially for Rudi thanks also for his help before I leave to Netherlands and during my fieldwork. Many thanks for all of my friends in ITC, especially for my classmates in Planning and Coordination specialisation (Biswash, Carlos, Catherine, Harriet, Humphrey, Martha and Martin). Special thanks for my nice clustermates : Aiman, Brando, Chalermchai, Phong, Ronald and Trang, with whom I share laughs, pressures and supports during my research period, so our cluster become a beautiful place with all of plants, food and fun. Especially for Aiman thanks also for his help and his contribution for my thesis. My greatest thanks for my family: my beloved aunt, Sukarni; my uncle, Sutadi; my beloved sisters: Neni, Widi and Susi; my brother in law: Adjie and Bob; and my beloved nephews and nieces: Aditya, Andika, Astika, Atyanta, Anindhyta and Agastya who always encourage me and above all prayed for me. I would like to thank also to Hadjarmukti family who always help me. Above all, I would like to express my thanks and gratitude to Allah Swt, the Most Beneficent, the Most Merciful whom granted my ability and willing to start and complete the thesis.

Retno Kuswandari March 2004

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TABLES OF CONTENTS ABSTRACT ............................................................................................................................................i ACKNOWLEDGEMENTS .................................................................................................................. ii TABLES OF CONTENTS....................................................................................................................iv LIST OF TABLES ................................................................................................................................vi LIST OF FIGURES..............................................................................................................................vii LIST OF APPENDICES .................................................................................................................... viii LIST OF ABBREVIATIONS ...............................................................................................................ix Chapter 1 : INTRODUCTION ...............................................................................................................1 1.1. Background .................................................................................................................................1 1.1.1. Sustainable Forest Management ..........................................................................................1 1.1.2. Sustainability Assessment ....................................................................................................1 1.1.3. Fuzzy Logic in Sustainability Assessment...........................................................................2 1.1.4. Forest Certification ..............................................................................................................3 1.2. Problem Statement ......................................................................................................................3 1.3. Research Objective......................................................................................................................4 1.4. Research Questions .....................................................................................................................5 1.5. Structure Of The Thesis ..............................................................................................................5 Chapter 2 : CERTIFICATION AND FUZZY DECISION MAKING..................................................6 2.1. Forest Certification .....................................................................................................................6 2.1.1. Global Forest Certification...................................................................................................6 2.1.2. The Sustainable Natural Production Forest Management (SNPFM) Certification System.7 2.1.3. Hierarchical Framework on the SNPFM Standard ..............................................................9 2.1.4. Decision Making Process of the SNPFM Forest Certification and Problem Asserted......12 2.2. Methods in Decision Making ....................................................................................................13 2.2.1. Analytical Hierarchy Process (AHP) .................................................................................13 2.2.2. Fuzzy Set Theory................................................................................................................14 2.2.3. Fuzzy AHP .........................................................................................................................15 2.2.4. Fuzzy Reasoning Model.....................................................................................................15 2.3. Fuzzy Decision Making.............................................................................................................17 Chapter 3: STUDY AREA...................................................................................................................19 3.1. General Overview of the Study Area ........................................................................................19 3.2. Justification to Select the Study Area .......................................................................................20 3.3. Forest Management Practice .....................................................................................................21 Chapter 4: METHODS.........................................................................................................................24 4.1. Data Collection..........................................................................................................................24 4.2. Research Process.......................................................................................................................24 4.3. Multi Criteria Decision Making................................................................................................26 4.3.1. Measurement and Standardisation .....................................................................................26 4.3.2. Spatial and Non Spatial Data Integration...........................................................................27 4.3.3. Sustainable Forest Management Grade..............................................................................28 IV

4.4. Analytical Hierarchy Process....................................................................................................29 4.5. Fuzzy AHP ................................................................................................................................31 4.6. Fuzzy Reasoning Method (Rule Based)....................................................................................32 4.7. Knowledge Building for Rule Based Assessment ....................................................................34 4.7.1. Cognitive Mapping.............................................................................................................34 4.7.2. Rule Base............................................................................................................................35 Chapter 5: RESULTS AND DISCUSSION.........................................................................................36 5.1. Cognitive Mapping Result ........................................................................................................36 5.1.1. Interaction between verifiers..............................................................................................36 5.1.2. Order of Importance ...........................................................................................................37 5.1.3. Role of Cognitive Mapping in Rule Base Assessment ......................................................42 5.2. Decision Tree ............................................................................................................................42 5.3. Assessment Using Analytical Hierarchy Process (AHP).........................................................43 5.4. Assessment Using Fuzzy AHP..................................................................................................48 5.5. Rule Base Assessment...............................................................................................................53 5.6. Comparison of the results..........................................................................................................58 5.6.1. Grade of Certification ........................................................................................................58 5.6.2. Actual Performance (Q) and Passing Performance (P)......................................................60 5.6.3. Upper Interval (U) and Lower Interval (L) ........................................................................60 5.6.4. Interpretation of the Results ...............................................................................................63 5.7. Strengths And Weaknesses .......................................................................................................64 5.7.1. Normal AHP.......................................................................................................................64 5.7.2. Fuzzy AHP .........................................................................................................................66 5.7.3. Rule Base Assessment (Fuzzy Reasoning Method) ...........................................................67 Chapter 6 : CONCLUSIONS AND RECOMMENDATIONS ............................................................69 REFERENCES.....................................................................................................................................73 APPENDICES......................................................................................................................................77

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LIST OF TABLES Table 1. Activities Specified in the TPTI System (Gardingen, 1999) .................................................21 Table 2. Final SFM Grade Making Process (LEI) ...............................................................................29 Table 3. Fundamental Scale used in Pairwise Comparison .................................................................30 Table 4. Conversion of crisp PCM to fuzzy PCM ...............................................................................31 Table 5. Order of importance of verifiers in FRS, FPS and BS Criteria .............................................41 Table 6. The Original Pairwise Comparison Matrice for Indicator P1.1.............................................44 Table 7. Normalized Pairwise Comparison Matrice for Indicator P1.1...............................................44 Table 8. Relative Performance for Indicator P1.1................................................................................44 Table 9. Result of Normal AHP Assessment for Actual Performance and Passing Performance.......46 Table 10. Original Pairwise Comparison Matrice of Indicator P1.1 ...................................................48 Table 11. Fuzzified Pairwise Comparison Matrice of Indicator P1.1..................................................48 Table 12. Result of Fuzzy AHP for Actual Performance.....................................................................50 Table 13. Result of Fuzzy AHP for Standard/Passing Performance....................................................51 Table 14. Normalisation of the Crisp Performance .............................................................................52 Table 15. Comparison of ranges for the Certification Grade...............................................................59 Table 16. Comparison of Passing and Actual Grade, Upper and Lower Interval................................60 Table 17. The Strengths and Weaknesses of the Normal AHP, the Fuzzy AHP and the Rule Base ...71

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LIST OF FIGURES Figure 1. The SNPFM Certification Process .........................................................................................8 Figure 2. The hierarchical framework of the SNPFM certification system.........................................11 Figure 3. The study area (Fauzi, 2001) ................................................................................................20 Figure 4. Research Approach...............................................................................................................25 Figure 5. Integrating Paths for Heterogeneous Data (Jeganathan, 2003) ............................................28 Figure 6. The adapted Fuzzy Reasoning Model for current research (adapted from Cornelissen, Berg et al. 2000)....................................................................................................................................33 Figure 7. Cognitive Map of Forest Resources Sustainability ..............................................................38 Figure 8. Cognitive Maps for Forest Products Sustainability..............................................................39 Figure 9. Cognitive Map for Business Sustainability Criterion...........................................................40 Figure 10. Decision Tree for Indicator P1.1. .......................................................................................43 Figure 11. Criteria structure for Production Principle .........................................................................45 Figure 12. Comparison of Actual Performance and Passing Performance In Normal AHP................47 Figure 13. Comparison of Actual and Passing Performance in Fuzzy AHP........................................53 Figure 14. Membership function for Forest Management in Forest Resources Sustainability Criteria54 Figure 15. Rules for Forest Management Process in Forest Resources Sustainability Criteria ..........55 Figure 16. Rule Implication for Forest Management Process in Forest Resources Sustainability Criteria..........................................................................................................................................55 Figure 17. Result of Rule Base Assessment for Actual Performance..................................................56 Figure 18. Result of Rule Base Assessment for Passing Performance ................................................57 Figure 19. Comparison of Lower Ranges for the Certification Grade.................................................59 Figure 20. Comparison of Upper Ranges for the Certification Grade .................................................59 Figure 21. Comparison of Passing and Actual Performances..............................................................60 Figure 22. Comparison of Upper Interval and Lower Interval.............................................................61 Figure 23. Comparison of Actual Performance from SFM assessment using Normal AHP, Fuzzy AHP and Rule Base (FRM)...................................................................................................................62 Figure 24. Comparison of Passing Performance from SFM assessment using Normal AHP, Fuzzy AHP and Rule Base (FRM)..........................................................................................................62 Figure 25. Comparison of the performances for the three methods.....................................................63 Figure 26. Comparison of the weight for Indicator P2.2. at different level in Fuzzy AHP. ................66

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LIST OF APPENDICES

A. B. C. D. E. F.

List of Indicators in Production Principle Knowledge Building Result of Cognitive Mapping Analysis Decision Trees Verifiers And Their Possible Standardization Procedure Pairwise Comparison Matrices of Labanan FMU Certification

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78 85 91 94 115 126

LIST OF ABBREVIATIONS

AHP APHI

: :

BFMP C&I FAO FMU FRM FSC GTZ IPB ITTO LEI MoF MoU SFM SNPFM STREK

: : : : : : : : : : : : : : :

TPTI

:

Analytical Hierarchy Process Asosiasi Pengusahaan Hutan Indonesia (Association of Indonesian Concession Holders) Berau Forest Management Project Criteria and Indicators Food and Agricultural Organization Forest Management Unit Fuzzy Reasoning Method The Forest Stewardship Council Gesellschaft für Technische Zusammenarbeit Institut Pertanian Bogor (Bogor Agriculture University) International Tropical Timber Organization Lembaga Ekolabeling Indonesia (Indonesian Ecolabelling Institute) Ministry of Forestry Memorandum of Understanding Sustainable Forest Management The Sustainable Natural Production Forest Management Silvicultural Techniques for the Regeneration of logged over forest in East Kalimantan Tebang Pilih Tanam Indonesia (Indonesian Selective Cutting and Planting)

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ASSESSMENT OF DIFFERENT METHODS FOR MEASURING THE SUSTAINABILITY OF FOREST MANAGEMENT

Chapter 1 : INTRODUCTION 1.1. Background 1.1.1. Sustainable Forest Management Sustainable Forest Management (SFM) is one of the important global issues. FAO annual report 2002 highlighted some of the recent technical, policy and institutional efforts to improve forest management and conservation, which reflects the balance of social, economic and environmental objectives (FAO 2002). For along time sustainability was almost concerned with sustained yield of wood. In a couple of years two major issues brought important changes: the contents of SFM were in general broadened, as ecological and socio cultural aspects were strengthened. Nowadays, the concept of SFM rests on three pillars, economic sustainability, ecological sustainability and social sustainability.

1.1.2. Sustainability Assessment Although sustainability is a goal for international and national policy makers, there is no measuring yardstick by which to assess it. Sustainability is difficult to define or measure because it is a vague and complex concept. There is a need for a practical tool to assess sustainability ((Phillis and Andriantiatsaholiniaina 2001). Criteria and Indicators (C & I) is a tool that has been developed to support the concept of sustainable forest management. Over the past several years, initiatives undertaken by governments and other institutions have helped to develop a better and a common understanding of the meaning of SFM (FAO 2002). C&I were developed in response to countries demands for practical ways of assessing and monitoring SFM at the national level and as benchmarks to measure and report progress towards sustainability. Initial emphasis has been on the development of C & I for application at the national level, but there is a need to be adapt C & I to the Forest Management Unit (FMU) where forest management decision are made and implemented. While the broad rationale underpinning C & I seem straight forward, the application of C&I to improve SFM raises major challenges of both a conceptual and practical nature (Raison, Brown et al. 2001). A major issue is how to adapt C&I developed for national-level use to the FMU. In several areas, the practical use of indicators as tools is constrained by the lack of data for adequate (relevant, reliable, and valid) indicators. This fact is a result of both of lack of in-depth knowledge about some objects of measurement and the difficulty of conveying accurate information on some aspects through indicators. In addition, the weaknesses of the methods related to SFM indicators are first; the majority of terms used are not, or not sufficiently, defined. Secondly, the methodical prescription of data measurement (units of measurement, spatial resolution, time scale/continuity, aggregation) is often weakly defined. Last, methods of evaluating measurements results, especially procedures for weighting and aggregation as well as for determining threshold levels for assessment of the 1


degree of SFM, are not sufficiently clearly defined and lead to wide variability in interpretation (Rametsteiner 2001). Many studies have been done in the development of C & I and their implementation at FMU. The problem is that C&I are often accused of being both perfectionist and comprehensive (Bas 2001). Many chosen indicators fail to measure up to the qualities where one must expect of them for defensibility and usefulness (Duinker 2002). The good indicator systems must be both pragmatic, in that they should be relevant to management needs and societal expectations, and realistic, in that their role as agents for improvement is well defined and their limitations known (Prabhu, Ruitenbeek et al. 2001).

1.1.3. Fuzzy Logic in Sustainability Assessment Sustainability is a vague concept, and unfortunately, unsustainability may not be easily reversible, because the environmental system is complex. In reality, the border between sustainability and unsustainability is not sharp but rather fuzzy. This means that it is not possible to determine exact reference values for sustainability, and a scientific evaluation of uncertainty must always be considered in the procedure of sustainability assessment (Phillis and Andriantiatsaholiniaina 2001). Forestry decision making today typically involves objectives and information concerning ecological, economic and social issues. The consequences of alternative forest management programmes might be hard to assess, predictions and assessments always include uncertainty (Kangas and Kangas 2002). Uncertainty can be dealt with using the fuzzy logic, which mostly deals with uncertainty due to the ambiguity of concepts. Moreover fuzzy logic, due to its capability to emulate skilled humans and its systematic approach to handling vague situations where traditional mathematics are ineffective, seems to be a natural technical tool to assess sustainability (Phillis and Andriantiatsaholiniaina 2001). Another important aspect of fuzzy logic is that it uses linguistic variables, thus performing computations with words. Evaluating sustainability forest management requires decision makers to consider multiple, conflicting definitions in an environment of risk, uncertainty, and incomplete or non-quantitative information. Zadeh’s fuzzy theory provides a rigorous, flexible approach to the problem of defining and evaluating sustainability (Ducey and Larson 1999). In existing system assessment of SFM in Indonesia, fuzziness comes at all level, starting from input, in constraints, in goal to finally in decision. Since sustainability is not quantitavely measurable by nature and it involves uncertainty and vagueness, fuzzy logic would provide a way to systematically formulate a base for quantifying such information.(Jeganathan 2003).

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1.1.4. Forest Certification Forest certification is a procedure whereby an independent certifier gives a written assurance that a forest is managed in accordance with agreed ecological, economic and social criteria. A label informs the consumers that the products they buy come from a certified forest. Thus, forest certification is a market instrument, which provides an incentive for SFM as it links producers and consumers in their responsible use of forest resources (GTZ 2003). Forest certification process is an evaluation of forest management practised following a predetermined set of criteria and indicators. Those indicators cover three main functions of forest that are economic, environmental and social function. The Sustainable Natural Production Forest Management (SNPFM) is one of the certification systems, which developed in Indonesia to assess forest management of natural production forest (Purbawiyatna 2002). The SNPFM certification standard was developed in hierarchical framework based on the existing forest management practised with respect to the diversity of environmental and social condition in order to achieve sustainable forest management as an overall objective. Decision making process in SNPFM certification system is to decide whether the forest management assessed pass the given ‘acceptable’ or not and to provide recommendation for forest management improvement. To achieve that decision-making objective in transparent and traceable way, Indonesian Ecolabelling Institute (LEI) apply the multi criteria evaluation method using Analytical Hierarchy Process (AHP). The decision makers panel in the performance evaluation stage is an ad-hoc team (expert panel II) established by the certification body with respect to the given qualification by LEI (Purbawiyatna 2002). In the SNPFM certification process, the equity principle is expected to be demonstrated in the whole process as far as possible. This implies that the people who involved in decision- making process will come from various background and experiences.

1.2. Problem Statement In general, the current SNPFM certification system has problems in input and in processing. The existing system is based on a top down management model, not participatory based. It is found that criteria and indicators developed have little connection with the actual forest management practices. Some indicators are not used by the Forest Management Unit and made only for certification. It also does not consider the new policies such as ongoing processes of decentralization and regional autonomy, and the institutional requirements in assessment. The system includes a large set of C & I which are difficult to assess, and requires time, resources, and a high expertise. The current attempts to measure and verify so many criteria, indicators, and verifiers (over 200) require large sets of information from the concessions. The proper acquisition, management and processing of such information is a massive and complex process. In some cases, non existence, in others non-availability, accessibility and questionable reliability of the data and information, data capture, collection and processing, the time and cost that involved, has made the proper implementation of certification in accordance with these excessive number of hierarchically structured indicators very difficult to implement (Sharifi 2003). The other problem is in integrating of the various data types (spatial and non spatial or quantitative and qualitative measurements). 3


Specific problem in the decision making process of the SNPFM certification is the use of AHP approach that requires a high expertise. In that method, words, for instance Excellent, Good, Fair, Poor, Bad, are considered as numbers and mathematically integrated. That process uses compensatory approach; it means that bad performance in one parameter can be compensated by the good performance in another parameter. The hierarchy considers only local interaction between indicators that is not realistic in the environment. The final result of the process is a crisp number that still needs interpretation to come to the decision. This approach also does not consider expert confidence, attitude and knowledge and uncertainty in making judgment. The Analytical Hierarchy Process (AHP) deals everything numerically and when integrating, the numerical compensation occurs, which in the environmental interaction is not realistic. For example the area that is affected by forest fire can not be compensated by having good a Early Warning system, the stakeholder disagreement can not be compensated by having a good boundary marking, the forest encroachment can not be compensated by having a good silviculture system, and the bad financial condition of the company can not be compensated by a good Management Information System. To overcome the shortcoming in the existing methods, it is necessary to employ a method, which can map the causal relationship between indicators and measuring the relative importance of each indicator in the achievement of the sustainable forest management. Jeganathan (2003) uses four different approaches of fuzzy logic: 2-tuple fuzzy linguistic; Fuzzy-AHP; Fuzzy Reasoning; and Type-2 Fuzzy Reasoning approach, to explore the alternative methods to assess the sustainability of the forest management. It is found that fuzzy reasoning based approaches gives more flexibility, transparency and full control on the processes involved in achieving the rational sustainability assessment. Since the study was done for only a small part of the big hierarchy, there is still a need to study the feasibility of this approach for the whole hierarchy. For a complex problem of decision making, such as assessment of the sustainable forest management, the result usually needs to be obtained through reasoning by the rules. Some study has found that rule base assessment in fuzzy reasoning model allows to link the human expectation about sustainability. So there is a need to explore the power of rule based assessment, which can relate the importance and performance of each indicator with the level of SFM. Fuzzy AHP, the extension of the AHP for handling uncertainty, is also explored as alternative method for SFM assessment.

1.3. Research Objective The main objective of the research is to explore the application of Rule Based method as well as Fuzzy AHP in the assessment of Sustainable Forest Management as alternatives to Analytical Hierarchy Process (AHP) in the existing SNPFM certification system in Indonesia. The study will focus on the following specific objectives: 1. To establish the logical and rational relationship between various indicators to identify the role of importance of each indicator in the achievement of the sustainable forest management. 4


2. 3.

To define proper rules which relate the performances of indicators with the level of sustainable forest management. To compare the Rule Based and the Fuzzy AHP method with the existing method (AHP) for assessing the Sustainable Forest Management

1.4. Research Questions Research questions related to the first specific objective are: 1. What is the interaction between verifiers and indicators in the hierarchy? 2. What is the order of importance of each verifier and indicator? Research questions related to the second specific objective are: 3. What is the role of each indicator in the achievement of sustainable forest management? 4. How can the performance of each indicator be measured? 5. What shall be the rules, which can relate the performances of indicators with the level of sustainability of forest management? Research question related to the third specific objective is: 6. What are the strengths and weaknesses of Rule Based and Fuzzy AHP comparing with AHP?

1.5. Structure Of The Thesis The content of the research is divided into six chapters. Chapter I describe the background of the research, problem, research objective, and research question, scope of the research and the assumptions used. Chapter II explains the Global Forest Certification issue, the existing SNPFM forest certification in Indonesia included its decision-making process and problem asserted, the Fuzzy Set Theory and the Fuzzy Decision Making in Forest Certification. Chapter III describes the Study Area, Justification of study area selection and Forest Management practise in the study area. Chapter IV explains the research process and explore some methods used in this research. Chapter V describes the results, analysis and discussion for actual certification result using AHP and rule based assessment, and the comparison of the Normal AHP, the Fuzzy AHP and the Rule based method based on it strengths and weaknesses. Chapter VI gives the conclusions and recommendation of the research.

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Chapter 2 : CERTIFICATION AND FUZZY DECISION MAKING 2.1. Forest Certification 2.1.1. Global Forest Certification Certification has long been a controversial issue concerning trade and the environment. Producer countries and trade groups have tended to highlight the trade-restrictive aspects of the practice, while consumer countries with the strong environmental lobbies have stressed its potential environmental benefits(FAO 2002). The certification system does not only aim as commercial tool but it also aims at reaching the sustainability of natural resources. In forest management context, forest certification shares the aim to promote SFM with another instrument, namely Criteria and Indicators (Rametsteiner and Simula 2003). The principles to be fulfilled in the certification system are: (a) on a voluntarybased; (b) established in a multi-stakeholder process; (c) standards applied must meet the principles agreed internationally; (d) a transparent process; and (e) implemented by a third independent party. Some interest groups from the environmental, social and economic sectors in many countries and regions have initiated forest certification processes since the early 1990s. The Forest Stewardship Council (FSC), a non-governmental organization on international level founded in 1993, has established an accreditation programme under which independent certification bodies can operate. FSC has developed globally applicable principles and criteria of responsible forest management. It encourages the working groups at national level to adapt these principles and criteria to the specific local context. (GTZ 2003). The total global area of certified forests are around 90 million ha, which represents only 2 percent of the world' s total forest area. Most certified forests are located in a limited number of temperate countries, and not in tropical countries. The area certified by FSC has reached 22.0 million ha. Most of this area is located in Europe and the United States, especially Sweden and Poland represent 61 percent of the total area and the United States for another 11 percent. Outside the FSC process there has been an rapid increase: 21.9 million ha of Finland' s forests have been certified under the Finnish Forest Certification System, 6.9 million ha of forest area in Norway and Sweden have been certified under national certification schemes; and some 44.0 million ha of forest area in Canada has been certified according to the ISO 14001 standard of the International Organization for Standardization (FAO 2002). Due to the forest certification trend, there is no other choice for Indonesia that it should develop and apply forest certification system. The first reason is the forest destruction has reached a worrying point. Indonesia can no longer tolerate any further destruction due to the exaggerating exploitation in the past time. On the other hand, a total stop on the forest utilization cannot be realised. Thus, the proper choice is to manage the forests into a context of balance between conservation, economic and social utilization. The second reason, due to the mistake of the forest management in the past time, Indonesia has an unpleasant credibility in the international forum, such as illegal logging, large scale

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of forest destruction, and corruption in forest management (Wibowo 2000). Therefore implementation of a credible certification system becomes a necessity if Indonesia wants to improve its credibility. The Indonesian Ecolabeling Institute (LEI) has developed criteria and indicators for the auditing of forest management on logging concessions, as well as the ecolabeling of products from these concessions. The LEI system is based on the International Tropical Timber Organization (ITTO) guidelines for SFM. A memorandum of understanding (MoU) has been signed with FSC on September 3, 1999. The realization of the cooperation through a Joint Certification Program based on joint certification protocol has been attached in the MoU. The decision making of certification will follow the procedure established by each certification system and only Forest Management Unit stated to pass both certification system which will obtain certificate (LEI 2000). 2.1.2. The Sustainable Natural Production Forest Management (SNPFM) Certification System Development of a certification system in Indonesia started on early 1993 when the Association of Indonesian concession Holders (APHI) tried to apply the ITTO criteria for the measurement of sustainable tropical forest management on several forest concessionaires, as their internal assessment of the forest management to enter the ecolabeling certification process. After some field test of C&I for SFM carried out, inputs from stakeholders and recommendations from both international and national workshop and discussion, in 1999 LEI conducted the refinement program of the certification system. These certification systems are for natural production forest management (SNPFM) and chain of custody (timber tracking). The first system assesses the performance of Forest management Unit (HPH) using a set of criteria and indicators with a certain procedure while the second system tries to ensure that the wood comes from sustainably managed resources. The final document of SNPFM certification system was stated by the LEI Board decree in November 1999 (Purbawiyatna 2002). The SNPFM certification system is written down in the document of certification system; LEI series 5000, series 99 and LEI Document 01 and 02 Components of the SNPFM certification system are standard, certification procedure, decision making process and requirement. All those system properties have been documented in a series of LEI Standards, Guidelines and LEI' s Documents. The SNPFM certification process is shown in Figure 1. a. Standard The SNPFM certification standards consist of sets of principles, criteria, indicators, verifiers, norm and verification guideline. In addition, a verification status is not ' legally binding'in context that the assessor can modify or improve their verification techniques in order to collect the information required to support the indicator' s conclusion as long as according to the indicator definition and norm. b. Certification Procedure The certification process is divided into four stages of activities, as follows:

7


Application of certification by forest management unit Forest managenet units' documents and basic data

Screening process by the Expert Panel 1

Pre-field assessment No

No

Decision for proceeding a field assessment

Yes

Field assessment process by Assessor team

Public announcement on certification process

Field assessment results

Inputs from community

Surveillance

Performance evaluation process by the Expert Panel 2

Field assessment and input from community

Performance evaluation and decision making process

Certification decision and Recommendation

Yes

Affirmation of certification decision

Affirmation of certification decission by certification body

Figure 1. The SNPFM Certification Process (1) Pre-Field Assessment This stage comprises of a series of activities, which is designed to increase the efficiency of evaluation process. The first decision-makers panel (Expert Panel I) decides whether the certification process can proceed to the next stages by assessing the basic information from applied FMU. The Expert Panel 1 carries out some activities, namely Document Evaluation, Field Scoping-visit, Decision making and Submission of Recommendation in this stage.

8


(2) Field Assessment and Input from Community The field assessors carry out data collection and data analysis based on the SNPFM criteria and indicators. Public consultation process is needed to provide the opportunity for community participation and to gain the information of FMU being evaluated. This process usually is carried out at provincial and or regional level prior or during the field assessment. The input from community also can be given to the certification body to be considered by decision makers (Expert Panel II) in making the final certification decision. (3) Performance Evaluation The performance evaluation is carried out by evaluating the FMU based on SNPFM criteria and indicators, through comparing the actual and the acceptable conditions in order to make a decision, rank of certification, and recommendation for management improvement. The sources of information are the field assessment report, the community inputs and the results of screening process by the Expert Panel I. Based on this information the Expert Panel II makes the SNPFM certification decision. (4) Certification Decision The certification decision is the process of endorsing the Expert panel II decision into a Certification Body Decree. If a FMU is granted certification, the Certification Body shall openly announce the event through the mass media, and disclose the decision in a sealed notification to all relevant parties in the government, NGOs and related groups or associations concerned. c. Decision Making Process Decision making process in the SNPFM certification system is to decide whether the forest management assessed pass the given ' acceptable level'or not and to provide recommendation for the improvement of FMU. The decision maker panel in the performance evaluation stage is an ad-hoc team established by a certification body with respect to the given qualification by LEI. d. Requirement LEI also defined a requirement of supporting instruments and conducted several trainings for related parties who will involve in operating the system to ensure that the system will be implemented in the proper way. 2.1.3. Hierarchical Framework on the SNPFM Standard The SNPFM certification standard was developed in a hierarchical framework based on the existing forest management practiced with respect to the diversity of environmental and social condition in order to achieve sustainable forest management as an overall objective. The hierarchical framework of the SNPFM is shown in Figure 2.2. The elements of that hierarchical framework are (LEI 2000): (1) Goal : Sustainable Forest Management (2) Principle : a. Sustainability of production function; refers to the assurance of continuity of forest product utilization and forestry-based business.

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b. Sustainability of ecological function; refers to the assurance of continuity of forest function as a support system to a variety of indigenous species and their ecosystem. c. Sustainability of social function; refers to the continuity and/or increase in forest production from time to time due to the improvement in the forest management efforts. (3) Criteria, each principle derived into several criteria as follow: a. Under sustainability of production function: - Sustainable forest resources; refers to the stability and security of natural production forest areas to ensure long term business certainty - Sustainable forest products; refers to the continuity and/or increase in forest production from time to time due to the improvement in the forest management efforts - Sustainable business; refers to the capability of management units in managing the natural production forest to generate profit with respect to the forest capability limitations. b. Under sustainability of ecological function principle: - Stable ecosystem: refers to the dynamic balance of forest structure and ecosystem function with its elements to ensure optimum production capacity within the ecological capability of renewal - Endemic/endangered/protected species survived; refers to the ability of endemic/endangered/protected flora and fauna species to adapt towards the natural production forest habitat. c. Under sustainability of social function: - Secured community-based forest tenure system: refers to the existence of series rights and obligations governing the claims and utilization of forest deriving from traditional customs and guarantee the community livelihood across generation should not be ignored due to the presence of management units, as clearly defined in the forest delineation and is approved by interested party. - Assured resilience and economic development of community and employees: refers to the continuity of economic activities and benefits to the welfare of community, including utilization of job and business opportunities for the continuity of community livelihood across generation. - Assured continuity of social and cultural integration of community and employees; refers to the assurance the social relationship is functioning properly. - Realisation of responsibility to rehabilitate nutritious status and anticipate the impact on health: refers to the efforts in maintaining and improving the nutritional status and preventing health deterioration. - Assured employees’ rights: refers to the implementation of workers’ rights as stipulated in the workers’ policies and norms (4) Process and Sub Process : - Area Management (AM): refers to a series of management activities in the forest area to ensure the security in the long-term, through stabilizing, regulating, and carrying out security activities in the area.

10


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AM : Area Management FM : Forest Management IA : Instititional Arrangement PM : Production Management EM : Environmental Management SM : Social Management HR : Human Resources OA : Organisational Arrangement FnM: Finance Management

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Figure 2. The hierarchical framework of the SNPFM certification system

11


- Forest Management (FM): refers to a series of production management activities aimed at regulating the use of forest products and preventing the negative impact of forest exploitations, including the presence of management units. - Institutional Arrangement (IA): refers to a series of management activities to improve managerial skills in forest utilization by strengthening the organization, human resources and finance. - Production Management (PM): refers to a series of activities to regulate and sustaining production function of the forest resources. - Environmental Management (EM): refers to a series of activities to minimize the negative impacts and to maximize the positive impacts of forest utilization upon species and their ecosystem. - Social Management (SM): refers to series of activities to increase the benefits and minimize the negative impacts of forest utilization, including impacts related to the presence of management units, towards the livelihood of local communities across generations. - Human Resources (HR): refers to a series of activities to improve employee capabilities in the management units in implementing and overcoming problems related to forest utilization. - Organizational Arrangement (OA): refers to a series of activities aimed at defining the mechanism of decision making to ensure that the implementation of managemen dimensions are in compliance with the principles of the management of sustainable production forests. - Financial Management (FnM): refers to a policy of a management unit to ensure the availability of fund allocation for reinvestment of resources in order to support the continuity of long-term business. (5) Indicators and Verifiers (Appendix A). The SNPFM indicators established by constructing the management dimension and output dimension matrix, which served to identify input, process, and outcome parameters that aimed at achieving the criterion defined. (6) Norm or intensity scale. The norm or intensity scale was constructed for each indicator from its verifiers. It describes the degree of fulfilment of indicators, from the excellent to the bad, in order to support the criterion and principle conclusion those indicators belong to. 2.1.4. Decision Making Process of the SNPFM Forest Certification and Problem Asserted Decision making process of the SNPFM certification is held by Expert Panel II, which is an ad-hoc panel selected by certification body with respect to the qualification defined by LEI. The panel consists of six persons, and three of them recommended are originated from the area where the FMU is located. The panel is divided into three commissions with two persons of each commission deals with production, ecological and social aspect. They have been informed about the SNPFM certification system in workshop or other socialization process of that system. 12


The sources of information for the decision making process are: (1) field assessment report, (2) clarification process with assessors, (3) result of screening process, (4) inputs from interested parties and (4) FMU documents. The field assessment report consists of indicators analysis and general information about FMU assessed including its typology analysis. Clarification process is assessor presentation to the panel for every field findings, the analysis as they wrote in the report and the field condition as they have seen. Based on the SNPFM decision making guideline, the evaluation processes are as follows: (1) Evaluation model construction is done by assigning level of importance of weight of each element in decision hierarchy. The weighting method applied is pairwise comparison from the top to bottom. (2) By understanding the SNPFM objective, FMU typology, problem and other related information, the panel discussing the level of performances of each indicator that ' should be achieved' with respect to the norm or intensity scale. That ' passing grade'or acceptable level of performance then assigned into each indicator in rating process, so the ' passing grade'value of each principle can be obtained. (3) The actual performance of each indicator as provided by the assessor assigned into the model in rating process to obtain the actual performances value (4) The ranking of overall performance defined by calculating the overall passing grade from each principle performances and its weights and then calculate the ranges for each level. Problems perceived in the SNPFM certification decision making are (1) complexity of elements that have to be considered; (2) different points of view of decision makers and (3) vague priority judgment due to lack of experience (Purbawiyatna 2002). In order to overcome this problem, the decision making process requires an appropriate selection of an evaluation method which enables to accommodate the complexity of elements involved, scales diversity, transparent and democratic ways of decision making. The Analytical Hierarchy Process (AHP) developed by Thomas L. Saaty has been chosen because it is considered can handle these problems.

2.2. Methods in Decision Making 2.2.1. Analytical Hierarchy Process (AHP) The AHP works by developing priorities for alternatives and the criteria used to judge the alternatives. The criteria are usually measured on different scales even when these criteria are quantitative and qualitative. Measurements on different scales cannot be directly integrated. First, priorities are derived for the criteria in terms of their contribution to achieve the goal then priorities are derived for the performance of the alternatives on each criterion. These priorities are derived based on pairwise comparison judgment. The process of prioritization solves the problem of handling the different types of scales, by interpreting their significance to these values from the users. Finally a weighting and adding process is used to obtain overall priorities for the alternatives as to how they contribute to the goal. This weighting and adding parallels what one would have done arithmetically prior to the AHP to combine alternatives measured under several criteria having the uniform scale to obtain an overall result. A multidimensional scaling problem is transformed in a one dimensional scaling problem by 13


using the AHP (Saaty 1999). The main advantages of AHP are the relative ease in handles multiple criteria and it can effectively handle both qualitative and quantitative data (Kahraman, Cebeci et al. 2004). This method also elicits preference information from the decision makers in such a way which they find easy to understand (Lootsma 1997). According to Herwijnen (1999), using AHP method in multicriteria decision making process one has to be aware that the result obtained allows compensatory rules. This means that a bad performance of certain criterion can be completely compensated by a good performance of another criterion. In AHP the alternatives that are deficient with respect to one or more objectives can be compensated by their performance with respect to other objectives. So that using the AHP model in the SNPFM decision making process to obtain the best choice of alternatives compared, which are the acceptable or passing grade performance with the actual performance, means allowing compensation of bad performance indicators by good indicators. For example although actual performances of ecological and social aspects in SFM assessment are below the acceptable level, it can be compensated by good performance of production aspect. Although the AHP is a popular method for tackling multicriteria analysis problems involving qualitative data, and has successfully been applied to many actual decision situations, this method is often criticized for various reasons: (a) for the fundamental scale to quantify verbal comparative judgement, and (b) because it calculates the final score of the alternatives via the aritmethic-mean aggregation rule. The original AHP is based upon ratio information so that the proposed algorithmic operations are inappropriate (Lootsma 1997). Another critic is for rank reversal of alternatives, which can occurred when a problem is changed to include an additional alternative that was not considered to be a candidate for selection when the problem was first stated (Bodin and Gass 2003). This method is also criticized for its inability to handle the uncertainty and imprecision associated with the mapping of the decision maker’s perception to a crisp number (Deng 1999) and in order to capture the experts knowledge, it still cannot reflect the human thinking style (Kahraman, Cebeci et al.). 2.2.2. Fuzzy Set Theory Fuzzines can be found in many areas in daily life, such as in engineering, in medicine, in meteorology, in manufacturing, and others, frequently in all area in which human judgment, evaluation or decision are important. There are the areas of decision making, reasoning, learning and soon. Most of our daily communication uses natural language, which the meaning of words is very often vague. The meaning of a word itself maybe well defined, but when using the word as a label for a set, the boundaries within which objects belong to the set or do not become fuzzy or vague (Zimmermann 1985). Fuzzy set theory is a mathematical theory designed to model the vagueness or imprecision of human cognitive processes that pioneered by Zadeh (Lootsma 1997). This theory is basically a theory of classes with unsharp boundaries. What is important to recognize is that any crisp theory can be fuzzified by generalizing the concept of a set within that theory to the concept of a fuzzy set. The stimulus for the transition from a crisp theory to a fuzzy one derives from the fact both the generality of a theory and its applicability to real world problems are enhanced by replacing the concept of a crisp set with a fuzzy set (Zadeh 1994). 14


2.2.3. Fuzzy AHP To overcome the inability of AHP to handle the imprecision and subjectiveness in the pairwise comparison process, Buckley and Laarhoven and Pedrycz extended Saaty’s AHP (Deng 1999). Triangular or trapezoidal fuzzy numbers are used to express the decision maker’s assessments on alternatives with respect to each criterion. After the criteria are weighted, the overall utilities of alternatives, known as fuzzy utilities (represented by fuzzy numbers), are aggregated by fuzzy arithmethic using Simple Additive Weighting method. To prioritize the alternatives, their fuzzy utilities need to be compared and ranked. However this comparison process can be quite complex and may produce unreliable results due to (a) considerable computations required, (b) inconsistent ranking outcomes with different ranking approaches, and (c) counter-intuitive ranking outcomes under some circumstances. To facilitate the pairwise comparison process and to avoid the complex and unreliable process of comparing utilities, Hepu Deng (1999) presents an multi attributes approach for effectively solving multi attributes problems involving qualitative data. Triangular fuzzy numbers are used in the pairwise comparison process to express the decision maker’s subjective assessments. The concept of fuzzy extent analysis is applied to solve the fuzzy reciprocal matrix for determining the criteria importance and alternative performance. To avoid the complex and unreliable process of comparing fuzzy utilities, the alpha-cut concept is used to transform the fuzzy performance matrix representing the overall performance of all alternatives with respect to each criterion into an interval performance matrix. Incorporated with the decision maker’s attitude towards risk, an overall performance index is obtained for each alternative across all criteria by applying the concept of the degree of similiraty to the ideal solution using the vector matching function. 2.2.4. Fuzzy Reasoning Model Fuzzy logic is a scientific tool that permits simulation of the dynamics without a detailed mathematical description (Andriantiatsaholiniaina, Kouikoglou et al. 2004). In Fuzzy Reasoning Model knowledge is represented by IF-THEN linguistic rules, which describes the logical evolution of the system according to the linguistic values of its principal characters that we call linguistic variables. Real values are transformed into linguistic values by an operation called fuzzification, and then fuzzy reasoning is applied in the form of IF – THEN rules. A final crisp value is obtained by defuzzification (Phillis and Andriantiatsaholiniaina 2001). Six step in Fuzzy Reasoning Model as follows : (1) defines model input; (2) defines linguistic variable (3) construct membership function; (4) fuzzification; (5) fuzzy inference and (6) defuzzification (Cornelissen, Berg et al. 2000). Linguistic Variable Linguistic variables are represented on an ordinal scale. When we try to represent such a variable in terms of human language, then it leads to “linguistic language”. Normally linguistic variables are not exactly measurable and may be categorized into any one of the linguistic variables, hence linguistic variable can also be called fuzzy variable, and are modelled by fuzzy sets (Sharifi and Herwijnen 2003). A linguistic variable is defined by four items, (a) the name of the variable (b) linguistic values, (c) the membership functions of the linguistic values, and (d) the physical domain over which the 15


variable takes its quantitative values (Phillis and Andriantiatsaholiniaina 2001). In the forest certification context, all level from Principle, Criteria, Indicators and Verifiers are considered as linguistic variables. Each of linguistic variables has its respective linguistic classes such as Excellent, Good, Fair, Poor and Bad. Membership Function The key idea of fuzzy set theory is that an element has a degree of membership in a fuzzy set (Negoita 1985), (Zimmermann 1985). The membership function represents the grade of membership of an element in a set. The membership values of an element vary between 1 and 0. Elements can belong to a set in a certain degree and elements can also belong to multiple set. Fuzzy set allows the partial membership of elements. Transition between membership and non membership is gradually (Cornelissen, Berg et al. 2000). Membership function maps the variation of value of linguistic variables into different linguistic classes. The adaptation of membership function for a given linguistic variable under a given situation is done in three ways: a) experts previous knowledge about the linguistic variable; b) using simple geometric forms having slopes (triangular, trapezoidal or s-functions) as per the nature of the variable; and c) by trial and error learning process (Jeganathan 2003). Rule Base Human knowledge is imprecise in nature. It is usually the case that the knowledge base is a collection of rules, which, for the most part, are neither totally certain nor totally consistent. For a problem of decision making, the result usually needs to be obtained through reasoning by the rules, which involves a complex process (Zheng 2001). The important issue of rules are as follows: (1) The fuzziness of antecedents and/or consequents in rules are fuzzy proportion; (2) Partial match between antecedent of a rule and a fact supplied by the user; (3) The presence of fuzzy quantifiers in the antecedent and/or the consequent of a rule (Zadeh 1983). Simulation of the evolution of the overall system is represented by rules of the form of IF (antecedents) – THEN (consequent), where the implication operator THEN and the connectives AND among antecedents are fuzzy. The antecedent part of the rules contains some linguistic values of the decision variables, and the consequence part consists of a linguistic value of the objective function (Carlsson and Fuller 2001). The rules are expressions of the role of interdependencies among factors of sustainability. It should be emphasized that the linguistic rule bases are derived from expert knowledge (Phillis and Andriantiatsaholiniaina 2001). Fuzzification, Fuzzy Inference and Defuzzification The fuzzification model transform the crisp values or normalized values into a linguistic variable in order to make it compatible with the rule base (Phillis and Andriantiatsaholiniaina 2001). It means fuzzification process converts the input values into a grade of memberships and corresponding linguistic classes. These fuzzified grades are evaluated through rule base for output grades. The next step is fuzzy inference, which refers to the internal mechanism for producing output values for a given value through fuzzy rules. In this process, rule implication evaluates individual rule over fuzzified grades and generates an output grade, then the aggregation does two things, first it truncates the con16


sequent fuzzy set according to the grade obtained and the second it does the union of all the fuzzy set Finally these output grades are converted back to real world crisp output values in a defuzzification process.

2.3. Fuzzy Decision Making Application of fuzzy set theory can already be found in many different sciences such as natural, life and social sciences, engineering, computer science, systems science and also in management and decision making (Klir and Folger 1988). In multi criteria decision analyses, the fuzzy set theory might be the most common method in dealing with uncertainty. The main reason for the popularity of the fuzzy set approach is the intuitive and computational ease of analysis (Kangas and Kangas 2002). Fuzzy logic also bridges the gap between scientific measurement and the fulfillment of social objectives and provides a way to translate a wide variety of information – objective data, qualitative information, subjective opinions, and social needs – into a common language for characterizing environmental effects (Silvert 2000). There are various studies that use fuzzy set theory to measure sustainability. Fuzzy mathematical models can be used to assess sustainable development based on context-dependent economic, ecological and societal sustainability indicators. Although a decision making process regarding sustainable development is subjective, fuzzy set theory links human expectations about development, expressed in linguistic proportions, to numerical data, expressed in measurements of sustainability indicators (Cornelissen, Berg et al. 2000). The other study has done by Phillis and Andriantiatsaholiniaina (2000) that developed a model called Sustainability Assessment by Fuzzy Evaluation (SAFE), which provides a mechanism for measuring development sustainability. One of environmental studies has found that the real power of the fuzzy logic methodology comes from the ability to integrate different kinds of observations in a way that permits a good balance between incommensurable effects such as social, economic, and biological impacts (Silvert 2000). Ducey and Larson (1999) stated that sustainability in forest management requires decision makers to consider multiple, conflicting definitions in an environment of risk, uncertainty, and incomplete or non-quantitative information. It appears that the important values in forest management, such as sustainability, lie beyond the threshold values. These values require appropriate conceptual and analytical tools for successful decisions. It suggests that sustainability can be approached using the basic ideas and methods of analysis using fuzzy sets. Cited from Seymour et al (1995), the importance of expert judgment of general is qualitative concerns in certifying sustainability. In a certification context, he notes that some strategic decisions and evaluations must be based on subjective expert opinion. Improving decisions about sustainability will require new approaches for integrating diverse value and information sources to address forest sustainability in the context of both ecological and social systems as well. Fuzzy decision methods provide possibilities for improvement, and simultaneously provide a simple but rigorous framework for rational decision making. Fuzzy methods are promising tools for SFM assessment because these methods are purposely designed for complex and illness defined problems such as sustainability assessment (Mendoza and Prabhu 2004).

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Jeganathan (2003) stated that the SNPFM certification procedure involves hierarchical evaluation of many parameters, which are defined in a vague manner using crisp verbal conditions. In the current system AHP is used for evaluation. However, this approach deals everything numerically and hence while integrating numerical compensation occurs, which does not reflect the real environmental interaction. In his research 4 different approaches: 2-tuple fuzzy linguistic approach; Fuzzy AHP; Fuzzy Reasoning approach; and Type-2 Fuzzy Reasoning approach, are explored to find the alternatives for the current system. The result shows that Fuzzy Reasoning based approach gives more flexibility, transparency and full control on the processes involved in achieving the rational sustainability assessment. It is also found that Fuzzy Reasoning model has helped the expert to include different level of his interpretational uncertainty for the value judgment and to understand its impact on the output. He can use his knowledge derived through his experiences on the ground processes as Fuzzy rules base. By using this approach, diverse data, uncertainty in the input data, expert’s confidence and attitude are better handled than in other methods. Mathematical compensation in this method is avoided by using rule base along proper compositional operators in the inference mechanism. Finally the output value can directly be represented as the Ecolabel grades instead of crisp number.

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Chapter 3: STUDY AREA 3.1. General Overview of the Study Area The chosen study area is Labanan Forest Management Unit, which is located in Berau District, East Kalimantan Province, Indonesia. The boundary of the study area lies between the latitude of 2o10’ N to 1o45’ N and longitude of 116o55’ and 117o20’ E (Fauzi 2001) as shown in Figure 3. The boundaries in the field are as follows, the northern part is Sambarata FMU of PT. Inhutani I, the eastern part is Meraang FMU of PT. Inhutani I, the southern part is eks PT. Alas Helau concessionare, and the western part is Tepian Buah FMU of PT. Inhutani I. Total FMU area is 83.240 ha and according to Forestry Land Use Plan (TGHK) consists of Limited Production Forest 26.997 ha, Production Forest 26.997 ha and Non Forest Area (Areal Penggunaan Lain) 1.676 ha. Based on Provincial Landuse Planning (RTRWP) this area is allocated for Forest Area (Kawasan Budidaya Kehutanan) 81.564 ha and allocated for Non Forest Area (Kawasan Budidaya Non Kehutanan) 1.676 ha. During the actual certification process, Labanan FMU is still a part of PT. Inhutani I, a government enterprise that owned by Ministry of Finance and holding forestry business. PT. Inhutani I got the concession in 1976 with a total area 2.2 million hectares. After the first period, 20 years, in 1995 Ministry of Forestry (MoF) extended the concession for the second period and the area was decreased to 1,185,249 hectares. Those areas divided into two units, which is Unit I Balikpapan area covers 444,133 ha and Unit II Tarakan area 741,116 ha. The Balikpapan Unit was control six forest management unit, which one of them is Labanan that cover 83.240 ha (Purbawiyatna 2002). In April 2000, the director of PT. Inhutani I stated Labanan forest management unit as self-management. The effective area for timber production is about 63% of the total area, and the rest area was excluded from production consideration for other purposes. Those excluded area consist of transmigration area 1,978 ha, community forest 7,122 ha and protection area 15,945 ha. In Labanan FMU there are several forest research has been carried out either by local or under intergovernmental-collaboration institutions. From 1989 untill 1996, the STREK (Silvicultural Techniques for the Regeneration of logged over forest in East Kalimantan) was carried out. The focus of the project was on the development of sylvicultural and management rules leading to sustained productivity of the forest in East Kalimantan. The project carried out under the authority of MoF through Forestry Research and Development Agency and PT. Inhutani I with the assistance of CIRAD-forest (Fauzi 2001). From 1996 to 2003, the Berau Forest Management Project (BFMP) has been carried out under cooperation of MoF and European Union. The focus of the project is on development, test and promoting a replicable example of sustainable forest management at operational level. To support that project, Ministry of Forestry and Estate was stated that Labanan FMU as a special status area under decree No. 866/Kpts-II/1999.

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Affecting by ongoing decentralization process in Indonesia, in 2002 there was a change in management and the company has a new name, PT. Hutan Sanggam Labanan Lestari. The share of the new company are 30 % owned by PT. Inhutani I, 20 % owned by Provincial government and 50 % owned by District government as the representative of the local community.

Figure 3. The study area (Fauzi, 2001)

3.2. Justification to Select the Study Area The focus of this research is method development, application of AHP, Fuzzy AHP and Rule Based to assess Sustainable Forest Management, so naturally it can be applicable to any study area. But the reason to choose Labanan as the study area was that it would be better if we have a test site where forest certification has already been done, because the availability of data for forest certification is the main concern of this study. The second reason was that some ITC students had done some studies related to forest certification in this area, Fauzi 2001, Wahyu 2002, Aguma 2002, Dahal 2002, Purbawiyatna 2002 and especially Jeganathan (2003) have done the earlier work using fuzzy logic in the study area. The last is the availability of the GIS and RS data to support the forest certification process, since the Labanan FMU is one of forest concessionaires who has applied Remote Sensing and Geographic Information System in their management.

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3.3. Forest Management Practice The focus of the research is in the Production Principle of the SNPFM certification system. The information about forest management practice implemented by the FMU is needed to help in understanding what is the meaning of indicators and verifiers, and how is the interaction among these in certification context. The forest type of Labanan FMU is often called by lowland mixed dipterocarp forest because of the dominance in the canopy and the emergent stratum of the Dipterocarpaceae family. It contributes about 25 % of the total tree density, 50 % of the total basal area and 60 % of stand volume (Sist and Saridan 1998 in Fauzi 2001). According to the STREK, Dipterocarpaceae and Euphorbiaceae were most abundant out of 35 trees families recorded in Labanan FMU. Silviculture system implemented in Labanan FMU is TPTI (Tebang Pilih Tanam Indonesia) or Indonesian Selective Cutting and Planting. The TPTI system is implemented through government regulations, originally through the decree of the Directorate General of Forest Utilisation (Ditjen Pengusahaan Hutan) Ministry of Forestry through KPTS 564/KPTS/IV-BPHH/1989, and modified as Number 151/KPTS/IV-BPHH/1993 (Gardingen 1999). The TPTI system is specified for a 35-year cutting cycle and the regulations list 12 activities that should be completed over a period from three years before harvesting through to the next cutting cycle. The TPTI system is essentially prescriptive; that is, it specifies a sequence of activities that must be carried out by the forest managers. Table 1. Activities Specified in the TPTI System (Gardingen, 1999) & +, !

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Note : Et : the harvesting year

22


In that system the production area divided into seven cutting blocks that will be harvested in five years (RKL). Then each cutting block divided into annually cutting compartments (RKT). The annual allowable cut defined by government based on stands survey results carried out by FMU. There are three stands surveys have to be carried out during the concession period. Which is survey for long term planning (20 years, RKPH) with 1 % intensity, survey for short term planning (5 years, RKL) with 5 % intensity and survey for annual planning (RKT) with 100 % intensity. According to the national guidelines, an average of 8 trees per hectare are logged at 35 years interval. Forest management practice in Labanan FMU is implemented according to TPTI system, and its performance is more favourable than the other concessionaires in Indonesia. Moreover BFMP and Inhutani have collaborate in research and development activities with the objective to improve forest management practice in Labanan FMU, because TPTI system has been criticized as being overly regulated and lacking in clear management targets or objectives. Based on the research it has identified a series of activities that could be improved within existing regulations (Gardingen 1999). The design should be based on the following principle : - The silvicultural system should be based upon clear management objectives, e.g. sustained production, environmental standards, maintain biodiversity. - Emphasize desired outcomes of forest management (as opposed to prescriptive based activities). - Include technical guidelines (not prescriptions). - Specify minimum data requirements for good forest management. - Require forest managers to achieve defined minimum standards, but not prevent them from exceeding these through alternative approaches to forest management. - Minimize the requirements for post-logging treatments. - Some treatments such as thinning should be optional and left to the discretion of the forest manager, based on economic decisions within the constraints of the environmental standards.

23


Chapter 4: METHODS 4.1. Data Collection The elements of the hierarchical framework of SNPFM are goal, principle, criteria, indicator and verifiers and norm or intensity scale (Appendix 1.). Considering the limited time for data collection and data processing, this research has been carried out only in one principle, namely Sustainability of Production Function that refers to the assurance of continuity of forest product utilisation and forestrybased business. This principle is derived into three criteria as follows: Sustainable Forest Resources, Sustainable Forest Products and Sustainable Business. The three criteria contain 21 indicators and 88 verifiers, and a five-norm or intensity scale (Excellent, Good, Fair, Poor and Bad). According to the LEI Guidelines, the SNPFM hierarchy is general for any certification case until indicator level, but for verifiers level it can be different depending on the typology of the assessed FMU, as long as it is according to the indicator definition and norm or intensity scale. The field observation is needed for the research to understand how the verifiers are measured by the assessor in the field and to select the relevant verifiers that are applicable for this study. The main activity of the data collection is to extract the experts’ knowledge that is used in every stages of the study. This information has been collected by discussion with two experts on forest certification from Bogor Agricultural University (IPB) and one expert from LEI. The reason to select these experts is that they are involved in forest certification since the establishing of the concept, four years before the SNPFM certification was applied in 1999, and nowadays they still are involved in forest certification practices and development of the forest certification system. At first stage the experts’ knowledge is used to select the relevant verifiers, and to modify or reformulate some verifiers to make better understanding of each indicator definition. The next step experts’ knowledge is used to map the interaction between verifiers in Cognitive Mapping technique. Then in the core step, experts’ knowledge is used to derive the rules for SFM assessment, which is based on their knowledge and experiences. In addition it is also used to make possible standardisation procedure for each verifier, select the membership function based on their confidence for their judgement. Since the Labanan FMU has followed a certification under a joint certification process between LEI and Forest Stewardship Council (FSC) scheme, the data of certification process is available in LEI and PT. Inhutani I.

4.2. Research Process The research process for this study is shown in Figure 3. There are two major lines in the research process : 1) AHP and Fuzzy AHP 24


In the two methods we can directly use the input (Pairwise Comparison Matrix) and follow the required procedures for these methods.

Actual Certification Data

Set of Verifiers & Indicators

Experts Knowledge

Field Observation

Experts Judgment

Pairwise Comparison Matrices Cognitive Mapping

Selected verifiers

Order of importances

Derive Rule Base

Rule Base

Revise Rule Base

Revised Rule Base

Normal AHP

Fuzzy AHP

Rule Base Assessment (FRM)

Grade of Certification



Comparison and Interpretation

Strengths and weaknesses

Figure 4. Research Approach 2) Rule Base from Fuzzy Reasoning Method Most of the research activities deal with this method. Field observation is needed to understand how the verifiers’ interaction in the field and help in derive the rules. And the most important work is how 25


to extract the experts’ knowledge in limited time, regarding the limited time availability from the experts for discussion. As the overall result this study tries to compare and interpret the results of the assessment from three different methods, and try to find out what are the strengths and weaknesses of each method.

4.3. Multi Criteria Decision Making In any planning and decision making process, a systematic and logical approach is used to arrive at the solution. This systematic process can be divided into 4 major phases as intelligence, design, choice and finally implementation as an add-on phase to achieve the decision in reality. Intelligence phase deals with problem identification, classification and decomposition. Generating, developing and analyzing possible courses of action for the problem situation are dealt in design phase. Decision phase deals with search, evaluation and recommendation of appropriate solution (Sharifi and Herwijnen 2003). The techniques adopted in the various approaches of decision analysis are called multicriteria decision methods (MCDMs). These methods incorporate explicit statements of preferences of decision-makers. Such preferences are represented by various quantities, weighting scheme, constraints, goal, utilities, and other parameters. They analyse and support decision through formal analysis of alternative options, their attribute, evaluation criteria, goals or objectives, and constraints. MCDM often requires the decision maker to provide qualitative assessments for determining (a) the performances of each alternative with respect to each criterion (b) the relative importance of the evaluation criteria with respect to the overall objective of the problem. As a result, uncertain, imprecise and subjective data are usually present which make the decision making process become complex and challenging. An evaluation criterion gives an indication about how well the alternatives achieve a certain objective. The performance of an alternative for a criterion can be measured in different measurement scales. There are four main levels of measurement. These levels, ranging from lowest to highest, are nominal, ordinal, interval, and ratio. Each level has all of the meaning of the levels below plus additional meaning. Measurements from scientific instruments are often ratio scale, the level of measurement in most social and decision context depends on the intent of the subject responding to a question or making a judgment.

4.3.1. Measurement and Standardisation The level of measurement in decision contexts depends on the intent of the subject responding to a question or making a judgement. A useful classification in the context of measurement scales is the distinction between natural and constructed scales. This classification is made based on the way attributes are measured. Natural scales are scales that have been established and enjoy common usage and interpretation. Because no value judgment is needed in constructing natural scales, these scales 26


are sometimes referred to as objective or external scales. If attributes are defined using the personal judgment of the decision maker or another person, for example an expert, then the scale is called a constructed scale. This scale is also referred to as a subjective scale (Sharifi and Herwijnen 2003). The norm (intensity scale) of verifiers for assessing the sustainable forest management is also included in a constructed scale. It uses a five-point scale ranging from Excellent to Bad. Given the variety of scales on which attributes can be measured, multi criteria decision analysis requires the values of the various criteria be transformed to comparable units. Only if the scales of the criteria are same, the scores of these criteria can be combined or compared. Making the scores of the criteria comparable is often called standardization or normalization. Through a standardization procedure the measurement units are made uniform and the scores lose their dimension along with their measurement unit (Sharifi and Herwijnen 2003). Various methods to standardize the scores are available. The method to use depends on the character of the problem and the character of the attributes. Since the different verifiers for assessing the sustainable of forest management are measured in different unist, it needs to be standardized to a uniform scale to allow fuzzy calculation. Different curves of standardization (maximum, minimum and interval) can be used according to needs and context (Phillis and Andriantiatsaholiniaina 2001). Standardization of the verifiers for assessing the sustainable forest management will be done individually with reference to its available minimum, maximum possible values and user preferred value of that verifier in satisfying sustainability view (Jeganathan 2003). The standardized values are in linguistic classes. The possible standardization for each verifier in this research can be seen in Appendix E. 4.3.2. Spatial and Non Spatial Data Integration In most of the decision related problems, the input data are heterogeneous and we should use the proper way to integrate these diverse data. There are three ways by which we can integrate these diverse of data. Herwijnen (1999) considered only two paths for converting spatial effect table. Path-1 converts all spatial element into non-spatial elements through ranking or rating and integrate them using normal Multi Criteria Decision Analysis (MCDA). In Path-2 every parameter is converted into spatial data and in the next step the composite spatial map derived trough overlay analysis is aggregated to derive useful information. Path-1 is prescribed when the input parameters are either implicitly spatial or non spatial. Path-2 is prescribed where spatial priorities play a key role partially (Jeganathan 2003). In some cases these two paths cannot be applied, for example remote sensing data cannot be converted into a single quantity and when spatial priorities play an important role in the final decision. In such cases Path-3 is prescribed, where the explicitly spatial elements are overlayed first (Jeganathan, 2003). Once they are overlayed, in the attribute table for each polygon we will get corresponding columns of each layer used for overlay. Each record in the table can be considered as one non-spatial quantity and shall be passed on to a model for processing. Finally processed information can be put in the corresponding polygon record and shall be seen spatially.

27


The major problems in making sets to assess the sustainability of forest management unit are as follows: a) the verifiers available are both spatial and non spatial type b) it involves experts knowledge c) needs input from different stakeholders and d) needs assessment from different expert (Jeganathan 2003). Consider the diverse input as explicitly spatial element and overlay them in GIS environment. Then each record of these overlayed spatial elements can be converted into value judgement using membership curves and linguistic classes defined by the expert. This can be done in Fuzzy Reasoning Model and follows the Path-3.

Figure 5. Integrating Paths for Heterogeneous Data (Jeganathan, 2003) Considering limited time for the research and non-availability of the primary data that have been collected by the assessor, the research based on the report of the field assessor and the expert panel II, in which everything has been translated into experts’ judgment. Thus the current research follows Path I. 4.3.3. Sustainable Forest Management Grade From the view of MCDM approach, the SNPFM certification system consists of 2 alternatives. The first alternative is "Passing performance", which the experts assign some standard performance for each indicator related to the typology of FMU, or the minimum requirement in order to qualify for certification. The second alternative is the "Actual performance", which represent the actual performance value of the assessed FMU. Table 2. shows the grades in the SNPFM certification system. The values P(Passing performance) & Q(Actual performance) is derived from weighted sum of individual performances over different hierarchical function, which uses pairwise comparison of AHP. The resultant values of actual performance are compared with the resultant value of standard passing performance for grading purpose. 28


The grade in the SNPFM certification system is classified into 5 grades as follow: Gold, Silver, Bronze, Cooper and Zinc. The total grade range considered is from 0 to 1. The range between passing performance and 1 is divided into 3 categories, and the interval for each category is called Upper Interval. The range between passing performance and 0 is divided into 2 categories, and the interval for each category is called Lower Interval. If the actual performance is more than the passing grade then it means that the FMU has a sustainable forest management. The surveillance will be done for the coming years depending on the grade achieved by the FMU. If the FMU gets Gold performance, then in the next 5-year period there will be 2 times surveillance. If the FMU gets Silver performance, then 3 times surveillance within 5 years. The last, if the FMU gets Bronze performance then 4 times surveillance will be done. Table 2. Final SFM Grade Making Process (LEI) Parameter Weight Passing Performance Actual Performance Overall Passing Value Overall Actual Value Upper Interval Lower Interval

Production Principle Ecological Principle X Y X1 Y1 X2 Y2 [ X * X1 + Y * Y1 + Z * Z1 ] = P [ X * X2 + Y * Y2 + Z * Z2 ] = Q (1-P)/3=U P/2= L

Ranking Gold

Ranges (P + 2U) to 1

Silver

(P + U) to {(P + 2U) - 0.001)

Bronze

P to {(P + U) - 0.001 }

Cooper

L to ( P – 0.001 )

Zinc

0 to ( L – 0.001 )

Social Principle Z Z1 Z2

4.4. Analytical Hierarchy Process AHP has the basic assumption that comparison of two elements is derived from their relative importance. In this context the alternatives are compared with another in achieving the given criteria, and these comparisons are called Pairwise Comparison. In making Pairwise Comparison the Decision Maker estimate the true but unknown weights based on experiences relative to the multi criteria decision problem (Bodin and Gass 2003).

29


According to Saaty (Sharifi and Herwijnen 2003) the basic principles and simple axioms of AHP is as follow : Basic principles of AHP : 1. Decomposition principle is used to structure a complex problems into hierarchical clusters. 2. Comparative judgments principle is applied to create Pairwise Comparison Matrices for all combinations of elements and is used to derive the weights or the preferences. 3. Hierarchical composition principle is applied to aggregate the local priorities of elements over the hierarchy into the final evaluation. The four simple axioms of AHP are : 1. Reciprocal axiom; if the pairwise comparison of two elements are A and B with respect to an element C is PC(EA,EB), then the comparison between B and C should be 1/PC(EA,EB). 2. Homogeneity axiom; elements being compared and arranged under a hierarchy should be homogeneous or comparable with an order of magnitude, which in the AHP scale ranges from 1 to 9. 3. Independency of judgment at each level axiom; judgments of the elements in a hierarchy do not depend on lower level elements. 4. Individuals who have reasons for their beliefs should make sure that their ideas are adequately represented for the result to match their expectations. Table 3. Fundamental Scale used in Pairwise Comparison Intensity of importance 1 2 3

Equal importance Weak Moderate importance

4 5

Moderate plus Strong importance

6 7

Strong plus Very strong or demonstrated importance Very, very strong Extreme importance

8 9

Qualitative Definition

Explanations Two activities contribute equally to the objective Experience and judgments slightly favour one activity over another Experience and judgments strongly favour one activity over another An activity is favoured very strongly over another and dominance is demonstrated in practice The evidence favouring one activity over another is of the highest possible order of affirmation

The weighted summation model is the aggregation that is used in original AHP, which is used in the current SNPFM certification system. The weighted summation method can be applied without difficulty in single dimension cases where all units of measurement are identical (Triantaphyllou and Lin 1996). If there are M alternative and N criteria in decision-making problem, then the best alternative, A*, is the one which satisfies the expression as follows:

30


P*WSM

= max

N

a ij W

(2)

j

i=1

M≥ i ≥ 1

Where P*WSM is the WSM priority score of the best alternative, aij is the measure of performance of the ith alternative in terms of the jth decision criterion, and wj is the weight of the jth criterion. The current research also uses the weighted summation method, because by using the experts’ judgment all the measurement become a single unit. The calculation has been done by using Mathlab and Microsoft Excell softwares.

4.5. Fuzzy AHP Inability of AHP to deal with the imprecision and subjectiveness in the pairwise comparison process have been improved in Fuzzy AHP. Instead of single crisp value, in Fuzzy AHP use a range of value to incorporate decision maker' s uncertainty. From this range decision maker can select the values that reflect his confidence and also he can specify his attitude like optimistic, pessimistic or moderate (Jeganathan 2003). Optimistic attitude is represented by the highest value of range, moderate attitude is represented by the middle value of the range and pessimistic attitude is represented by the lowest value of the range. Laarhoven and Pedrycz, Buckley, and Boender et al. introduced fuzzy number operations in Saaty’s AHP method by using triangular fuzzy numbers to replace fuzzy number (Triantaphyllou and Lin 1996). In Fuzzy AHP triangular or trapezoidal fuzzy number are used to represent the decision maker’s assessments on alternatives with respect to each criterion. The concept of fuzzy extent analysis is applied to solve the fuzzy reciprocal matrix for determining the criteria importance and alternative performance. The alpha-cut analysis is used to transform the fuzzy performance matrix representing the overall performance of all alternatives with respect to each criterion into an interval performance matrix, to avoid the complex and unreliable process of comparing fuzzy utilities. An overall performance index is obtained for each alternative across all criteria by applying the concept of the degree of similiraty to the ideal solution using the vector matching function (Deng 1999). Conversion from crisp Pairwise Comparison Matrices to Fuzzy Pairwise Comparison Matrice are done as follows : Table 4. Conversion of crisp PCM to fuzzy PCM Crisp PCM value 1 2 3 5 7 9

Fuzzy PCM value (1,1,1), if diagonal (1,1,3) otherwise (1,2,4) (1,3,5) (3,5,7) (5,7,9) (7,9,11)

Crisp PCM value 1/1 1/2 1/3 1/5 1/7 1/9 31

Fuzzy PCM value (1/1, 1/1, 1/1) if diagonal (1/3,1,1) otherwise (1/4, 1/2, 1/1) (1/5, 1/3, 1/1) (1/7, 1/5, 1/3) (1/9, 1/7, 1/5) (1/11, 1/9, 1/7)


The steps required for Fuzzy AHP developed by Hepu Deng (1999) and then modified by Jeganathan (2003) for the assessment of SFM is as follows: 1. Acquisition of Normal (crisp) Pairwise Comparison Matrices (PCM) 2. Fuzzifying the crisp PCM to Fuzzy PCM 3. Fuzzy Extent Analysis for Calculation of Performance ratings 4. Weightage Multiplication from Hierarchy 5. Alpha cut analysis for embedding uncertainty of Decision Maker confidence 6. Lambda function for embedding Attitude of the Decision Maker 7. Normalizing the Effect table 8. Positive and Negative Similarity Vector Identification 9. Similarity measurement using Vector Matching Function 10. Final Performance Index Measurement. The current research uses the same procedure from step 1 until step 7, then using the weight summation method and normalization to obtain the final actual performance and passing performance. To perform Fuzzy AHP assessment the current research use Mathlab and Microsoft Excell software.

4.6. Fuzzy Reasoning Method (Rule Based) Six step in Fuzzy Reasoning Method is as follows: (1) defines model input; (2) defines linguistic variable (3) construct membership function; (4) fuzzification; (5) fuzzy inference and (6) defuzzification (Cornelissen, Berg et al. 2000). The adapted Fuzzy Reasoning model for this study is shown in Figure 6. Number of linguistic classes and the related membership functions for the current research are selected from the experts’ preference. In the current LEI system the number of linguistic classes is three at the verifier level, namely Good, Fair and Bad. The number of linguistic classes at indicator level, sub process level, process level and criteria level are five as Excellent, Good, Fair, Poor and Bad. Then the number of linguistic classes at Production principle is five that directly represent the certification grade as Gold, Silver, Bronze, Cooper and Zinc. The construction of membership function should be based on expert knowledge (Cornelissen, Berg et al. 2000). The shapes of the membership function represent the uncertainty or confidence level of the decision maker in allocating range of value in certain class. Triangular membership curve with steep slope represent the decision maker uncertainty about the highest degree of membership, but he is confident about his range. Triangular membership curves with moderate slope reveal that he is not only uncertain about the highest degree of membership but also the ranges. Trapezoidal membership curve represent that decision maker is very confidence about the highest degree of membership at the middle range, and he is only uncertain about the edges of each class. Then Gaussian curve reflect the more moderate decision maker than triangular curve (Jeganathan 2003).

32


Step 1

Define

Step 2

Define

Step 3

Construct

Sustainability Indicators and Verifier

Model Input

Linguistic variables and Linguistic values for Antecedents and Consequences

Mem bership function

Fuzzy Model

Step 4

Com pute

Degree of m em bership (fuzzification)

Fuzzy inference (Rule im plication and fuzzy integration)

Step 5

Determ ine

Step 6

Convert

Fuzzy conclusion into num erical assessm ent (defuzzification)

Assess

Sustainable Forest Managem ent

Model O utput

Figure 6. The adapted Fuzzy Reasoning Model for current research (adapted from Cornelissen, Berg et al. 2000) In the current research the experts select triangular membership curves with moderate slope to represent their level of confidence. To perform all steps in Fuzzy Reasoning Method is need to use Fuzzy Logic Toolbox in Mathlab Software.

33


4.7. Knowledge Building for Rule Based Assessment Knowledge building is needed for deriving the rules that will be used in Rule Based Assessment. Expert knowledge is used in different level of Rule Based (Fuzzy Reasoning Method) assessment. It help in making the Causal Relationship Diagram among the verifiers and indicators at the base level, then at medium level in selecting the standardization method, assigning the number of linguistic classes and the shape of the membership curve and at the main level his knowledge and experience is needed as Rules in judging the interaction (Jeganathan 2003). The Causal Relationship is obtained from Cognitive Mapping process, and the collection of rules from expert judgment is structured in Rule Base. The important note is according to LEI Guidelines from principles, criteria and indicators are generally applicable in any SFM assessment in the certification system, but for verifiers is optional and not legally binding in the context that assessor can modify some verifiers as long as according to the indicator definition and intensity scale (norm). For the research the experts from Bogor Agricultural University (IPB) and LEI has made modification for some verifiers. The reasons for that modification are the first one in the guidelines perceive too many verifier, some of them very correlated and can be joined in one modified verifier. The second reason is verifiers for some indicator not yet represent the indicator meaning so is needed to modify, and in some indicator is needed to add the new verifier. The last is to facilitate the good model for cognitive mapping, which should not be too complex but still represent the indicators definition and whole interaction in a particular system. 4.7.1. Cognitive Mapping The first idea of Cognitive maps was introduced by Tolman (1948) in a paper entitled Cognitive maps in rats and men, which intended as a model in psychology (Marchant 1999). Cognitive mapping is a soft qualitative methodology for examining the interactions, connectivities, and linkages of indicators. For application in sustainability assessment, this analysis allowed a more holistic assessment of the indicators by examining how they affect each other directly and indirectly as well as individually and collectively. Indicators, like individual elements of an ecosystem, generally do not affect sustainability by themselves. Consequently, it is more meaningful and insightful to assess their impact by examining their interrelationship and linkages in order to look their overall cumulative dynamic effects. There are three primary analytical constructs based on cognitive mapping that are useful for sustainability assessment: domain, centrality, and criticality (Mendoza and Prabhu 2003). Domain (direct force) reflects the density or number of indicators directly linked to a particular indicator regardless of the direction or the density or number of indicators they are directly connected with (i.e. affecting or affected by them). Centrality (central force) represents not only the direct impact of an indicator through its direct connection to other indicators, but also its indirect connection. The central score considers the number of indicators directly linked to the indicator and the number of indicators indirectly connected at different levels or nodes beyond the indicator’s direct connections. It means that in Level 1 connections are direct, Level 2 is one connection removed and Level 3 is two connection removed, and so on. The formula for calculating the central score is as follows:

34


First level connection + 2nd level + 3rd level + ….. + nth Centrality = ------------------------- ---------- --------------1 2 3 n

(1)

The central score calculation for the current research based on only for three levels, regarding to the default value for the Decision Explorer software. Criticality or critical importance of indicator is calculated based on the number of indicator connected, or linked, to an indicator. Related concept in criticality: 1) Critical indicator is described in terms of how ‘critical’ or how close the condition of an indicator is relative to its perceived target or desired condition. 2) Path is a chain of indicators connected by a directed graph. Forward path is a set of “tail” arrows leading to the succeeding indicators and conversely backward path is a chain of indicators with a “head: of the arrow ending at the indicator. The function of forward path is for evaluating overall impact in terms of identifying causal factors. On the other hand backward path has function for tracing or explaining the sources of impacts. In the existing forest certification model, different parameters have been arranged in different hierarchical level and in sub-branches, due to the assumption that those parameters only have potential interaction at that branch. In contrast, this does not represent the reality in the field, because in the environmental system the dynamic behaviour has much wider impact. To remove this constraint, it is necessary to map overall interaction dynamic behaviour by using Cognitive Mapping (Jeganathan 2003). 4.7.2. Rule Base The most important part in Fuzzy Reasoning Method is building the Rule base which acquires the rules from Indicator level to the higher level, Criteria and Principle. The number of rules depends on the number of inputs and number of linguistic classes of the inputs, for example indicator P2.1. (Landuse planning should ensure the continuity of production at all planning and implementation level) has three verifiers (“Plans on forest division”, “Arrangement on layout and time frame” and “Compatibility of above plan with reality in the field”). Each verifier has 3 linguistic classes, so the total number of rules needed for inferencing of Indicator P2.1 is 3*3*3 = 27 rules. But in practice the experts can optimize or reduce the number of rules based on his knowledge and experience. The overall rule base used in this study is represented in Decision Trees from Indicator, Criteria and Principle. The arguments used by the expert for giving the result of the rule are important to note in building the rule base. It is difficult to derive the rules due to huge amount of inputs in limited time, but the main arguments that keep the logic consistent, should be noted with the expert concurrence. The main arguments used for building rule base for the current research are briefly explained in Appendix B.

35


Chapter 5: RESULTS AND DISCUSSION 5.1. Cognitive Mapping Result Three cognitive maps have been produced in the current research for each criterion in Production Principle, namely Forest Resource Sustainability, Forest Product Sustainability, and Business Sustainability that are shown in Figure 7, 8 and 9. The reason to make separate cognitive map for each criterion is to obtain the good model to represent whole interaction in a particular system, which is not too complex but still reflect the complexity of the system, and to get a better view of the interaction between verifiers within this criteria by using the manageable number of verifiers. Another reason is the limitation of the software (Decision Explorer Demo Version) that only allows for using not more than 30 concepts in one cognitive map; in this research we consider each verifier as a concept. 5.1.1. Interaction between verifiers In cognitive mapping technique, the interaction or causal relationship between verifiers is represented by arrows. The heads of the arrow show the verifiers that affect another verifiers, and the tails of the arrow represent the verifiers that affected by another verifiers. For example looking at Forest Production Sustainability Criteria (Figure 8) the verifier “Plans on forest division/compartmentalization (1)” affect the verifier “Compatibility the plan with reality in the field (3)”; and subsequently “Exploitation factor(10)”, “Percentage and form of stands damage(12)”, and “Potential supply of regeneration(14)”. And for the other way around, verifier “Type and amount of waste (11)” are directly affected by verifiers “Annual production (9)”, “Compatibility the compartmentalization plan with field reality (3)”, “Exploitation factor(10)” and “Implementation of infrastructure(19)”. The interaction between verifiers is indicated by the number of link concepts, forward routes and backward routes that is shown in Appendix C. The important verifiers usually have a higher number of link concept than the others verifiers, or perceived as the “busy” concept in cognitive maps with many arrows (heads and or tails). It means that the verifiers play important roles in whole interaction at a particular system either affect the other verifiers or affected by the other verifiers. By using the cognitive mapping we also can find the different routes where each verifier can participate in affecting other verifiers. The first are the forward routes that also called explanations, because they will affect the other verifiers to the forward direction, and the second are backward routes that also called consequences, because they are affected by the other verifier from the backward directions.

36


5.1.2. Order of Importance The order of importance is indicated by the central score. The calculation for central score uses the formula that has been explained in chapter 4. In this research calculation of central scores is generated by Decision Explorer Software (Demo Version). The calculation is based only on three levels as the default value of that software (Mendoza and Prabhu 2003). The bigger number of central score will have the higher order of importance, because the central score not only calculate the direct impact of an indicator through its direct connection to other indicators, but also its indirect connection to other indicators. The order of importance reflects the role of each verifier in achieving Sustainable Forest Management. A higher order means that the verifier has more contribution in achieving the SFM than the other verifier. For example in Forest Resource Sustainability criteria (Figure 7.), verifier “Forest fire damage (7)” is in the first order with 12 central score, and is followed by verifiers “Plan as per Forest Classification (5)”, “Community and institution participation (13)”, “Early Warning System (11)” and “Availability of Skilled Labour (12)” which has value 10 for central score as the second order. The implication of the order of importance for achieving the Forest Resource Sustainability a FMU should have area that free or less area affected by forest fire damage, because the area affected by forest fire cannot be used for timber production and cannot be compensated with other verifiers. So this verifier plays the most important role in achieving SFM at FRS criterion level. The other verifiers that have direct connection to verifier “Forest fire damage (7)” such as “Early warning system (11)”, “Availability of Skilled Labour (12)” and “Community and institution participation (13)” will have high order of importance. Then in Forest Product Sustainability criteria (Figure 8.), three verifiers, “Annual Production (9)”, “Compatibility of the forest compartmentalization plan with reality (3)” and “Percentage and form of stands damage (12)” are in the first order with value 13 for central score, and are followed by verifier “Exploitation Factor (10)” which has value 10 and verifier “Type and amount of waste(11)” which has value 9. Implication of the order of importance for verifiers in this criterion that an assessed FMU should has sufficient annual production but not exceeded the Annual Allowable Cut (AAC) and regeneration level, has a good compartment and boundary marking in the field and has low of percentage and form of stands damage to achieve sustainability in forest products criterion. In Business Sustainability criteria (Figure 9.), verifier “Mean of Supply (16)” is in the first order with value 11 for central score. It is followed by verifiers “Management system (7)”, “amount of funding (14)” and “Number of professional staff (13)”, which have value 10 for central score. The implication of the order of importance in the criterion is the FMU should have steady mean of supply of capital, good management system, rational amount of funding and sufficient number of professional staff to achieve sustainability in business activities. The complete order of importance for verifiers on Forest Resource Sustainability, Forest Product Sustainability and Business Sustainability Criteria are shown in Table 5.

37


Figure 7. Cognitive Map of Forest Resources Sustainability

Note : A A

B : A affects B B : A affected by B 38


Figure 8. Cognitive Maps for Forest Products Sustainability

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B : A affects B B : A affected by B

39


Figure 9. Cognitive Map for Business Sustainability Criterion

Note : A B : A affects B B : A affected by B A 40


Table 5. Order of importance of verifiers in FRS, FPS and BS Criteria ) ,,

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200%, solvability & remunerativeness is greater than or equal to the interest rate. Good: Liquidity > 100 – 200%, solvability % remunerativeness is greater than or equal to the interest rate. Fair: Liquidity < 100 %, solvability & remunerativeness is greater than or equal to the interest rate. Poor: Liquidity < 100 %, solvability & remunerativeness < the interest rate. Excellent: The fulfilment of tax/fee obligations, absorption of local manpower, sufficient infrastructure that can be used by the community and a partnership with the local community is in effect. Good: The fulfilment of tax/fee obligations, the absorption of local manpower & sufficient infrastructure to be used by the local community. Fair: Tax and Fee obligations met. Bad: Has not fulfilled tax and fee obligations

82


INDICATOR

DEFINITION

VERIFIERS

P3.3 Management Information System (MIS)

Managerial policies in terms of the commitment of the management unit in attaining a sustainable production can be identified by all of their Management Information System (SIM) hardware. There needs to be a monitoring and managing system that is proportional to the management unit area size and a clear chain of command (decision making) mechanism that synchronizes all the organizational decisions of each unit (planning, production and training as well as the support group unit).

1. 2. 3. 4. 5. 6. 7.

Management System MIS equipment Organization management facilities Management decision-making mechanism Organizational structure Job description Role of special division in handling ecological and environment matters 8. Role of biodiversity and environmental information system 9. Internal Monitoring Unit (Satuan Pengawasan Internal)

P3.4 The availability of professional staff for planning, protection, production, and management of forest and business

To ensure sustainable forest resources, forest product and effort in management unit, there needs to be professional & sufficient staff available for planning, forest protection, production and management of forest & business

Number and qualification of planning, protection, production, forest counselling and business management professionals Frequency of training and number of participants Manager camp qualification Human resources specifically assigned in protected area and biodiversity management Human resources specifically assigned in management of protected plant and wild animal species

P3.5 Investment and re-investment in forest management

To accommodate a sustainable forest management, sufficient funding is needed for forest arrangement, protection of protected area and its bio-diversity, including protected/endemic/rare species, production and management of the forest, market and consumer services, means of structure/infrastructure & work instrumentation and the development of human resources.

1. Realization and fund allocation for forest arrangement 2. Realization and fund allocation for forest protection 3. Available funds allocated for management of ecological/environment related aspects 4. Compatibility of funds allocated for management of ecological/environment related aspects towards the implementation of its activities 5. Realization of fund allocation for forest production 6. Realization of fund allocation for forest counselling 7. Realization of fund allocation for establishment of facilities and infrastructure 8. Realization and fund allocation for market services 9. Realization and fund allocation for human resources development 83

NORM (INTENSITY SCALE)

Excellent: Information, organization and action (SOP) monitoring as well as Management Information System instrumentation available for all levels (within the organizational structure) which can be controlled by the Internal Monitoring System (IMS). Decision making between planning & production is synchronized (the planning as part of work reference). Good: Information, organization & action monitoring instrumentation available, which can be controlled by the IMS, but the SIM instrumentation can only be used on certain levels within the organizational structure. Fair: All levels cannot use information, organization & action monitoring instrumentation available, which can be controlled by the SPI, but the SIM instrumentation. Poor: Information, organization & action monitoring instrumentation available, however SPI function not maximized and the Management Information System (SIM) cannot be used at all levels. Bad: No synchronization in decision making between planning & production and Internal Monitoring System (SPI) not fully functional. Excellent: There are professional staff for planning, forest protection, production and forest management available in sufficient numbers Good: Professional staff in planning, forest protection, production, and management of forest & business available but not in sufficient numbers. Fair: There is an effort to improve staff competence. Poor: No effort to improve staff competence. Bad: No professional staff available in planning forest protection, production and management of forest and business Excellent: Large & rational amount of funding available and a steady means of supply. Good: Large & rational amount of funds available however means of supply not as smooth. Poor: Not enough funds available however means of supply is steady. Bad: Not sufficient funding and unsteady means of supply.


INDICATOR

P3.6 The growth of forest capital

DEFINITION

Increasing the capital, which is re-invested back into the forest and (its) business, is a form of long-term investment.

VERIFIERS

Estimating forest value periodically

NORM (INTENSITY SCALE)

Excellent: Capital funds available, which are re-invested back into the forest so that the value of the forest as capital is constantly increasing. Good: Capital funds available which is re-invested back into the forest, but the value of the forest as capital remains as is Fair: Capital funds available but not re-invested back into the forest. Bad: No capital funds available.

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APPENDIX B KNOWLEDGE BUILDING

Indicator P1.1. Guarantee of Land Utilization as a forest area The verifier “Stakeholders agreement” is considered as the most important, then “Boundary Quality” is the second and the least important is “Legal fulfilment”. Stakeholders agreement has important role in indicator P1.1 performance. If stakeholders agreement good then guarantee of land utilization as a forest area should be good, and if it is bad then everything will be bad. Although the management unit has a good boundary and legal fulfilment but the stakeholders do not agree, then there will be no guarantee of land utilization as a forest area.

Indicator P1.2. Forest planning and use based on forest types and functions According to the definition of indicator P1.2, the management unit are division according its allotment is based physical characteristics of the land, which takes the form of watershed areas, sea surface level, slope, land sensitivity to erosion and rain intensity. It is also known that forest types have various characteristics that need to developed, so thereby produce forest classification that will reflect on the prescriptions need to be made by the management. The verifier “Production and protected area plan as per land capability” is considered the most important, the second is “Implementation of Production and Protected area plan”, and the least important is “Production and protected area plan as per forest class”.

Indicator P1.3. The level of change in land cover due to the encroachment and conversion of forest, fire, and other factors According to the definition of the indicator P1.3, theft and other means of forest clearing, change of forest function and other impediments, including inappropriate management tactics can alter the form of (forest land) closure. The scope of the form of closure can cause drastic changes on the forest type and ecological landscape. The verifier “Forest encroachment” is considered as the most important, the second is “Intensity of forest fire”, the third is “the other disturbances”, and the least important is “Overcutting”. Forest encroachment has important role in indicator P1.3 performance. If the forest encroachment is high then the performance cannot be good, because forest encroachment usually lead to forest fire and forest conversion. In Labanan FMU case, the local community usually convert the forest to agriculture land by burning. Forest fire as a natural disaster is rarely happened.

Indicator P1.4. A forest fire management system According to the definition of the indicator P1.3, forest fire is a natural disaster, which its occurrence is unpredictable, making the readiness of a special task force (management) essential in order to manage this well in the framework of prevention as well as containment. The verifier “Early Warning System” is considered the most important, then “Institution and Community Participation” and “Availability of skill labour” are equally important and can compensate for 85


each other. Early Warning System has important role in indicator P1.4 performance. If Early Warning System is bad then the other verifiers

Indicator P1.5. The selection and implementation of silvicultural system in compliance with the local forest ecosystem According to the definition of indicator P1.5, selection, planning, determining and the application of a silviculture system should be in compliance with force of area support, namely the forest and its ecosystem. The verifier “Implementation of silviculture activity” is considered as most important, the second is “Compliance with the local ecosystem”, then the others verifiers are considered equally together as the least important. Implementation of silviculture activity has important role in the indicator P.1 performance. If the implementation of silviculture activity is good then the indicator P.1 performance should be good.

Indicator P1.6. Maintenance of a variety of non timber forest products is guaranteed According the definition of indicator P1.6, the existence and potential of non-timber forest products provide continuous prospects and guarantee of a good forest. The verifier “Type of non timber forest product” is considered the most important and then “Potential extractions of NFTP”.

Indicator P2.1. Landuse planning should ensure the continuity of production at all planning and implementation level. The verifier “Plans on forest division (forest parts and compartmentalization)” is considered as most important, next “Arrangements on lay out and time frame” and last “Compatibility of above plan with reality in the field”. Plans on forest division plays a role in indicator P2.1. performance, if the plans consider forest type, forest class and forest function then it should be ensure the continuity of production. And assessment will be done for the next verifier “Arrangements on lay out and time frame” and “Compatibility of above plan with reality in the field”. If the plan is bad then the other verifiers cannot be good.

Indicator P2.2. Observation of stand development and their level of production. According to the definition of indicator P2.2, Repeated measurements of growth and increments serve as a basis to determine the total amount of yearly harvest. The right growth predictions can be achieved if appropriate scientific measures are used (object representation, methodology, experts, funds, documentation and monitoring of data). The verifier “Procedure and intensity of growth data gathering” is considered as the most important, then “Documentation and monitoring” and the least important is “Resulting data has been used in formulating the management plan or its revision”. Procedure and growth data gathering plays important role in indicator P2.2 performance, if it have been done according to standard operating scientific norm, we will know the condition of stand de-

86


velopment and their level of production. If the growth measurement has not been done then the rest verifiers become not applicable.

Indicator P2.3. Annual production in accordance with the capability of forest product According to the definition of indicator P2.3, Natural/ecological sustainability of production can be achieved if the amount of Annual Allowable Cut (AAC) does not exceed the forest reproduction capabilities. The structured harvesting of timber forest products that are in line with the level of success in replanting/regenerating (shifting cultivation) according to its appropriate periods will simplify the sustainable management on its next rotation/cycle. The verifier “ Forest production management plan” is considered as the most important, the second is “Data and basic map” and the last is “Annual Production”. Forest production management plan plays important role in indicator P2.3. performance, if it is compiled based on the data and basic map, then the annual production should be in accordance with the capability of forest product. And if it is not compiled based on the data and basic map, then the annual production cannot be good.

Indicator P2.4. Efficiency of forest utilization According to the definition of indicator P2.4, high productivity levels that are reflected trough the ratio of production against forest potential can be achieved if the collections of forest products are done efficiently. Exploitation factors, minimal waste and utilization of type are key elements in applying forest production collection techniques. The verifier "Exploitation factor" is considered as most important and the second is "Type and amount of waste". Exploitation factor is calculated by dividing the actual production with forest potency from cruising result, and it plays important role in indicator P2.4 performance. If the exploitation factor high then the efficiency of forest utilization should be good, and if it is low then the efficiency of forest utilization cannot be good.

Indicator P2.5. Condition of residual stand According to the definition of indicator P2.5, the level of stand damage caused by harvesting can hinder a natural/artificial regeneration process and the future potential supply of stand. The verifier "Percentage and form of stand damage" is considered as most important, then the second is "Natural regeneration" and "Potential supply of regeneration" is the least important. Percentage and form of stand damage plays important role in indicator P2.5 performance. If the percentage and form of stand damage is low then the condition of residual stand should be good. And if the percentage and form of stand damage is high then it is impossible to have a good condition of residual stand.

Indicator P2.6. Validity of timber tracking system in the forest The verifier "Internal timber administration system" is considered as most important, then "Timber flow in the field" is as the second and the last is "Production statistic". The internal timber administration system plays important role in indicator P2.6 performance. If the internal timber administration system is good then the validity of timber tracking system in the forest is good, because the manage-

87


ment unit can ensure/guarantee control over the timber collection, both in the total amount as well as its source. If the internal timber administration system is bad (not available) then the validity of timber tracking system in the forest cannot be good.

Indicator P2.7. Infrastructure of the forest management unit In harvesting products, the effort in determining the road density and total road length (main, side and incline), the location and quality of the road and bridge construction and the logging and skidding methods are done in such a way to minimize damage to the environment. Also the construction of erosion control structures on the transportation route, logyard and building area need o be done. The verifier "Design of infrastructure for harvesting" is considered as most important and the second is "Implementation of infrastructure for forest harvesting". So the design of infrastructure for harvesting plays important role in indicator P2.7 performance. If the design of infrastructure for harvesting is good then the infrastructure of the forest management unit should be good, and if it is bad then the infrastructure of the forest management cannot be good.

Indicator P2.8. Implementation of Reduced Impact Logging (RIL) According to the definition of Indicator P2.8, every phase in the harvesting of forest products has an impact on the environment, specially the important negative impacts has to be clearly mapped. The location, type or weight of the impact should be mapped. In consequence, a controlled effort can be determined, such as the implementation of “Reduced Impact Logging” within the context of minimizing the impact, if the activity causing the impact is done in the relevant location. The verifier "Significant impact indicator map" is considered the mot important, the second is "Monitoring, Organization, Standard Operating Procedure of Reduced Impact Logging", the third is " Reduced Impact Logging implementation" and the last is "Impact".

Indicator P2.9. Availability of forest product to local community The verifier "Accommodation of the local community interest" is considered as the most important, the second is "Information to local community" and the least important is "Standard Operating Procedure and Monitoring tools". Accommodation of the interest of the local community whose livelihood has dependent on the forest plays important role in indicator P2.9 performance. If the management unit accommodate the interest of the local community then the availability of forest product to local community should be good. But if they do not accommodate the interest of the local community then everything will be bad and the other verifiers become not applicable.

Indicator P3.1. Financial condition of the company The verifier "Liquidity" is considered as the most important, the second is "Solvability" and "Rentability" is the least important. A profitable entrepreneurship, liquid and solvable is reflecting a good business management. The liquidity plays important role in indicator P3.1 performance. If the liquidity is good then it reflects the good financial condition of the company, and if it is bad the financial condition of the company cannot be good.

88


Indicator P3.2. Contribution to regional economic development According to the definition of indicator P3.2, contribution to regional economic development that directly measurable, are from tax and fee components that are paid by management unit and the absorption of manpower from the community. As indirect contribution are the infrastructure and development of economic ventures (local partnerships). The verifier "Taxes and levies fulfilment" is considered as the most important, then "Local labour absorption" is as the second, and "Infrastructure that can be used by local community" is as the least important. Taxes and levies fulfilment has important role in indicator P3.2 performance. If the taxes and levies fulfilment is good then the contribution to regional economic development should be good, and if it is bad it will overruled the other rules or everything will be bad. Then if the management unit cannot fulfil the taxes usually they do not provide the infrastructure that can be used by the local community.

Indicator P3.3. Management Information System (MIS) According to the definition of indicator P3.3. managerial policies in terms of the commitment of the management unit in attaining a sustainable production can be identified by all Management Information System (MIS) hardware. The verifier "Management System" is considered as the most important, the second is "Management Information System Equipment", then the third is "Organizational Structure" and the least important is "Decision making mechanism". Management System plays important role in indicator P3 performances.

Indicator P3.4. The availability of professional staff for planning, protection, production and management of forest and business According to the definition of indicator P3.4, to ensure sustainable forest resources, forest product and effort in management unit, it needs professional and sufficient staff available for planning, forest protection, production and management of forest and business. The verifier "Qualification of professional staff" is considered as the most important, then "Human resources development" is as the second, and "Number of professional staff" is as the least important. Qualification of professional staff has important role in indicator P3.4 performance. If qualification of professional staff is good then the availability of professional staff for planning, protection, production and management of forest and business should be good, but it is still need to consider the performance of another verifiers, namely human resources development and the sufficiency of the number of professional staff.

Indicator P3.5. Investment and reinvestment in forest management According to the definition of indicator P3.5, to accommodate a sustainable forest management, sufficient funding is needed for forest arrangement, protection of protected area and its bio-diversity, including protected/endemic/rare species, production and management of the forest, market and consumer services, means of structure and work instrumentation and the development of human resources.

89


The verifier "Amount of funding" is considered as the most important, the second is "Fund allocation" and the least important is "Means of Supply". The amount of funding has important role in indicator P3.5 performance. If the amount of funding is large and rational then investment and reinvestment in forest management should be good, and if it is not enough fund available than everything will be bad.

Indicator P3.6. The growth of forest capital Increasing the capital, which is reinvested back into the forest and its business, is a form of long-term investment. The verifier "Availability of capital funds" is considered as the most important, then "Reinvestment into the forest" is as the second and "Forest stock" is as the least important. Availability of capital funds plays important role in indicator P3.6 performance. If the capital fund is good then the growth of forest should be good, and if it is bad then everything will be bad..

90


APPENDIX C RESULT OF COGNITIVE MAPPING ANALYSIS

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APPENDIX F PAIRWISE COMPARISON MATRICES OF LABANAN FMU CERTIFICATION FOREST RESOURCES SUSTAINABILITY CRITERIA P1.1 : Guarantee of land utilization as a forest area P1.1 Excellent Good Fair Poor Bad

Excellent 1 0.3333 0.2000 0.1429 0.1111

Good

Fair

Poor

3 1 0.5000 0.2000 0.1429

5 2 1 0.3333 0.2000

7 5 3 1 0.3333

Bad 9 7 5 3 1

P1.2 : Forest planning and use based on forest types and functions P1.2 Excellent Good Fair Poor Bad

Excellent 1 0.5 0.3333 0.1429 0.1111

Good

Fair

Poor

2 1 0.5000 0.2000 0.1429

3 2 1 0.2500 0.1667

7 5 4 1 0.3333

Bad 9 7 6 3 1

P1.3 : The level of change in land cover due to encroachment and conversion of forest, fires, and other factors P1.3 Excellent Fair Poor Bad

Excellent 1 0.3333 0.1667 0.1111

Fair

Poor

3 1 0.3333 0.1667

6 3 1 0.3333

Bad 9 6 3 1

P1.4 : A forest fire management system P1.4 Good Fair Poor Bad

Excellent 1 0.5000 0.3333 0.1429

Fair

Poor

2 1 0.5000 0.2000

3 2 1 0.2500

Bad 7 5 4 1

P1.5 : The selection and implementation of silvicultural systems in compliance with the loca forest ecosystem P1.5 Excellent Fair Poor Bad

Excellent 1 0.2000 0.1429 0.1111

Fair

Poor

5 1 0.3333 0.2500

7 3 1 0.5000

Bad 9 4 2 1

126


P1.6 : Maintenance of a variety of non timber forest products in guaranteed P1.6 Excellent Fair Poor Bad

Excellent 1 0.2500 0.1667 0.1111

Fair

Poor

4 1 0.2000 0.1667

6 5 1 0.5000

Bad 9 6 2 1

P1.2 2.6 1 0.6667 0.5882

P1.3 2.5 1.5 1 0.6667

P1.4 2.4 1.7 1.5 1

Area Management P1.1 P1.1 P1.2 P1.3 P1.4

1 0.3846 0.4000 0.4167

Forest Management P1.5 P1.5 P1.6

1 0.6667

P1.6 1.5 1

Forest Resources Sustainability AM AM FM

1 0.3448

FM 2.9 1

FOREST PRODUCTS SUSTAINABILITY CRITERIA P2.1 : Land use planning should ensure the continuity of production at all planning implementation P2.1 Excellent Fair Poor Bad

Excellent 1 0.5000 0.1667 0.1111

Fair

Poor

2 1 0.2000 0.1667

6 5 1 0.5000

Bad 9 6 2 1

P2.2 : Observation of stand development and their level of production P2.2 Excellent Good Fair Poor Bad

Excellent 1 0.5 0.2000 0.1429 0.1111

Good

Fair

Poor

2 1 0.2500 0.1667 0.1429

5 4 1 0.5000 0.2500

7 6 2 1 0.5000

127

Bad 9 7 4 2 1


P2.3 : Annual production in accordance with the capability of forest productivity P2.3 Excellent Good Fair Poor Bad

Excellent 1 0.3333 0.2000 0.1429 0.1111

Good

Fair

Poor

3 1 0.2500 0.1667 0.1429

5 4 1 0.3333 0.2500

7 6 3 1 0.5000

Bad

Good

Poor

Bad

3 1 0.2000 0.2000

7 5 1 1.0000

9 7 4 2 1

P2.4 : Efficiency of forest utilization P2.4 Excellent Good Poor Bad

Excellent 1 0.3333 0.1429 0.1429

7 5 1 1

P2.5 : Condition of residual stands P2.5 Excellent Good Fair Poor Bad

Excellent 1 0.3333 0.2000 0.1429 0.1111

Good

Fair

Poor

3 1 0.3333 0.2000 0.1429

5 3 1 0.3333 0.2000

7 5 3 1 0.2500

Bad

P2.6 : Validity of a timber tracking system in the forest P2.6 Excellent Fair Bad

Excellent 1 0.3333 0.1429

Fair

Bad

3 1 0.2000

7 5 1

P2.7 : Infrastructure of the forest management unit P2.7 Excellent Good Fair Poor

Excellent 1 0.2500 0.1429 0.1111

Good 4 1 0.2500 0.1429

Fair

Poor

7 4 1 0.3333

9 7 3 1

P2.8 : Implementation of reduced impact logging P2.8 Excellent Fair Poor Bad

Excellent 1 0.2500 0.1429 0.1429

Fair

Poor

4 1 0.2000 0.2000

7 5 1 1.0000

Bad 7 5 1 1

128

9 7 5 3 1


P2.9 : Availability of forest products for local community P2.9 Excellent Good Fair Bad

Excellent 1 0.2500 0.1667 0.1250

Good

Fair

4 1 0.3333 0.1667

6 3 1 0.2500

Bad 8 6 4 1

Production Management P2.2

P2.3

P2.2 P2.3 P2.4 P2.5 P2.6

1 2.1 0.9091 4.0000 0.3333

P2.7

1

0.47619 1 0.2941 2.7000 0.2128 0.33333 3

P2.4

P2.5

PM 1 0.3846 0.0794

EM 2.6 1 0.5000

0.25 0.37037 0.833333 1 0.5263

3 4.7 1.1 1.9 1

1 3 0.909091 1.6 0.416667

1.1

0.625

2.4

1

SM 12.6 2 1

Forest Products Sustainability AM2 AM2 FM2

1 4.7000

FM2 0.212766 1

BUSINESS SUSTAINABILITY CRITERIA P3.1 : Financial condition of the company P3.1 Excellent Good Fair Poor

Excellent 1 0.3333 0.2000 0.1111

Good 3 1 0.3333 0.1429

P2.7

1.1 3.4 1 1.2000 0.9091

Forest Management (2)

PM EM SM

P2.6

Fair 5 3 1 0.2000

Poor 9 7 5 1

129


P3.2 : Contribution to regional ecnomic development P3.2 Excellent Good Fair Bad

Excellent 1 0.3333 0.2000 0.1111

Good

Fair

3 1 0.3333 0.1429

5 3 1 0.2000

Bad 9 7 5 1

P3.3 : Management Information System P3.3 Excellent Good Fair Poor Bad

Excellent 1 0.3333 0.1667 0.1250 0.1111

Good

Fair

Poor

3 1 0.2500 0.1429 0.1250

6 4 1 0.2000 0.1429

8 7 5 1 0.5000

Bad 9 8 7 2 1

P3.4 : The ability of professional staff for planning, protection, production, and management of forest

and business P3.4 Excellent Good Fair Poor Bad

Excellent 1 0.5000 0.3333 0.1429 0.1111

Good

Fair

Poor

2 1 0.5000 0.2000 0.1429

3 2 1 0.2500 0.2500

7 5 4 1 1.0000

Bad

P3.5 : Investment and re-investment in forest management P3.5 Excellent Good Poor Bad

Excellent 1 0.5000 0.1429 0.1111

Good

Poor

2 1 0.2000 0.1429

7 5 1 0.5000

Bad 9 7 2 1

P3.6 : The growth of forest capital P3.6 Excellent Good Fair Bad

Excellent 1 0.5000 0.1667 0.1250

Good

Fair

2 1 0.2500 0.1667

6 4 1 0.2500

Bad 8 6 4 1

Foret Management (3) P3.1 P3.1 P3.2

1 0.2500

P3.2 4 1

130

9 7 4 1 1


Financial Management P3.5 P3.5 P3.6

P3.6

1 1.0000

1 1

Institutional Arrangement OA OA HR FnM

1 4.0000 4.0000

HR 0.25 1 1.0000

FnM 0.25 1 1

Business Sustainability FM3 FM3 IA

1 3.0000

IA 0.333333 1

PRODUCTION PRINCIPLE FRS FRS FPS BS

1 0.7692 0.7692

FPS 1.3 1 0.5000

BS 1.3 2 1

131