Governance and Institution in the Indian Forest Sector: An Analytical Study 3031347455, 9783031347450

Citation preview

Jyotish Prakash Basu

Governance and Institution in the Indian Forest Sector An Analytical Study

Governance and Institution in the Indian Forest Sector

Jyotish Prakash Basu

Governance and Institution in the Indian Forest Sector An Analytical Study

Jyotish Prakash Basu West Bengal State University Kolkata, West Bengal, India

ISBN 978-3-031-34745-0 ISBN 978-3-031-34746-7 (eBook) https://doi.org/10.1007/978-3-031-34746-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Foreword

Since the FAO initiated its five-yearly remote sensing-based global forest resource assessment in 1990, it has documented disturbing deforestation and forest degradation rates in the Global South. The underlying causes are highly complex and vary from place to place, but most documented degradation rarely follows official political priorities. On the contrary, deforestation and forest degradation are often categorized as policy failures. Following Nobel Price Laurette Elinor Ostrom’s groundbreaking publication, Governing The Commons: The Evolution of Institutions for Collective Action in 1990, various forms of decentralized forest governance have been implemented across the Global South to improve rural livelihoods and conserve forest resources. Since then, a substantial amount of research has investigated whether and the degree to which different forms of decentralization have delivered on the stated objectives. Many single-case or small-n studies trace decentralized forest governance’s biophysical, socioeconomic, and socio-political effects, but large-n studies are rare. This book offers a large-n study, including 36 villages, 844 households, 10 gram panchayats, 12 beat offices, and 36 forest protection committees in West Bengal. It also develops a forest governance index that allows a quantitative comparison across many cases. Hence, the results may be generalized for larger units within a formal forest governance hierarchy. This is not a trivial matter. Generalizations based on a few case studies that criticize the outcomes of existing power structures are often (rightly and wrongly) dismissed (by those in power) as non-representative. Through a rigorous collection of standardized data and index calculation on the rule of law; transparency; accountability; participation; degree of inclusiveness and equitability; and efficiency and effectiveness, the governance of forests become directly comparable across villages and administrative units. Most people in the studied villages are very poor and thus highly dependent on forests for their livelihoods. However, the combined forest governance index only qualifies as good in two of the investigated villages. The forest governance is poor in three villages, while the rest rank as medium. There is room for improvement in all villages, and the governance indexes, including their subcomponents, tell us which parameters and decision-making processes to focus on for further improvement. v

vi

Foreword

Hence, the book will inspire fellow scientists investigating the effects of decentralized forest governance and practitioners striving to make forest decentralization work for forests and rural people. Thorsten Treue Associate Professor, Ph.D. in Forest Policy and Economics The University of Copenhagen Copenhagen, Denmark

Preface

Forests play an important role in achieving the 17 Sustainable Development Goals (SDGs) of the United Nations, 2030 Agenda, and SDG 16th have also focused on institutions and non-discriminatory laws and policies relating to forests and forest life. The forest management practice has led to the degradation of forests in some developing countries, especially in Latin America, South East Asia, and Congo. As a result, the forest management paradigm shifted from timber production to sustainable forest management. To achieve sustainable forest management, forest governance research has received keen attention to address environmental degradation, forest conservation, deforestation, and climate change issues at the global level along with livelihood generation and poverty reduction of forest-dependent communities at the local level in the socio-ecological and political system in West Bengal, India. The study of forest governance is largely influenced by the functioning of institutions. In India, 97% of the total forest area is governed by the government out of which 93% is controlled by the state forest department and the rest 4% by the state revenue department. The rest 3% of forest land is owned by private entities and communities. In India, about 27–30% of the population are forest-dependent communities living in the forest fringe areas. About 28% of the forest area is protected or managed in collaboration with communities under the Joint Forests Management (JFM) program. There are more studies on forest governance and institutions available at the crosscountry level and national levels based on secondary data while very few studies are at the local level. The study provides an exhaustive analysis of forest governance quantitatively at the local level based on primary data collection. The study attempts to address the quantitative analysis of forest governance, enforcement, monitoring; participation, and institution which are the important pillars of the study. The study was conducted in the two regions of West Bengal, India. One is the South Bengal forest division while the other is the North Bengal forest division in West Bengal, India, during 2020–21. The total number of selected households in the two forest divisions comprises 844, and the number of villages is 35. In addition, the study has also surveyed 12 beat offices, 10 gram panchayats, and 34 forest protection committees in both forest divisions. The study has utilized various governance indicators, namely Rule of Law, Transparency, Accountability, Participation, Inclusive vii

viii

Preface

and Equitable, and Efficient and Effective to formulate the forest governance index. In addition, the monitoring index, enforcement index, and forest dependency index have been constructed at the household level. Different statistical and econometric models are applied to identify the determinants of forest governance at the household level. Besides, the study also attempts to address the impact of governance in the forest sector based on macro-level data in India. Particular attention is given to the forest policies and Acts of India during preand post-independence periods and other Asian, African, and Latin American countries. Special importance is given to the study of governance trends in South Asian countries. Kolkata, West Bengal, India

Jyotish Prakash Basu

Acknowledgements

I would like to express my gratitude to the Indian Council of Social Science Research (ICSSR), New Delhi, for financial support to conduct the study. The study is based on secondary data and primary data. During the field survey, I received generous support, guidance, and assistance from several people, including students in different colleges, Ph.D. scholars, local people, and many others in the selected nearby villages, as well as government officials in West Bengal. The field survey would not be possible without their active participation and cooperation. Special thanks in this regard should be given to Dr. Sourav Kumar Das, the project assistant, for spending his tireless effort to make a successful field survey. Again thanks to Aishwarya Basu, Ph.D. scholar, Department of Economics, the University of Burdwan, for her participation in the field survey. I am further grateful to my university authority for giving me infrastructural facilities including space and a library.

ix

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 6

2

Review of Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Literature on Institutions and Enforcement . . . . . . . . . . . . . . . . . . . 2.2 Literature on Forest Livelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Literature on Forest Participation . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Literature on Governance like the Rule of Laws, Transparency, Accountability, Inclusive and Equitable . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11 11 12 13

Data Base and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Description of the Purulia Forest Division in the Purulia District . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Sampling Technique for Data Collection in Purulia Forest Division . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 Description of Bankura (South) Forest Division in the Bankura District of South Bengal . . . . . . . . . . . . . . . 3.1.4 Sampling Technique for Data Collection in Bankura (South) Forest Division . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.5 Description of Rupnarayan Forest Division of Paschim Medinipur District, South Bengal . . . . . . . . . . 3.1.6 Sampling Techniques for Data Collection in the Rupnarayan Forest Division of Paschim Medinipore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.7 Description of Alipurduar Forest Division of North Bengal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.8 Sampling Technique for Data Collection in Alipurduar Forest Division . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Analytical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Forest Participation Index . . . . . . . . . . . . . . . . . . . . . . . . . . .

21 21

3

14 16

22 24 26 28 28

30 30 30 32 32 xi

xii

Contents

3.2.2 3.2.3 3.2.4 3.2.5 3.2.6 3.2.7 3.2.8

Institutional Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monitoring Index and Enforcement Index . . . . . . . . . . . . . Forest Dependence Index . . . . . . . . . . . . . . . . . . . . . . . . . . . Forest Governance Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . Factors Affecting Forest Governance Index . . . . . . . . . . . . Forest Governance and Forest Dependency Index . . . . . . . Impact of Forest Governance on Forest Cover in India Based on Secondary Data . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

5

6

Governance in South Asian Countries and Impact of Governance on Forest Cover in India . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Forest Area, Annual Change in Forest Area and Forest Ownership Across South Asian Countries . . . . . . . . . . . . . . . . . . . . 4.2 Governance Structure Across South Asian Countries . . . . . . . . . . 4.2.1 Forest Cover of India as a Whole and West Bengal in Particular . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Forest Policies and Acts in India During Pre and Post Independence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Forest Policies and Acts in Pre Independence India (1857–1947) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Forest Policies and Acts in the Post-independence India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Impact of Governance on Forest Cover in India . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Socio-economic Analysis of Sample Households in the South Bengal and The North Bengal Forest Division . . . . . . . . . . . . . . . . . . . . 5.1 Socio-economic Conditions of Households in the Purulia Forest Division . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Socio-economic Conditions of Bankura (South) Forest Division . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Socio-economic Conditions of Rupnarayan Forest Division in the PaschimMedinipur District . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Socio-economic Conditions of the Households in the Alipurduar Forest Division, North Bengal . . . . . . . . . . . . . . Analysis of Forest Protection Committee (FPC), Forest Beat Office and Gram Panchayat in South and North Bengal Forest Divisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 FPC of Purulia Forest Division, South Bengal . . . . . . . . . . . . . . . . 6.2 FPC of Bankura (South) Forest Division, South Bengal . . . . . . . . 6.3 FPC of Rupnarayan Forest Division of Paschim Medinipur, South Bengal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 FPC of Alipurduar Forest Division, North Bengal . . . . . . . . . . . . .

35 36 37 38 41 42 43 44 45 45 46 48 54 54 55 58 62 63 63 74 83 97

109 109 113 116 119

Contents

xiii

6.5

Forest Beat Office Across South Bengal and North Bengal Forest Divisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Gram Panchayat Across South Bengal and North Bengal Forest Divisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

6.6 7

8

9

Institutions and Enforcement at Local Level . . . . . . . . . . . . . . . . . . . . . 7.1 Institutional Index and Enforcement Index in the Purulia Forest Division, South Bengal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Institutional Index and Enforcement Index in Bankura (South) Forest Division, South Bengal . . . . . . . . . . . . . . . . . . . . . . . 7.3 Institutional Index and Enforcement Index in Rupnarayan Forest Division of Paschim Medinipur in South Bengal . . . . . . . . 7.4 Institutional Index, Enforcement Index and Monitoring Index in Alipurduar Forest Division in North Bengal . . . . . . . . . . 7.5 Monitoring, Institutional, and Enforcement Indices in South and North Bengal Forest Divisions . . . . . . . . . . . . . . . . . . 7.6 Formal Institutions and NGOs in the Forest Sector in West Bengal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.1 West Bengal Forest Development Corporation Limited (WBFDCL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.2 Large-Sized Adivasi Multipurpose Cooperative Societies (LAMPS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.3 The Ramkrishna Mission Lok Shiksha Parishad (RKM-LP) (NGO) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.4 The Indian Institute for Bio-social Research and Development (IBRAD) . . . . . . . . . . . . . . . . . . . . . . . . . Measurement of Participation of Communities in the Planning, Monitoring and Implementation Stages . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Forest Participation of Purulia Forest Division, South Bengal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Forest Participation of Bankura (South), South Bengal . . . . . . . . . 8.3 Forest Participation in Rupnarayan Forest Division of Paschim Medinipur South Bengal . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Forest Participation of Alipurduar Forest Division, North Bengal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Comparative Analysis of Forest Participation Index Across South Bengal and North Bengal Forest Division . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

129 129 130 132 133 135 136 137 137 137 138 145 145 151 156 163 169 171

Measurement of Forest Governance in South Bengal and North Bengal Forest Divisions at Household Level . . . . . . . . . . . . 173 9.1 Forest Governance of the Purulia Forest Division, South Bengal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 9.2 Forest Governance of the Bankura (South), Forest Division, South Bengal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180

xiv

Contents

9.3 9.4 9.5

Forest Governance of Rupnarayan Forest Division of Paschim Medinipur, South Bengal . . . . . . . . . . . . . . . . . . . . . . . . 184 Forest Governance of Alipurduar Forest Division of North Bengal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 Comparative Analysis of Forest Governance Across Different Forest Divisions of South Bengal and North Bengal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197

10 Forest Dependency and Forest Governance in South Bengal and North Bengal Forest Divisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Forest Dependency Index (FDI) in Purulia Forest Division . . . . . 10.2 Relation Between Forest Dependency and Forest Governance in Purulia Forest Division . . . . . . . . . . . . . . . . . . . . . . . 10.3 Forest Dependence Index (FDI) in Bankura (South) Forest Division . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Relation Between Forest Dependence and Forest Governance in Bankura (South) Forest Division . . . . . . . . . . . . . . 10.5 Forest Dependence Index (FDI) of the Households in Rupnarayan Forest Division of Paschim Medinipur . . . . . . . . . 10.6 Relation Between Forest Dependence and Forest Governance in Rupnarayan Forest Division of Paschim Medinipore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7 Forest Dependence Index (FDI) of the Households in Alipurduar Forest Division, North Bengal . . . . . . . . . . . . . . . . . 10.8 Relation Between Forest Dependence and Forest Governance in Alipurduar Forest Division, North Bengal . . . . . . 11 Conclusions and Policy Recommendations . . . . . . . . . . . . . . . . . . . . . . . 11.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Government Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

203 203 204 208 210 212

214 216 218 223 223 228 229 231

Chapter 1

Introduction

Societies are benefitted from forests which provide different services to them. It provides goods for commercial trade like timber, non-timber forest products, and tourism services as well. On the other hand, it sustains livelihood generation, critical life support inputs like food, medicine, fodder, fuel wood, and construction poles, etc. It also provides local utility services like watershed management and soil conservation along with global public services like climate change mitigation, biodiversity conservation, and carbon sequestration. Forests play an important role in achieving the 17 Sustainable Development Goals (SDGs) of the United Nations, 2030 Agenda (United Nations 2015). SDG 16th has focused on institutions and non-discriminatory laws and policies relating to forests and forest life. The study of forest governance is one of the instruments for achieving forest conservation and management goals (Mohanty and Saha 2012). Forest governance research has received keen attention to deal with issues like environmental degradation, deforestation, and climate change at a global scale. Until the 20th Century, forests were managed across the globe for the production of timber as well as catering to industrial demand (Ghosh and Sinha 2016). This management practice has led to the degradation of forests in the countries, especially in Latin America, South East Asia, and Congo (FAO 2010). As a result, the forest management paradigm had a shift from timber production to sustainable forest management. The present research endeavors to address important issues like forest conservation, livelihood generation of forest-dependent communities, and equity in the socio-ecological and political systems in West Bengal, India. The study is associated with appropriate monitoring and enforcement of forest policy with the active participation of local communities. India’s previous forest policy, such as the Forest Policy of 1894; 1952, the importance was given to agriculture over forestry which helped conversion of forest lands into agricultural purposes (Joshi et al. 2011). The previous forest policies and the Forest Act of (1865, 1878, 1927) highlighted the importance of timber production and revenue generation (Gosh and Sinha 2016). On the other hand, three important forest policies after independence are associated with forest governance in India, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. P. Basu, Governance and Institution in the Indian Forest Sector, https://doi.org/10.1007/978-3-031-34746-7_1

1

2

1 Introduction

specifically, the 1952 Forest Policy, the National Commission on Agriculture (NCA) 1976, and the New Forest Policy 1988 (Saxena 2000). In India, 97% of the total forest area is governed by the government out of which 93% is controlled by the state forest department and the rest 4% by the state revenue department. The rest 3% of forest land is owned by private entities and communities (MoEF 2006). Though the forest land is owned by the government the communities’ involvement in the management and protection of forest has been increasing over time. About 28% of the forest area is managed in collaboration with communities under the Joint Forests Management (JFM) program (Aggarwal et al. 2009). In India forest is considered to be the home to tribal and other communities and it contributes to the livelihood security of such populations. The size of forestdependent communities which are most vulnerable varies from 300 to 400 million residing in the forested regions (Saha and Guru 2003), and about 1.73 lac villages are located in such regions (Basu 2017). This large number of forest-dependent communities (about 27–30% of the population) has benefitted from getting forest tenure rights, forest uses rights, the right to protect, right to regenerate, and conserve community forests under Forest Right Act (FRA) which has already been enacted in the parliament in 2006. Some studies have advocated that security of tenure rights or security of property rights helped to govern forests relatively well (Netting 1976; McKean and Ostrom 1995; Gibson 2001; Ghate 2004; Tucker 2004). The effectiveness of forest governance is largely influenced by the proper functioning of institutions. The institutions may be of various forms. Institutions at the global level are Work Bank, UNDP, and FAO while institutions at the local level are Joint Forest Management Committees (JFMC)/Forest Protection Committees (FPCs), Community Forest Management groups, Van Panchayats, Village Councils (schedule VI area) and Biodiversity Management Committees, eco-development committees, NGOs and Self Help Groups, etc. Besides, Panchayati Raj Institutions (PRIs) are constitutionally mandated bodies for decentralized development planning and execution at the local level. Many countries in Africa and Latin America like Tanzania, Brazil, Bolivia, and Mexico have recognized that community forestry absorbs a substantial amount of carbon to fight against climate change and the establishment of forest rights showed a substantial amount of carbon dividends. In Nepal, about 32% of the population derived benefits from community forestry and it improved the better conditions of forests health, tackle climate change, and increased economic benefits to the communities. In Asia, the community-based forest management policy is successful in the protection of public forests in India, Nepal, the Philippines, China, Indonesia, Cambodia, Thailand, and Vietnam. Apart from community-based forest governance, it is important to mention other two forms of governance including private and public governance. Private forest governance results in the deterioration of forests (Mwangi 2007). On the other hand, the experience of public governance is not satisfactory for some countries (for example the countries like Indonesia, Malaysia, the Philippines, Ghana, Liberia, and the Ivory Coast) where deforestation took place (Repetto and Gillis 1988).

1 Introduction

3

In developed countries, forest governance is used to mean for ensuring timber legality. Timber legality deals with industrial forestry which is characterized by timber exploitation for commercial purposes. The democratization wave in the late twentieth century stimulated the involvement of non-state actors such as Businesses, Non-governmental Organizations (NGOs), and private financial intuitions in the formulation of forest policies. A lot of debates have been arising on the efficient management of forest resources. Some researchers are in favor of state-led forest management (Terborgh 1999; Lovejoy 2006), while other scholars suggested that privatization of common resources is the only way to resolve the tragedy of the commons and conservation of natural resources (Smith 1981; Simmons et al. 1996). Many other scholars put forward in favor of local communities who are the actual guardians of the forests (Agrawal 2001; Andersson et al. 2014). Local communities are the managers of natural resource management (Ostrorm 1990). The concept of community self-governance plays a key role in natural resource management recognized by many researchers (Feeny et al. 1990; Ascher 1995; Agrawal and Ostrom 2001; Brown et al. 2003; Agrawal and Chhatre 2006; Bowler et al. 2010). They found that community-based governance regimes could address the Tragedy of the Commons compared to state-based governance regimes. There have been many successful cases all over the world where community self-governance systems have effectively managed common resources (Agrawal and Chhatre 2006; Pagdee et al. 2006; Ellis and Porter-Bolland 2008; Somanathan et al. 2009; Tan et al. 2009; Baland et al. 2010; Bowler et al. 2010). Local forest management with communities proved effective and adaptive to forest management practice for future forest governance (Ostrom and Nagendra 2006; Pandey 1993, 2003). In addition, local management of forests through community involvement is likely to be more cost-effective than state management (Baland et al. 2010). The involvement of local communities in forest management reduces conflicts with local authorities (Basu 2021) and contributes to better governance (Newig and Fritsch 2009). Enforcement and monitoring are the two pillars of good forest governance. In some studies, local enforcement and monitoring are associated with the likelihood of forest regeneration (Hockings and Phillips 1999; Igoe 2004). It is argued that national parks are the most effective means of protecting forests’ biodiversity (Bruner et al. 2001). Others argue that local forest users are important in protected activities, including monitoring and enforcement (Hayes 2006; Stevens 1997; Wells and Branda 1992). Appropriate monitoring and enforcement by community-based institutions reduce deforestation and favor forest regeneration (Nagendra and Gokhale 2008). The appropriate level of local enforcement and collective action reduces the probability of forest degradation and increases the scope of forest regeneration (Chhatre and Agrawal 2008). The involvement of local communities is necessary for the protection and development of forestlands for the sustainability of forest management (Basu 2021; FAO 2015; Chirenje et al. 2013). Without the involvement of the community in effective

4

1 Introduction

implementation, the basic objectives of the conservation of forest resources would be unsuccessful (Basu 2021). There is a large body of research in India have focused on forest-dependent communities who are maintaining and managing forests within their boundaries (Gadgil and Berkes 1991; Gadgil and Guha 1992; Gadgil and Subhash Chandran 1992). Many other studies across the world have shown that the users of a commonpool resource enforce basic rules more efficiently than the rules imposed on them externally (Tang 1992; Wade 1994; Baland and Platteau 1996). Governance matters for inclusive development. Under inclusive development, women, disabled persons, the other marginalized classes of people are included in the decision-making process. Good forest governance is necessary condition for attaining environmentally-sustainable or inclusive development and developing capacity-building measures like income generation, job/employment creation, education, health facilities, housing, infrastructure, communication, sanitation, etc. of the forest-dependent communities for sustainable livelihood generation. There is no such broadly accepted definition of forest governance. Forest governance is defined how various stakeholders like communities, government, civil society, NGOs, smallholders, and other institutions enforce their decisions for the management, use, and conservation of forest resources (FAO 2006). Governance may be good or poor. Good governance is characterized by the respect for rule of law, transparency, control of corruption, high participation and equity, and high levels of accountability (Kaufmann et al. 2008; Mayers et al. 2002; UNDP 2006; World Bank 2006). Sustainable forest management and policies are directly linked with forest governance shown in Fig. 1.1. The success or failure of a particular forest governance regime is to be assessed by either forest conditions like forest cover and forest biodiversity (Agrawal and Chhatre 2006; Pagdee et al. 2006) or by social performances like social equity, the livelihood of local dwellers (Ostrom 2007), or by forest use rights of the local community and local participants in forest management (Sikor and Tan 2011; Arts 2014). Research Questions: The study intends to address the major issues about the context of forest governance. First, the question is how does improved governance contribute to sustainable forest management? Second, what governance arrangements best empower local communities to ensure livelihood and to create employment opportunities in their area? Third, how the forest rights over forest products of marginal and vulnerable groups of people protected? Fourth, what evidence is there of transferable good practices in balancing biodiversity and livelihood priorities? Fifth, what are the barriers to generating social and economic development with metrics of environmental health and sustainability? And Sixth, how forests are managed, and how are decisions on forest use taken? What is done to enforce forest laws and policies for sustainable forest development? Research gap: Forests provide ecological services and livelihood security for the disadvantaged section of the communities like tribals and others who are living in and around forest areas in India. People are at the center of development. The present

1 Introduction

5

Fig. 1.1 Roots and fruits of forest governance. Source Bodegon et al. (2012)

study examines what governance arrangement is the best to empower local communities to ensure sustainable livelihood and to create employment opportunities. The previous studies did not consider how good governance helped develop capacitybuilding measures for forest-dependent communities for their sustainable livelihood generation. The study addresses local institutions, monitoring, and enforcement by local communities for governing the forest better. In addition, the present study also tries to identify the practices for balancing between conservation of forests and livelihood priorities. Having the above backdrop delineated, the objectives of the study are as follows: The first is to examine forest policies and acts before and after the independence of India. In addition, the present study attempts to examine the impact of governance on forest cover in India at the macro level.

6

1 Introduction

Second is to construct a forest dependence index, institutional index, monitoring index, and enforcement index for the households who are living in the forest fringes villages in different forest divisions in West Bengal. Third, is to quantify the level of forest participation of the households as well as different disadvantaged classes of households like women, landless, small and marginal farmers, SC/ST, educated and illiterate in different stages of participation, and to attempt to investigate if there are any significant differences in participation between male and female-headed households, between SC/ST and non-SC/ST households, between illiterate and formally educated households, between elders (20–40 years) and aged households (beyond 40 years) and between landless and small/marginal farmers. Fourth is to measure forest governance in terms of forest governance indices based on different indicators at the household level and at the village level in different forest divisions of West Bengal and to identify the villages following good, medium, and poor governance in terms of governance index. In addition, the present study estimates the factors responsible for forest governance at the household level across four forest divisions in West Bengal. The fifth is to examine the impact of forest governance on forest dependence of the households across four forest divisions of West Bengal. Lastly, is to examine the functioning and performance of forest protection committees in different forest divisions of West Bengal in protecting forest resources. In addition, the study also attempts to examine the role of Panchayat, Self-help groups, and NGOs in the context of forest management in the study area. The following hypotheses are made in the study. First, the North Bengal forest division performs good forest governance compared to the South Bengal forest division. Second, the participation index is higher in the North Bengal forest division compared to the South Bengal forest division. Third, monitoring, institutional, and enforcement indices are very effective in the North Bengal forest division than South Bengal forest division. Fourth, good governance has a positive impact on the rise in forest cover in India at the macro level.

References Aggarwal A, Paul V, Das S (2009) Forest resources: livelihoods, degradation, and climate change in Green India 2047 renewed: looking back to change track. The Energy and Resources Institute, New Delhi, pp 91–108 Agrawal A (2001) Common property institutions and sustainable governance of resources, Montreal. World Dev 29:1649–1672 Agrawal A, Chhatre A (2006) Explaining success on the commons: community forest governance in the Indian Himalayas. World Dev 34:149–166 Agrawal A, Ostrom E (2001) Collective action, property rights, and decentralization in resource use in India and Nepal. Politics Soc 29:485–514 Andersson K, Benavides JP, León R (2014) Institutional diversity and local forest governance. Environ Sci Policy 36:61–72

References

7

Arts B (2014) Assessing forest governance from a “Triple G” perspective: government, governance, governmentality. Forest Policy Econ 49:17–22 Ascher W (1995) Communities and sustainable forestry in developing countries. ICS Press San Francisco Baland J, Platteau J (1996) Halting degradation of natural resources: is there a role for rural communities? Clarendon Press, Oxford, England Baland JM, Bardhan P, Das S, Mookherjee D (2010) Forests to the people: decentralization and forest degradation in the Indian Himalayas. World Dev 38:1642–1656 Balmford A, Bruner A, Cooper P, Costanza R, Farber S, Green RE, Jenkins M, Jefferiss P, Jessamy V, Madden J (2001) Economic reasons for conserving wild nature. Science 297:950–953 Basu JP (2017) Climate change adaptation and forest dependent communities an analytical perspective of different agro-climatic regions of West Bengal, India. Springer. https://doi.org/10.1007/ 978-3-319-52325-5 Basu JP (2021) Forest participation of local communities: a study of a tribal dominated region in India. J Soc Econ Dev 23(1):180–201 Bodegon PM, Van OmmenKloeke AEE, Douma JC, Ordonez JC, Reick PB (2012) Global quantification of contrasting leaf life span strategies for deciduous and evergreen species in response to environmental conditions. Global Ecol Biogeog 21:224–235. https://doi.org/10.1111/j.14668238.2011.00667 Bowler D, Buyung-Ali L, Healey JR, Jones JP, Knight T, Pullin AS (2010) The evidence base for community forest management as a mechanism for supplying global environmental benefits and improving local welfare. CEE Rev 08–011 Brown D, Vabi MB, Nkwinkwa R(2003) Governance reform in the forest sector: a role for community forestry. Paper presented at XII World Forestry Congress, Quebec City, Canada Chhatre A, Agrawal A (2008) Forest commons and local enforcement. Proc Natl Acad Sci 105(36):13286–13291 Chirenje L, Richard A, Emmanuel G, Musamba B (2013) Local communities participation in decision-making processes through planning and budgeting in African countries. Chinese J Popul Resour Environ 11:10–16 Ellis EA, Porter-Bolland L (2008) Is community-based forest management more effective than protected areas?: A comparison of land use/land cover change in two neighboring study areas of the Central Yucatan Peninsula, Mexico. Forest Ecol Manage 256:1971–1983 FAO (2006) Food and agriculture organization of the United Nations, Rome FAO (2010) Global forest resource assessment. Key Findings. FAO, Rome, p 12 FAO (2015) Community forestry statistics in Cambodia (2015). Phnom Penh (Cambodia): Forestry Administration (FA) Feeny D, Berkes F, McCay BJ, Acheson JM (1990) The tragedy of the commons: twenty-two years later. Human Ecol 18:1–19 Gadgil M, Berkes F (1991) Traditional resource management systems. Resour Manage Optim 8(3–4):127–141 Gadgil M, Guha R (1992) This fissured land: an ecological history of India. Oxford University Press, Delhi Gadgil M, Subash Chandran MD (1992) Sacred groves. India Int Centre Q 19(1–2):183–187 Ghate R (2004) Uncommons in the commons: community initiated forest resource management. Concept Publishing Company, New Delhi Ghosh M, Sinha B (2016) Impact of forest policies on timber production in India: a review. Nat Resour Forum 40. https://doi.org/10.1111/1477-8947.12094 Gibson C (2001). Forest resources: institutions for local governance in Guatemala. In: Burger J, Ostrom E, Norgaard RB, Policansky D, Goldstein B (eds) Protecting the commons: a framework for resource management in the Americas. Island Press, Washington, DC, pp 71–89 Hayes TH (2006) Parks, people, and forest protection: an institutional assessment of the effectiveness of protected areas. World Dev 34(12):2064–2075

8

1 Introduction

Hockings M, Phillips A (1999) How well are we doing? Some thoughts on the effectiveness of protected areas. Parks 9(2):5–16 Igoe J (2004) Conservation and globalization: a study of national parks and indigenous communities from East Africa to South Dakota. Wadsworth, Riverside, CA Joshi AK, Pant P, Kumar P, Giriraj A, Joshi PK (2011) National forest policy in India: critique of targets and implementation. Small Scale Forest 10(1):83–96 Kaufmann D, Aart K, Massimo M (2008) Governance matters VII: aggregate and individual governance indicators, 1996–2007. World Bank Policy Research Working Paper No. 4654, 24 June 2008 Lovejoy TE (2006) Protected areas: a prism for a changing world. Trends Ecol Evol 21:329–333 Mayers J, Stephen B, Duncan M (2002) The pyramid: a diagnostic and planning tool for good forest governance. International Institute for Environment and Development McKean, Margaret A, Ostrom E (1995) Common property regimes in the forest: just a relic from the past? Unasylva 46(1):3–15 MoEF (2006) Report of the national forest commission. Ministry of Environment and Forests, Government of India, New Delhi, India Mohanty B, Sahu G (2012) An empirical study on elements of forest governance: a study of JFM implementation models in Odisha. Proc Soc Behav Sci 37:314–323. https://doi.org/10.1016/j. sbspro.2012.03.297 Mwangi E (2007) Subdividing the commons: Distributional conflict in the transition from collective to individual property rights in Kenya’s Maasailand. World Dev 35(5):815–834 Nagendra H, Gokhale Y (2008) Management regimes, property rights, and forest biodiversity in Nepal and India. Environ Manage 41(5):719–733 Netting RM (1976) What alpine peasants have in common: observations on communal tenure in a Swiss village. Human Ecol 4(2):135–146 Newig J, Fritsch O (2009) Environmental governance: participatory, multi-level and effective? Environ Policy Governance 19:197–214 Ostrom E (1990) Governing the commons: the evolution of institutions for collective action. Cambridge University Press, New York Ostrom E (2007) A diagnostic approach for going beyond panaceas. Proc Natl Acad Sci 104(39):15181–15187. https://doi.org/10.1073/pnas.0702288104 Ostrom E, Nagendra H (2006) Insights on linking forests, trees, and people from the air, on the ground, and in the laboratory. Proc Natl Acad Sci 103(51) Pagdee A, Kim Y-S, Daugherty PJ (2006) What makes community forest management successful: a meta-study from community forests throughout the world. Soc Nat Resour 19:33–52 Pandey DN (1993) Wildlife, national parks and people. Indian Forester 119(7):521–529 Pandey DN (2003) Cultural resources for conservation science. Conserv Biol 17(2):633–635 Repetto R, Gillis M (1988) Public policies and the misuse of forest resources. Cambridge University Press, Cambridge, UK, p 1988 Saha A, Guru B (2003) Poverty in remote rural areas in India: a review of evidence and issues. GIDR Working Ahmedabad Gujarat Inst Deve Res 139:69 Saxena R (2000) Joint forest management in Gujarat: policy and managerial issues. Working Paper 149, Institute of Rural Management, Anand Sikor T, Tan NQ (2011) Realizing forest rights in Vietnam: addressing issues in community forest management. RECOFTC-The Center for People and Forests, Bangkok, Thailand Simmons R, Smith F Jr, Georgia P (1996) The tragedy of the commons revisited: politics versus private property. The Center for Private Conservation, Washington, DC Smith RJ (1981) Resolving the tragedy of the commons by creating private property rights in wildlife. Cato J 1:439 Somanathan E, Prabhakar R, Mehta BS (2009) Decentralization for cost-effective conservation. Proc Natl Acad Sci 106:4143–4147 Stevens S (1997) New alliances for conservation. In: Stevens S (ed) Conservation through cultural survival. Island Press, Washington, DC, pp 33–62

References

9

Tan NQ, Thanh TN, Tuan HH (2009) Community forestry and poverty alleviation: a synthesis of project findings from field activities. Forest Governance Learning Group (FGLG), Vietnam, Hanoi Tang SY (1992) Institutions and collective action: self governance in irrigation systems. ICS Press, San Francisco Terborgh J (1999) Requiem for nature. Shearwater Books, Washington, DC, Island Tucker CM (2004) Aiming for sustainable community forest management: the experiences of two communities in Mexico and Honduras. In: Zarin D, Alavalapati J, Putz FE, Schmink MC (eds) Working forests in the tropics: conservation through sustainable management? Columbia University Press, New York, pp 178–199 UNDP (2006) Good governance practices for the protection of human rights, office of the United Nations High Commissioner for Human Rights, UN United Nations (2015) Transforming our world by 2030: a new agenda for global action Zero. Draft of the outcome document for the UN Summit to adopt the Post-2015 Development Agenda. New York: United Nations Wade R (1994) Village republics: economic conditions for collective action in South India. ICS Press, San Francisco, CA Wells M, Brandon (1992) People and parks: linking protected area management with local communities. World Bank, Washington, DC World Bank (2006). Strengthening forest law enforcement and governance: addressing a systemic constraint to sustainable development. Environment and agriculture and rural development departments. Sustainable Development Network. Report No. 36638-GLB, Washington DC

Chapter 2

Review of Literature

Abstract The present chapter reviews the literature on institutions, enforcement, monitoring, poverty and livelihood, participation, inclusive development, and forest governance.

2.1 Literature on Institutions and Enforcement According to North (1990), Institutions mean the “rules of the game in a society” (North 1990). Institutions may be “formal and informal”. Formal institutions consist of “constitutions, laws, and contracts” while informal institutions are based on “customs, traditions, and codes of human behavior” (Basu 2021). Formal and informal rules taken together constitute the institutional framework where all individual action takes place (North 1990). According to Demsetz (1967), the main function of property rights is to internalize the externalities. In this circumstance, individual rights happen in the case of open access resources due to population growth or changes in technology. Since Hardin (1968) there have been a lot of debates on how to efficiently manage the common forest resources. Some researchers are in favor of state-led forest management while other scholars suggested that privatization of common resources is the only way to resolve the tragedy of the commons and conserve natural resources. Many other scholars argued that local communities are seen to be better guardians of the forest. Ostrom (1990) emphasized the role of institutions in solving the problems of common forest resources. According to her, the tragedy of the commons can be resolved through the formation of voluntary organizations. Good forest condition depends on the appropriate enforcement of rules and regulations (Yadav et al. 2003). Monitoring and local enforcement are the necessary conditions for efficient resource management (Gibson et al. 2005). This hypothesis has been tested using data on 178 user groups and other important factors like social capital, formal organization, and dependence on forest products (Gibson et al. 2005). The study concluded that although monitoring and enforcement are necessary but not sufficient conditions for the successful management of local forest resources. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. P. Basu, Governance and Institution in the Indian Forest Sector, https://doi.org/10.1007/978-3-031-34746-7_2

11

12

2 Review of Literature

Nagendra and Gokhale (2008) compared the management of forest resources involving communities in Nepal and India. In Nepal, they selected three categories of state-initiated programs and in India, they also selected the state-initiated Joint Forest Planning and Management program. They concluded that state-initiated approaches to involving communities are helpful for biodiversity conservation and livelihood generation of the people. Umemiya et al. (2010) investigated the relationship between governance quality and deforestation based on national-level data. Results showed that good quality of governance is necessary to condition for the decrease in deforestation and long-term carbon storage globally. Chhatre and Agrawal (2008) examined the relationship between local enforcement and forests used. The study covers 152 cases from 9 countries. The selected countries are the United States (4 cases), Mexico (6 cases), Guatemala (5 cases), Bolivia (10 cases), Kenya (11 cases), Uganda (25 cases), Nepal (51 cases), India (34 cases) and Tanzania (6 cases). The result of the study showed that the probability of degradation of forest declines and the probability of regeneration of forests increases with increases in the level of local enforcement. Many other studies across the world have shown that the users of a commonpool resource enforce basic rules more efficiently than the rules imposed on them externally (Tang 1992; Wade 1994; Baland and Platteau 1996).

2.2 Literature on Forest Livelihood A group of researchers has shown that forest resources are important in the economy of the forest people in different parts of the world, especially in developing countries (Shackleton et al. 2007; Reddy and Chakravarty 1999; Adam et al. 2014). Some studies have pointed out that forest resources are important from an environmental point of view (Arnold and Bird 1999; Cavendish 1999; Adhikari 2005). The other group of researchers studied the contribution of Non-timber forest products (NTFPs) helped household income in African and Asian countries including India (Cavendish 2000; Reddy and Chakravarty 1999; Fisher 2004; Mamo et al. 2007; Bwalya 2013; Cordova et al. 2013; Babulo et al. 2008). Some other studies showed that forests act as safety nets for the rural poor during the period of drought (Shackleton and Shackleton 2004; Angelsen and Wunder 2003; Paumgarten 2005). The other group of researchers has explored the nexus between forest dependence and forest-based poverty alleviation strategies (Sunderlin et al. 2004; Vedeld et al. 2007; Nielsen et al. 2012). There are some studies measuring forest dependency in terms of forest income and livelihood generation. The first approach is income from the forest as the measure of forest dependence (Cavendish 2000; Campbell and Luckert 2002; Wunder et al.

2.3 Literature on Forest Participation

13

2014; Rustin 2008). The second approach was a livelihood-based approach of forest dependence (Shackleton et al. 2011; Timko et al. 2010). However, there are several studies have shown that community forestry is not always in favor of the equitable distribution of forest products (Neupane 2003; Adhikari et al. 2004; Adhikari 2005).

2.3 Literature on Forest Participation In the early 1960s, participation was meant for political participation and in favor of voting and the power-hankering process (Islam et al. 2013). In the 1970s, participation was an important aspect of the development approaches (Samad 2003; Chowdhury 2004). In the early 1980s, the community participation approach focused on pro-poor and benefits sharing for the poor (Oakley 1991; Cornwall 2002). Eilola et al. (2015) argued that participation means empowering local communities which calls for inclusive development. FAO (2012) defined participation as a process through which stakeholders influence policy formulation, and investment choices, share control over development initiatives and management decisions, and establish the necessary sense of ownership among local communities. On the other hand, participation means the involvement of conventional as well as conventional stakeholders in the planning, implementing, and evaluating activities (Basu 2021; Reed et al. 2009). Community participation plays a pivotal role in forest protection, management, and livelihood generation (Ranjit 2014). According to Chowdhury (2004), participation includes decision-making, identifying problems and planning the allocation of resources, implementing activities, evaluating, and monitoring activities. Maier et al. (2014) compared top-down decision-making with community participation (bottom-up) modes of decision-making. They concluded that community participation is more cost-efficient and cost-effective from viewpoint of sustainability and environmental conditions compared to the top-down approach. There are some other works on community participation in developmental activities including the planning stage, implementation, and monitoring stages (Obadire et al. 2014; Chowdhury 2004; Reed et al. 2009; Sharma et al. 2011; Bagdi and Kurothe 2014; Islam et al. 2013). It is important to mention that the participation of local communities in forest management is for ensuring decision-making (Basu 2021; Jumbe and Angelson 2007; Agrawal and Gupta 2005; Enuameh-Agbolosoo et al. 2015). The determinants of participation in forest management are demographic, socioeconomic, biophysical, and other institutional variables (Degeti 2003; Salam et al. 2005; Dolisca et al. 2006; Getacher and Tafere 2013; Agrawal and Chhatre 2006).

14

2 Review of Literature

2.4 Literature on Governance like the Rule of Laws, Transparency, Accountability, Inclusive and Equitable Mohanty and Saha (2012) studied forest governance in Orissa using the forest governance index. This study is based on a field survey that was conducted in four forest divisions of Odisha. In the study, various stakeholders like VSS members, PRI members, forest personnel, and NGOs were involved. The study is based on data from 36 VSS, 4 DFOs, 8 Rangers, 16 foresters, 12 PRI members, and 288 households through interviews and group discussions method. The results of the study revealed that there has been unanimous co-operation held among the forest department officials, villagers, NGOs as well as different village-level organizations. The role of local forestry institutions has affected the forest ecosystem services and social equity in Meghalaya, Northeast India (Oberlack et al. 2015). This study is based on Ostrom’s Social–Ecological Systems (SES) framework. The study is based on a field survey in 2013 and 26 households were interviewed through a structured questionnaire. Different issues like livelihood opportunities and wealth distribution, privatization, and commercialization, de-privatization, and gender equity were examined. The result of the study revealed that Mawlyngbna’s forests provide important sources of livelihood benefits for the villagers and reached ecological outcomes and maintained the social equity pattern of forest governance. Good forest governance occurs when there exists a rule of law, persistent minimum level of corruption, accountability, transparency, and voluntary participation (The World Bank 2009). According to UNDP (2006), good governance emphasized that priority should be given to the poorest and most vulnerable people’s interest for the allocation of resources. On the other hand, poor governance results in social exclusions, unaccountable bureaucracies, inequitable resource allocation, and widespread corruption (Mayers et al. 2002; Tacconi 2007; World Bank 2006). Contreras-Hermosilla (2002) studied that illegal logging activities are the major constraint to the global economy. The study addressed enhancing the enforcement of forest law, and implementation of detection. Martínez et al. (2009) focused on the poor management of forest resources across the world resulting in deforestation and degradation of the forest. The study highlights forest ecosystems, biodiversity of the forest, watershed protection, and soil management for the reduction of degradation and deforestation. Callister (1999) analyzed the deforestation of the world’s forests as the result of the development of the economy. Proper rule of law for forest management and controlling and improvements in air pollution levels have helped forests to recover and grow in developed countries. EIA (2001) examined illegal logging in the forest sector. Many countries, for example, Bolivia, Indonesia, and Cameroon have launched a program for law of enforcement as an objective for strengthening rule of law.

2.4 Literature on Governance like the Rule of Laws, Transparency …

15

Baird (2001) focused on forest crime as a limitation on development, reiterated the moral degradation and deterioration of the social fabric associated, and advocated international cooperation and a multi-year program with proper goals and mechanisms to track progress on reducing forest crime. McGrath and Grandalski (2000) addressed different types of corruption, crime, and penalty in the forest sector which leads to environmental problems. Amha bin Buang (2001) advocated that the law should be simple, enforceable, and consistent for forest resource management in East Asia. Governance in the forest sector in developing countries depends on transparency and accountability in the management of such sectors (World Bank 2009). Glastra (1999) studied illegal logging and timber trade threatened the development of forest management. The study has given concentration on the transparency level for proper forest management. Caddy et al. (2007) pointed out that transparency has been exercised through government policymakers, business activities, and international NGO operations. Darby (2010) studied transparency and accountability in emerging economies in natural resource management. According to him, local government has to be transparent, accountable, and respectful of the laws of natural resources. Hsu (2016) formulated Environmental Performance Index (EPI) that studied the sustainability state across the world using 32 performance indicators across 11 issues categories on the environmental ecosystem. Bovens (2007) addresses the defects of accountability of resource management in the EU. Frank and Prince (2017) focussed on the initiative of democratic institutions which help to strengthen the community in the forest governance in Ghana. In Ghana, the Social Responsibility Agreement (SRA) is a scheme of forest management that helps people who suffer from the failure of accountability. Castrén and Madhavi (2011) analyzed accountability in the governance of forest resources for quality control and quality assurance of the data. The study highlighted tenure rights, reduction of corruption, and ensuring equitable and sustainable use of forest resources. Hale (2008) highlighted the implications of transparency-based accountability for global governance. Brinkerhoff (2005) focused on the donor governance reconstruction agendas and governance of security, legitimacy, and effectiveness. Nuesiri (2016) examined five dimensions of accountability. The study concluded that government has is to create an environment of accountable management of natural resources for the economy. Rydin and Pennington (2000) emphasized the roots of participatory activities in the incentive structures. They focused on social capital, environmental planning, and collective action. The present study is different from other studies in the literature on the following grounds; First, the present study constructs a forest governance index formulating different indicators at the household level or the micro level. Second, the study examines the

16

2 Review of Literature

quantitative analysis of the participation of households who are involved in the planning, implementation, and monitoring stages. Third, the study is especially important for quantifying the involvement of disadvantaged sections of society like women, landless, marginal and small farmers, illiterate, youth, and the elderly in the forest planning, implementation, and monitoring stages for ensuring inclusive development. Fourth, the study is significant for addressing local livelihood issues of forestdependent households and on the other hand, it examines the role of institutions and enforcement in determining forest dependency. Fifth, this study measures monitoring and enforcement indices based on community involvement at the micro level in West Bengal. Summing Up From the above literature, it is observed that very few pieces of literature addressed quantitative measurement of forest dependence and forest governance at the local or household level. Most of the studies are qualitative analyses executed either at the national or global level. In addition, there is a lack study available in the context of forest governance in West Bengal, which is the first pioneer in initiating the concept of joint forest management in India.

References Adam YO, Abdalla M, Tayeb EL (2014) Forest dependency and its effect on conservation in Sudan: a case of Sarf-Saaid reserved forest in Gadarif State. Agric Forest 60(3):107–121 Adhikari B (2005) Poverty, property rights, and collective action: understanding the distributive aspects of common property resource management. Environ Dev Econ 10(1):7–31 Adhikari B, Falco SD, Lovett JC (2004) Household characteristics and forest dependency: evidence from common property forest management in Nepal. Ecol Econ 48:245–257 Agrawal A, Chhatre A (2006) Explaining success on the commons: community forest governance in the Indian Himalayas. World Dev 34:149–166 Agrawal A, Gupta K (2005) Decentralization and participation: the governance of common pool resources in Nepal’s Terai. World Dev 1101–1114 Amha bin Buang (2001) Summary report of the forest law enforcement and governance east asia ministerial conference. Sustain Dev 60:1–15 Angelsen A, Wunder S (2003) Exploring the forest-poverty link: key concepts, issues and research implications. Economist 40:68 Arnold JEM, Bird P (1999) Forest and the poverty-environment nexus. Paper presented at the UNDP-EC Expert workshop on poverty and the environment, Brussels, Belgium Babulo B, Muys B, Nega F, Tollens E, Nyssen J, Deckers J, Mathijs E (2008) Household livelihood strategies and forest dependence in the highlands of Tigray, Northern Ethiopia. Agric Syst 98(2):147–155 Bagdi GL, Kurothe R (2014) People’s participation in watershed management programmes: evaluation study of Vidarbha region of Maharashtra in India. Int Soil Water Conserv Resour 2:57–66 Baird M (2001) Forest crimes as a constraint to development in East Asia. In: Speech delivered at the Forest Law Enforcement and Governance: East Asia Ministerial Conference, Bali, Indonesia, 11–13 September 2001

References

17

Baland J, Platteau J (1996) Halting degradation of natural resources: is there a role for rural communities? Clarendon Press, Oxford, England Barham BL, Coomes OT, Takasaki Y (1999) Rain forest livelihoods: income generation, household wealth and forest use. Unasylva 50(198):34–42 Basu JP (2021) Forest participation of local communities: a study of a tribal dominated region in India. J Soc Econ Dev 23(1):180–201 Bovens M (2007) Addresses the defects of accountability of resource management in EU Brinkerhoff DW (2005) Rebuilding governance in failed states and post-conflict societies: core concepts and cross-cutting themes. Public Adm Dev 25(1):3–14 Bwalya SM (2013) Household dependence on forest income in rural Zambia. Zambia Soc J 2(1):67– 86 Caddy J, Peixoto T, McNeil M (2007) Beyond public scrutiny: stocktaking of social accountability in OECD countries. World Bank, Washington, DC Callister DJ (1999) Corrupt and illegal activities in the forestry sector: current understandings, and implications for world bank forest policy. Discussion Draft, May 1999. http://wbln0018.worldb ank.org/essd/forestpole.nsf/ Campbell B, Luckert M (2002) Uncovering the hidden harvest: valuation methods for woodland and forest resources. VAEarth Scan Publications, Sterling, London Castrén T, Madhavi P (2011) Forest governance 2.0: a primer on ICTs and governance. http://documents.worldbank.org/curated/en/574391468346443493/pdf/637550WP0For es00Box0361527B0PUBLIC0 Cavendish W (1999) Poverty, inequality and environmental resources: quantitative analysis of rural households. Working Paper Series 99-9, Centre for the Study of African Economies, Oxford Cavendish W (2000) Empirical regularities in the poverty-environment relationship of rural households: evidence from Zimbabwe. World Dev 28(11):1979–2003 Chambers R, Conway GR (1991) Sustainable rural livelihoods: practical concepts for the 21st century. Institute of Development Studies DP Chhatre A, Agrawal A (2008) Forest commons and local enforcement. Proc Natl Acad Sci 105(36):13286–13291 Chowdhury SA (2004) Participation in forestry: a study of people’s participation on the social forestry policy in Bangladesh: myth or reality [M Phil dissertation]. Department of Administration and Organization Theory, University of Bergen, Bergen (Norway) Contreras-Hermosilla A (2002) Law compliance in the forestry sector: an overview Córdova JPP, Wunder S, Smith-Hall C, Börner J (2013) Rural income and forest reliance in highland Guatemala. Environ Manage 51(5):1034–1043 Cornwall A (2002) Introduction: new democratic space? The politics and dynamics of institutionalized participation. IDS Bull 3(2):2 Darby S (2010) Natural resource governance: new frontiers in transparency and accountability. Transparency and Accountability Initiative, London Degeti T (2003) Factors affecting people’s participation in participatory forest management: the case of IFMP Adaba-Dodola in Bale Zone of Oromia Region [MA dissertation]. Addis Ababa University, Ethiopia Demsetz H (1967) Toward a theory of property rights. Am Econ Rev 57:347–359 Dolisca F, Carter R, McDaniel M, Shannon A, Jolly M (2006) Factors influencing farmers’ participation in forestry management programs: a case study from Haiti. Forest Ecol Manage 236:324–331 EIA (2001) Environmental investigation agency and Telepak, Indonesia Eilola S, Fagerholm N, Maki S, Khamis M, Kayhko N (2015) Realization of participation and spatiality in participatory forest management a policy–practice analysis from Zanzibar, Tanzania. J Environ Plann Manage 58:1242–1269 Enuameh-Agbolosoo AD, Enuameh ASK, Kotoka JJ, Ziggah-Aborta F, Dabi M (2015) Community participation in forest resource management as a tool in reducing the effects of climate change and enhancing sustainable development. Int J Human Soc Stud 3(8):285–293

18

2 Review of Literature

FAO (2012) Manual for integrated field data collection. national forest monitoring and assessment, 1st edn. Food and Agricultural Organization of the United Nations, Rome Fisher M (2004) Household welfare and forest dependence in Southern Malawi. Environ Dev Econ 9(2):135–154 Frank KA, Prince O-WA (2017) Representation without accountability in forestry: experiences from the Social Responsibility Agreement in Ghana. For Policy Econ 80:34–43 (July) Getacher T, Tafere A (2013) Explaining the determinants of community based forest management: evidence from Alamata. Ethiopia Int J Common Dev 1:63–70 Gibson C (2001) Forest resources: institutions for local governance in Guatemala. In: Burger J, Ostrom E, Norgaard RB, Policansky D, Goldstein B (eds) Protecting the commons: a framework for resource management in the Americas. Island Press, Washington, D.C., pp 71–89 Gibson CC, Williams JT, Ostrom E (2005) Local enforcement and better forests. World Dev 33(2):273–284 Glastra R (1999) Cut and run: illegal logging and timber trade in the tropics. International Development Research Center, IDRC, Ottawa, Canada Hale TN (2008) Transparency, accountability, and global governance. Global governance: a review of multilateralism and international organizations 14(1):73–94 Hardin G (1968) The tragedy of commons. Science 162 Hsu A (2016) Environmental performance index. Yale University, New Haven. http://epi.yale.edu/ sites/default/files/2016EPI_Full_Report_opt.pdf Islam KK, Rahman M, Fujiwara T, Sato N (2013) People’s participation in forest conservation and livelihoods improvement: experience from a forestry project in Bangladesh. Int J Biodivers Sci Ecosyst Serv Manage 9(1):30–43 Jumbe CB, Angelsen A (2007) Forest dependence and participation in CPR management: empirical evidence from forest co-management in Malawi. Ecol Econ 62:661–667 Maier C, Lindner T, Winkel G (2014) Stakeholders perceptions of participation in forest policy: a case study from Baden-Wurttemberg. Land Use Policy 39:166–176 Mamo G, Sjaastad E, Vedeld P (2007) Economic dependence on forest resources: a case from Dendi District, Ethiopia. Forest Policy Econ 9(8):916–927 Martínez ML, Pérez-Maqueo O, Vázquez G, Castillo-Campos G, García-Franco J, Mehltreter K, Landgrave R (2009) Effects of land use change on biodiversity and ecosystem services in tropical montane cloud of Mexico forests. Forest Ecology Manage 258:1856–1863 Mayers J, Stephen B, Duncan M (2002) The pyramid: a diagnostic and planning tool for good forest governance. Int Inst Environ Dev McGrath, Grandalski (2000) Forest law enforcement-policies, strategies and technologies, Mekong basin symposium on forest law enforcement. Phnom Penh, Cambodia Mohanty B, Sahu G (2012) An empirical study on elements of forest governance: a study of JFM implementation models in Odisha. Proc Soc Behav Sci 37:314–323. https://doi.org/10.1016/j. sbspro.2012.03.297 Nagendra H, Gokhale Y (2008) Management regimes, property rights, and forest biodiversity in Nepal and India. Environ Manage 41(5):719–733 Neupane H (2003) Contested impact of community forestry on equity: some evidences from Nepal. J Forest Livelihood 2:55–61 Nielsen MR, Pouliot M, Kim BR (2012) Combing income and assts measures to include the transitory nature of poverty in assessments of forest dependence: evidence from democratic Republic of Congo. Ecol Econ 78:37–46 North D (1990) Institutions, institutional change and economic performance. Cambridge University Press, Cambridge Nuesiri EO (2016) Decentralised forest management: towards a utopian realism. Geogr J 182(1):97– 103 Oakley P (1991) Project with people: the practice of participation in rural development. International Labor Organization, Geneva (Switzerland), p 304

References

19

Obadire OS, Mudau MJ, Zuwarimwe J, Meusah RS (2014) Participation index analysis for CRDP at Muyexe in Limpopo province, South Africa. J Hum Ecol 48(2):321–328 Oberlack C, LaHaela Walter P, Schmerbeck J, Tiwari BK (2015) Institutions for sustainable forest governance: robustness, equity, and cross-level interactions in Mawlyngbna, Meghalaya, India. Int J Commons 9(2):670–697. https://doi.org/10.18352/ijc.538 Ostrom E (1990) Governing the commons: the evolution of institutions for collective action. Cambridge University Press, New York Paumgarten F (2005) The role of non-timber forest products as safety nets: a review of evidence with a focus on South Africa. GeoJournal 64(3):189–197 Ranjit Y (2014) Determinants of people’s participation in forest protection and management: a study in Kaski, Nepal. Econ J Dev Issues 17–18(1–2):175–186 Reddy SRC, Chakravarty SP (1999) Forest dependence and income distribution in a subsistence economy: evidence from India. World Dev 27(7):1141–1149. https://doi.org/10.1016/S0305750X(99)00057-1 Reed MS, Graves A, Dandy N, Posthumus H, Hubacek K, Morris J, Prell C, Quin CH, Stringer LC (2009) Who’s in and why? A typology of stakeholder analysis methods for natural resource management. J Environ Manage 90:1933–1949 Rustein SO (2008) The DHS wealth index: approaches for rural and urban areas. Demographic Health Res 60 Rydin Y, Pennington M (2000) Public participation and local environmental planning: the collective action problem and the potential of social capital. Local Environ 5(2):153–169 Salam MA, Noguchi T, Koike M (2005) Factors influencing the sustained participation of farmers in participatory forestry: a case study in central Sal forests in Bangladesh. J Environ Manage 74:43–51 Samad M (2003) Participation of the rural poor: in government and NGO programs. Dhaka (Bangladesh), Mowla Brother Shackleton C, Shackleton S (2004) The importance of non-timber forest products in rural livelihood security and as safety nets: a review of evidence from South Africa. S Afr J Sci 100(11):658–664 Shackleton CM, Shackleton SE, Buiten E, Bird N (2007) The importance of dry woodlands and forests in rural livelihoods and poverty alleviation in South Africa. Forest Policy Econ 9(5):558– 577 Shackleton S, Delang CO, Angelsen A (2011) From subsistence to safety nets and cash income: exploring the diverse values of non-timber forest products for livelihoods and poverty alleviation. Non-timber forest products in the global context. Springer Berlin Heidelberg, Berlin, pp 55–81 Sharma R, Singh P, Padaria RN (2011) Social processes and people’s participation in watershed development in India. J Comm Mobilization Sustain Dev 6:168–173 Sunderlin WD, Angelsen A, Wunder S (2004) Forests and poverty alleviation. State of the world’s Forests 10 Tacconi L (2007) Illegal logging: law enforcement, livelihoods and the timber trade. Earthscan Forestry Library. Tang SY(1992) Institutions and collective action: self governance in irrigation systems. ICS Press, San Francisco Thorbecke E (2006) The evolution of the development doctrine: 1950–2005. UNU-WIDER Discussion Paper 2006/155. World Institute for Development Economics Research of the United Nations University, Helsinki Timko JA, Waeber PO, Kozak RA (2010) The socio-economic contribution of non-timber forest products to rural livelihoods in Sub-Saharan Africa: knowledge gaps and new direction. Int for Rev 12(3):284–294 Umemiya C, Rametsteiner E, Kraxner F (2010) Quantifying the impacts of the quality of governance on deforestation. Environ Sci Policy 13(8):695–701 UNDP (2006) Good governance practices for the protection of human rights, office of the United Nations High Commissioner for Human Rights, UN

20

2 Review of Literature

Vedeld P, Angelsen A, Bojo J, Sjaastad E, Kobugabe Berg G (2007) Forest environmental incomes and the rural poor. Forest Policy Econ 9(7):869–879 Wade R (1994) Village republics: economic conditions for collective action in South India. ICS Press, San Francisco, CA World Bank (2006) Strengthening forest law enforcement and governance: addressing a systemic constraint to sustainable development. Environment and Agriculture and Rural Development Departments. Sustainable Development Network. Report No. 36638-GLB, Washington DC World Bank (2009) Roots for good forest outcomes: an analytical framework for governance reforms. The World Bank, Agriculture and Rural Development Department, Washington, DC Wunder S, Angelsen A, Belcher B (2014) Forests, livelihoods and conservation: broadening the empirical base. World Dev 64(S1):S1–S11 Yadav NP et al (2003) Forest management and utilization under community forestry. J Forest Livelihood 3(1)

Chapter 3

Data Base and Methodology

Abstract This chapter describes the database and methodology used for the analysis of data. The data are collected both from field survey and secondary sources. Different statistical and econometric techniques are applied to analyze data in this chapter.

Data: Both primary data and secondary data.

3.1 Study Area South Bengal and North Bengal Forest Divisions in West Bengal. The study was conducted in the state of West Bengal which is in the eastern part of India and the choice of the state was because it is one of the pioneering states in India, originated and implemented the first joint forest management or participatory management program (JFM) (Basu, 2021). The first concept of JFM originated in the “Arabari experiment” of the Midnapore district of South West Bengal in the 1970s. The forest cover of this state is 16,901.51 sq km which is 19.04% of the geographical area in 2019 and this State is one of the tribal-dominated states of India. As per the census 2011, the tribal population in West Bengal is 5,296,963, which is about 5.8% of the total population of the state (% of the tribal population in India is 8.6% as per the census 2011). We have considered three forest divisions of South Bengal say Purulia forest division, Bankura (south) forest division, and Rupnarayan forest division of Paschim Medinipore. Under these three forest divisions, we have selected the forest range office, beat office, Gram Panchayat, forest protection committees, villages, and households. The same procedure was followed for the Alipurduar forest division. The schematic diagram (Fig. 3.1) shows the administrative structure of the forests in West Bengal. The location map of the study areas in South Bengal and North Bengal forests is shown in Figs. 3.2, 3.3, and 3.4 respectively. The structure of sampling design of field survey of South Bengal and North Bengal Forest divisions is shown in Fig. 3.5.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. P. Basu, Governance and Institution in the Indian Forest Sector, https://doi.org/10.1007/978-3-031-34746-7_3

21

22

3 Data Base and Methodology Principal Chief Conservator of Forests, Research, Monitoring & Development, West Bengal

Addl. Principal Chief Conservator of Forests, Research & Monitoring

Chief Conservator of Forests, Research & Development

Conservator Forest, Research

Conservator Forest, Development

Division al Forest Officer

Division al Forest Officer

Division al Forest Officer

(DFO),

(DFO),

(DFO),

Silvicult ure (Hills)

Silvicult ure (North)

Silvicult ure (South)

Chief Conservator of Forests, Soil Conservation

Chief Conservator of Forests, Monitoring & Evaluation

Forestry Training Centre,

Forester School

Hijli DFO

Conservator of Forests,

Conservator of Forests, Soil Conservation, North

Monitoring

Divisional Forest Officer

Divisional Forest Officer

Divisional Forest Officer

Divisional Forest Officer

(DFO),

(DFO),

(DFO),

(DFO),

Monitoring (South)

Monitoring (North)

Kurseong Soil

Jalpaiguri Soil

Range Office Beat Office

Fig. 3.1 Schematic diagram of administrative structure of forest in West Bengal

3.1.1 Description of the Purulia Forest Division in the Purulia District Geographically, the district of Purulia in West Bengal lies between the two states, Jharkhand on the North and Orissa on the South. This district is a rain-fed district and its rainfall was 1189.2 mm in the year 2015–16. The major sources of livelihood are agriculture and forestry (Basu, 2021). The district had a forest cover of 20.95% of the geographical area in 2000 and it gradually reduced to 14% in 2018. The district is poverty-ridden in West Bengal with a poverty rate of 32.7%. The Purulia forest division has eight range offices including Bagmundi (Basu, 2021).

3.1 Study Area

Fig. 3.2 Location of range and Beat offices in South West Bengal

Fig. 3.3 Location of villages under Beat offices in South West Bengal

23

24

3 Data Base and Methodology

Fig. 3.4 Location of different villages under Beat offices in Alipurduar forest division in North Bengal

3.1.2 Sampling Technique for Data Collection in Purulia Forest Division The present study is based on the primary data collected from the selected Bagmundi forest range under the Purulia forest division in the district of Purulia, West Bengal. A multistage sampling technique has been used because it addresses the selection of small sample households and time and cost-effectiveness. Primary data have been collected in January 2020. A questionnaire method is followed to collect data from the head of households. In this division, nine villages have been selected purposively under the Bagmundi forest range. We have taken three Beat offices purposively like Baghmundi, Kalimati, and Burda, and three Gram Panchayat like Charida, Perorgoria, and Icchakota. In addition, we have selected nine Forest Protection Committees sayIchakota, Rabidi, Barnajora-Dulkibera, Nischintapur, Tarpenia, Charida, Bagti, Kalimati, and Perorgoria, etc. Besides, we have selected nine villages in the Bagmundi range office. They are Charida, Rabidi, Nischintapur, BarnajoraDulkibera-Badhghutu, Tarpenia, Kalimati/Lawadi, Bagti, Perorgoria, and Ichakota. After the selection of villages, 20% of households from each village are selected randomly. Thus, the total number of sample households consists of 252. Table 3.1 shows sample households corresponding to different villages of different beat offices in the Purulia forest division.

3.1 Study Area

25

Sampling Design of Field survey

Forest Division South Bengal Forest Division

Purulia FD

Bankura FD

Range

Purposive sampling

Beat

Gram Panchayat

Forest Protection Committee

Village

Random sampling

Households

Fig. 3.5 Schematic diagram of the sampling design

Paschim Medinipur FD

26 Fig. 3.5 (continued)

3 Data Base and Methodology

Forest Division North Bengal Forest Division

Alipurduar FD

Range

Beat

Gram Panchayat

Forest Protection Committee

Village

Households

3.1.3 Description of Bankura (South) Forest Division in the Bankura District of South Bengal The present study was also conducted in the district of Bankura, which is one of the Western districts of West Bengal. Our study area is the Bankura (south) forest division in the district of Bankura of West Bengal. Bankura (south) forest division consists of twelve (12) range offices such as Bankura, Indpur, Fulkushma, Jhilimili, Ranibundh, Pirrorgari, Khatra, Hirabandh, Kamalpur, Simlapal, Sarenga, and Motgoda.

Bagmundi

Purulia FD

Source Field survey

Name of range

Name of forest division

Charida

Perorgoria

Icchakota

Kalimati

Burda

Gram Panchayat

Bagmundi

Name of beat

Perorgoria Total

Ichakota

Perorgoria

Kalimati

Tarpenia

Tarpenia Bagti

Barnajora-Dulkibera-Badhghutu

Nischintapur Kalimati/Lawadi

Nischintapur

Barnajora-Dulkibera

Bagti

Rabidi

Charida

Charida

Rabidi

Name of the sample villages

Ichakota

FPC

1248

85

156

222

220

110

75

130

100

150

Total number of households

252

18

32

44

43

23

13

26

22

31

No. of sample household (20% of the hhs)

Table 3.1 Details of sample households, sample beat offices, sample Gram Panchayat, FPCs in Bagmundi range in Purulia forest division, South Bengal

3.1 Study Area 27

28

3 Data Base and Methodology

3.1.4 Sampling Technique for Data Collection in Bankura (South) Forest Division The present study is based on the primary data collected from the Ranibundh forest range office in the Bankura (south) forest division in the district of Bankura, West Bengal. We have chosen the Ranibundh Block of this District of Bankura (south) because this block experienced the highest tribal population. A multistage sampling technique has been used to collect data and data have been collected in the month February 2020. A questionnaire method is followed to collect data from the head of households. Ten JFM villages under the Ranibundh forest range are selected purposively. After the selection of villages, 20% of households from each village have been selected randomly. Thus, the total number of sample households consists of 228. Table 3.2 shows sample households corresponding to different villages of different beat offices in the Ranibundh range in the Bankura district.

3.1.5 Description of Rupnarayan Forest Division of Paschim Medinipur District, South Bengal The study area is the Rupnarayan forest division of Paschim Medinipore district which is the place from where the concept of Joint Forest Management in Arabari during the 1970s in India had been initiated. We have chosen the Amlagora range office in Paschim Medinipore forest division purposively. The Rupnarayan forest division comprises five (05) range offices such as Amlagora, Garbeta, Goaltor, Hoomgarh, and Mahalisai. Our study area is the Amlagora range office. Paschim Medinipur district belongs to the drought-prone district of West Bengal and high poverty-ridden and low literacy rate (78%). Agriculture, wage labor, and forestry are the major sources of livelihood for the people in this district. In this district, the percentage of SC and ST population to total population is 33.96 (Census 2011). Geographically, the district of Paschim Medinipur in West Bengal lies between the two districts Jharkhand on the West and Orissa on the South. The actual rainfall in the district was 1541.1 mm in the year 2016–17. This district is a rainfed district. The location of the district is shown in the following Fig. 3.3.

Ranibundh

Bankura (south) FD

Source Field survey

Name of range

Name of forest division Sonagara

Rudra

Ranibundh

Punshiya

Ranibundh

Gram panchayat

Ambikanagar

Name of beat

130 1138

Jamgeria Mahadebsinan Total

Mahadebsinan Mitham

130

50

Barpatcha

186

Mitham

120

114

Barapatcha

Kadmagarh

Kadmagarh

120

40

85

163

Total number of households

Jamgeria

Dhankura

Dhankura

Bagdiha

Makhnu Kalabani

Makhnu

Bagdiha Kalabani

Kamo

Name of the sample villages

Kamo

FPC

228

26

26

10

38

24

22

24

8

17

33

No. of sample households (20% of the hhs)

Table 3.2 Details of sample households, sample beat offices, sample Gram Panchayat, FPCs in Ranibundh range office in Bankura (south) forest division, West Bengal

3.1 Study Area 29

30

3 Data Base and Methodology

3.1.6 Sampling Techniques for Data Collection in the Rupnarayan Forest Division of Paschim Medinipore The primary data was collected from the Amlagora forest range office in Paschim Medinipore forest division in the district of Paschim Medinipore, West Bengal. The multistage sampling technique has been used. Primary data have been collected in September 2020. A questionnaire method is adopted to collect data from the head of households. Ten JFM villages under the Amlagora forest range are selected purposively. The names of the FPCs and the names of the villages are the same. After the selection of villages, 20% of households from each village are selected randomly. Thus, the total number of sample households consists of 213. Table 3.3 shows sample households corresponding to different villages of different beat offices in the Amlagora forest range in Paschim Medinipore district.

3.1.7 Description of Alipurduar Forest Division of North Bengal Alipurduar forest division is situated in the district of Alipurdua, the Northern part of West Bengal. Alipurduar is the outskirts of Bhutan and the entry point to the northeastern states of India. The boundary of this district is very closer to the international border with Bhutan. Buxa Tiger Reserve, Jaldapara National Park, Chilapata Forest, and Jayanti Hills are located in the Alipurduar forest division. There are so many rare endangered species of animals like tigers, rhinoceros, and elephants living in this area. The beauty of this forest division is not only confined to the availability of tea gardens but also a place of dense jungles. More than 80% of the population are scheduled castes and scheduled tribes. There are various ethnic tribes such as Rajbanshi, Santhals, Bodo and Toto, Oraons, etc. living in this district. The overall literacy rate is 64.7% out of which the male literacy rate is 36.25% and the female rate is 28.47%. Major livelihood is agriculture and forestry. This forest division is a combination of rivers, hills, tea gardens, and forests.

3.1.8 Sampling Technique for Data Collection in Alipurduar Forest Division The present study is based on the primary data collected from the Buxa Tiger Reserve forest range office in the Alipurduar forest division in North Bengal. A multistage sampling technique has been used. Primary data have been collected in July 2021. A questionnaire method is adopted to collect data from the head of households. Six JFM villages under the Buxa Tiger Reserve forest range are selected purposively.

Amlagora

Rupnarayan forest division of Paschim Medinipur

Source Field survey

Name of range

Name of forest division Pathrisole

Roshkundu

Chandabila

Roshkundu

Chandabila

Gram panchayat

Pathrisole

Name of beat

52 969

Ichalkoda Total

Ichalkoda

151

Chandabila

65

67

Chandabila

Fulsason

Fulsason

142

Dharnadihi

Godasole

Godasole

40 150

Darnadihi

Aushbandi

Dhabani

Dhabani Aushabandi

Kastokura

Kastokura

41

Lower Pathrisole 126

Lower Pathrisole

Total number of households

Upper Pathrisole 135

Name of the sample villages

Upper Pathrisole

FPC

213

16

34

20

14

29

31

8

9

28

24

No. of sample households (20% of the hhs)

Table 3.3 Details of sample households, sample beat offices, sample Gram Panchayat, FPCs in Amlagora range office in Rupnarayan forest division of Paschim Medinipur, South Bengal

3.1 Study Area 31

32

3 Data Base and Methodology

After the selection of villages, 20% of households from each village are selected randomly. Thus, the total number of sample households consists of 151. Table 3.4 shows sample households corresponding to different villages of different beat offices in the Boxa Tigar Reserve forest in the Alipurduar district. Thus, the total number of selected households in the South West Bengal Forest Division is 844 from 29 villages and 151 households from 6 villages in the North Bengal forest division. Thus, the total number of sample households for the South Bengal and North Bengal forest divisions is 844.

3.2 Analytical Methods 3.2.1 Forest Participation Index The quantitative analysis of the level of forest participation is measured by the participation index. We divide participation into three stages the planning stage, the implementation stage, and the monitoring stage. The indicators in each stage of participation are selected in line with Tadesse et al. (2017). Table 3.5 shows different indicators of participation in different stages. In the planning stage, implementation stage, and monitoring stage every head of the households was asked to respond with their views on a three-point measurement scale (Yes = 3, No = 2, Don’t know = 1) on all indicators’ statements. To formulate the index we normalize the value of the responses say 3, 2 and 1 for each household on each indicator. The normalized value lies between 0 and 1. “0” shows the minimum value and “1” means the maximum value. The following Eq. (3.1) is used for this purpose. Yij =

yi j − Min(yi j ) Max(yi j ) − Min(yi j )

(3.1)

Min(yi j ) and Max(yi j ) are the minimum and maximum values of the jth indicator. Yij shows the normalized score of the ith household on jth indicator. After normalization, we take the averages of all indicators. The index value is constituted by the following formula.  Zi =

Yij k

(3.2)

where Z i is the index of the ith household. Using Eq. (3.2), the participation index in the planning stage (PP), participation index in the implementation stage (PI), and participation index in the monitoring stage (PM) are calculated. Then overall participation index is measured by the averages of participation in the planning (PP) stage, participation in the implementation stage (PI), and participation in the monitoring stage (PM). That is,

Buxa Tiger Reserve

Alipurduar

Source Field survey

Name of range

Name of forest division

Rajabhatkawa

Gram panchayat

Total

Jayanti

Santrabari

West Rajabhatkawa

Name of beat

Jayanti

28 Basti

28 Basti (EDC) Jayanti (JFMC)

Santrabari

Rabhabasti

Pampubasti

Garobasti

Name of the sample villages

Santrabari (EDC)

Rabhabasti

Garopampu

FPC/JFMC/ EDC

720

152

123

125

72

248

Total number of households

151

31

25

25

15

29

26

No. of sample households (20% of the hhs)

Table 3.4 Details of sample households, sample beat offices, sample Gram Panchayat, FPCs in Buxa Tiger Reserve range office in Alipurduar forest division, North Bengal

3.2 Analytical Methods 33

34

3 Data Base and Methodology

Table 3.5 Description of indicators of Planning, Implementation and Monitoring stages of participation Planning stage Indicators

Implementation stage Descriptions Indicators

Monitoring stage

Descriptions Indicators

Descriptions

Whether the Yes = 3, No households = 2, Don’t participate in know = 1 forest boundary demarcation?

Does Yes = 3, No household = 2, Don’t participate in know = 1 reforestation of degraded forest areas?

Does household follow up forest management bylaw?

Yes = 3, No = 2, Don’t know = 1

Whether the households participate in identifying forest users?

Yes = 3, No = 2, Don’t know = 1

Does Yes = 3, No household = 2, Don’t participate in know = 1 planting of fruit bearing trees?

Whether the households participate in forest patrols?

Yes = 3, No = 2, Don’t know = 1

Whether the households participate in participatory forest resource assessment?

Yes = 3, No = 2, Don’t know = 1

Does household participate in planting trees and management?

Yes = 3, No = 2, Don’t know = 1

Whether the Yes = 3, No households = 2, Don’t participate in know = 1 curbing of illegal forest activities?

Does household participate in forest management committee election?

Yes = 3, No = 2, Don’t know = 1

Whether the households participate in nursery establishment?

Yes = 3, No = 2, Don’t know = 1

Whether the Yes = 3, No households = 2, Don’t participate to know = 1 supervise forest management plan implementation?

Whether the household encourage others to participate?

Yes = 3, No = 2, Don’t know = 1

Whether the households participate in bee keeping?

Yes = 3, No = 2, Don’t know = 1

Whether the households participate in maintain forest boundary?

Whether the households participate in preparing forest management plan?

Yes = 3, No = 2, Don’t know = 1

Whether the households participate in forest fire fighting activities?

Yes = 3, No = 2, Don’t know = 1

Whether the households participate in developing forest management bylaws?

Yes = 3, No = 2, Don’t know = 1

Whether the households participate in attending meeting in the general body?

Yes = 3, No = 2, Don’t know = 1

Yes = 3, No = 2, Don’t know = 1

(continued)

3.2 Analytical Methods

35

Table 3.5 (continued) Planning stage

Implementation stage

Indicators

Descriptions Indicators

Whether the households participate in the approval of forest management agreement?

Yes = 3, No = 2, Don’t know = 1

Monitoring stage

Descriptions Indicators

Descriptions

Whether the Yes = 3, No households = 2, Don’t participate in know = 1 knowledge and skill developing training program?

Source Basu (2021)

Overall Participation Index (PI) = 1/3[PP + PI + PM]

(3.3)

The participation index takes a value that belongs to 0–1. The higher values of the index show a higher level of participation and vice versa.

3.2.2 Institutional Index Institutions are defined here as the rules and regulations framed by village-level institutions like forest protection committees (FPCs). To formulate an institutional index we have taken three main indicators. First is the participation index, second is the monitoring index and the third is the perception index (Abebe 2011). Each main indicator is subdivided into two or more indicators. Every household was asked to respond with their views on three Likert-type of scales (Yes = 1, No = 2, Don’t know = 3) on all indicators’ statements. For the participation index (PI) we take the responses from the households on their participation in the afforestation program organized by the forest department and their presence in the meeting of the forest protection committee’s general body. For the monitoring index (MI) we take the responses from the households on whether the forest department follows ups forest management bylaws, supervises forest management plan implementation, and maintains forest boundaries. For the perception index (PcI) we also take the responses from the households regarding whether there is any existing informal rule for the use of forest products, whether there are government rules regarding forest use, and whether the community members obey government rules. After normalizing the indicators as per Human Development Index (UNDP 2006), we construct an Institutional Index (II) which is given by the following Institutional Index = 1/3[PI + MI + PcI]

(3.4)

36

3 Data Base and Methodology

3.2.3 Monitoring Index and Enforcement Index For quantifying index values of monitoring and enforcement, each main indicator is subdivided into two or more indicators. Every household was asked to respond with their views on three Likert-type of scales (Yes = 1, No = 2, Don’t know = 3) on all indicators’ statements. For the monitoring index (MI) we take the responses from the households whether the households under the forest protection committee at the village level follow ups forest management bylaws, supervise forest management plan implementation, and maintain forest boundaries. For the construction of the enforcement index, we take the responses from the households regarding whether they have been protecting against illegal logging and helping forest patrolling, whether there is any rule for getting permission to collect forest products beyond the specified, and whether the permission is issued by the village level forest protection committee. To formulate an index we need to normalize the values of each indicator for each household. The normalized value lies between 0 and 1. “0” shows the minimum value while “1” indicates maximum values. This normalization procedure is done as per the methodology of the Human Development Index (UNDP 2006). After normalization, we take the averages of all indicators to get the index value. Higher Table 3.6 Description of main indicators and sub-indicators of monitoring and enforcement indices Main indicators

Sub-indicators

Descriptions

Monitoring

Follow ups forest management bylaws

Yes = 1, No = 2, Don’t know = 3

Supervise forest management plan implementation

Yes = 1, No = 2, Don’t know = 3

Forest boundary maintenance

Yes = 1, No = 2, Don’t know = 3

Monitoring index Enforcement Preventing illegal logging Forest patrolling

Whether the communities detecting an illegal logging? (Yes = 1, No = 2, Don’t know = 3) Whether the communities’ patrols work for forest? (Yes = 1, No = 2, Don’t know = 3)

Rules for getting Whether there is any strict rule for getting permission to permission to collect collect forest products beyond the specified amount? (Yes forest product = 1, No = 2, Don’t know = 3) Permissions issued by appropriate authority Enforcement index Source Author’s calculation

Whether the permission is issued by Forest Protection Committee? (Yes = 1, No = 2, Don’t know = 3)

3.2 Analytical Methods

37

values of enforcement and monitoring indices represent successful enforcement and monitoring and vice versa. The indicators along with their descriptions are presented in Table 3.6.

3.2.4 Forest Dependence Index Forest dependency is measured by the forest dependence index (FDI). To formulate the forest dependency index we have taken forest collection importance (FCI), a physical asset (PA), wealth (Wh), and non-forest livelihood strategy (NFLS) as the main indicators in line with Lauren et al. (2020). For Forest Collection Importance, we have added two sub-indicators the number of forest products collected (FPC) and Forest dependent households (FDH). FPC takes the values 1, 2, and 3 corresponding to one forest product collected, two forest products collected, and three forest products collected respectively. FDH = 1 if the household is forest-dependent and 0 otherwise. For Physical Asset, we have taken four sub-indicators the distance between residence and forest (D) in KM, average time spent by the household (hours per day) for non-timber forest products (NTFPs) collection (TNTFP), percentage of households engaged in the collection of NTFPs (PHNTFP) and gender engaged in the collection of NTFPs, {Male = 1, Female = 2, Both = 3, Nil = 0}(GCNTFPs). For Wealth, we have taken four indicators like forest land {Yes = 1, No = 0} (FL), Agricultural land (acre) (AL), livestock owner {Yes = 1, No = 0} (LW), and type of house {pacca = 1, kacha = 2, thatch hut = 3, traditional tribal hut = 4, other = 5} (TH). Non-Income livelihood strategies consist of income from agriculture (IAG), income from petty business (IPB), income from services (ISER), income from wage (IW), and income from handicrafts (IH). To formulate the index we take the normalized value of each indicator. The normalized value lies between 0 and 1. “0” shows the minimum and “1” shows the maximum values. Once normalization is made we take the averages of all sub-indicators (Table 3.7). FCI = PA =



(FPC + FDH)/2

(3.5)

 (D + TNTFP + PHNTFP + GCNTFP)/4

(3.6)

 (FL + AL + LW + TH)/4

(3.7)

Wh = NFLS =

 (IAG + IPB + ISER + IW + IH)/5

(3.8)

38

3 Data Base and Methodology

Table 3.7 Description of main indicators and sub-indicators of forest dependence index Main indicators

Sub-indicators

Description

Forest collection importance

Collected forest products

In numbers

Household dependent on forest

Yes = 1, No = 0

Physical asset

Distance from home to forest

In KM

Average time spent by households for Hours per day collecting NTFP

Wealth

Non forest livelihood strategies

Household engaged in collection NTFP

Percentage of households engaged in the collection of NTFPs

Gender engaged in collection NTFP

Male = 1, Female = 2, Both = 3, Others = 4, No = 0

Forest land

Yes = 1, No = 0

Land holdings

In acre

Livestock owner

Yes = 1, No = 0

Types of house

Pacca = 1, Kachha = 2, Thatch Hut = 3, Traditional Tribal Hut = 4, Others = 5

Income from agricultural (monthly)

(Rupees)

Income from petty business (monthly)

(Rupees)

Income from service (monthly)

(Rupees)

Income from wages (MGNREGA and non-MGNREGA) (monthly)

(Rupees)

Income from handicraft (monthly)

(Rupees)

Source Author’s calculation from primary data

Forest Dependency Index =

 (FCI + PA + Wh + NFLS)/4

(3.9)

3.2.5 Forest Governance Index Forest governance is measured by the forest governance index (FGI). According to the FAO definition of governance, we have taken six main indicators like Rule of Law (RL), Transparency (T), Accountability (A), Participation (P), Inclusive and Equitable (IE), and Efficient and Effective (EE). A description of main indicators and sub-indicators relating to forest governance is presented in the selection of indicators is done in line with some literature and local conditions prevailing in the study areas. To formulate the index we take to normalize the responses of the households (say 0, 1, 2, 3, 4) on each sub-indicator. The normalized value lies between 0 and 1. “0” shows the minimum and “1” shows the maximum values. This normalization procedure was followed by the methodology of the Human Development Index (UNDP 2006).

3.2 Analytical Methods

39

Table 3.8 Description of main indicators and sub-indicators of forest governance index Main indicators

Sub indicators

Response of the households in the three scale

Rule of law

Is there any formal rule regulating for forest use?

Yes clear rules = 1, Yes but vague = 2, No = 3, Don’t know = 4

Is there any informal rule for the use of forest product?

Yes, but unclear = 1, Yes, clear rules = 2, No = 3, Don’t Know = 4

Do you know the timber brokers helping for deforestation due to leakage of forest laws?

Yes = 1, No = 2, Don’t know = 3

Is there any weak forest administration?

Yes = 1, No = 2, Don’t know = 3

Is there political intervention for illegal encroachment and illegal logging?

Yes = 1, No = 2, Don’t know = 3

Is there any strong administration which helps to save reserve forest?

Yes = 1, No = 2, Don’t know = 3

Do you know permission to be taken from the forest protection committees beyond their specified level of forest product use?

Yes, need to inform the authorities = 1, Yes, written permission needed = 2, No = 3, Don’t Know =4

Is there any money involvement for getting permission for the extra collection of forest product?

Yes = 1, No = 2, Don’t know = 3

Do you know any decisions taken from meeting of executive committee?

Yes = 1, No = 2, Don’t know = 3

Do you know the agenda of meeting placed before the general body meeting?

Yes = 1, No = 2, Don’t know = 3

Are you regularly present in the general body meeting?

Yes = 1, No = 2, Don’t know = 3

Do you have any experience of tackling conflict if any?

Yes = 1, No = 2, Don’t know = 3

Do you know the community members obey government rules?

Yes by everyone = 1, Yes by some = 2, No = 3, No particular rules = 4, Not aware = 5

Planning index

Yes = 1, No = 2, Don’t know = 3

Transparency

Accountability

Participation

Forest boundary demarcation Identifying forest users Participatory forest resource assessment Forest management committee election Encouraging others to participate

(continued)

40

3 Data Base and Methodology

Table 3.8 (continued) Main indicators

Sub indicators

Response of the households in the three scale Preparing forest management plan Developing forest management byelaws Approval of forest management agreement

Implementation index

Reforestation of degraded forest areas

Yes = 1, No = 2, Don’t know = 3

Planting of fruit bearing trees such as mahua and mango Planting trees and management Nursery establishment Beekeeping Forest fire fighting Attending meetings Participations in knowledge and skill developing training Monitoring index Follow up forest managements byelaws

Yes = 1, No = 2, Don’t know = 3

Helps forest patrolling Preventing illegal timber logging Supervise forest management plan implementation Forest boundary maintenance Inclusive and equitable

Do you know female members forms Self-Help Group?

Yes = 1, No = 2, Don’t know = 3

Efficient and effective

Do you know that there has been an increased in availability of wood and non-timber forest products in last 5 years?

Increased = 1, No change = 2, Decline = 3, Don’t know = 4

(continued)

3.2 Analytical Methods

41

Table 3.8 (continued) Main indicators

Sub indicators

Response of the households in the three scale

Do you know the dependency of forest resources go down due to the successful implementation of poverty eradication programmes of the Government?

Yes = 1, No = 2, Don’t know = 3

Source Author’s calculation

After normalization, we are to take the averages of all sub-indicators. Once indices values of all sub-indicators are made, we can have separate indices of main indicators like indices of Rule of Law (RL), Transparency (T), Accountability (A), Participation (P), Inclusive and Equitable index (IE) and Efficient and Effective index (EE) (Table 3.8). The overall forest governance index is measured by the averages of the Rule of Law index (RL), Transparency index (T), Accountability index (A), Participation index (P), Inclusive and Equitable index (IE), and Efficient and Effective index (EE). That is,  Forest Governance Index = (3.10) (RL + T + A + P + IE + EE)/6

3.2.6 Factors Affecting Forest Governance Index Multivariate Regression Model To identify factors affecting forest governance we use multiple regression model. Model specification: To investigate socio-economic and other determinants of forest governance at the household level, the linear regression model is applied. The linear model is given below: Yi = α + β1 X1i + β2 X2i + β3 X3i + β4 X4i + β5 X5i + β6 X6i + εi where Y is Forest Governance Index (FGI) Xi = Enforcement Index of the ith household X2 = Educational Index X3 = Caste of the household head X4 = Land Holding (in Acre) X5 = Forest income as percentage of total income (Rs per month)

(3.11)

42

3 Data Base and Methodology

X6 = Trust between communities and Forest Dept. (If No = 1, Yes = 2, Don’t Know = 3) εi is the random disturbance term. The model is heteroscadastic adjustment model.

3.2.7 Forest Governance and Forest Dependency Index The objective of this section is to examine the impact of forest governance on forest dependency in the three forest divisions like Purulia, Bankura, and Paschim Medinipore in South West Bengal. We have taken six indicators of governance like Rule of law, transparency, accountability, participation, inclusive and equitable, and efficiency and effectiveness. We have also taken four socio-economic variables like education, caste, land holding, and forest income. Thus, we have the 10 explanatory variables and forest dependency index (FDI) as the dependent variable. To avoid the multi-collinearity problem we have applied a step-wise regression model. Table 3.9 shows the dependent and explanatory variables. Step-Wise Regression Model A general multiple regression model can be written as yi = β0 + β1 xi1 + β2 xi2 + · · · + βk xik + u i ; For i = 1, 2, … n.

Table 3.9 The dependent and explanatory variables of the model

Dependent variable FDI index (FDI) Independent variable Rule of Law index (RLI) Transparency index (TI) Accountability index (AI) Participation index (PI) Inclusive and equitable index (IEI) Efficient and effective index (EEI) Educational index (EDUI) Caste (SC = 1, ST = 2, GEN = 3, OTH = 4) (C) Land holding (in acre) (LH) % of forest income to total income (FI)

(3.12)

3.2 Analytical Methods

43

In matrix form, we can write Eq. (3.1) as ⎡

⎤ y1 ⎢ .. ⎥ ⎣ . ⎦ yn

⎡

n∗1

1 x11 x12 · · · ⎢ .. .. =⎣. . 1 xn1 xnk · · ·

⎤ x1k .. ⎥ . ⎦ xnk

⎡

⎤ β0 ⎢ .. ⎥ ⎣ . ⎦ βk

n∗(k+1)



Y = Xβ + U; U ∼ 0, σ

⎤ u1 ⎢ ⎥ + ⎣ ... ⎦ ⎡

(k+1)∗1

un

n∗1

 2

The least squared estimator is given in matrix form as

−1  β = XX XY

(3.13)

Stepwise regression is the step-by-step interactive construction of a regression model that involves the selection of independent variables to be used in a final model. This also means that procedure independent variables are to be included or deleted at each step of regression.

3.2.8 Impact of Forest Governance on Forest Cover in India Based on Secondary Data The objective of this section is to examine the impact of governance on forest cover change in India. The data have been taken from 2002 to 2019 from the sources of the World Bank Group. Data relating to governance are rule of law, control of corruption, voice and accountability, political stability, regulatory quality, and government effectiveness. In addition, we have taken data on GDP per capita and globalization and forest cover, etc. We have taken the Two Stage Least Squares (2SLS) instrumental variable model because the conditions for 2SLS hold, that is, the number of instrumental variables is greater than the number of endogenous variables. The Two-Stage Least Squares Estimation Let’s consider a population model: y1 = α1 y2 + β0 + β1 x1 + β2 x2 + · · · + βk xk + u

(3.14)

where y1 and y2 are two endogenous variables. We also assume that there are m instrumental variables. Assume that instruments, z = (1, x1 , …, xk , z1 , …, zm ), are correlated with y2 . The reduced form equation of y2 is the function of all exogenous variables plus instruments.

44

3 Data Base and Methodology

Thus, we have y2 = δ0 + δ1 x1 + δ2 x2 + · · · + δk xk + δk+1 z 1 + · · · + δk+m z m + ε (3.15)

y2 = y2 + ε

(3.16)



where y2 is a linear combination of y2 with all exogenous variables.

References Abebe M (2011) Social and institutional predictors of entrepreneurial career intention: evidence from Hispanic adults in the U.S. J Enterp Culture (JEC) 20(1):1–23 Basu JP (2021) Forest participation of local communities: a study of a tribal dominated region in India. J Soc Econ Dev 23(1):180–201 Census (2011) https://censusindia.gov.in/census.website/ Lauren N, Jeanine MR, Hisham Z (2020) Forest dependence is more than forest income: development of a new index of forest product collection and livelihood resources. World Dev 125:104689 Tadesse S, Woldetsadik M, Senbeta F (2017) Forest users’ level of participation in a participatory forest management program in southwestern Ethiopia. Forest Sci Technol 13(4):164–173. https:/ /doi.org/10.1080/21580103.2017.1387613 UNDP (2006) Good governance practices for the protection of human rights. Office of the United Nations High Commissioner for Human Rights, UN

Chapter 4

Governance in South Asian Countries and Impact of Governance on Forest Cover in India

Abstract This chapter attempts to study forest area, forest ownership, and governance structure across South Asian Countries and to examine state-wise India’s forest cover, and district-wise forest cover in West Bengal. This chapter also examines different forest policies and forest acts and rules and regulations in pre-independence and post-independence India. In addition, this chapter attempts to examine the impact of governance on forest cover in India based on secondary data.

4.1 Forest Area, Annual Change in Forest Area and Forest Ownership Across South Asian Countries In South Asia, 19% of the total land area is under forests. Across the South Asian countries, the percentage area under forest ranges from 69% in Bhutan to 2% in Pakistan (Table 4.1). South Asian forests are under tremendous pressure and their conversion to alternative uses is widespread. The annual change in forest area across South Asian countries is shown in Table 4.2. South Asian countries like Bhutan, India, and Maldives, have recorded forest increases while other countries have lost their forests since 1990 (Table 4.2). The positive trend in forest area in South Asia is largely explained by the increased forest area of Bhutan and India. And the positive trend was due to the afforestation/reforestation effort. The trends in forest loss in Bangladesh, Pakistan, and Sri Lank are due to an expansion of agricultural land (Table 4.2). The forest ownership in South Asia is presented in Table 4.3. About 86% of forest land is owned by the Government in South Asia. 100% of forest land is owned by the government in Nepal, and Bhutan while 92% of forest land is owned by the government in Sri Lanka, and 86% is owned by the government in India (Table 4.3).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. P. Basu, Governance and Institution in the Indian Forest Sector, https://doi.org/10.1007/978-3-031-34746-7_4

45

46

4 Governance in South Asian Countries and Impact of Governance …

Table 4.1 Forest area, forests as percentage of land area and forest area per 1000 persons, 2010 Country

Forest area (1000 ha)

Forest area as % of land

(ha/1000 persons)

Bangladesh

1442

11

9

Bhutan

3249

69

4750

India

68,434

23

58

Maldives

1

3

3

Nepal

3636

25

126

Pakistan

1687

2

10

Sri Lanka

1860

29

93

South Asia

80,309

19

51

Asia–Pacific

740,383

26

195

World

4,033,060

31

597

Source FAO (2011)

Table 4.2 Annual change in forest area across South Asian Countries Country/region

1990–2000 1000 ha/year −3

Bangladesh Bhutan India Maldives

2000–2005 % − 0.18

1000 ha/year −3

2005–2010 % − 0.18

1000 ha/year −3

% − 0.18

11

0.34

11

0.34

11

0.34

145

0.22

464

0.7

145

0.21

0

0

0

Nepal

− 92

0

− 2.09

− 53

− 1.39

0

0

Pakistan

− 41

− 1.76

− 43

− 2.11

− 43

− 2.37

Sri Lanka

− 27

− 1.2

− 30

− 1.47

− 15

− 0.77

South Asia

−7

− 0.01

347

0.44

96

0.12

− 708

− 0.1

2315

0.32

494

0.07

− 8323

− 0.2

− 4841

Asia–Pacific World

0

0

− 0.12

− 5581

− 0.14

Source FAO (2010)

4.2 Governance Structure Across South Asian Countries Corruption and weak governance are the major causes of deforestation in most South Asian countries. The lack of implementation of policy, and corruption in the political and administrative process are responsible for weak governance. The corruption perception index (CPI) represents the degree to which corruption is perceived to exist among public officials and politicians. It reveals the level of corruption. The score value is given on a scale of 0 (highly corrupt) to 100 (very clean). The score value of the CPI score value with its global rank is presented in Table 4.4. Of the

4.2 Governance Structure Across South Asian Countries

47

Table 4.3 Forest ownership in South Asia Country/region

Public ownership

Private ownership

Other ownership

1990

2000

2005

1990

2000

2005

1990

2000

2005

Bangladesh

62

62

62

36

36

36

2

2

2

Bhutan

100

100

100

0

–

–

0

0

0

India

86

86

86

14

14

14

0

0

0

Maldives

–

–

–

–

–

–

–

–

–

Nepal

100

100

100

0

0

0

0

0

0

Pakistan

66

66

66

34

34

34

0

0

0

Sri Lanka

92

92

93

8

8

7

0

0

0

South Asia

86

86

86

14

14

14

0

0

0

Source FAO (2010)

Table 4.4 Corruption perception score value across South Asian countries in 2022

Country

CPI score, 2022

CPI global rank

Bangladesh

25

147

Bhutan

68

25

India

40

85

Maldives

40

85

Nepal

34

110

Pakistan

27

140

Sri Lanka

36

101

Source Author’s calculation

South Asian countries the CPI score value for Bhutan is found to be the highest (68) followed by India (40), Maldives (40), and Nepal (34) (Table 4.4 and Fig. 4.1). To understand the trends in governance we have taken three main indicators of governance say control of corruption, rule of law, and government effectiveness for the year 2020 over the year 2000. The governance score values lie between − 2.5 and + 2.5. Weak governance shows − values while strong governance shows + values. The score values of control of corruption for the year 2020 across all the South Asian countries are negative (weak governance) except Bhutan and Sri Lanka while the trends of control of corruption for all of South Asia are positive except Pakistan. In the case of the rule of law, the score values for all South Asian countries are negative (weak governance) except Bhutan in 2020 while the trends of the core values of rule of law are positive for Bangladesh, Bhutan, and Pakistan, and is negative for India, Maldives, Nepal, and Sri Lanka (Table 4.5). Again, the score values of government effectiveness are positive (strong governance) for India and Bhutan and are negative for all other South Asian countries in 2020 while the trend values of government effectiveness are positive for India, Pakistan, and Sri Lanka and are negative for other South Asian countries like Bangladesh, Bhutan, Maldives and Nepal (Table 4.5).

48

4 Governance in South Asian Countries and Impact of Governance …

CPI score , 2022 80 70 60 50 40 30 20 10 0 Bangladesh

Bhutan

India

Maldives

Nepal

Pakistan

Sri Lanka

Fig. 4.1 Corruption perception index for South Asian countries

Table 4.5 Trend in Governance score across South Asian countries Country

Governance score (− 2.5 to + 2.5) Control of corruption

Rule of law

Government effectiveness

2000

2020

Trend

2000

2020

Trend

2000

2020

Trend

Bangladesh

− 1.1

− 0.98

+

− 0.91

− 0.57

+

− 0.59

− 0.78

−

Bhutan

0.99

1.65

+

0.13

0.59

+

0.83

0.37

−

India

− 0.35

− 0.24

+

0.33

− 0.02

−

− 0.13

0.39

+

Maldives

− 0.38

− 0.34

+

0.07

− 0.33

−

0.37

− 0.11

−

Nepal

− 0.67

− 0.58

+

− 0.25

− 0.49

−

− 0.46

− 0.94

−

Pakistan

− 0.84

− 0.84

0

− 0.93

− 0.69

+

− 0.6

− 0.55

+

Sri Lanka

− 0.18

0.31

+

0.15

− 0.05

−

− 0.3

− 0.07

+

Source Author’s calculation

4.2.1 Forest Cover of India as a Whole and West Bengal in Particular Geographical area India occupies 2.5% of the total world area and the forest area of India constitutes 1.8% of the total world forest cover. Nearly 27% of the total population of the country is dependent on forests for livelihood. The forest cover of India with different states is presented in Table 4.6.

196,244

44,212

55,673

Gujrat

Haryana

Himachal Pradesh

3113

28

378

538

79,716

191,791

38,852

308,252

307,713

Jharkhand

Karnataka

Kerala

Madhya Pradesh

Maharashtra

8721

6676

1935

4501

2603

4281

222,236

3702

Goa

6.72

7068

Total

1483

Delhi

78

135,192

Chhattisgarh

333

169,421*

94,163

Bihar

2795

21,095

#(UT of Ladakh)

78,438

Assam

4203

83,743

Arunachal Pradesh

1994

VDF

#(UT of J 53,258* & K)

162,968

Andhra Pradesh

Jammu and Kashmir #

Geo. area (GA)

State/UT

20,572

34,341

9508

21,048

9687

8612

660

7952

7126

451

5092

576

56.42

32,198

3280

10,279

30,557

13,938

MDF

Table 4.6 Forest cover in the states/UTS in India (area in km2 )

21,485

36,465

9701

13,026

11,321

10,719

1752

8967

5195

1123

9387

1123

132.3

16,345

3693

15,253

15,036

13,205

OF

50,778 (05)

77,482 (01)

21,144 (12)

38,575 (06)

23,611 (11)

23,612

2490 (28)

21,122 (13)

15,434 (20)

1602 (31)

14,857 (21)

2237 (29)

195.44 (33)

55,611 (03)

7306 (25)

28,327 (08)

66,688 (02)

29,137 (07)

Total forest cover (2019 assessment)

16.5 (28)

25.14 (20)

54.42 (10)

20.11 (22)

29.62 (18)

10.62

1.47 (37)

39.66 (15)

27.72 (19)

3.62 (36)

7.57 (32)

60.43 (09)

13.18 (29)

41.13 (14)

7.76 (31)

36.11 (16)

79.63 (04)

17.88 (27)

% of GA

96 (11)

68 (13)

823 (03)

1025 (01)

58 (15)

371

23 (18)

348 (04)

334 (05)

14 (19)

100 (10)

8 (21)

3.03 (24)

64 (14)

7 (23)

222 (07)

− 276 (36)

990 (02)

Change in forest cover w.r.t ISFR 2017

0.19

0.09

4.05

2.73

0.25

1.60

0.93

1.68

2.21

0.88

0.68

0.36

1.57

0.12

0.10

0.79

− 0.41

3.52

Change % w.r.t ISFR 2017

(continued)

4256

6002

13

4484

688

548

298

250

315

154

2994

0

0.3

610

250

173

229

8255

Scrub

4.2 Governance Structure Across South Asian Countries 49

Geo. area (GA)

22,327

22,429

21,081

16,579

155,707

50,362

342,239

7096

130,060

112,077

10,486

240,928

53,483

88,752

8249

114

491

111

State/UT

Manipur

Meghalaya

Mizoram

Nagaland

Odisha

Punjab

Rajasthan

Sikkim

Tamil Nadu

Telangana

Tripura

Uttar Pradesh

Uttarakhand

West Bengal

A & N Islands

Chandigarh

Dadra and Nagar Haveli

Daman and Diu

Table 4.6 (continued)

1.4

0

1.36

5678

3019

5047

2617

654

1608

3605

1102

78

8

6970

1273

157

489

905

VDF

5.69

80

14.24

684

4160

12,805

4080

5236

8787

11,030

1552

4342

801

21,552

4534

5801

9267

6386

MDF

13.4

127

6.43

381

9723

6451

8109

1836

10,187

11,729

688

12,210

1040

23,097

6679

12,048

7363

9556

OF

20.49 (37)

207 (32)

22.03 (36)

6743 (26)

16,902 (17)

24,303 (10)

14,806 (22)

7726 (24)

20,582 (14)

26,364 (09)

3342 (27)

16,630 (19)

1849 (30)

51,619 (04)

12,486 (23)

18,006 (15)

17,119 (16)

16,847 (18)

Total forest cover (2019 assessment)

18.46 (25)

42.16 (13)

19.32 (23)

81.74 (03)

19.04 (24)

45.44 (12)

6.15 (33)

73.68 (08)

18.36 (26)

20.27 (21)

47.1 (11)

4.86 (34)

3.67 (35)

33.15 (17)

75.31 (07)

85.41 (02)

76.33 (05)

75.46 (06)

% of GA

− 0.16 − 0.99 − 0.02

− 27 (34) − 180 (35) − 3 (33)

0 (30)

0 (28)

0.47 (26)

1 (25)

55 (17)

8 (21)

127 (09)

0 (27)

163 (08)

83 (12)

− 2 (32)

58 (15)

12 (20)

0.00

0.00

2.18

0.01

0.33

0.03

0.87

0.00

0.80

0.32

− 0.06

0.35

0.65

0.53

− 2.88

− 499 (37)

274 (06)

Change % w.r.t ISFR 2017

Change in forest cover w.r.t ISFR 2017

(continued)

0.19

5

0.10

1

146

383

587

29

3615

715

307

4760

33

4327

635

1

600

1181

Scrub

50 4 Governance in South Asian Countries and Impact of Governance …

30

490

3,287,469

Lakshadweep

Puducherry

Total

99,278

0

0

VDF

308,472

17.66

16.09

MDF

304,499

34.75

11.01

OF

712,249

52.41 (34)

27.1 (35)

Total forest cover (2019 assessment)

Asterisk in this Table means Union Territories of J & K and Ladakh as on October, 2019 Source India State of Forest Report (2019) Note Figures in () indicate rank

Geo. area (GA)

State/UT

Table 4.6 (continued)

21.67

10.7 (30)

90.33 (01)

% of GA

3976

− 1.26 (31)

0 (29)

Change in forest cover w.r.t ISFR 2017

0.56

− 2.35

0.00

Change % w.r.t ISFR 2017

46,297

0.00

0.00

Scrub

4.2 Governance Structure Across South Asian Countries 51

52

4 Governance in South Asian Countries and Impact of Governance …

According to the 2019 assessment, the total forest cover in India is 712,249 km2 which is 21.67% of its geographical area. The area of very dense forest is 99,278 km2 , moderately dense forest is 308,472 km2 , and the open forest is 304,499 km2 in India. The very dense forest is highest in the state of Arunachal Pradesh (21,095 km2 ) followed by Maharashtra (8721 km2 ), Chhattisgarh (7068 km2 ), Odisha (6970 km2 ), and so on. The moderately dense forest is highest in the state of Madhya Pradesh (34,341 km2 ) followed by Chhattisgarh (32,198 km2 ), Arunachal Pradesh (30,557 km2 ), Odisha (21,552 km2 ), and so on. The open forest is highest in the state of Madhya Pradesh (36,465 km2 ) followed by Odisha (23,097 km2 ), Maharashtra (21,485 km2 ), and Chhattisgarh (16,345 km2 ), and so on. Total forest cover is highest in Madhya Pradesh state (77,482 km2 ) followed by Arunachal Pradesh (66,688 km2 ), Chhattisgarh (55,611 km2 ), and so on. The lowest forests cover is in the united territory Daman and Diu (20.49 km2 ). The total forest cover in West Bengal is 16,902 km2 which is 19.04% of its geographical area. According to total forest cover the rank of West Bengal is 17. The percentage of forest cover is highest in Lakshadweep followed by Mizoram, A and N Islands, Arunachal Pradesh, and so on concerning their geographical area. The change in forest cover is highest in the state Karnataka (1025 km2 /2.73%) followed by Andhra Pradesh (990 km2 /3.52%), Kerala (823 km2 /4.05%), and so on. The forest area change in West Bengal is 55 km2 (0.33%) concerning the 2017 assessment and the rank is 17. The forest area change is lowest in the state of Manipur (− 499 km2 ). The total area of forest cover change in India is 3976 km2 concerning the 2017 assessment. The area of scrub in India is 46,297 km2 . The area of scrub is highest in the state of Andhra Pradesh (8255 km2 ). The forest cover of West Bengal is shown in Table 4.7. The forest cover in the state of West Bengal is 16901.51 km2 which is 19.04% of the state’s geographical area. In terms of forest density classes, the state has 3018.52 km2 under Very Dense Forest (VDF) which is 3.40% of the state’s geographical area, 4160.26 km2 under Moderately Dense Forest (MDF) which is 4.69% of the state’s geographical area and 9722.73 km2 under Open Forest (OF) which is 10.95% of geographical area. The area under Scrub is 146.12 km2 which is only 0.17% of the state’s geographical area (Table 4.7). Table 4.7 Forest cover of West Bengal (in km2 )

Class

Area

% of GA

Very dense forest (VDF)

3018.52

Moderately dense forest (MDF)

4160.26

4.69

Open forest (OF)

9722.73

10.95

16,901.51

19.04

146.12

0.17

Total Scrub

3.40

Source India State of Forest Report (2019), West Bengal

4.2 Governance Structure Across South Asian Countries

53

District-wise forest cover in West Bengal is shown in Table 4.8. The total forest cover in the state of West Bengal is 16901.51 km2 which is 19.04% of its total geographical area. The forest cover is highest in Jalpaiguri district (2862.40 km2 ) followed by South Twenty-Four Parganas (2788.71 km2 ), Darjiling (2367.80 km2 ) and Paschim Medinipur (2161.54 km2 ) and so on. The area under very dense forest is highest in South Twenty-Four Parganas (983.10 km2 ) followed by Jalpaiguri (724.22 km2 ), Darjiling (720.76 km2 ), Paschim Medinipur (256.21 km2 ) and so on. The area under moderately dense forest is highest in South Twenty-Four Parganas (745.03 km2 ) followed by Darjiling (654.52 km2 ), Paschim Medinipur (591.64 km2 ), Jalpaiguri (434.92 km2 ) and so on. Lastly, the area under open forest is highest in Jalpaiguri (1703.26 km2 ) followed by Paschim Medinipur (1313.69 km2 ), South Twenty Four Parganas (1060.58 km2 ), Darjiling (992.52 km2 ) and so on. According to the district’s geographical area the highest percent of forest cover in Darjiling district followed by Jalpaiguri, South Twenty Four Parganas, Paschim Medinipur and so on. The forest cover has increased highest in Bankura in 2019 assessment compared to 2017 assessment. Forest cover has increased in West Bengal Table 4.8 District-wise forest cover in West Bengal (in km2 ) 2019 assessment District

Geographical area (GA)

Very dense forest

Mod. dense forest

Bankura

6882

222.33

395.27

Barddhaman

7024

57.53

Birbhum

4545

Dakshin Dinajpur

Open forest

Total

% of GA

Change w.r.t. 2017 assessment

Scrub

667.98

1285.58 (05)

18.68 (06)

15.58 (01)

28.59

91.78

190.00

339.31 (13)

4.83 (16)

4.31 (06)

7.35

1.00

34.14

149.66

184.8 (16)

4.07 (17)

7.80 (04)

8.90

2219

0.00

5.83

81.29

87.12 (18)

3.93 (18)

0.12 (09)

0.00

Darjiling

3149

720.76

654.52

992.52

2367.80 (03)

75.19 (01)

2.80 (07)

9.21

Haora

1467

0.00

50.00

253.77

303.77 (14)

20.71 (05)

− 0.23 (17)

0.00

Hugli

3149

0.00

14.00

146.00

160.00 (17)

5.08 (15)

0.00 (12)

0.00

Jalpaiguri

6227

724.22

434.92

1703.26

2862.40 (01)

45.97 (02)

5.40 (05)

39.65

Koch Bihar

3387

0.00

27.00

322.06

349.06 (11)

10.31 (12)

0.06 (10)

0.00

Kolkata

185

0.00

0.00

1.00

1.00 (19)

0.54 (19)

0.00 (13)

0.00

Maldah

3733

0.00

209.04

282.65

491.69 (09)

13.17 (10)

0.69 (08)

0.00

Murshidabad

5324

0.00

53.06

291.83

344.89 (12)

6.48 (14)

− 1.11 (18)

0.00 (continued)

54

4 Governance in South Asian Countries and Impact of Governance …

Table 4.8 (continued) 2019 assessment District

Geographical area (GA)

Very dense forest

Mod. dense forest

Nadia

3927

1.00

160.16

North Twenty Four Parganas

4094

13.02

Paschim Medinipur

9368

Purba Medinipur

Open forest

Total

% of GA

Change w.r.t. 2017 assessment

Scrub

318.84

480.00 (10)

12.22 (11)

0.00 (14)

0.00

184.98

524.98

722.98 (08)

17.66 (07)

− 0.02 (15)

0.00

256.21

591.64

1313.69

2161.54 (04)

23.07 (04)

10.54 (03)

20.24

4713

1.99

197.96

620.1

820.05 (07)

17.40 (08)

0.05 (11)

2.50

Puruliya

6259

37.36

306.94

571.58

915.88 (06)

14.63 (09)

11.88 (02)

28.68

South Twenty Four Parganas

9960

983.10

745.03

1060.58

2788.71 (02)

27.99 (03)

− 3.29 (19)

1.00

Uttar Dinajpur

3140

0.00

3.99

230.94

234.93 (15)

7.48 (13)

− 0.07 (16)

0.00

Grand total

88,752

3018.52

4160.26

9722.73

16,901.51

19.04

54.51

146.12

Source India State of Forest Report (2019), West Bengal Note Figures in () indicate rank

by 54.51 km2 in 2019 assessment compared to the previous assessment report, 2017, followed by Puruliya (11.08 km2 ), Paschim Medinipur (10.54 km2 ) and so on. The area under scrub is highest in Jalpaiguri District (39.65 km2 ) followed by Puruliya (28.68 km2 ), Bankura (28.59 km2 ) and so on. The area under scrub is 146.12 km2 in total West Bengal (Table 4.8).

4.3 Forest Policies and Acts in India During Pre and Post Independence 4.3.1 Forest Policies and Acts in Pre Independence India (1857–1947) During British rule, forests were used as an important source of revenue for the government (Guha and Gadgil 1989). The objective was not only revenue earning but also it used for enhancing the railway network as well as military purposes. Under this rule, forest land was converted to agricultural land (Shyamsunder and Parameswarappa 1987; Mukerji 2003). The objective of the first Indian Forest Act (1865) was revenue generation by the sale of timber and forest produce (GoI 1865). The Forest Act of 1878 classified forests as reserved, protected, and village, based on access and use (Haeuber 1993;

4.3 Forest Policies and Acts in India During Pre and Post Independence

55

GoI 1878). Under this act, the individual rights over forests and forest products from reserved and protected forests were restricted. “The first national forest policy during the British Colonial Government was introduced on 19 October 1894 (GoI 1976)”. The objectives of the policy were enhancing revenue collection through the sale of valuable timber although attention was given to maintaining adequate forest cover. The Indian Forest Act (1927) emphasized the control of timber and other forest products in transit, as well as implemented fines and penalties related to forest offenses (GoI 1927). Under this act, there was a provision for the imposition of duties on timber and other forest products by the central government and the state government was responsible to implement forest laws.

4.3.2 Forest Policies and Acts in the Post-independence India The chronology of different forest policies and acts and orders in India is presented in Table 4.9. In 1952, the National Forest policy was introduced and the main objective of this policy was the sustained production of timber. Under this forest policy, top priority was given to the commercial exploitation of forests depriving the needs of local communities. This is the first policy in which a target was set up of 33% of the total land area of the country to be brought under forest cover (60% in the Himalayan and other mountain regions and 20% in the plain). Under this policy, forest land was given to industries and businessmen for industrial development, which had a significant impact on forest and forest-dependent communities (Bandi 2011: 79). In 1972 Wildlife Protection act was passed and importance was given to the limited access to resources and the human entrance was prohibited in the protected areas. Under this act national parks and wildlife sanctuaries etc. are created. In 1976, National Commission on Agriculture was set up and the main objective was the maximization of forest products through the creation of a Forest Corporation. In addition, the commission introduced ‘social forestry’ in 1975 to develop forestry in the unproductive non-forest government and community lands after realizing the importance of locals’ support for the protection of forests. The initiative on Social Forestry taken up by the Government of India in the Fifth Five-year Plan (1975–1980) had its impact in West Bengal, where the social forestry project was taken up from 1975–90 under the sponsorship of the World Bank. Activities on social forestry were initially taken up by the Panchayat Department in 1975 under the leadership scheme to motivate the local community and impart technical training in planting and nurturing trees. In 1977 forests were recognized in the concurrent list from the state list. The 1980 forest conservation act protected and improved forest cover. The act prevents deforestation and helps to arrest soil erosion and land degradation. The act also curbs the diversion of forest land for non-forest purposes. The 1986 Environmental Protection act emphasized that the Central Government empowers to take requisite measures to protect and improve the environment with the help of state governments.

56

4 Governance in South Asian Countries and Impact of Governance …

Table 4.9 Chronology of India’s forest policy and forest acts during post-independence period Year

Forest policy

1952

Forest act/laws

Forest rules and regulations

Amended

National forest – policy

–

–

1972

–

–

1982, 1986, 1993, 2003 and 2006

1975

–

IUCN and World Parks Congress recognize rights of indigenous community

–

1977

–

–

Forests enters the – concurrent list from the state list

1980

–

Forest conservation act

–

–

1982

–

–

–

–

1986

Environment protection act

–

–

–

1988

New national forest policy

–

–

–

Wildlife protection act

1996

Panchayat Extension to Scheduled Areas

2002

–

Biological diversity act

–

–

2006

–

Forest right act

–

–

2013

–

Right to fair compensation and transparency in land acquisition, rehabilitation and resettlement act

–

–

Source Author’s calculation

In 1988, the introduction of the National Forest Policy of India was people-centric and emphasized the protection, forest regeneration, and provision of employment to the tribal people who are living in the forest fringe areas (MoEF 1988). Under the National forest policy of 1988 importance has been given to environmental stability, ecological balance, and conservation. Besides, the important part of this national forest policy is the involvement of local communities including women in a holistic manner. Giving priority to the National forest policy of 1988 the Government of India implemented Joint Forest Management (JFM) in 1990 involving local communities to reach the target of 33% of the geographical area under forest cover. To achieve this target the task force on Green India, the Planning commission, and the Government of India proposes 43 million hectares of degraded land to be brought under forest

4.3 Forest Policies and Acts in India During Pre and Post Independence

57

(out of which regeneration in 15 million hectares degraded forests under JFM, 10 million hectares irrigated land under agroforestry and 18 million hectares rain-fed area under agroforestry. In 1996 Government of India enacted a law in the parliament named Panchayats (Extension to Scheduled Areas) Act or PESA focusing on the importance of Gram Shaba to cater to the improvement of the living status of the people in the scheduled areas. More specifically, PESA empowers Gram Shaba to take necessary action for restoring unlawfully alienated land of scheduled tribes. The Gram Shaba must ensure that no land of scheduled tribes has been transferred to non-scheduled tribes. Under this act, the Gram Shaba also involves the selection of beneficiaries under different poverty alleviation programs and they are the custodian of minor forest produce. The Biological Diversity Act, of 2002, was passed by the parliament in India to protect biodiversity and promote sustainable management of biological resources within the local communities. This act was enacted consistent with the requirements given by the United Nations Convention on Biological Diversity (CBD) because India belongs to the party. Under this act, the focus has been given to the conservation and sustainability of biological diversity, regulation of access to biological resources, securing sharing of benefits with local people, and lastly protection and rehabilitation of threatened species. The customary forest right of tribal and other traditional forest dwellers (OTFD) have been recognized under Forest Right Act (FRA) in 2006. “The objective of this FRA Act is to recognize and vest the Forest Rights and Occupancy in forest land in forest dwelling Scheduled Tribes and other Traditional Forest Dwellers who have been residing in such forests for a generation but whose rights could not be recorded (Ministry of Tribal Affairs, Govt. of India and UNDP 2014; Desor 2013)”. “The recognized rights include responsibility and authority of sustainable use; conservation of biodiversity; and maintenance of ecological balance while ensuring livelihood and food security of the forest-dwelling scheduled tribes and other traditional forest dwellers (Ministry of Tribal Affairs, Govt. of India and UNDP 2014)”. Over this issue, there has emerged a series of conflicts between two groups of institutions of the government based on different ideologies. The groups are the Ministry of Tribal Affairs (MOTA) and the Ministry of Environment and Forestry (MoEF) department of the government of India. Ultimately the forest land right of the communities under FRA has been rejected by the order of the Hon’ble Supreme Court of India on February 13, 2019. The Right to Fair Compensation and Transparency in Land Acquisition, Rehabilitation, and Resettlement Act was passed in 2013 (Duggal 2014). This act aimed to replace the colonial land acquisition of 1894. Under the new act of 2013, the land is to be acquired for the public interest, to be ensured the rehabilitation and resettlement of those affected through land acquisition, and to be re-evaluated the amount of compensation given in the process of land acquisition. Forest policies and laws of different countries are presented in Table 4.10.

58

4 Governance in South Asian Countries and Impact of Governance …

Table 4.10 Different countries’ forest policies, laws or orders for forest conservation Country

Years

Event

Features

Myanmar

2014

Log export ban

Bans wood exports

Indonesia

1985

Log export ban

Promotes domestic timber industry in the country

Malaysia

1992

National forest policy

Sustainable forest management certification practices

Thailand

1989

Logging ban

Encourages forest protection

Ecuador

1996–97

Logging ban

Bans logging in natural forests

Costa Rica

2007

Forest management policy directives

Protects forests

USA

1926, 1990

Log export ban

Bans wood exports

Brazil

1969

Log export ban

Bans certain wood exports

Ghana

1995

Log export ban (LEB) policy

Substitutes export of log to timber products; reduces the rate of exploitation of natural forest resources

Cameroon

1994

Forestry law

Checks illegal logging and unsustainable forest management

Source Ghosh and Sinha (2016)

4.4 Impact of Governance on Forest Cover in India This section attempts to study the impact of governance on the forest cover in India. To examine this we apply two stage least square (2SLS) method. 2SLS instrumental variable model has been described in Chap. 3. Data relating to governance are rule of law, control of corruption, voice and accountability, political stability, regulatory quality, government effectiveness. We have formulated a governance index using the indicators of governance. In addition, we have also taken data on GDP per capita and globalization and forest cover etc. Here, we have taken forest cover change as dependent variable while all other variables are treated as independent variables. We have one endogenous variable say governance index while rule of laws, control of corruption, voice of accountability, political stability, regulatory quality and government effectiveness are known as an instrumental variables. Here total number of instrumental variables is greater than the total number of endogenous variables. Therefore, we may apply 2SLS model (Figs. 4.2 and 4.3). We have chosen 2SLS regression model for our analysis. Table 4.11 shows the results of regression with instrumental variable. Here, Change in forest cover is dependent variable and globalization, per capita gross domestic, and rule of law are instruments of the model. The endogenous variable in this case is governance index. The regression model is run on 18 observations. The Wald Chi2(3) is 18 and it is statistically significant as p-value is less than 0.05. The goodness-of-fit measure Rsquared accounts for 37% of the overall variation in the model. It is observed that the

4.4 Impact of Governance on Forest Cover in India

59

1.5 1 0.5 0 Law

Account

Regu

Pol.sta

C Corrup

Govt. eff

-0.5 -1 -1.5 -2 2003

2019

Fig. 4.2 Indicators of forest governance over the years 2003–2019

Governance Index 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Fig. 4.3 Governance index

coefficient of governance index is positive and is highly significant. This means that the governance has positive impact on forest cover. That is, forest cover will increase with the good forest governance and vice versa. The coefficient of gross domestic product, on the other hand, is negative and statistically significant. This means that

60

4 Governance in South Asian Countries and Impact of Governance …

Table 4.11 Results Instrumental variables where instruments are globalization, GDP and rules of law and instrumented: Governance Index Instrumental variables (2SLS) regression Dependent variable = Forest cover change

Coef.

Robust SE

Z

P > |Z|

Governance index

0.3123

0.093

3.35

0.001

Globalization

0.0093

0.007

1.32

0.207

GDP per capita

− 0.00003

0.00001

− 3.0

0.001

Const.

− 0.43587

0.41615

− 1.04

0.29

N = 18 Wald Chi 2(3) = 18 Prob > Chi2 = 0.004 Adj. R2 = 0.3712

Source Author’s calculation

deforestation will take place with the increase in gross domestic product. That is, economic growth has negative impact on forest cover in India. Table 4.12 shows the regression with instrumental variable. Here, glob, GDP, and regulatory quality are instruments of the model. The endogenous variable in this case is governance index. The Wald Chi2(3) is 20 and it is statistically significant as indicated by p-value. The goodness-of-fit measure R-squared accounts for 41% of the overall variation in the model. We see that the coefficient of governance index is positive and is highly significant. None of the other coefficients are significant. Table 4.13 shows the regression with instrumental variable. Here, glob, GDP, and control of corruption are instruments of the model. The endogenous variable in this case is governance index. The Wald Chi2(3) is 22.66 and it is statistically significant as shown by p-value. The goodness-of-fit measure R-squared accounts for 39% of the overall variation in the model. We see that the coefficient of governance index is positive and is highly significant. The coefficient of GDP, on the other hand, is negative and statistically significant. Table 4.14 shows the regression with instrumental variable. Here, glob, GDP, and government effectiveness are instruments of the model. The endogenous variable in this case is governance index. The Wald Chi2(3) is 26.84 and it is statistically significant. The goodness-of-fit measure R-squared accounts for 41% of the overall variation in the model. It is found that that the coefficient of governance index is Table 4.12 Results instrumental variables where instruments are globalization, GDP and Regulatory quality and instrumented: Governance Index Instrumental variables (2SLS) regression Dependent variable = Forest cover change

Coef.

Governance index Globalization

Robust SE

0.2188

0.0680

Z

P > |Z| 3.21

0.001

0.87

0.38

0.0062

0.0071

GDP per capita

− 0.00002

0.00001

− 2.0

0.10

Const.

− 0.2504

0.3917

− 0.63

0.52

Source Author’s calculation

N = 18 Wald Chi 2(3) = 20 Prob > Chi2 = 0.002 Adj. R2 = 0.4113

4.4 Impact of Governance on Forest Cover in India

61

Table 4.13 Results instrumental variables where instruments are globalization, GDP and control of corruption and instrumented: Governance Index Instrumental variables (2SLS) regression Dependent variable = Forest cover change

Coef.

Robust SE

Z

P > |Z|

Governance index

0.2935

0.0765

3.83

0.000

Globalization

0.0087

0.0069

1.26

0.20

GDP per capita

− 0.00003

0.00001

− 3.0

0.001

Const.

− 0.3986

0.3802

− 1.05

0.29

N = 18 Wald Chi 2(3) = 22.66 Prob > Chi2 = 0.000 Adj. R2 = 0.3894

Source Author’s calculation

Table 4.14 Results instrumental variables where instruments are globalization, GDP and government effectiveness and instrumented: Governance Index Instrumental variables (2SLS) regression Dependent variable = Forest cover change

Coef.

Robust SE

Z

P > |Z|

Governance index

0.2342

0.0600

3.90

0.000

Globalization

0.0067

0.0065

1.03

0.30

GDP per capita

− 0.000027

0.000014

− 1.92

0.052

Const.

− 0.20090

0.3594

− 0.56

0.53

N = 18 Wald Chi 2(3) = 26.84 Prob > Chi2 = 0.000 Adj. R2 = 0.4134

Source Author’s calculation

positive and is highly significant. The coefficient of GDP, on the other hand, is negative and statistically significant. A Summing Up The positive trend in forest area in South Asia is largely explained by the increased forest area of Bhutan and India. And the positive trend was due to the afforestation/ reforestation effort. The score values of control of corruption for the year 2020 across all the South Asian countries are negative (weak governance) except Bhutan and Sri Lanka while the trends of control of corruption for all of South Asia are positive except Pakistan. The forest cover in the state of West Bengal is 19.04% of the state’s geographical area. In terms of forest density classes, the state of West Bengal contributes 10.95% of the geographical area under open forest. The forest cover trend in India as well as the state of West Bengal is increasing. India’s forest policy of 1988 and the forest conservation act of 1980 revealed the rise in forest cover. Different countries like Indonesia, Myanmar, Thailand, Costa Rica, Brazil, Ghana, and Ecuador banned logging by formulating scientific forest policies and acts. The macroeconomic impact on forest cover in India showed that forest cover has increased with the increase in governance and vice versa. This means that good governance

62

4 Governance in South Asian Countries and Impact of Governance …

has a positive impact on forest cover in India. On the other hand, economic growth reflected by GDP per capita has a negative impact on forest cover in India.

References Bandi M (2011) Forest Governance in India with particular reference to Andhra Pradesh: A review of policy shift from state control to community participation. Man Dev 33(4):75–90 (December) Desor S (2013) Report of the National Consultation on Forest Rights Act and Protected Areas. New Delhi: Future of Conservation Network Duggal S (2014) Land acquisition in India. https://www.lawctopus.com/academike/wp-content/upl oads/2014/08/By-Swati-Duggal.pdf FAO (2010) Global forest resource assessment 2010. Main report. FAO Forestry paper 163, Rome FAO (2011) State of world’s forests. FAO, Rome Ghosh M, Sinha B (2016) Impact of forest policies on timber production in India: A review: Mili Ghosh and Bhaskar Sinha/Natural Resources Forum. Nat Resour Forum 40. https://doi.org/10. 1111/1477-8947.12094 GoI (Government of India) (1865) Indian Forest Act 1865. http://lawmin.nic.in/legislative/textof centralacts/1865.pdf (accessed 21 March 2016) GoI (Government of India) (1878) Indian Forest Act 1878. http://lawmin.nic.in/legislative/textof centralacts/1878.pdf (accessed 21 March 2016) GoI (Government of India) (1927) Indian Forest Act 1927. http://lawmin.nic.in/legislative/textof centralacts/1927.pdf (accessed 21 March 2016) GoI (Government of India) (1976) Report of the National Commission on Agriculture, Part IX, Forestry. National Commission on Agriculture, Ministry of Agriculture and Irrigation, Government of India, New Delhi, India Guha R, Gadgil M (1989) State forestry and social conflict in British India. Past Present 123:141–177 Haeuber R (1993) Indian Forest Policy in two eras: Continuity or change? Environ Hist Rev 17(1):49–76 India State of Forest Report (2019). https://fsi.nic.in/forest-report-2019 MoEF (1988). https://www.indiascienceandtechnology.gov.in/sites/default/files/file-uploads/sci encetechnologypolicies/1527506823_introduction-nfp.pdf Mukerji AK (2003) Forest policy reforms in India–evolution of the joint forest management approach. Paper submitted to the XII World Forestry Congress, Quebec City, Canada Shyamsunder S, Parameswarappa S (1987) Forestry in India: the forester’s view. Ambio 16:332–337 UNDP (2014). https://www.undp.org/publications/undp-annual-report-2014

Chapter 5

Socio-economic Analysis of Sample Households in the South Bengal and The North Bengal Forest Division

Abstract The present chapter offers a visualization of the socio-economic conditions of the sample households across four forest divisions of South Bengal and North Bengal. This chapter also analyses dependency on non-timber forest products (NTFPs) and other benefits from derived forests like soil protection, climate protection, water conservation, tourism, and livestock grazing across forest divisions of South Bengal and North Bengal.

5.1 Socio-economic Conditions of Households in the Purulia Forest Division The socio-economic characteristics of the sample households (in terms of caste, gender, age of headed households, education, average family size, economic status, and landholdings) of the Purulia district are shown in Table 5.1. It is found from Table 5.1 that about 67.45% of households belong to scheduled caste (SC) and scheduled tribe (ST) while 3.57% are in general caste and about 29% belong to other backward classes (OBC). It is also observed that Ichakota village has the highest percentage of tribal households (100%) followed by Tarpenia (95.65%), Bandhghutu (84.62%), and others. Whereas in the case of other backward classes Charida) the village has the highest concentration (83.87) followed by Perorgoria (75) and others. It is also found that about 90% are male-headed households while 10% of femaleheaded households in the Purulia forest division. Bandhghutu village (100) has the highest percentage of male-headed households and Tarpenia (26.09) has the highest percentage of female-headed households (Table 5.1). In the case of the age of headed households, it is found that 45.24% of headed households belong from 41 to 60 years, followed by 38.89% of 21–40 years and 15.87% of 60 years and above. The education structure of the sample households of the Purulia forest division shows that more than 58% of household heads are illiterate while 42% of the household head got formal education. Ichakota village has the highest number of illiterate-headed households (89) followed by Nischintapur (81), Tarpenia (78), Bagti (68), and others. Rabidi (36.36) has the highest percentage of primarily educatedheaded households followed by Perorgoria (34.38), Charida (29.03), and others. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. P. Basu, Governance and Institution in the Indian Forest Sector, https://doi.org/10.1007/978-3-031-34746-7_5

63

11 (84.62)

–

2 (15.38)

ST

General

OBC

13 (100)

Male

6 (46.15)

1 (7.69)

41–60 years

above 60 years

7 (53.85)

2 (15.38)

4 (30.77)

–

5

Illiterate

Primary

Secondary

Above secondary

Average of family size

Education

6 (46.15)

21–40 years

Age of the head of households

–

Female

Gender

–

SC

Social status

4 (12.5)

24 (75)

–

1 (3.23)

6.08

1 (4.35)

2 (8.70)

2 (8.70)

N = 18

Ichakota

3 (6.98)

–

18 (100)

4 (9.09)

2 (11.11)

18 (40.91) –

2 (4.55)

18 (41.86) 19 (43.18) 6 (33.33)

8 (25)

–

5.4

2 (6.25) 4.8

1 (4.55)

2 (9.09)

11 (34.38) 8 (36.36) 9 (28.13)

3 (11.54)

–

–

19 (73.08)

7 (26.92)

N = 26

5 (11.63)

7 (15.91)

4 (22.22)

4 (15.38)

12 (46.15)

10 (38.46)

5

1 (3.23)

5.2

3 (6.98)

4.1

–

4 (9.09)

5.1

–

–

10 (23.26) 10 (22.73) 2 (27.78)

11 (35.48) 6 (13.95)

9 (29.03)

5

–

–

5 (19.23)

5.08 (continued)

8 (3.17)

38 (15.08)

59 (23.41)

147 (58.33)

40 (15.87)

114 (45.24)

98 (38.89)

227 (90.08)

25 (9.92)

73 (28.97)

9 (3.57)

118 (46.83)

52 (20.63)

No. of HH = 252

Nischintapur Purulia

10 (32.26) 24 (55.81) 30 (68.18) 16 (88.89) 21 (80.77)

6 (19.35)

14 (43.75) 10 (45.45) 18 (58.06) 20 (46.51) 18 (40.91) 8 (44.44)

18 (78.26) 10 (31.25) 11 (50)

5 (21.74)

8 (34.78)

3 (6.98)

26 (60.47) 5 (11.36)

26 (83.87) –

2 (6.45)

3 (9.68)

N = 44

Bagti

14 (32.56) 19 (43.18) –

N = 43

Lawadi

20 (90.91) 30 (96.77) 40 (93.02) 40 (90.91) 16 (88.89) 23 (88.46)

2 (9.09)

3 (13.64)

2 (9.09)

6 (27.27)

10 (43.48) 10 (31.25) 12 (54.55) 7 (22.58)

17 (73.91) 28 (87.5)

6 (26.09)

–

–

11 (50)

–

N = 31

–

Charida

N = 32

N = 22

Perorgoria Rabidi

22 (95.65) 8 (25)

1 (4.35)

Socio-economic Bandhghutu Tarpenia variables N = 13 N = 23

Table 5.1 Socio-economic conditions of the sample households in the Purulia forest division

64 5 Socio-economic Analysis of Sample Households in the South Bengal …

–

APL

2 (15.38)

9 (69.23)

< 1 Acre

> =1 Acre

3 (9.38)

–

–

N = 44

Bagti N = 18

Ichakota

2 (4.65)

4 (9.09)

–

–

2 (4.65)

–

–

19 (61.29) 16 (37.21) 20 (45.45) 6 (33.33)

1 (3.23)

5 (19.23)

2 (7.69)

–

26 (100)

N = 26

153 (60.71)

10 (3.97)

89 (35.32)

10 (3.97)

242 (96.03)

No. of HH = 252

Nischintapur Purulia

12 (52.17) 27 (84.38) 13 (59.09) 12 (38.71) 25 (58.14) 24 (54.55) 12 (66.67) 19 (73.08)

1 (4.35)

9 (40.91)

–

29 (90.63) 22 (100)

N = 43

Lawadi

30 (96.77) 41 (95.35) 40 (90.91) 18 (100)

N = 31

10 (43.48) 5 (15.63)

–

23 (100)

Charida

N = 32

N = 22

Perorgoria Rabidi

Source Author’s calculation; Note Figures in parentheses show percentage of total households

2 (15.38)

Land less

Land holding (acre)

13 (100)

BPL

Economic status

Socio-economic Bandhghutu Tarpenia variables N = 13 N = 23

Table 5.1 (continued)

5.1 Socio-economic Conditions of Households in the Purulia Forest Division 65

66

5 Socio-economic Analysis of Sample Households in the South Bengal …

Charida (35.48) village has most percentage of secondary educated headed households followed by Bandhghutu (30.77), Perorgoria, and others. The average family size of the household in the Purulia district is 5.08. Tarpenia has the highest average family size (6.08). Economic status is a good indicator of social livelihood. From Table 5.1 it is found that 96% of sample households in Purulia are living below the poverty line. All the sample households of Bandhghutu, Tarpenia, Rabidi, Ichakota, and Nischintapur villages are living below the poverty line. The economic structure of the sample household is determined by their land holdings. It is observed from Table 5.1 that 35% of households in the Purulia forest division are landless farmers while others are marginal and small farmers (65%). Perorgoria (84%) village has the highest number of households having more than 1 acre of land followed by Nischintapur (73%), Bandhghutu (69%), Ichakota (66%), Rabidi (59%), Lawadi (58%), Bagti (44%), Tarpenia (52%) and Charida (38%). The infrastructural availabilities such as sanitation facilities, type of sanitation, accessibility of public health care facilities, facilities from banks, loan facilities, and housing conditions are presented in Table 5.2. It is found that 54% of households in the Purulia district have sanitation facilities and others have no sanitation. Charida (90.32) village has the highest percentage of sanitation facilities followed by Perorgoria (78.13), Bandhghutu (76.92), Lawadi (69.77), and others. On the other hand, Nischintapur (100) and Tarpenia (100) have the highest percentage of no sanitation facility followed by Ichakota (72.22), Rabidi (54.55), and others. In the case of types of sanitation, it is found that 46% of households have pucca sanitation in the Purulia district. Table 5.2 reveals that more than 89% of households in the Purulia district have access to public healthcare facilities. Charida (100%) and Nischintapur (100%) have the highest percentage of public health care accessibility followed by Lawadi (97%), Bagti (90.91%), Ichakota (88.89%), and others. Table 5.2 shows that 98% of households have taken banking facilities in the Purulia forest division. Table 5.2 shows that no households have taken loans from the bank in the Purulia forest division. In the case of housing conditions, it is found that more than 82% of households in the Purulia forest division have kaccha houses and the remaining 17% have pucca houses. Nischintapur (100) and Ichakota (100) have the highest percentage of each house, whereas Bandhghutu (76.92) has the highest number of pucca houses (Table 5.2). The occupational structure of the households is shown in Table 5.3. The occupational structure shows that more than 50% of households have three occupations and more than 40% of households have double occupations for subsistence in the Purulia forest division. Charida (83.9%) has the highest percentage of double occupational households followed by Rabidi (59.1%), Nischintapur (42.3%), and others. In the case of triple occupational households Ichakota (77.7%) has the highest percentage followed by Bandhgutu (69.2%), Lawadi (62.8%), Tarpenia (60.9%), Perorgoria (59.4%), and others. Income is an important index that reflects the economic status of the households. The sources of livelihood of the sample households are agriculture, forest product collection, casual labor, petty business, handicrafts, government services, etc. It is found from Table 5.4 that more than 86% of households are engaged in forest product collection, about 90% of households are engaged in casual labor and about 56% are

N = 23

N = 13

10 (76.92)

Yes

–

–

3 (23.08)

Normal

Others

No

23 (100)

–

–

–

–

8 (61.54)

Yes

No

13 (100)

13 (100)

Yes

Loan facility

–

No

Banking facility

5 (38.46)

No

23 (100)

23 (100)

–

19 (82.61)

4 (17.39)

Accessibility of public health care facility

10 (76.92)

Pucca

Type of sanitation

3 (23.08)

No

23 (100)

Tarpenia

Bandhghutu

Sanitation facility

Infrastructure

32 (100)

30 (93.75)

2 (6.25)

28 (87.5)

4 (12.5)

8 (25)

–

4 (12.5)

20 (62.5)

25 (78.13)

7 (21.88)

N = 32

Perorgoria

22 (100)

22 (100)

–

16 (72.73)

6 (27.27)

12 (54.55)

1 (4.55)

1 (4.55)

8 (36.36)

10 (45.45)

12 (54.55)

N = 22

Rabidi

31 (100)

30 (96.77)

1 (3.23)

31 (100)

–

3 (9.68)

–

3 (9.68)

25 (80.65)

28 (90.32)

3 (9.68)

N = 31

Charida

43 (100)

43 (100)

–

42 (97.67)

1 (2.33)

13 (30.23)

–

3 (6.98)

27 (62.79)

30 (69.77)

13 (30.23)

N = 43

Lawadi

Table 5.2 Infrastructural facilities of the sample households in the Purulia forest division

44 (100)

43 (97.73)

1 (2.27)

40 (90.91)

4 (9.09)

17 (38.64)

–

5 (11.36)

22 (50)

27 (61.36)

17 (38.64)

N = 44

Bagti

18 (100)

18 (100)

–

16 (88.89)

2 (11.11)

13 (72.22)

–

1 (5.56)

4 (22.22)

5 (27.78)

13 (72.22)

N = 18

Ichakota

26 (100)

26 (100)

–

26 (100)

–

26 (100)

–

–

–

–

26 (100)

N = 26

Nischintapur

(continued)

252 (100)

248 (98.41)

4 (1.59)

226 (89.68)

26 (10.32)

118 (46.83)

1 (0.40)

17 (6.75)

116 (46.03)

135 (53.57)

117 (46.43)

No. of HH = 252

Purulia

5.1 Socio-economic Conditions of Households in the Purulia Forest Division 67

12 (92.31)

Kachha

20 (86.96)

3 (13.04)

25 (78.13)

7 (21.88)

–

N = 32

Perorgoria

15 (68.18)

7 (31.82)

–

N = 22

Rabidi

19 (61.29)

12 (38.71)

–

N = 31

Charida

36 (83.72)

7 (16.28)

–

N = 43

Lawadi

Source Author’s calculation; Note Figures in parentheses show percentage of total households

10 (76.92)

Pucca

–

N = 23

N = 13

–

Tarpenia

Bandhghutu

Housing condition

Yes through Bank/Money lender

Infrastructure

Table 5.2 (continued)

38 (86.36)

6 (13.64)

–

N = 44

Bagti

18 (100)

–

–

N = 18

Ichakota

26 (100)

–

–

N = 26

Nischintapur

209 (82.94)

43 (17.06)

–

No. of HH = 252

Purulia

68 5 Socio-economic Analysis of Sample Households in the South Bengal …

9 (69.2)

–

13 (100)

Triple occupation

More than three occupation

All

23 (100)

1 (4.3)

14 (60.9)

8 (34.8)

32 (100)

1 (3.1)

19 (59.4)

10 (31.3)

2 (6.2)

N = 32

Perorgoria

22 (100)

–

8 (36.3)

13 (59.1)

1 (4.6)

N = 22

Rabidi

31 (100)

–

2 (6.4)

26 (83.9)

3 (9.7)

N = 31

Charida

43 (100)

2 (4.6)

27 (62.8)

14 (32.6)

–

N = 43

Lawadi

Source Author’s calculation; Note Figures in parentheses show percentage of total households

4 (30.8)

Double occupation

–

N = 23

N = 13

–

Tarpenia

Bandhghutu

Single occupation

Occupational Structure

Table 5.3 Occupational Structure of the sample households in the Purulia forest division

44 (100)

5 (11.4)

24 (54.6)

15 (34.0)

–

N = 44

Bagti

18 (100)

–

14 (77.7)

4 (22.3)

–

N = 18

Ichakota

26 (100)

–

14 (53.8)

11 (42.3)

1 (3.9)

N = 26

Nischintapur

252 (100)

9 (3.6)

131 (51.9)

105 (41.7)

7 (2.8)

No. of HH = 252

Purulia

5.1 Socio-economic Conditions of Households in the Purulia Forest Division 69

70

5 Socio-economic Analysis of Sample Households in the South Bengal …

in agriculture in the Purulia district. All the sample households of Bandhghutu, Tarpenia, Lawadi, Bagdi, Ichakota, and Nischintapur villages are engaged in forest product collection. The sample households of Charida village do not engage themselves in forest product collection whereas all the households are engaged as casual laborers. In the case of agriculture, Bandhghutu (84.6%) village has the highest percentage of household engagement followed by Perorogoria (84.4%), Ichakota (72.2%), and others (Table 5.4). Monthly income per household has been analyzed in Table 5.5. About 20% of income come comes from agriculture, 24% from forestry, and 41% from casual labor in the Purulia forest division. The average monthly income per household is Rs 8714.02 in the Purulia forest division. Among the livelihood groups, the highest monthly income per household is observed for handicraft workers (Rs 8735.29) followed by businessmen (Rs 7500), service holders (Rs 5986.96), casual labor (Rs 3697.33), cultivators (Rs 3103.50) and households who collect forest product (Rs 2082.72). The income distribution of the households in the Purulia forest division has been presented in Table 5.6. More than 60% of households have income lies between Rs 5001–10,000, nearly 21% of households have income lies between Rs 10,001– 15,000, 12% of households have less than Rs 5000 and the remaining 7.1% of households have income more than Rs 15,000 in Purulia forest division. The livelihoods of sample households are dependent on the collection of NTFPs like fuelwood, mahua flower, food, fodder, mushrooms, and herbal products from the forest. The percentage of households collecting these NTFPs in the Purulia forest division is shown in Fig. 5.1. More than 86% of households in Purulia forest divisions are engaged in the collection of fuelwood, followed by the collection of Mahua flower (49.21%), Mushroom (29.76%), Herbals (3.17%), and others. It is also observed that more than 80% of households have collected mahua flowers in Bandhghutu and Rabiti villages. The households in the village of Charida do not collect any type of NTFPs from the forest as they have an alternative source of livelihood (Table 5.7). Dependency on NTFPs is depicted in Fig. 5.1. In the study area of the Purulia forest division, we observe that various types of benefits are derived from the households from the forest. In this connection, households were asked to rank their perception of forest benefits like soil protection, climate protection, employment provision, water conservation, tourism, livestock grazing, and shade for livestock. It is found from Table 5.8 that 72.62% of households have given 1st preference on soil protection followed by climate protection (23.81%) and so on. More than 58% of households have chosen climate protection as their 2nd preference followed by soil protection (20.63%) and so on. Again 58.33% of households have chosen livestock grazing as their 3rd preference followed by water conservation (17.06%) and so on. On the other hand, only 21% of households have given preferences on livestock grazing as their 4th preference. The ranking of benefits from the forest is shown in Fig. 5.2.

13 (100)

11 (84.6)

–

–

2 (15.4)

Casual labour

Business

Handicraft

Service

2 (8.7)

–

1 (4.3)

19 (82.6)

23 (100)

14 (60.9)

6 (18.8)

–

1 (3.1)

20 (62.5)

30 (93.8)

27 (84.4)

N = 32

Perorgoria

3 (13.6)

–

1 (4.5)

16 (72.7)

21 (95.5)

11 (50)

N = 22

Rabidi

4 (12.9)

17 (54.8)

13 (41.9)

31 (100)

–

1 (3.2)

N = 31

Charida

3 (7.0)

–

3 (7.0)

41 (95.3)

43 (100)

27 (62.8)

N = 43

Lawadi

Source Author’s calculation; Note Figures in parentheses show percentage of total households

11 (84.6)

N = 23

N = 13

Forest product collection

Tarpenia

Bandhghutu

Agriculture

Sources of livelihoods

Table 5.4 Sources of livelihoods of the sample households in the Purulia forest division

– –

2 (4.5)

1 (5.6)

18 (100)

18 (100)

13 (72.2)

N = 18

Ichakota

–

4 (9.1)

44 (100)

44 (100)

24 (54.5)

N = 44

Bagti

1 (3.8)

–

–

25 (96.2)

26 (100)

15 (57.7)

N = 26

Nischintapur

23 (9.1)

17 (6.7)

24 (9.5)

225 (89.3)

218 (86.5)

143 (56.7)

No. of HH = 252

Purulia

5.1 Socio-economic Conditions of Households in the Purulia Forest Division 71

2363.64 (24.55)

–

–

Casual labour

Businessmen

Handicraft workers

8146.15 (100) 8546.09 (100)

3000 (3.05)

–

428.57 (4.58)

3631.58 (35.1)

2937.39 (34.37)

N = 22

Rabidi

8088.13 (100)

4866.67 (11.28)

–

3000 (1.16)

8692.73 (100)

8000 (12.55)

–

4000 (2.09)

3135 (24.23) 3687.5 (30.85)

1864 (21.61) 2297.14 (25.22)

4000 (41.73) 5090.91 (29.28)

N = 32

Perorgoria

14,190.32 (100)

7250 (6.59)

8735.29 (33.76)

10,153.85 (30.01)

4077.42 (28.73)

–

4000 (0.91)

N = 31

Charida

7491.16 (100)

4500 (4.19)

–

6000 (5.59)

4036.59 (51.38)

1595.81 (21.3)

2092.59 (17.54)

N = 43

Lawadi

Source Author’s calculation; Note Figures in parentheses show percentage of total households

All

Service holders 9000 (17)

2261.54 (27.76)

Forest product collection

3214.29 (22.89)

N = 23

N = 13

2954.55 (30.69)

Tarpenia

Bandhghutu

Agriculture

Sources of income

Table 5.5 Monthly income (Rs) per household by Livelihood groups in the Purulia forest division

7794.18 (100)

5000 (2.92)

–

2500 (2.92)

3718.18 (47.7)

3718.18 (24.94)

3075 (21.52)

N = 44

Bagti

8332.22 (100)

–

–

4000 (2.67)

3761.11 (45.14)

2182.22 (26.19)

3000 (26)

N = 18

Ichakota

7248.77 (100)

8000 (4.24)

–

–

3680 (48.81)

2287.23 (31.55)

1933.33 (15.39)

N = 26

Nischintapur

8714.02 (100)

5986.96 (4.98)

8735.29 (3.96)

7500 (5.59)

3697.33 (40.56)

2440.77 (24.39)

3103.50 (20.51)

No. of HH = 252

Purulia

72 5 Socio-economic Analysis of Sample Households in the South Bengal …

9 (69.2)

3 (23.1)

–

–

13 (100)

10,001–15,000

15,001–20,000

above 20,000

All

23 (100)

–

1 (4.4)

6 (26.1)

14 (60.8)

2 (8.7)

32 (100)

–

2 (6.3)

4 (12.5)

21 (65.6)

5 (15.6)

N = 32

Perorgoria

22 (100)

–

1 (4.6)

3 (13.6)

17 (77.2)

1 (4.6)

N = 22

Rabidi

31 (100)

5 (16.1)

6 (19.4)

15 (48.4)

4 (12.9)

1 (3.2)

N = 31

Charida

43 (100)

–

1 (2.4)

6 (13.9)

28 (65.1)

8 (18.6)

N = 43

Lawadi

Source Author’s calculation; Note Figures in parentheses show percentage of total households

1 (7.7)

N = 23

N = 13

5001–10,000

Tarpenia

Bandhghutu

≤ 5000

Income

Table 5.6 Distribution of income of the sample households in the Purulia forest division

44 (100)

–

1 (2.2)

11 (25)

27 (61.4)

5 (11.4)

N = 44

Bagti

18 (100)

–

–

2 (11.2)

15 (83.3)

1 (5.5)

N = 18

Ichakota

26 (100)

–

1 (3.8)

2 (7.7)

17 (65.4)

6 (23.1)

N = 26

Nischintapur

252 (100)

5 (2.0)

13 (5.1)

52 (20.7)

152 (60.3)

30 (11.9)

No. of HH = 252

Purulia

5.1 Socio-economic Conditions of Households in the Purulia Forest Division 73

74

5 Socio-economic Analysis of Sample Households in the South Bengal …

Percentage of Households

PURULIA 100 90 80 70 60 50 40 30 20 10 0 Fuelwood

Fodder

Herbals

Mahua Flower

Mushroom

Others

Fig. 5.1 Dependency on NTFPs in the Purulia forest division. Source Author’s calculation

5.2 Socio-economic Conditions of Bankura (South) Forest Division The socio-economic characteristics of the sample households (in terms of caste, gender, age of headed households, education, average family size, economic status, and landholdings) of the Bankura district are shown in Table 5.9. It is found from Table 5.9 that about 68.42% of households belong to the scheduled tribe (ST) and nearly 10% belong to the scheduled caste (SC) while 3.95% are in general caste and 17.54% belong to other backward classes (OBC). It is also observed that Jamgeria village experiences the highest percentage of tribal households (100%) followed by the villages like Mahadebsinan (96.15), Makhnu (94.12), and others. It is also found that about 90% of households are male-headed while 10% of female-headed households are in the Bankura forest division. Mitham village (97.37%) has the highest percentage of male-headed households and Barapatcha (20%) has the highest percentage of female-headed households (Table 5.9). In the case of the age of headed households, it is found that 54.82 percentage of households belong to the age group 41–60 years, followed by 24.56% of 21–40 years and 20.61% of 60 years and above. The education structure of the sample households of Bankura forest division shows that more than 51.32% of the household’s heads are illiterate while 48.68% of the household’s heads got formal education. The average family size of the household in the Bankura forest division is 4.5. Economic status is a good indicator of social livelihood. From Table 5.9 it is found that 98.68% of sample households in Bankura are living below the poverty line. All the sample households of villages like Kamo, Makhnu, Bagdiha, Mitham, Jamgeria, Barapatcha, Mahadebsinan, Kalabani, and Dhankuravillage are living below the poverty line. The economic structure of the sample household is determined by their land holdings. It is observed from Table 5.9 that 6.14% of households are landless farmers having no land while others are marginal and small farmers (93.86%).

–

–

11 (84.62)

–

–

Herbals

Mahua flower

Mushroom

Others

–

16 (69.57)

15 (65.22)

–

–

23 (100)

1 (3.13)

19 (59.38)

22 (68.75)

–

–

30 (93.75)

N = 32

Perorgoria

–

10 (45.45)

19 (86.36)

–

–

21 (95.45)

N = 22

Rabidi

–

–

–

–

–

–

N = 31

Charida

3 (6.98)

4 (9.30)

9 (20.93)

–

–

43 (100)

N = 43

Lawadi

Source Author’s calculation; Note Figures in parentheses show percentage of total households

13 (100)

N = 23

N = 13

Fodder

Tarpenia

Bandhghutu

Fuelwood

NTFPs

–

15 (34.09)

20 (45.45)

2 (4.55)

2 (4.55)

44 (100)

N = 44

Bagti

2 (11.11)

1 (5.55)

10 (55.55)

–

1 (5.55)

18 (100)

N = 18

Ichakota

Table 5.7 Dependency on NTFP of the sample households across different villages in the Purulia forest division

–

10 (38.46)

18 (69.23)

6 (23.08)

–

26 (100)

N = 26

Nischintapur

6 (2.38)

75 (29.76)

124 (49.21)

8 (3.17)

3 (1.19)

218 (86.51)

No. of HH = 252

Purulia

5.2 Socio-economic Conditions of Bankura (South) Forest Division 75

76

5 Socio-economic Analysis of Sample Households in the South Bengal …

Table 5.8 Benefits from forests by the sample households in the Purulia forest division Purulia

Yes

No Benefits

Rank 1

Rank 2

Rank 3

Rank 4

Soil protection

183 (72.62)

52 (20.63)

8 (3.17)

–

9 (3.57)

Climate protection

60 (23.81)

147 (58.33)

13 (5.16)

–

32 (12.70)

Employment

–

2 (0.79)

19 (7.54)

–

231 (91.67)

Water conservation

1 (0.40)

14 (5.56)

43 (17.06)

–

194 (76.98)

Tourism

1 (0.40)

–

8 (3.17)

–

243 (96.43)

Livestock grazing

6 (2.38)

34 (13.49)

147 (58.33)

53 (21.03)

12 (4.76)

Shade for livestock

–

–

3 (1.19)

1 (0.40)

248 (98.41)

Source Author’s calculation; Note Figures in parentheses show percentage of total households

Purulia Forest Division Percentage of household

Fig. 5.2 Ranking of benefits from forest in Purulia forest division

80 60 40 20 0 Rank 1

Rank 2

Rank 3

Soil Protection Climate Protection Livestock Grazing

The infrastructural facilities such as sanitation facilities, type of sanitation, accessibility of public health care facilities, facilities of loan from banks, and housing conditions are presented in Table 5.10. It is found that 65.35% of households in the Bankura forest division have sanitation facilities. Table 5.10 reveals that more than 87.72% of households in the Bankura forest division have access to public healthcare facilities. Table 5.10 shows that 98.25% of households have taken banking facilities. In the case of housing conditions, it is found that 81.14% of households in the Bankura forest division have kaccha houses and the remaining 18.86% have pucca houses. Dhankura (100) has the highest percentage of each house, whereas Bagdiha (50) has the highest number of pucca houses (Table 5.10). The occupational structure of the households is shown in Table 5.11. The occupational structure shows that more than 60% of households have three occupations and 25% of households have double occupation for subsistence in the Bankura forest division. Income is an important index which reflects economic status of the households. The sources of livelihood of the sample households are agriculture, forest product collection, casual labour, petty business, handicrafts and government services. It is found from Table 5.12 that 96.1% of households are engaged in forest product collection, about 82.5% households are engaged in casual laborer, and about 89.5% are in the agriculture in Bankura forest division.

N = 17

N = 33

–

1 (3.03) –

General

OBC

27 (81.82)

Male

16 (94.12)

1 (5.88)

–

8 (24.24)

15 (45.45)

10 (30.30)

21–40 years

41–60 years

Above 60 years

7 (87.5)

1 (12.5)

2 (25)

2 (25)

4 (50)

–

N = 08

Bagdiha

1 (5.88)

12 (70.59)

1 (12.5)

7 (87.5)

4 (23.53) –

Age of the head of households

6 (18.18)

Female

Gender

27 (81.82)

ST

16 (94.12)

5 (15.15)

1 (5.88)

Makhnu

Kamo

SC

Social status

Socio-economic variables

5 (13.16)

23 (60.53)

10 (26.32)

37 (97.37)

1 (2.63)

–

5 (13.16)

26 (68.42)

7 (18.42)

N = 38

Mitham

5 (19.23)

11 (42.31)

10 (38.46)

24 (92.31)

2 (7.69)

–

–

26 (100)

–

N = 26

Jamgeria

2 (20)

7 (70)

1 (10)

8 (80)

2 (20)

–

1 (10)

9 (90)

–

N = 10

Barapatcha

6 (23.08)

14 (53.85)

6 (23.08)

22 (84.62)

4 (15.38)

–

–

25 (96.15)

1 (3.85)

N = 26

Mahadebsinan

Table 5.9 Socio-economic conditions of the sample households in the Bankura forest division

6 (25)

10 (41.67)

8 (33.33)

23 (95.83)

1 (4.17)

23 (95.83)

–

–

1 (4.17)

N = 24

Kadmagarh

5 (20.83)

17 (70.83)

2 (8.33)

22 (91.67)

2 (8.33)

3 (12.5)

1 (4.17)

12 (50)

8 (33.33)

N = 24

Kalabani

6 (27.27)

9 (40.91)

7 (31.82)

20 (90.91)

2 (9.09)

11 (50)

–

11 (50)

–

N = 22

Dhankura

(continued)

47 (20.61)

125 (54.82)

56 (24.56)

206 (90.35)

22 (9.65)

40 (17.54)

9 (3.95)

156 (68.42)

23 (10.09)

No. of HH = 228

Bankura

5.2 Socio-economic Conditions of Bankura (South) Forest Division 77

N = 17

N = 33

13 (39.39)

–

4.7

Secondary

Above secondary

Average of Family Size

–

APL

1 (3.03) –

31 (93.94)

< 1 Acre

> =1 Acre

–

5 (62.5)

2 (25)

1 (12.5)

–

8 (100)

5

1 (12.5) 4.5

1 (3.85)

8 (30.77)

5 (19.23)

12 (46.15)

N = 26

Jamgeria

22 (57.89)

5 (13.16)

11 (28.95)

–

26 (100)

–

–

–

38 (100) 26 (100)

4.3

3 (7.89)

18 (47.37)

3 (7.89)

14 (36.84)

N = 38

Mitham

10 (10)

–

–

–

10 (10)

4.8

–

5 (50)

–

5 (50)

N = 10

Barapatcha

24 (92.31)

1 (3.85)

1 (3.85)

–

26 (100)

4.9

1 (3.85)

7 (26.92)

1 (3.85)

17 (65.38)

N = 26

Mahadebsinan

Source Author’s calculation; Note Figures in parentheses show percentage of total households

17 (100)

1 (3.03) –

–

17 (100)

3.8

–

Land less

Land holding (acre)

33 (100)

BPL

Economic status

2 (6.06) –

Primary

4 (50)

N = 08

Bagdiha

2 (11.76) 3 (37.5)

18 (54.55)

15 (88.24)

Makhnu

Kamo

Illiterate

Education

Socio-economic variables

Table 5.9 (continued)

24 (100)

–

–

3 (12.5)

21 (87.5)

4.4

3 (12.5)

9 (37.5)

4 (16.67)

8 (33.33)

N = 24

Kadmagarh

23 (95.83)

1 (4.17)

–

–

24 (100)

4.1

1 (4.17)

10 (41.67)

–

13 (54.17)

N = 24

Kalabani

22 (100)

–

–

–

22 (100)

4.7

–

9 (40.91)

2 (9.09)

11 (50)

N = 22

Dhankura

204 (89.47)

10 (4.39)

14 (6.14)

3 (1.32)

225 (98.68)

4.5

10 (4.39)

84 (36.84)

17 (7.46)

117 (51.32)

No. of HH = 228

Bankura

78 5 Socio-economic Analysis of Sample Households in the South Bengal …

N = 17

N = 33

20 (60.61)

Yes

–

–

13 (39.39)

Normal

Others

No

–

–

8 (100)

8 (100)

2 (11.76) –

–

1 (5.88)

14 (82.35)

15 (88.24)

27 (81.82)

Yes

30 (90.91)

17 (100)

3 (9.09) –

Yes

13 (76.47)

8 (100)

–

7 (87.5)

4 (23.53) 1 (12.5)

No

Banking facility

6 (18.18)

No

Accessibility of public health care facility

20 (60.61)

Pucca

Type of sanitation

13 (39.39)

No

N = 08

Bagdiha

2 (11.76) –

Makhnu

Kamo

Sanitation facility

Infrastructure N = 26

Jamgeria

–

–

17 (65.38)

17 (65.38)

37 (97.37)

1 (2.63)

36 (94.74)

2 (5.26)

26 (100)

–

23 (88.46)

3 (11.54)

8 (21.05) 9 (34.62)

–

–

30 (78.95)

30 (78.95)

8 (21.05) 9 (34.62)

N = 38

Mitham

10 (100)

–

9 (90)

1 (10)

1 (10)

–

–

9 (90)

9 (90)

1 (10)

N = 10

Barapatcha

26 (100)

–

21 (80.77)

5 (19.23)

10 (38.46)

–

–

16 (61.54)

16 (61.54)

10 (38.46)

N = 26

Mahadebsinan

Table 5.10 Infrastructural facilities of the sample households in the Bankura forest division

24 (100)

–

24 (100)

–

8 (33.33)

–

–

16 (66.67)

16 (66.67)

8 (33.33)

N = 24

Kadmagarh

24 (100)

–

20 (83.33)

4 (16.67)

12 (50)

–

–

12 (50)

12 (50)

12 (50)

N = 24

Kalabani

22 (100)

–

20 (90.91)

2 (9.09)

16 (72.73)

–

–

6 (27.27)

6 (27.27)

16 (72.73)

N = 22

Dhankura

(continued)

224 (98.25)

4 (1.75)

200 (87.72)

28 (12.28)

79 (34.65)

–

1 (0.44)

148 (64.91)

149 (65.35)

79 (34.65)

No. of HH = 228

Bankura

5.2 Socio-economic Conditions of Bankura (South) Forest Division 79

25 (65.79)

13 (34.21)

2 (5.26)

36 (94.74)

N = 38

Mitham

21 (80.77)

5 (19.23)

–

26 (100)

N = 26

Jamgeria

7 (70)

3 (30)

–

10 (100)

N = 10

Barapatcha

21 (80.77)

5 (19.23)

–

26 (100)

N = 26

Mahadebsinan

Source Author’s calculation; Note Figures in parentheses show percentage of total households

4 (50)

31 (93.94)

Kachha

14 (82.35)

2 (6.06) 3 (17.65) 4 (50)

–

8 (100)

N = 08

Bagdiha

Pucca

Housing condition

1 (5.88)

–

Yes through Bank

N = 17

N = 33

33 (100) 16 (94.12)

Makhnu

Kamo

No

Loan facility

Infrastructure

Table 5.10 (continued)

21 (87.5)

3 (12.5)

4 (16.67)

20 (83.33)

N = 24

Kadmagarh

19 (79.17)

5 (20.83)

–

24 (100)

N = 24

Kalabani

22 (100)

–

5 (4.58)

17 (77.27)

N = 22

Dhankura

185 (81.14)

43 (18.86)

12 (5.26)

216 (94.74)

No. of HH = 228

Bankura

80 5 Socio-economic Analysis of Sample Households in the South Bengal …

20 (60.6)

2 (6.1)

33 (100)

Triple occupation

More than three occupation

All

17 (100)

–

11 (64.7)

6 (35.3)

8 (100)

–

3 (37.5)

5 (62.5)

–

N = 08

Bagdiha

38 (100)

6 (15.8)

12 (31.6)

20 (52.6)

–

N = 38

Mitham

26 (100)

1 (2.6)

23 (60.5)

2 (5.3)

–

N = 26

Jamgeria

10 (100)

2 (20)

8 (80)

–

–

N = 10

Barapatcha

26 (100)

2 (7.7)

19 (73.1)

5 (19.2)

–

N = 26

Mahadebsinan

Source Author’s calculation; Note Figures in parentheses show percentage of total households

9 (27.3)

Double occupation

–

N = 17

N = 33

2 (6.1)

Makhnu

Kamo

Single occupation

Occupational structure

Table 5.11 Occupational structure of the sample households in the Bankura forest division

24 (100)

1 (4.2)

22 (91.6)

1 (4.2)

–

N = 24

Kadmagarh

24 (100)

1 (4.2)

16 (66.6)

7 (29.2)

–

N = 24

Kalabani

22 (100)

–

20 (90.9)

2 (9.1)

–

N = 22

Dhankura

228 (100)

15 (6.6)

154 (67.5)

57 (25)

2 (0.9)

No. of HH = 228

Bankura

5.2 Socio-economic Conditions of Bankura (South) Forest Division 81

N = 17

N = 33

N = 08

1 (3.0)

–

– 6 (15.8)

2 (7.7)

– 2 (20)

– 2 (7.7)

–

1 (3.8)

19 (73.1)

26 (100)

25 (96.2)

N = 26

Source Author’s calculation; Note Figures in parentheses show percentage of total households

–

–

1 (2.6)

–

Service

–

Handicraft

1 (3.8)

3 (7.9)

–

27 (71.1) 22 (84.6) 9 (90)

–

1 (3.0)

10 (100)

10 (100)

N = 10

2 (8.3)

–

1 (4.2)

22 (91.7)

24 (100)

24 (100)

N = 24

22 (100)

N = 22

–

–

2 (8.3)

1 (4.5)

–

–

20 (83.3) 21 (95.5)

16 (7.0)

1 (0.4)

9 (3.9)

188 (82.5)

219 (96.1)

204 (89.5)

No. of HH = 228

Dhankura Bankura

19 (79.2) 22 (100)

24 (100)

N = 24

Barapatcha Mahadebsinan Kadmagarh Kalabani

Business

35 (92.1) 26 (100)

22 (57.9) 26 (100)

N = 26

Jamgeria

Casual Labour 27 (81.8) 14 (82.4) 7 (87.5)

8 (100)

N = 38

Bagdiha Mitham

32 (96.1) 14 (82.4) 5 (62.5)

Makhnu

Kamo

Forest product 32 (96.1) 17 (100) collection

Agriculture

Sources of livelihoods

Table 5.12 Sources of livelihoods of the sample households in the Bankura forest division

82 5 Socio-economic Analysis of Sample Households in the South Bengal …

5.3 Socio-economic Conditions of Rupnarayan Forest Division …

83

The monthly income per household has been presented in Table 5.13. It is found that about 32.24% of income comes from agriculture, 23.30% from forestry and 30.35% from casual labor, and 11.95% comes from the service sector in the Bankura forest division. The average monthly income per household in the Bankura forest division is found to be Rs 7467.69. Among the livelihood groups, the highest monthly income per household is observed from service (Rs 12,718.75) followed by business (Rs 3688.89), handicraft (Rs 3500), casual labor (Rs 2748.4), agriculture (Rs 2690.98), and forest Product collection (Rs 1811.75). The income distribution of the households in the Bankura forest division has been shown in Table 5.14. More than 69% of households have income lies between Rs 5001–10,000, 6.6 percent of households have income between Rs 10,001–15,000, more than 20% of households have income less than Rs 5000 and the remaining 3.5% of households have income more than 15,000 in Bankura forest division (Table 5.14). The livelihoods of sample households are dependent on the collection of nontimber forest products (NTFPs). The dependency on Non-Timber Forest Products (NTFP) of the sample households of the Purulia district has been shown in Fig. 5.3. More than 96% of households collect fuelwood, 23.25% collect mahua flowers, 10.96% collect Sal seeds, etc. The village-wise distribution of the collection of NTFP is presented in Table 5.15. Villages like Makhnu, Bagdhia, Jamgeria, Barapatcha, Mahadebsinan, Kadmagarh, and Dhankura have collected fuelwood from all the sample households. It is also observed that more than 58% of households of village Makhnu, 45% of the household of village Kamo and 38% of the household of village Mahadebsinan have collected mahua flower from the forest. In Bankura (south) forest division, households were asked to rank their perception of forest benefits. It has been found from Table 5.16 that 62.28% of households have given soil protection as their 1st preference followed by climate protection (25.88%) and livestock grazing (11.84). More than 42% of households have chosen climate protection as their 2nd preference followed by soil protection (28.07%), livestock grazing (17.54), and others. Again 60% of households have chosen livestock grazing as their 3rd preference followed by soil protection (7.89%), shade for livestock (7.02), and others (Table 5.16) (Fig. 5.4).

5.3 Socio-economic Conditions of Rupnarayan Forest Division in the PaschimMedinipur District The socio-economic characteristics of the sample households (in terms of caste, gender, and age of headed households, education, average family size, economic status, and landholdings) of the Rupnarayan forest division in Paschim Medinipur district are presented in Table 5.17. It is found from Table 5.17 that about 70.89% of households belong to scheduled caste (SC) and scheduled tribe (ST) while 4.23% are in general caste and about 24.88% belong to other backward class (OBC). It is also observed that Kastokura, Dhabani, Godasole, and Dharnadihi villages have 100%

3366.67 (39.17))

5000 (2.15)

–

5000 (2.15)

7032 (100)

Casual labour

Business

Handicraft

Service

Total

6741.18 (100)

–

–

–

2578.57 (31.50)

2000 (29.67)

7325 (100)

–

–

–

3157.14 (37.73)

1987.5 (27.13)

4120 (35.15)

N = 08

Bagdiha

8722.21 (100)

21,666.67 (39.22)

3500 (1.05)

3666.67 (3.31)

3174.07 (25.86)

1425.54 (15.05)

2334.09 (15.49)

N = 38

Mitham

7219.62 (100)

4500 (4.79)

–

1200 (0.64)

2559.09 (29.99)

1917.31 (26.56)

2744.62 (38.02)

N = 26

Jamgeria

10,031 (100)

15,000 (29.90)

–

–

1866.67 (16.75)

2226 (22.19)

3125 (31.15)

N = 10

Barapatcha

7868.08 (100)

9250 (9.04)

–

3000 (1.47)

2552.63 (23.71)

1902.69 (24.18)

3404 (41.60)

N = 26

Mahadebsinan

Source Author’s calculation; Note Figures in parentheses show percentage of total households

1645.5 (22.67)

Forest product collection

3178.57 (38.83)

N = 17

N = 33

2453.13 (32.83)

Makhnu

Kamo

Agriculture

Sources of income

Table 5.13 Monthly income (Rs) per household by livelihood groups in the Bankura forest division N = 24

Kalabani

6229.33 (100)

4000 (5.35)

–

4000 (2.67)

2572.73 (37.86)

6255 (100)

–

–

4500 (5.99)

2050 (27.31)

1596 (25.62) 1553.68 (19.66)

1775 (28.49) 2941.67 (47.03)

N = 24

Kadmagarh

7836.36 (100)

3000 (1.73)

–

–

2985.71 (36.09)

2496.36 (31.61)

2413.64 (30.57)

N = 22

Dhankura

7467.69 (100)

12,718.75 (11.95)

3500 (0.21)

3688.89 (1.95)

2748.4 (30.35)

1811.75 (23.30)

2690.98 (32.24)

No. of HH = 228

Bankura

84 5 Socio-economic Analysis of Sample Households in the South Bengal …

–

–

15,001–20,000 1 (3.0)

–

33 (100)

Above 20,000

All

8 (100)

–

–

2 (25)

1 (12.5)

N = 26

Jamgeria

–

N = 10

38 (100)

3 (7.9)

–

2 (5.3)

26 (100)

–

–

4 (15.4)

10 (100)

1 (10)

1 (10)

1 (10) –

24 (100)

26 (100)

–

–

20 (83.3)

4 (16.7)

N = 24

1 (3.8)

1 (3.8)

3 (11.5)

16 (61.5)

5 (19.2)

N = 26

2 (9.1)

N = 22

24 (100)

–

–

1 (4.2)

22 (100)

–

–

1 (4.5)

228 (100)

5 (2.2)

3 (1.3)

15 (6.6)

159 (69.7)

46 (20.2)

No. of HH = 228

Dhankura Bankura

16 (66.6) 19 (86.4)

7 (29.2)

N = 24

Barapatcha Mahadebsinan Kadmagarh Kalabani

21 (55.2) 15 (57.7) 7 (70)

12 (31.6) 7 (26.9)

N = 38

Source Author’s calculation; Note Figures in parentheses show percentage of total households

17 (100)

1 (5.9)

10,001–15,000 –

2 (11.7)

26 (78.8) 14 (82.4) 5 (62.5)

N = 08

Bagdiha Mitham

6 (18.2)

N = 17

N = 33

5001–10,000

Makhnu

Kamo

≤ 5000

Income

Table 5.14 Distribution of income of the sample households in the Bankura forest division

5.3 Socio-economic Conditions of Rupnarayan Forest Division … 85

86

5 Socio-economic Analysis of Sample Households in the South Bengal …

BANKURA Percentage of Households

120 100 80 60 40 20 0 Fuelwood

Herbals

Sal Seeds

Mahua Flower

Others

Axis Title Fig. 5.3 Dependency on NTFPs in the Bankura (south) forest division. Source Author’s calculation

of tribal households followed by Ichalkoda (56.25), Aushbandi (48.39), and others. It is also found that about 92% are male-headed households while nearly 8% are female-headed households in the Rupnarayan forest division of Paschim Medinipur district (Table 5.17). The education structure of the sample households shows that nearly 48% of household heads are illiterate while 52% of the household head got formal education. Chandabila village has the highest number of illiterate (73.53%) households followed by Godasole, Dharnadihi, Kastokura, and others. The average family size of the household in Rupnarayan forest division of Paschim Medinipur district is 4. From Table 5.17 it is found that 82.63% of sample households are living below the poverty line. The economic structure of the sample household is determined by their land holdings. It is observed from Table 5.17 that 27.70% of households are landless farmers while 72.3% are marginal and small farmers. The infrastructural availabilities such as sanitation facilities, accessibility of public health care facilities, facilities of loans from banks, and housing conditions are presented (Table 5.18). It is found that 37.56% of households in the Rupnarayan forest division of Paschim Medinipur district have sanitation facilities and others do not have such facilities. Table 5.18 reveals that 96.24% of households have access to public healthcare facilities. Table 5.18 shows that 97.65% of households have taken banking facilities. In the case of housing conditions, it is found that 89.67% of households have kaccha houses and the remaining 10.33% have a pucca house. The occupational structure shows that nearly 59% of households have three occupations while 36% of households have double occupations for subsistence in the Rupnarayan forest division of the Paschim Medinipur district (Table 5.19). The sources of livelihood of the sample households are agriculture, forest product collection, casual labor, petty business, handicrafts, and government services. It is found from Table 5.20 that 93% of households are engaged in forest product collection, about 90% of households are engaged in casual labor and about 71% are in agriculture in the Rupnarayan forest division of Paschim Medinipur district.

2 (6.06)

15 (45.45)

4 (12.12)

Sal seeds

Mahua flower

Others

–

2 (25)

–

2 (11.67) 7 (87.5)

10 (58.82)

1 (5.88)

–

8 (100)

N = 08

Bagdiha

–

26 (100)

N = 26

Jamgeria

16 (42.11)

14 (53.85)

7 (18.42) 5 (19.23)

6 (15.79) 3 (11.54)

–

35 (92.11)

N = 38

Mitham

2 (20)

2 (20)

–

–

10 (100)

N = 10

Barapatcha

22 (84.62)

10 (38.46)

7 (26.92)

–

26 (100)

N = 26

Mahadebsinan

Source Author’s calculation; Note Figures in parentheses show percentage of total households

–

Herbals

17 (100)

N = 17

N = 33

32 (96.97)

Makhnu

Kamo

Fuelwood

NTFP

19 (79.17)

–

–

–

24 (100)

N = 24

Kadmagarh

Table 5.15 Collection of NTFP of the sample households across different villages in the Bankura forest division

2 (8.34)

1 (4.17)

1 (4.17)

–

21 (87.5)

N = 24

Kalabani

17 (77.27)

3 (13.64)

3 (13.64)

1 (4.55)

22 (100)

N = 22

Dhankura

105 (46.05)

53 (23.25)

25 (10.96)

1 (0.44)

221 (96.93)

No. of HH = 228

Bankura

5.3 Socio-economic Conditions of Rupnarayan Forest Division … 87

88

5 Socio-economic Analysis of Sample Households in the South Bengal …

Table 5.16 Forest benefits of the sample households in the Bankura (south) forest division Bankura

Yes

No

Rank 1

Rank 2

Rank 3

Rank 4

Soil protection

142 (62.28)

18 (7.89)

64 (28.07)

–

4 (1.75)

Climate protection

59 (25.88)

6 (2.63)

96 (42.11)

–

67 (29.39)

Employment

–

–

1 (0.44)

–

227 (99.56)

Water conservation

–

9 (3.95)

6 (2.63)

–

213 (93.42)

Tourism

–

1 (0.44)

1 (0.44)

–

226 (99.12)

Livestock grazing

27 (11.84)

137 (60.09)

40 (17.54)

3 (1.32)

21 (9.21)

Shade for livestock

–

16 (7.02)

18 (7.89)

2 (0.88)

192 (84.21)

Source Author’s calculation; Note Figures in parentheses show percentage of total households

Percentage of household

Bankura(South) 70 60 50 40 30 20 10 0 Rank 1

Rank 2

Rank 3

Soil Protection

Livestock Grazing

Climate Protection

Fig. 5.4 Ranking of benefits from forest in Bankura (south) forest division

About 35% of income comes from agriculture, about 16% from forestry, about 34% from casual labor, and 12.76% comes from the service sector in the Rupnarayan forest division of Paschim Medinipur district. (Table 5.20). The monthly income per household has been presented in Table 5.21. It is observed from Table 5.21 that the highest monthly income per household is from service (Rs 8734.1) followed by agriculture (Rs 4161.84), casual labor (Rs 3380.54), business (Rs 2240), and forest product collection (Rs 1432.22). The income distribution of the households in the Rupnarayan forest division of Paschim Medinipur district has been shown in Table 5.22. More than 56% of households have income lies between Rs 5001–10,000, 11.3% of households have income between Rs 10,001–15,000, more than 22.1% of households have income less than Rs 5000 and the remaining 9.8% of households have income more than Rs 15,000 in Paschim Medinipur district (Table 5.22). The livelihoods of sample households are dependent on the collection of NTFPs. The dependency of non-timber forest products (NTFP) of the sample households

N = 28

N = 24

7 (29.17) 1 (3.57)

17 (70.83)

–

General

OBC

24 (100)

Male

7 (77.78)

2 (22.22)

7 (29.17) 4 (14.29) 5 (55.56)

11 (45.83)

1 (4.17)

Illiterate

Primary

Education

Above 60 years

6 (21.43) 2 (22.22)

6 (21.43) 5 (55.56)

2 (22.22)

11 (45.83)

41–60 years

15 (53.57)

6 (25)

9 (32.14) 2 (22.22)

27 (96.43)

1 (3.57)

–

–

9 (100)

21–40 years

Age of the head of households

–

Female

1 (3.57)

23 (82.14)

–

ST

Gender

N=9

Kastokura

3 (10.71) –

Lower Patrisole

Upper Patrisole

SC

Social status

Socio-economic variables

1 (12.5)

3 (37.5)

1 (12.5)

2 (25)

5 (62.5)

6 (75)

2 (25)

–

–

8 (100)

–

N=8

Dhabani

5 (16.13)

10 (32.26)

–

9 (29.03)

22 (70.97)

31 (100)

–

3 (9.68)

7 (22.58)

15 (48.39)

6 (19.35)

N = 31

Aushbandi

4 (13.79)

19 (65.52)

6 (20.69)

14 (48.28)

9 (31.03)

25 (86.21)

4 (13.79)

–

–

29 (100)

–

N = 29

Godasole

–

N = 20

Dharnadihi

–

3 (15)

8 (40)

9 (45)

18 (90)

2 (10)

1 (7.14)

2 (10)

9 (64.29) 12 (60)

–

7 (50)

7 (50)

14 (100)

–

5 (35.71) –

–

2 (14.29) 20 (100)

7 (50)

N = 14

Fulsason

4 (11.76)

25 (73.53)

5 (14.71)

11 (32.35)

18 (52.94)

28 (82.35)

6 (17.65)

–

–

9 (26.47)

25 (73.53)

N = 34

Chandabila

Table 5.17 Socio-economic conditions of the sample households in the Rupnarayan forest division (Paschim Medinipur)

2 (12.5)

2 (12.5)

3 (18.75)

8 (50)

5 (31.25)

16 (100)

0

–

6 (37.5)

9 (56.25)

1 (6.25)

N = 16

Ichalkoda

(continued)

28 (13.15)

102 (47.89)

34 (15.96)

87 (40.85)

92 (43.19)

196 (92.02)

17 (7.98)

9 (4.23)

53 (24.88)

109 (51.17)

42 (19.72)

No. of Hh = 213

Medinipur

5.3 Socio-economic Conditions of Rupnarayan Forest Division … 89

4.2

Average of family size

2 (8.33)

15 (62.5) 20 (71.43)

> =1 Acre 5 (62.5)

–

3 (37.5)

–

8 (100)

5.5

–

4 (50)

N=8

Dhabani

21 (67.74)

2 (6.45)

8 (25.81)

3 (9.68)

28 (90.32)

3.8

–

16 (51.61)

N = 31

Aushbandi

19 (65.52)

6 (20.69)

4 (13.79)

2 (6.90)

27 (93.10)

3.9

–

6 (20.69)

N = 29

Godasole N = 20

Dharnadihi

19 (95)

3.7

1 (5)

8 (57.14) 11 (55)

2 (14.29) 3 (15)

4 (28.57) 6 (30)

2 (14.29) 1 (5)

12 (85.71)

3.8

–

4 (28.57) 5 (25)

N = 14

Fulsason

Source Author’s calculation; Note Figures in parentheses show percentage of total households

8 (88.89)

4 (14.29) 1 (11.11)

7 (29.17) 4 (14.29) –

< 1 Acre

–

9 (100)

3.9

–

2 (22.22)

N=9

Kastokura

Land less

Land holding (acre)

9 (37.5)

APL

15 (53.57)

15 (62.5) 13 (46.43)

4.1

2 (7.14)

BPL

Economic status

1 (4.17)

Above secondary

14 (50)

N = 28

N = 24

11 (45.83)

Lower Patrisole

Upper Patrisole

Secondary

Socio-economic variables

Table 5.17 (continued)

4 (11.76)

9 (26.47)

21 (61.76)

1 (2.94)

33 (97.06)

3.9

–

5 (14.71)

N = 34

Chandabila

13 (81.25)

1 (6.25)

2 (12.5)

4 (25)

12 (75)

4

–

12 (75)

N = 16

Ichalkoda

124 (58.22)

30 (14.08)

59 (27.70)

37 (17.37)

176 (82.63)

4

4 (1.88)

79 (37.09)

No. of Hh = 213

Medinipur

90 5 Socio-economic Analysis of Sample Households in the South Bengal …

N = 28

N = 24

4 (16.67) 1 (3.57)

–

9 (37.5)

Normal

Others

No

8 (88.89)

–

1 (11.11)

–

1 (11.11)

8 (88.89)

N=9

Kastokura

24 (100)

Yes

–

24 (100)

No

Yes

Banking facility

–

No

26 (92.86)

2 (7.14)

28 (100)

–

9 (100)

–

9 (100)

–

Accessibility of public health care facility

7 (25)

–

11 (45.83)

Pucca

20 (71.43)

15 (62.5) 21 (75)

Yes

Type of sanitation

9 (37.5)

No

7 (25)

Lower Patrisole

Upper Patrisole

Sanitation facility

Infrastructure

8 (100)

–

8 (100)

–

8 (100)

–

–

–

–

8 (100)

N=8

Dhabani

30 (96.77)

1 (3.23)

29 (93.55)

2 (6.45)

22 (70.97)

–

–

9 (29.03)

9 (29.03)

22 (70.97)

N = 31

Aushbandi

29 (100)

–

27 (93.10)

2 (6.90)

17 (58.62)

–

1 (3.45)

11 (37.93)

12 (41.38)

17 (58.62)

N = 29

Godasole

13 (92.86)

1 (7.14)

13 (92.86)

1 (7.14)

10 (71.43)

–

–

4 (28.57)

4 (28.57)

10 (71.43)

N = 14

Fulsason

20 (100)

–

18 (90)

2 (10)

15 (75)

–

–

5 (25)

5 (25)

15 (75)

N = 20

Dharnadihi

Table 5.18 Infrastructural facilities of the sample households in the Rupnarayan forest division (Paschim Medinipur)

34 (100)

–

33 (97.06)

1 (2.94)

29 (85.29)

–

–

5 (14.71)

5 (14.71)

29 (85.29)

N = 34

Chandabila

15 (93.75)

1 (6.25)

16 (100)

–

8 (50)

–

–

8 (50)

8 (50)

8 (50)

N = 16

Ichalkoda

(continued)

208 (97.65)

5 (2.35)

205 (96.24)

8 (3.76)

133 (62.44)

–

7 (3.29)

73 (34.27)

80 (37.56)

118 (55.40)

No. of HH = 213

Medinipur

5.3 Socio-economic Conditions of Rupnarayan Forest Division … 91

8 (88.89)

N=9

Kastokura

20 (83.33)

7 (77.78)

2 (22.22) 8 (100)

–

1 (12.5)

7 (87.5)

N=8

Dhabani

29 (93.55)

2 (6.45)

4 (12.90)

27 (87.10)

N = 31

Aushbandi

27 (93.10)

2 (6.90)

–

29 (100)

N = 29

Godasole

13 (92.86)

1 (7.14)

1 (7.14)

13 (92.86)

N = 14

Fulsason

Source Author’s calculation; Note Figures in parentheses show percentage of total households

21 (75)

4 (16.67) 7 (25)

Kachha

9 (32.14) 1 (11.11)

Pucca

Housing condition

14 (58.33)

Yes through Bank

19 (67.86)

N = 28

N = 24

10 (41.67)

Lower Patrisole

Upper Patrisole

No

Loan facility

Infrastructure

Table 5.18 (continued)

19 (95)

1 (5)

4 (20)

16 (80)

N = 20

Dharnadihi

34 (100)

–

9 (26.47)

25 (73.53)

N = 34

Chandabila

13 (81.25)

3 (18.75)

3 (18.75)

13 (81.25)

N = 16

Ichalkoda

191 (89.67)

22 (10.33)

46 (21.60)

167 (78.40)

No. of HH = 213

Medinipur

92 5 Socio-economic Analysis of Sample Households in the South Bengal …

9 (37.5)

3 (12.5)

24 (100)

Triple occupation

More than three occupation

All

2 (22.2)

–

N=9

Kastokura

9 (100)

–

8 (100)

–

7 (87.5)

1 (12.5)

–

N=8

Dhabani

31 (100)

–

20 (64.5)

11 (35.5)

–

N = 31

Aushbandi

29 (100)

–

23 (79.3)

6 (20.7)

–

N = 29

Godasole

1 (5)

20 (100)

14 (100)

11 (55)

8 (40)

–

N = 20

Dharnadihi

–

10 (71.4)

4 (28.6)

–

N = 14

Fulsason

Source Author’s calculation; Note Figures in parentheses show percentage of total households

28 (100)

1 (3.6)

19 (67.9) 7 (77.8)

10 (41.7) 8 (28.6)

Double occupation

–

N = 28

N = 24

2 (8.3)

Lower Patrisole

Upper Patrisole

Single occupation

Occupational structure

34 (100)

1 (2.9)

11 (32.4)

21 (61.8)

1 (2.9)

N = 34

Chandabila

Table 5.19 Occupational structure of the sample households in the Rupnarayan forest division (Paschim Medinipur)

16 (100)

2 (12.5)

8 (50)

6 (37.5)

–

N = 16

Ichalkoda

213 (100)

8 (3.8)

125 (58.7)

77 (36.2)

3 (1.4)

No. of HH = 213

Medinipur

5.3 Socio-economic Conditions of Rupnarayan Forest Division … 93

18 (75)

19 (79.2)

1 (4.2)

–

4 (16.7)

Casual labour

Business

Handicraft

Service

4 (14.3)

–

3 (10.7)

22 (78.6)

25 (89.3)

22 (78.6)

–

–

–

9 (100)

9 (100)

8 (88.9)

N=9

Kastokura

–

–

–

8 (100)

8 (100)

7 (87.5)

N=8

Dhabani

–

–

1 (3.2)

28 (90.3)

30 (96.8)

23 (74.2)

N = 31

Aushbandi

–

–

–

28 (96.6)

29 (100)

24 (82.8)

N = 29

Godasole

–

–

–

13 (92.9)

14 (100)

11 (78.6)

N = 14

Fulsason

Source Author’s calculation; Note Figures in parentheses show percentage of total households

19 (79.2)

N = 28

N = 24

Forest product collection

Lower Patrisole

Upper Patrisole

Agriculture

Sources of livelihoods

– 1 (2.9)

1 (5)

2 (5.9)

33 (97.1)

33 (97.1)

11 (32.4)

N = 34

Chandabila

–

–

20 (100)

20 (100)

12 (60)

N = 20

Dharnadihi

Table 5.20 Sources of livelihoods of the sample households in the Rupnarayan forest division (Paschim Medinipur)

5 (31.3)

–

1 (6.25)

12 (75)

12 (75)

14 (87.5)

N = 16

Ichalkoda

15 (7.0)

–

8 (3.8)

192 (90.1)

198 (93.0)

151 (70.9)

No. of HH = 213

Medinipur

94 5 Socio-economic Analysis of Sample Households in the South Bengal …

3140 (23.13)

400 (1.55)

–

13,050 (20.24)

10,747.5 (100)

Casual labour

Business

Handicraft

Service

Total

11,926.96 (100)

15,691 (18.79)

–

6000 (7.19)

3126.27 (20.59)

1568.32 (13.15)

9191.67 (100)

–

–

–

3071.11 (33.41)

1453.89 (15.82)

5250 (50.77)

N=9

Kastokura

7282.5 (100)

–

–

–

3975 (54.58)

1782.5 (24.48)

1742.86 (20.94)

N=8

Dhabani

3149.29 (47.09)

1202.59 (18.62)

2675 (34.28)

N = 29

Godasole

7202.97 (100)

–

–

6457.07 (100)

–

–

9000 (4.03) –

3301.43 (41.40)

1107.5 (14.88)

3852.17 (39.68)

N = 31

Aushbandi

8562.14 (100)

–

–

–

3436.92 (37.27)

1556.43 (18.18)

4854.55 (44.55)

N = 14

Fulsason

Source Author’s calculation; Note Figures in parentheses show percentage of total households

1437.78 (10.03)

Forest product collection

6113.64 (40.27)

N = 28

N = 24

6115.79 (45.05)

Lower patrisole

Upper patrisole

Cultivators

Sources of income

8253.5 (100)

25,000 (15.15)

–

–

3246 (39.33)

1362.5 (16.51)

3991.67 (29.02)

N = 20

Dharnadihi

6580.29 (100)

18,000 (8.05)

–

4000 (3.58)

3506.06 (51.71)

1794.85 (26.47)

2072.73 (10.19)

N = 34

Chandabila

Table 5.21 Monthly income (Rs) per household by livelihood groups in the Rupnarayan forest division (Paschim Medinipur)

12,325.63 (100)

15,600 (39.55)

–

3000 (1.52)

3853.33 (17.36)

1055.83 (6.42)

4950 (35.14)

N = 16

Ichalkoda

8853.02 (100)

8734.1 (12.76)

–

2240 (2.96)

3380.54 (33.96)

1432.22 (15.48)

4161.84 (35.21)

No. of HH = 213

Medinipur

5.3 Socio-economic Conditions of Rupnarayan Forest Division … 95

–

3 (12.5)

24 (100)

15,001–20,000

Above 20,000

All

9 (100)

–

1 (11.1) 8 (100)

–

–

–

8 (100)

–

N=8

Dhabani

31 (100)

1 (3.2)

1 (3.2)

2 (6.5)

19 (61.3)

8 (25.8)

N = 31

Aushbandi

29 (100)

–

–

1 (3.4)

21 (72.4)

7 (24.1)

N = 29

Godasole

14 (100)

–

1 (7.1)

4 (28.6)

6 (42.9)

3 (21.4)

N = 14

Fulsason

Source Author’s calculation; Note Figures in parentheses show percentage of total households

28 (100)

3 (10.7)

4 (14.3)

1 (11.1)

6 (25)

5 (17.9)

11 (45.8) 12 (42.9) 6 (66.7)

10,001–15,000

1 (11.1)

N=9

5001–10,000

4 (14.3)

N = 28

N = 24

Kastokura

4 (16.7)

Lower Patrisole

Upper Patrisole

≤ 5000

Income

20 (100)

1 (5)

1 (5)

2 (10)

12 (60)

4 (20)

N = 20

Dharnadihi

Table 5.22 Distribution of income of the sample households in the Rupnarayan forest division (Paschim Medinipur)

34 (100)

1 (2.9)

–

1 (2.9)

18 (52.9)

14 (41.2)

N = 34

Chandabila

16 (100)

3 (18.75)

1 (6.25)

2 (12.5)

8 (50)

2 (12.5)

N = 16

Ichalkoda

213 (100)

12 (5.6)

9 (4.2)

24 (11.3)

121 (56.8)

47 (22.1)

No. of HH = 213

Medinipur

96 5 Socio-economic Analysis of Sample Households in the South Bengal …

5.4 Socio-economic Conditions of the Households in the Alipurduar Forest …

97

Percentage of Households

Rupnarayan (Paschim Medinipur) 90 80 70 60 50 40 30 20 10 0 Fuelwood

Fodder

Sal Seeds

Mushroom

Others

Axis Title

Fig. 5.5 Dependency on NTFPs in Runarayan forest division, Paschim Medinipur. Source Author’s calculation

is shown in Fig. 5.5. More than 83% of households collect fuelwood, followed by the collection of mushrooms, Sal seed, fodder, and others. The village-wise distribution of the collection of NTFPs is presented in Table 5.23. The sample villages like Kastokura, Habana, Godasole, and Fulsason are fully (100%) dependent on fuelwood. The households were asked to rank their preferences on forest benefits taken from nearby forests in the Rupnarayan forest division of Paschim Medinipur district. It has been found from Table 5.24 that 82.63% of households have given soil protection as their 1st preference followed by livestock grazing (14.08%) and climate protection (2.82%). More than 57% of households have chosen climate protection as their 2nd preference followed by livestock grazing (26.29%), shade for livestock (6.57), and others. Again 49.30% of households have chosen livestock grazing as their 3rd preference followed by shade for livestock (23%), soil protection (11.74%), and others (Fig. 5.6).

5.4 Socio-economic Conditions of the Households in the Alipurduar Forest Division, North Bengal The socio-economic characteristics of the sample households (in terms of caste, gender, age of headed households, education, average family size, economic status, and landholdings) of the Alipurduar forest division are shown in Table 5.25. It is found from Table 5.25 that about 64.9% of households belong to the scheduled tribe (ST) and scheduled caste (SC) while 35.10% are of general caste. It is also found from Table 5.25 that about 82.78% of households are male-headed households while 17.22% are female-headed households in the Alipurduar forest division. In the case of the age of headed households, it is found that 45.03% of headed households belong

–

3 (12.5)

11 (45.83)

Sal seeds

Mushroom

Others

–

15 (53.57)

10 (35.71)

6 (66.67)

5 (55.56)

3 (10.71) –

2 (7.14)

9 (100)

N=9

Kastokura

7 (87.5)

3 (37.5)

–

–

8 (100)

N=8

Dhabani

11 (35.48)

2 (6.45)

–

–

30 (96.77)

N = 31

Aushbandi

17 (58.62)

5 (17.24)

–

–

29 (100)

N = 29

Godasole

7 (50)

2 (14.29)

–

–

14 (100)

N = 14

Fulsason

Source Author’s calculation; Note Figures in parentheses show percentage of total households

1 (4.17)

Fodder

24 (85.71)

N = 28

N = 24

18 (75)

Lower Patrisole

Upper Patrisole

Fuelwood

NTFP

8 (40)

1 (5)

–

2 (10)

19 (95)

N = 20

Dharnadihi

18 (52.94)

3 (8.82)

3 (8.82)

–

33 (97.05)

N = 34

Chandabila

2 (12.5)

1 (6.25)

–

1 (6.25)

12 (75)

N = 16

Ichalkoda

Table 5.23 Collection of NTFP of the sample households across different villages in the Rupnarayan forest division (Paschim Medinipur)

91 (42.72)

32 (15.02)

6 (2.82)

5 (2.35)

178 (83.57)

No. of HH = 213

Medinipur

98 5 Socio-economic Analysis of Sample Households in the South Bengal …

5.4 Socio-economic Conditions of the Households in the Alipurduar Forest …

99

Table 5.24 Forest benefits taken by the sample households in Rupnarayan forest division (Paschim Medinipur) Paschim Medinipur

Yes

No benefits

Rank 1

Rank 2

Rank 3

Rank 4

Soil protection

176 (82.63)

7 (3.29)

25 (11.74)

–

5 (2.35)

Climate protection

6 (2.82)

122 (57.28)

9 (4.23)

–

76 (35.68)

Employment

–

1 (0.47)

–

–

212 (99.53)

Water conservation

–

7 (3.29)

13 (6.10)

–

193 (90.61)

Tourism

–

–

1 (0.47)

–

212 (99.53)

Livestock grazing

30 (14.08)

56 (26.29)

105 (49.30)

–

22 (10.33)

Shade for livestock

–

14 (6.57)

49 (23.00)

–

150 (70.42)

Source Author’s calculation; Note Figures in parentheses show percentage of total households

Percentage of household

Paschim Medinipur 100 80 60 40 20 0 Rank 1

Rank 2

Rank 3

Soil Protection

Climate Protection

Livestock Grazing

Fig. 5.6 Ranking of benefits from forest in Rupnarayan forest division, Paschim Medinipur

to the age group of 41–60 years. The education structure of the sample households of Alipurduar forest division shows that more than 33% of households are illiterate while 66.89% of households got formal education. The average family size of the households of the Alipurduar forest division is 3.76. From Table 5.25 it is found that 85.43% of sample households in the Alipurduar forest division are living below the poverty line. The economic structure of the sample household is determined by their land holdings. It is observed from Table 5.25 that 10.60% of households in the Alipurduar district are landless farmers while marginal and small farmers constitute 89.4%. The infrastructural facilities such as sanitation facilities, types of sanitation, accessibility of public health care facilities, access to banking facilities, loan facilities, and housing conditions are presented in Table 5.26. It is found that 92.72% of households have sanitation facilities in the Alipurduar forest division. In the case of sanitation, it is found that 87.42% of households have pucca sanitation. Table 5.26 reveals that 99.34% of households can access public healthcare facilities in the Alipurduar forest division. Table 5.26 shows that 100% of households have taken banking facilities in the Alipurduar forest division. In the case of housing conditions, it is found that more than 92.72% of households have traditional tribal huts (Table 5.26).

100

5 Socio-economic Analysis of Sample Households in the South Bengal …

Table 5.25 Socio-economic conditions of the sample households in the Alipurduar forest division Socio-economic Garobasti Pampubasti Rabhabasti Santrabari 28 Jayanti Alipurduar variables Basti N = 26

N = 29

N = 15

N = 25

N= 25

N = 31 N = 151

SC

3 (11.54)

3 (10.34)

2 (13.33)

2 (8)

1 (4) 7 18 (11.92) (22.58)

ST

14 (53.85)

16 (55.17)

7 (46.67)

16 (64)

18 (72)

9 80 (52.98) (29.03)

General

9 (34.62)

10 (34.48)

6 (40)

7 (28)

6 (24)

15 53 (35.10) (48.39)

Female

2 (7.69)

5 (17.24)

3 (20)

8 (32)

3 (12)

5 26 (17.22) (16.13)

Male

24 (92.31)

24 (82.76)

12 (80)

17 (68)

22 (88)

26 125 (83.87) (82.78)

Social status

Gender

Age of head of households 21–40 years

11 (42.31)

18 (62.07)

6 (40)

10 (40)

14 (56)

7 66 (43.71) (22.58)

41–60 years

10 (38.46)

8 (27.59)

8 (53.33)

13 (52)

10 (40)

19 68 (45.03) (61.29)

above 60 years

5 (19.23)

3 (10.34)

1 (6.67)

2 (8)

1 (4) 5 17 (11.26) (16.13)

Illiterate

10 (38.46)

6 (20.69)

7 (46.67)

8 (32)

10 (60)

9 50 (33.11) (29.03)

Primary

5 (19.23)

9 (31.03)

2 (13.33)

8 (32)

4 (16)

5 33 (21.85) (16.13)

Secondary

9 (34.62)

12 (41.38)

6 (40)

8 (32)

10 (40)

15 60 (39.74) (48.39)

Above secondary

2 (7.69)

2 (6.90)

–

1 (4)

1 (4) 2 8 (5.30) (48.39)

Average of Family Size

4.42

3.31

4.2

3.68

3.52

3.68

BPL

25 (96.15)

27 (93.10)

14 (93.33)

22 (88)

22 (88)

19 129 (61.29) (85.43)

APL

1 (3.85)

2 (6.90)

1 (6.67)

3 (12)

3 (12)

12 22 (14.57) (38.71)

Education

3.76

Economic status

(continued)

5.4 Socio-economic Conditions of the Households in the Alipurduar Forest …

101

Table 5.25 (continued) Socio-economic Garobasti Pampubasti Rabhabasti Santrabari 28 Jayanti Alipurduar variables Basti N = 26

N = 29

N = 15

N = 25

N= 25

N = 31 N = 151

Land holding (acre) Land less

1 (3.85)

–

–

–

–

15 16 (10.60) (48.39)

< 1 Acre

20 (76.92)

26 (89.66)

13 (86.67)

20 (80)

22 (88)

16 117 (51.61) (77.48)

> =1 Acre

5 (19.23)

3 (10.34)

2 (13.33)

5 (20)

3 (12)

–

18 (11.92)

Source Author’s calculation; Note Figures in parentheses show percentage of total households

The occupational structure of households is shown in Table 5.27. The occupational structure shows that more than 50% of households have double occupations while 35.10% of households have triple occupation for subsistence in the Alipurduar forest division. The sources of livelihood of the sample households are agriculture, forest product collection, casual labor, petty business, handicrafts, and government services. It is found from Table 5.28 that 78.15% of households are engaged in forest product collection, 96.69% of households are engaged in casual labor, 29.14% are in agriculture and 21.85% are in business in Alipurduar forest division (Table 5.28). The monthly income per household has been presented in Table 5.29. About 13% of income comes from agriculture, 6.92% from forestry, 54.88% from casual labor, 11.09% from business, and 13.95% from service in the Alipurduar forest division. The average monthly income per household in the Alipurduar forest division is found to be Rs 9496.39. Among the livelihood groups, the highest monthly income per household is observed for casual labor (Rs 5211.26) followed by service (Rs 1324.5), agriculture (Rs 1250.33), business (Rs 1052.98), and forest product collection (Rs 657.32). The income from forest product collection seems to be lowest because the area is protected and they can use it for their own purposes, not for sale. The income distribution of the households in the Alipurduar forest division has been presented in Table 5.30. More than 65% of households have income lies between Rs 5001–10,000, nearly 21% of households have income between Rs 10,001–15,000, 3.31% of households have income between 15,001–20,000, 5.96% of households have income less than Rs 5000 and the remaining 3.97% of households have income more than Rs 20,000 in Alipurduar forest division. The livelihoods of sample households are dependent on the collection of NTFPs. The dependency of non-timber forest products (NTFP) of the sample households of the Alipurduar forest division is shown in Table 5.31 and Fig. 5.7. It is found from Table 5.31 that about 78.81% of households collect fuelwood, followed by the collection of mushrooms, honey, fodder, herbals, and others.

102

5 Socio-economic Analysis of Sample Households in the South Bengal …

Table 5.26 Infrastructural facilities of the sample households in the Alipurduar forest division Infrastructure Garobasti N = 26

Pampubasti Rabhabasti Santrabari 28 Jayanti Alipurduar Basti N = 29

N = 15

N = 25

N= 25

N = 31 N = 151

Sanitation facility No

24 (92.31) 29 (100)

15 (100)

22 (88)

23 (92)

27 140 (87.10) (92.72)

Yes

2 (7.69)

–

3 (12)

2 (8)

4 11 (7.28) (12.90)

–

Type of sanitation Pucca

21 (80.77) 29 (100)

15 (100)

18 (72)

22 (88)

27 132 (87.10) (87.42)

Normal

3 (11.54)

–

–

4 (16)

1 (4)

–

8 (5.30)

Others

–

–

–

–

–

–

–

No

2 (7.69)

–

–

3 (12)

2 (8)

4 11 (7.28) (12.90) –

Accessibility of public health care facility No

–

–

–

1 (4)

–

Yes

26 (100)

29 (100)

15 (100)

24 (96)

25 31 (100) (100)

1 (0.66) 150 (99.34) –

Banking facility No

–

–

–

–

–

Yes

26 (100)

29 (100)

15 (100)

25 (100)

25 31 (100) (100)

–

151 (100)

No

–

–

–

–

–

–

–

Yes through Bank

–

–

–

–

–

–

–

Loan facility

Housing condition Pucca

3 (11.54)

–

2 (13.33)

–

–

1 (3.23)

6 (3.97)

Kachha

–

–

–

–

–

–

–

Thatch Hut

5 (19.23)

–

–

–

–

–

5 (3.31)

Traditional Tribal Hut

18 (69.23) 29 (100)

13 (86.67)

25 (100)

25 30 140 (100) (96.77) (92.72)

Source Author’s calculation; Note Figures in parentheses show percentage of total households

5.4 Socio-economic Conditions of the Households in the Alipurduar Forest …

103

Table 5.27 Occupational structure of the sample households in the Alipurduar forest division Occupational Garobasti structure

Pampubasti Rabhabasti Santrabari 28 Jayanti Alipurduar Basti

N = 26

N = 29

N = 15

N = 25

N= 25

N = 31 N = 151

Single occupation

2 (7.69)

2 (6.90)

1 (6.67)

–

3 (12)

5 13 (8.61) (16.13)

Double occupation

8 (30.77)

17 (58.62)

13 (86.67)

13 (52)

14 (56)

15 80 (52.98) (48.39)

Triple occupation

14 (53.85) 10 (34.48)

1 (6.67)

11 (44)

6 (24)

11 53 (35.10) (35.48)

More than three occupation

2 (7.69)

–

–

1 (4)

2 (8)

–

All

26 (100)

29 (100)

15 (100)

25 (100)

25 31 (100) (100)

5 (3.31)

151 (100)

Source Author’s calculation; Note Figures in parentheses show percentage of total households

Table 5.28 Sources of livelihoods of the sample households in the Alipurduar forest division Major sources of livelihoods

Garobasti

Pampubasti Rabhabasti Santrabari 28 Basti

Jayanti

Alipurduar

N = 26

N = 29

N = 31 N = 151

N = 15

N = 25

N= 25

Agriculture 17 (65.38) 9 (31.03)

2 (13.33)

7 (28)

8 (32) 1 (3.23) 44 (29.14)

Forest product collection

21 (80.77) 20 (68.97)

9 (60)

25 (100)

19 (76)

Casual labour

24 (92.31) 28 (96.55)

15 (100)

25 (100)

25 29 146 (96.69) (100) (93.55)

Business

4 (15.38)

6 (20.69)

1 (6.67)

7 (28)

4 (16) 11 33 (21.85) (35.48)

Handicraft

–

–

–

–

–

–

Service

3 (11.54)

3 (10.34)

4 (26.67)

–

1 (4)

4 15 (9.93) (12.90)

24 118 (78.15) (77.42)

–

Source Author’s calculation; Note Figures in parentheses show percentage of total households

The results of forest benefit and their ranking are shown in Table 5.32. It is found that about 80.13% of households have given their 1st preference to tourism. Nearly 57% of households have given their 2nd preference on climate protection. Again 54.97% of households have given their 3rd preference for soil protection (Table 5.32) (Fig. 5.8).

104

5 Socio-economic Analysis of Sample Households in the South Bengal …

Table 5.29 Monthly income (Rs) per household by sources in the Alipurduar forest division Sources of income

Garobasti Pampubasti Rabhabasti Santrabari 28 Basti Jayanti

Alipurduar

N = 26

N = 29

N = 15

N = 25

N = 31

N = 151

Agriculture 3442.31 (31.76)

1344.83 (15.14)

666.67 (5.45)

812 (9.30) 1120 (13.24)

64.52 (0.71)

1250.33 (13.17)

Forest product collection

565.77 (5.22)

384.48 (4.33)

507.87 (4.15)

1412.48 (16.18)

630.68 (7.46)

474.13 (5.23)

657.32 (6.92)

Casual labour

4638.46 (42.79)

4996.55 (56.26)

6100 (49.83)

5304 (60.77)

5668 (67.01)

5019.35 5211.26 (55.31) (54.88)

Business

807.69 (7.54)

1275.86 (14.37)

666.67 (5.45)

1200 (13.75)

760 (8.98)

1354.84 1052.98 (14.93) (11.09)

Handicraft

–

–

–

–

–

–

Service

1384.62 (12.77)

879.31 (9.90)

4300 (35.13)

–

280 (3.31)

2161.29 1324.5 (23.82) (13.95)

Total

10,838.85 8881.03 (100) (100)

12,241.2 (100)

8728.48 (100)

8458.68 9074.13 9496.39 (100) (100) (100)

N = 25

–

Source Author’s calculation; Note Figures in parentheses show percentage of total households

Table 5.30 Distribution of income of the sample households in the Alipurduar forest division Income

Garobasti Pampubasti Rabhabasti Santrabari 28 Jayanti Alipurduar Basti N = 26

N = 29

N = 15

N = 25

N= 25

N = 31 N = 151

≤ 5000

–

1 (3.45)

–

–

3 (12)

5 9 (5.96) (16.13)

5001–10,000

17 (65.38)

19 (65.52)

10 (66.67)

20 (80)

16 (64)

18 100 (58.06) (66.23)

10,001–15,000 6 (23.08)

8 (27.59)

2 (13.33)

5 (20)

3 (12)

7 31 (20.53) (22.58)

15,001–20,000 1 (3.85)

1 (3.45)

–

–

3 (12)

–

5 (3.31)

Above 20,000

2 (7.69)

–

3 (20)

–

–

1 (3.23)

6 (3.97)

All

26 (100)

29 (100)

15 (100)

25 (100)

25 31 (100) (100)

151 (100)

Source Author’s calculation; Note Figures in parentheses show percentage of total households

5.4 Socio-economic Conditions of the Households in the Alipurduar Forest …

105

Table 5.31 Collection of NTFP of the sample households across different villages in the Alipurduar forest division NTFP

Garobasti

Pampubasti Rabhabasti Santrabari 28 Jayanti Basti

N = 26

N = 29

Alipurduar

N = 15

N = 25

N= 25

N = 31 N = 151

Fuelwood

21 (80.77) 20 (68.97)

9 (60)

25 (100)

20 (80)

24 119 (78.81) (77.42)

Fodder

–

2 (6.90)

1 (6.67)

5 (20)

5 (20)

8 21 (13.91) (25.81)

Herbals

–

–

–

21 (84)

–

–

21 (13.91)

Grass

–

–

–

5 (20)

–

–

5 (3.31)

Fruits

–

–

–

2 (8)

–

–

2 (1.32)

Sal seeds

–

–

–

1 (4)

–

–

1 (0.66)

Honey

1 (3.85)

3 (10.34)

1 (6.67)

15 (60)

5 (20)

1 (3.23) 26 (17.22)

Mushroom 8 (30.77)

6 (20.69)

7 (46.67)

20 (80)

7 (28)

11 59 (39.07) (35.48)

Others

–

–

–

–

–

2 (7.69)

2 (1.32)

Source Author’s calculation; Note Figures in parentheses show percentage of total households

% NTFP Collection

Alipurduar 90 80 70 60 50 40 30 20 10 0

Fig. 5.7 Dependency on NTFPs in Alipurduar forest division. Source Author’s calculation

106

5 Socio-economic Analysis of Sample Households in the South Bengal …

Table 5.32 Forest benefits of the sample households in the Alipurduar forest division Alipurduar

Yes

No Benefits

Rank 1

Rank 2

Rank 3

Rank 4

Soil protection

13 (8.61)

13 (8.61)

83 (54.97)

–

42 (27.81)

Climate protection

1 (0.66)

86 (56.95)

33 (21.85)

–

31 (20.53)

Employment

4 (2.65)

13 (8.61)

9 (5.96)

–

125 (82.78)

Water conservation

2 (1.32)

4 (2.65)

2 (1.32)

–

143 (94.7)

Tourism

121 (80.13)

7 (4.63)

13 (8.61)

–

10 (6.62)

Livestock grazing

10 (6.22)

28 (18.54)

5 (3.31)

–

108 (71.52)

Shade for livestock

–

1 (0.66)

5 (3.31)

–

145 (96.03)

Source Author’s calculation; Note Figures in parentheses show percentage of total households

Percentage of household

Alipurduar forest division 90 80 70 60 50 40 30 20 10 0 Rank 1

Rank 2

Rank 3

Tourism

Climate Protection

Soil Protection

Fig. 5.8 Ranking of benefits from forest in Alipurduar forest division, North Bengal

Summing Up The socio-economic conditions of the sample households are weak across the Purulia, Bankura, Paschim Medinipur, and Alipurduar Forest divisions of South and North Bengal. Most of the households comprise landless, marginal, and small landholdings, scheduled tribes, and scheduled castes. These four forest divisions like Purulia, Bankura, and Rupnarayan in Paschim Medinipur and Alipurduar are pre-dominated by marginalized tribal poor people. There have been high illiteracy rates across these forest divisions. Most households have access to infrastructural facilities like sanitation, health care, banking facilities, etc. The majority of households are living in kaccha houses. The occupational structure shows that more than 50% of households have three occupations in Purulia, Bankura, and Paschim Medinipur forest divisions whereas more than 25% of households have a double occupation in these forest divisions of South Bengal. On the other hand, the occupational structure shows that more than 52% of households have two occupations in the Alipurduar forest division in North Bengal.

5.4 Socio-economic Conditions of the Households in the Alipurduar Forest …

107

Major sources of livelihood of the households are found to be forest product collection, casual labor, and agriculture in the selected forest divisions of Purulia, Bankura, and Rupnarayan of Paschim Medinipur. The monthly income per household differs substantially across three districts of West Bengal. In Purulia forest division monthly income per household is Rs 8714.02, in Bankura (south) forest division is Rs 7467.69; in the Rupnarayan forest division of Paschim Medinipur district, it is Rs 8853.02 while in Alipurduar forest division it is Rs 9496.39. Based on the average income per household it is noted that the people are better off in the Alipurduar forest division compared to other forest divisions in South Bengal. On the other hand, there is substantial variation in income derived from forest products across different forest divisions. The percentage of income from forest product collection is 24.39 in the Purulia forest division, 23.3 in Bankura (south) forest division, and 15.48 in the Rupnarayan forest division while in Alipurduar it is 6.92%. Income from forest products is highest in the South Bengal forest division compared to the North Bengal forest division. The households in both the South and North Bengal forest divisions are dependent on Non-Timber Forest Products (NTFP). More than 86% of households collect fuelwood in Purulia, Bankura, and Paschim Medinipur forest divisions while 78% of households are engaged in the collection of NTFPs in the Alipurduar forest division. In addition, they also collect a smaller amount of sal leaves, mahua flowers, mushrooms, honey, fodder, and others. In addition, households derive other benefits from forests like soil protection, climate protection, livestock grazing, tourism, etc. Households have been given a rank based on their preferences. It is observed that soil protection as 1st the preference, climate protection as 2nd preference, and livestock grazing as 3rd preference across Purulia, Bankura (south), and Rupnarayan of Paschim Medinipur of forest divisions. The majority of the households of South West Bengal have expressed that they are not getting benefits from tourism. On the other hand, households have given tourism as 1st preference, climate protection as their 2nd preference, and soil protection as their 3rd preference in the Alipurduar forest division of North Bengal.

Chapter 6

Analysis of Forest Protection Committee (FPC), Forest Beat Office and Gram Panchayat in South and North Bengal Forest Divisions

Abstract This chapter analyses the basic features of selected FPCs, the participation rate in the general body meeting and performance in terms of afforestation, SHGs formation, and control of illegal logging, and provisions of training facilities for forest fire prevention across different forest divisions of South and North Bengal forest divisions. In addition, this chapter also examines the role of forest beat offices and Gram Panchayats in strengthening forest governance issues.

6.1 FPC of Purulia Forest Division, South Bengal In the Purulia forest division, we have selected nine forest protection committees (FPCs) under the Bagmundi forest range namely Ichakota, Rabidi, BarnajoraDulkibera, Nischintapur, Tarpenia, Charida, Bagti, Kalimati and Perorgoria. Most of these FPCs formed during the 1990s and few were in 2002. Of these FPCs, Nischintapur FPC has the highest forest cover while Charida FPC has the lowest forest cover. The major trees planted across the FPCs are Akashmani, Amlaki, Sal trees, Koroncho, etc. The basic features of the sample FPCs are presented in Table 6.1 and FPC-wise forest cover is shown in Fig. 6.1. As per the constitution of Joint Forest Management, the involvement of female members in the executive committee as well as the general body is mandatory. In the study area, it is observed that 40% of female members are in the executive committee, and in the general body it varies from 17 to 35%. Table 6.2 provides membership information on female and male members across nine FPCs in the Purulia forest division. Table 6.3 presents the participation rate of female and male members in the general body (GB) meeting across different FPCs in the Purulia forest division. It is found that the overall participation rate in the GB meeting is 52.50%, out of which female members’ participation is 46.88%, and that for male members’ participation is 54.62%. Out of nine FPCs, only two FPCs’ participation rate in GB meetings is above the average participation. It is also observed from Table 6.3 that the female participation rate is highest in Kalimati FPC (84.44%) while it is lowest in Tarpenia FPC (25%). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. P. Basu, Governance and Institution in the Indian Forest Sector, https://doi.org/10.1007/978-3-031-34746-7_6

109

110

6 Analysis of Forest Protection Committee (FPC), Forest Beat Office …

Table 6.1 Basic features of the sample FPCs in Purulia forest division, South Bengal Name of FPC

Years of formation

Forest cover (hec)

Total households

Major planted trees

Ichakota

1994

250

85

Akashmani, Amlaki, Sal

Rabidi

2002

157

100

Akashmani, Amlaki, Sal

Barnajora-Dulkibera

2002

271

75

Akashmani, Amlaki, Sal

Nischintapur

1994

849

130

Akashmani, Amlaki, Sal

Tarpenia

2002

177

110

Akashmani, Amlaki, Sal

Charida

1993

133

150

Akashmani, Amlaki, Sal

Bagti

1991

500

222

Akashmani, Amlaki, Sal

Kalimati

1991

411

220

Akashmani, Koroncho

Perorgoria

1991

212

156

Akashmani, Amlaki, Sal

Area of Forest (in ha)

Sources Attendance copy of beat meeting, 2019–20

Purulia forest division 900 800 700 600 500 400 300 200 100 0

FPC wise Forest cover

Fig. 6.1 FPC wise forest cover of Purulia forest division, West Bengal. Source Field survey

It has been asked of the FPCs official relating to election procedure, the number of meetings held per year, and maintaining resolution/ meeting books for the meeting. All FPC replied that the election of the members was held by popular votes by raising hands, the number of meetings held per year lies between 10 and 15 days, and agenda-wise resolutions are written in the meeting book and maintained such meeting book. Table 6.4 presents meeting-related information of the FPCs.

6.1 FPC of Purulia Forest Division, South Bengal

111

Table 6.2 Membership information of female and male in the executive and general committee across sample FPCs in Purulia forest division, South Bengal Name of FPC

Executive committee members

General body members

Female

Male

Total

Female

Male

Total

Ichakota

2 (40)

3 (60)

5 (100)

10 (30.30)

23 (69.70)

33 (100)

Rabidi

2 (40)

3 (60)

5 (100)

23 (25.56)

67 (74.44)

90 (100)

Barnajora-Dulkibera

2 (40)

3 (60)

5 (100)

16 (17.78)

74 (82.22)

90 (100)

Nischintapur

2 (40)

3 (60)

5 (100)

31 (34.07)

60 (65.93)

91 (100)

Tarpenia

2 (40)

3 (60)

5 (100)

16 (21.05)

60 (78.95)

76 (100)

Charida

2 (40)

3 (60)

5 (100)

46 (35.94)

82 (64.06)

128 (100)

Bagti

2 (40)

3 (60)

5 (100)

10 (28.57)

25 (71.43)

35 (100)

Kalimati

2 (40)

3 (60)

5 (100)

45 (22.73)

153 (77.27)

198 (100)

Perorgoria

2 (40)

3 (60)

5 (100)

27 (34.62)

51 (65.38)

78 (100)

Sources Attendance copy of beat meeting, 2019–20

Table 6.3 Participation rate of the FPC members in the general committee meeting in Purulia forest division, South Bengal Name of FPC

General body members

Participation in the GB meeting

Participation rate (%)

Female Male Total Female Male Total Female Male

Total

Ichakota

10

23

33

5

12

17

50.00

52.17 51.52

Rabidi

23

67

90

7

25

32

30.43

37.31 35.56

Barnajora-Dulkibera

16

74

90

6

37

43

37.50

50.00 47.78

Nischintapur

31

60

91

11

26

37

35.48

43.33 40.66

Tarpenia

16

60

76

4

28

32

25.00

46.67 42.11

Charida

46

82

128

18

47

65

39.13

57.32 50.78

Bagti

10

25

35

3

10

13

30.00

40.00 37.14

Kalimati

45

153

198

38

105

143

84.44

68.63 72.22

Perorgoria

27

51

78

13

35

48

48.15

68.63 61.54

224

595

819

105

325

430

46.88

54.62 52.50

All

Sources Attendance copy of beat meeting, 2019–20

The performance of each FPC is judged by the number of man days created for afforestation, formation of self-help groups especially SC/ST women members, control of illegal timber logging, help to implement poverty reduction measures, and training facilities for controlling forest fire. In respect of man-day creation for afforestation, out of nine FPC only six FPC creates more than 20 man-days, and the rest 3 FPC offers below 15 man-days yearly. All FPCs are successful in controlling illegal timber logging. Out of 9 FPCs, five FPCs helped to implement poverty

112

6 Analysis of Forest Protection Committee (FPC), Forest Beat Office …

Table 6.4 Meeting related information of the FPCs in Purulia forest division, South Bengal Name of FPC

Election held by popular vote by Meetings Availability of meeting raising hands (Yes = 1, No = 0) held in FPC book (Yes = 1, No = 0) (year)

Ichakota

1

15

1

Rabidi

1

15

1

Barnajora-Dulkibera 1

14

1

Nischintapur

1

14

1

Tarpenia

1

15

1

Charida

1

14

1

Bagti

1

15

1

Kalimati

1

10

1

Perorgoria

1

15

1

Sources Attendance copy of beat meeting, 2019–20

Table 6.5 Performance of the FPCs in Purulia forest division, South Bengal Name of FPC

Number of man days (per year) involved in afforestation activities

SHG formation by FPC (SC/ST)

Ichakota

14

Rabidi

22

Control of illegal timber logging (Yes = 1, No = 0)

Help to implement poverty reduction measures (Yes = 1, No = 0)

Training facilities for controlling forest fire (Yes = 1, No = 0)

13 (72.22) 1

0

0

14 (63.64) 1

1

1

Barnajora-Dulkibera 12

10 (76.92) 1

1

1

Nischintapur

24

19 (73.08) 1

1

1

Tarpenia

18

12 (52.17) 1

1

1

Charida

22

22 (70.97) 1

0

0

Bagti

28

17 (38.64) 1

0

0

Kalimati

24

19 (44.19) 1

1

0

Perorgoria

26

20 (62.5)

0

0

1

Source Field survey, 2019–20; Note Figures in the parentheses represent percentage of SC/ST members

reduction measures in the study area while others are not involved. In respect of SHGs formation, especially SC/ST women, six out of nine FPCs formed SHGs involving more than 60% SC/ST women for their empowerment. It is also observed from Table 6.5 that four out of nine FPCs arranged training programs for forest fire prevention while rest five FPCs did not do so.

6.2 FPC of Bankura (South) Forest Division, South Bengal

113

6.2 FPC of Bankura (South) Forest Division, South Bengal In Bankura (south) forest division we have selected ten forest protection committees (FPCs) namely Kamo, Bagdiha, Makhnu, Kalabani, Dhankura, Kadmagarh, Jamgeria, Barapatcha, Mahadebsinan, and Mitham under Ranibundh forest range. All FPCs started their journey in the 1990s except one in 1985. Of these FPCs, Barapatcha FPC has the highest forest cover while Bagdiha FPC has the lowest forest cover. The major trees planted across the FPCs are Akashmani, Sirish, Sisu, Sal, etc. The basic features of the sample FPCs are presented in Table 6.6. FPC-wise forest cover in Bankura (south) is shown in Fig. 6.2. Table 6.7 provides such basic membership information of females and male across the selected FPCs. In the study area, it is observed that female members in the executive committee vary from 16 to 40% while in the general body (GB) it varies from 28 to 42%. Makhnu FPC experiences the highest number of female members both in the executive committee and general body. The participation level of female and male members in the general body meeting in different FPCs in the Bankura (south) forest division is shown in Table 6.8. It is found that the overall participation rate in the GB meeting is 66.40%, out of which female members’ participation is 62.21%, and that for male members’ participation is Table 6.6 Basic features of the sample FPCs in Bankura (south) forest division, South Bengal Name of FPC

Years of formation Forest cover (hec) Total households Major planted trees of FPC

Kamo

1990

100.23

163

Akashmani, Sirish, Sisu

Bagdiha

1994

46.80

38

Akashmani, Sirish, Sisu,

Makhnu

1990

154.25

110

Akashmani, Sirish, Sisu

Kalabani

1990

297.49

150

Akashmani, Sirish, Sisu, Sal

Dhankura

1990

93.10

45

Akashmani, Sirish, Sisu, Sal

Kadmagarh

1993

146.49

200

Akashmani, Sirish, Sisu, Sal

Jamgeria

1991

50.65

117

Akashmani, Sirish, Sisu

Barapatcha

1985

401

60

Akashmani, Sirish, Sisu

Mahadebsinan 1990

313

133

Akashmani, Sirish, Sisu

Mitham

139.09

183

Akashmani, Sirish, Sisu

1993

Sources Attendance copy of beat meeting, 2019–20

114

6 Analysis of Forest Protection Committee (FPC), Forest Beat Office …

Area of Forest (in ha)

Bankura (South) forest division 450 400 350 300 250 200 150 100 50 0

FPC wise forest cover

Fig. 6.2 FPC wise forest cover of Bankura (south) forest division, West Bengal. Source Field survey

Table 6.7 Membership information of female and male in the executive and general committee across sample FPCs in Bankura (south) forest division, South Bengal Name of FPC

Executive committee members

General body members

Female

Female

Male

Total

Male

Total

Kamo

–

6 (100)

6 (100)

103 (32.70)

212 (67.30)

315 (100)

Bagdiha

–

6 (100)

6 (100)

35 (27.56)

92 (72.44)

127 (100)

Makhnu

2 (40)

3 (60)

5 (100)

79 (42.25)

108 (57.75)

187 (100)

Kalabani

1 (20)

4 (80)

5 (100)

130 (31.71)

280 (68.29)

410 (100)

Dhankura

–

7 (100)

7 (100)

37 (38.14)

60 (61.86)

97 (100)

Kadmagarh

1 (16.67)

5 (83.33)

6 (100)

50 (27.78)

130 (72.22)

180 (100)

Jamgeria

3 (42.86)

4 (57.14)

7 (100)

49 (37.12)

83 (62.88)

132 (100)

Barapatcha

2 (28.57)

5 (71.43)

7 (100)

35 (30.43)

80 (69.57)

115 (100)

Mahadebsinan

–

6 (100)

6 (100)

40 (36.36)

70 (63.64)

110 (100)

Mitham

3 (18.75)

13 (81.25)

16 (100)

85 (41.46)

120 (58.54)

205 (100)

Sources Attendance copy of beat meeting, 2019–20

68.58%. Out of ten FPCs, six FPCs’ participation rate in the GB meeting is above the average participation. It is also observed from Table 6.8 that the female participation rate is highest in Mitham FPC (100%) while it is lowest in Makhnu FPC (22.78%). Table 6.9 presents meeting-related information on the selected FPCs in Bankura (south) forest division. It has been asked of the FPCs relating to election procedure, the number of meetings held per year, and maintaining resolution/meeting books for the general body meeting. All FPCs except two replied that the election of the members was held by popular votes by raising hands while two FPCs told that election was held by secret ballot. The number of meetings held per year lies between 5 and

6.2 FPC of Bankura (South) Forest Division, South Bengal

115

Table 6.8 Participation level of the FPC members in the general committee meeting in Bankura (south) forest division, South Bengal Name of FPC

Kamo

General body members

Participation in the GB meeting

Participation rate (%)

Female

Female

103

Male

Total

Male

Total

Female

Male

Total

212

315

33

180

213

32.04

84.91

58.47

Bagdiha

35

92

127

26

72

98

74.29

78.26

76.27

Makhnu

79

108

187

18

65

83

22.78

60.19

41.48

Kalabani

130

280

410

122

204

326

93.85

72.86

83.35

Dhankura

37

60

97

28

45

73

75.68

75.00

75.34

Kadmagarh

50

130

180

50

58

108

100.00

44.62

72.31

Jamgeria

49

83

132

10

39

49

20.41

46.99

33.70

Barapatcha

35

80

115

23

72

95

65.71

90.00

77.86

Mahadebsinan

40

70

110

5

32

37

12.50

45.71

29.11

Mitham All

85

120

205

85

80

165

100.00

66.67

83.33

643

1235

1878

400

847

1247

62.21

68.58

66.40

Sources Attendance copy of beat meeting, 2019–20

Table 6.9 Meeting related information of the FPCs in Bankura (south) forest division, South Bengal Name of FPC

Election held by popular vote (Yes = 1, No = 0)

Meetings held in FPC (year)

Meeting book is availability (Yes = 1, No = 0)

Kamo

1

5

1

Bagdiha

1

5

1

Makhnu

1

6

1

Kalabani

1

12

1

Dhankura

1

6

1

Kadmagarh

1

8

1

Jamgeria

1

8

1

Barapatcha

0

15

1

Mahadebsinan

0

13

1

Mitham

1

12

1

Sources Attendance copy of beat meeting, 2019–20

15 days and agenda-wise resolutions are written in the meeting book and maintained such meeting book. The performance of each FPC is judged by the number of man days created for afforestation, formation of self-help groups especially among SC/ST women members, controlling illegal timber logging, helping to implement poverty reduction measures, and training facilities for controlling forest fire. In respect of man-day

116

6 Analysis of Forest Protection Committee (FPC), Forest Beat Office …

Table 6.10 Performance of the FPCs in Bankura (south) forest division, South Bengal Name of FPC

Number of man days (per year) involved in afforestation activities

SHG formation by FPC (SC/ST)

Kamo

27

Control of Illegal logging (Yes = 1, No = 0)

Help to implement Poverty reduction measures (Yes = 1, No = 0)

Training facilities for controlling forest fire (Yes = 1, No = 0)

11 (33.33) 1

1

1

Bagdiha

5

3 (37.5)

1

1

1

Makhnu

11

9 (52.94)

1

1

1

Kalabani

16

5 (20.83)

1

1

0

Dhankura

12

17 (77.27) 1

1

1

Kadmagarh

22

21 (87.5)

1

1

0

Jamgeria

22

14 (53.85) 1

1

0

7 (70)

1

1

1

Barapatcha

8

Mahadebsinan

21

9 (34.62)

1

1

1

Mitham

35

20 (52.63) 1

1

1

Source Field survey

creation for afforestation, out of ten FPC five FPC create opportunities for more than 20 man-days per year while the rest five FPC offer below 15 man-days yearly. All selected FPCs are successful in controlling illegal timber logging and helping to implement poverty reduction measures in the study area. In respect of SHGs formation, especially among SC/ST women, three out of ten FPC formed SHGs involving more than 60% SC/ST women for their empowerment. It is also observed from Table 6.10 that seven out of ten FPCs arranged training programs for forest fire prevention while rest three FPCs did not do so.

6.3 FPC of Rupnarayan Forest Division of Paschim Medinipur, South Bengal In the Rupnarayan Forest division of Paschim Medinipur, we have selected ten FPCs like Upper Pathrisole, Lower Pathrisole, Kastokura, Dhabani, Aushbandi, Godasole, Fulsason, Darnadihi, Chandabila, and Ichalkoda, etc. All FPCs have been working since the 1980s. Fulsason FPC has the highest forest cover while Dhabani FPC has the lowest forest cover. The major trees planted across the FPCs are Sal, Mehul, Bahara, Piyasal, Amlaki, Akashmani, Sirish, Sisu, etc. The basic features of the sample FPCs are presented in Table 6.11 and FPC-wise forest cover is shown in Fig. 6.3. Table 6.12 provides basic membership information for females and males across the FPCs. The members of the executive committee vary from 6 to 14. It is observed

6.3 FPC of Rupnarayan Forest Division of Paschim Medinipur, South Bengal

117

Table 6.11 Basic features of the sample FPCs in Rupnarayan forest division of Paschim Medinipur, South Bengal Name of FPC Years of formation of FPC

Forest cover (hec)

Total household members

Major planted trees

Upper Pathrisole

1985

120

135

Sal, Mehul, Bahara, Piyasal, Amlaki, Akashmani

Lower Pathrisole

1985

20

126

Sal, Mehul, Bahara, Piyasal, Amlaki, Akashmani

Kastokura

1985

32

41

Sal, Mehul, Bahara, Piyasal, Amlaki, Akashmani

Dhabani

1986

10.3

40

Sal, Mehul, Bahara, Piyasal, Amlaki, Akashmani

Aushbandi

1982

72

150

Sal, Mehul, Bahara, Piyasal, Amlaki

Godasole

1986

18

142

Sal, Mehul, Bahara, Piyasal, Amlaki

Fulsason

1997

195

67

Sal, Mehul, Bahara, Piyasal, Amlaki

Dharnadihi

1981

82

65

Sal, Sirish, Sisu

Chandabila

1982

150

151

Sal, Sirish, Sisu

Ichalkoda

1983

70

52

Sal, Sirish, Sisu

Sources Attendance copy of beat meeting, 2020–21

Area of Forest (in ha)

Forest cover 250 200 150 100 50 0

FPCs in Rupnarayan Forest division

Fig. 6.3 FPC wise forest cover of Rupnarayan forest division of Paschim Medinipur, South Bengal. Source Field survey

that female members in the executive committee vary from 22 to 50% while in the general body, it varies from 28 to 44% (Table 6.12). The participation rates of female and male members in the general body meeting in different FPCs in Rupnarayan forest division, Paschim Medinipur are shown in

118

6 Analysis of Forest Protection Committee (FPC), Forest Beat Office …

Table 6.12 Membership information of female and male in the executive and general committee across sample FPCs in Paschim Medinipur forest division, South Bengal Name of FPC

Executive committee members

General body members

Female

Male

Total

Female

Male

Total

Upper Pathrisole

–

8 (100)

8 (100)

130 (41.4)

184 (58.6)

314 (100)

Lower Pathrisole

–

10 (100)

10 (100)

102 (35.92)

182 (64.08)

284 (100)

Kastokura

2 (33.33)

4 (66.67)

6 (100)

101 (35.94)

180 (71.43)

281 (100)

Dhabani

2 (22.22)

7 (77.78)

9 (100)

23 (30.67)

52 (69.33)

75 (100)

Aushbandi

–

14 (100)

14 (100)

76 (37.62)

126 (62.38)

202 (100)

Godasole

–

–

–

128 (35.75)

230 (64.25)

358 (100)

Fulsason

2 (33.33)

4 (66.67)

6 (100)

234 (44.49)

292 (55.51)

526 (100)

Dharnadihi

2 (28.57)

5 (71.43)

7 (100)

215 (48.10)

232 (51.90)

447 (100)

Chandabila

2 (25)

6 (75)

8 (100)

52 (28.26)

132 (71.74)

184 (100)

Ichalkoda

5 (50)

5 (50)

10 (100)

65 (38.24)

105 (61.76)

170 (100)

Sources Attendance copy of beat meeting, 2020–21

Table 6.13 Participation rate of the FPC members in the general committee meeting in Paschim Medinipur forest division, South Bengal Name of FPC

General body members Participation in the GB meeting

Participation rate (%)

Female

Male

Total

Male

Total

Female

Male

Total

Upper Pathrisole

130

184

314

Female 111

172

283

85.38

93.48

89.43

Lower Pathrisole

102

182

284

102

182

284

100

100

100

Kastokura

101

180

281

93

174

267

92.08

96.67

94.37

Dhabani

23

52

75

16

42

58

69.57

80.77

75.17

Aushbandi

76

126

202

76

126

202

100

100

100

Godasole

128

230

358

0

0

0

0.00

0

0

Fulsason

234

292

526

234

292

526

100

100

100

Darnadihi

215

232

447

215

232

447

100

100

100

Chandabila

52

132

184

52

132

184

100

100

100

Ichalkoda

65

105

170

65

105

170

100

100

100

1126

1715

2841

964

1457

2421

85.61

84.96

85.22

All

Sources Attendance copy of beat meeting, 2020–21

Table 6.13. It is found that the overall participation rate in the GB meeting is 85.22%, out of which female members’ participation is 85.61%, and that for male members’ participation is 84.96%. Out of ten FPCs, six FPCs’ participation rate in the GB meeting is 100%. Table 6.14 presents meeting-related information on the FPCs in the Rupnarayan forest division. It is observed from Table 6.14 that the election of the members was

6.4 FPC of Alipurduar Forest Division, North Bengal

119

Table 6.14 Meeting related information of the FPCs in Paschim Medinipur forest division, South Bengal Name of FPC

Election held by popular vote (Yes = 1, No = 0)

Meetings held in FPC (year)

Meeting book is availability (Yes = 1, No = 0)

Upper Pathrisole

1

4

1

Lower Pathrisole

1

1

1

Kastokura

1

4

1

Dhabani

1

5

1

Aushbandi

1

2

1

Godasole*

0

0

0

Fulsason

1

4

1

Dharnadihi

1

2

1

Chandabila

1

7

1

Ichalkoda

1

4

1

Sources Attendance copy of beat meeting, 2020–21 Note * Shows Godasole FPC was defunct by forest department, because the committee was involved in illegal timber logging

held by popular votes by raising hands. The number of meetings held per year lies between 1 and 7 days and agenda-wise resolutions are written in the meeting book and maintained such meeting book. The performance of each FPC is judged by the number of man days created for afforestation, formation of self-help groups especially among SC/ST women members, controlling of illegal timber logging, helping to implement poverty reduction measures, and training facilities for controlling forest fire. In respect of man-day creation for afforestation, out of ten FPC three FPC created more than 20 man-days per year while rest seven FPC offers below 15 man-days yearly. All FPCs are successful in controlling illegal timber logging except one FPC and helping to implement poverty reduction measures in the study area. In respect of SHGs formation, especially among SC/ST women, one out of ten FPC formed SHGs involving more than 60% SC/ST women for their empowerment. It is also observed from Table 6.15 that no FPCs arranged any training program for forest fire prevention in the Rupnarayan forest division of Paschim Medinipur.

6.4 FPC of Alipurduar Forest Division, North Bengal In Alipurduar forest division we have selected three FPCs and two eco-development committees (EDC) namely Garopampu (FPC), Rabhabasti (FPC), Santrabari (EDC), 28 Basti (EDC) and Jayanti (FPC). All FPCs are functioning from 1990s. The highest

120

6 Analysis of Forest Protection Committee (FPC), Forest Beat Office …

Table 6.15 Performance of the FPCs in Paschim Medinipur forest division, South Bengal Name of FPC

Number of households involved in afforestation

SHG formation by FPC (SC/ST)

Control of Illegal logging (Yes = 1, No = 0)

Helping to Implement Poverty reduction measures taken by FPC (Yes = 1, No = 0)

Training facilities for controlling forest fire (Yes = 1, No = 0)

Upper Pathrisole

17

16 (66.67)

0

1

0

Lower Pathrisole

23

7 (25)

1

1

0

Kastokura

8

5 (55.56)

1

1

0

Dhabani

7

1 (12.5)

1

1

0

Aushbandi

27

10 (32.26)

1

1

0

Godasole

17

15 (51.72)

1

1

0

Fulsason

14

0 (0)

1

1

0

Darnadihi

18

9 (45)

1

1

0

Chandabila

28

18 (52.94)

1

1

0

Ichalkoda

11

1 (6.25)

1

1

0

Source Field survey

Table 6.16 Major features of sample FPC of Alipurduar, North Bengal Name of FPC

Years of formation of FPC

Forest Cover (hec)

Total Household members

Major planted forests

Garopampu (FPC)

1993

3500

124

Sal, Segun, Siris, Lali

Rabhabasti (FPC)

1993

2500

72

Sal, Segun, Siris, Lali

Santrabari (EDC)

2006

7500

125

Sal, Segun, Gakul, Lampati, Parari

28 Basti (EDC)

1993

6500

123

Sal, Segun, Gakul, Lampati, Parari

Jayanti (FPC)

1992

800

152

Sal, Segun, Siris, Bon Kathal

Sources Attendance copy of beat meeting, 2020–21

forest cover is found in Santrabari EDC while lowest forest cover is in Jayanti FPC. The major trees are planted across the FPCs are Sal, Segun, Siris, Lali, Gakul, Lampati, Parari and Bon Kathal etc. (Table 6.16) and FPC wise forest cover is shown in Fig. 6.4. Table 6.17 provides basic membership information of female and male across the FPCs/EDCs.

6.4 FPC of Alipurduar Forest Division, North Bengal

121

Forest Cover of FPCs Area of Forest (in ha)

8000 7000 6000 5000 4000 3000 2000 1000 0 Garopampu

Rabhabasti

Santrabari (EDC)

28 Basti (EDC)

Jayanti (JFMC)

Fig. 6.4 FPC wise forest cover in Alipurduar forest division, North Bengal. Source Field survey

Table 6.17 Membership information of female and male in the executive and general committee across sample FPCs of Alipurduar, North Bengal Name of FPC/EDC

Executive committee members

General body members

Female

Male

Total

Female

Male

Total

Garopampu

1 (16.67)

5 (83.33)

6 (100)

118 (45.38)

142 (54.62)

260 (100)

Rabhabasti

–

6 (100)

6 (100)

85 (44.04)

108 (55.96)

193 (100)

Santrabari

2 (40)

3 (60)

5 (100)

140 (43.75)

180 (56.25)

320 (100)

28 Basti

1 (16.67)

5 (83.33)

6 (100)

228 (42.22)

312 (57.78)

540 (100)

Jayanti

–

6 (100)

6 (100)

250 (41.67)

350 (58.33)

600 (100)

Sources Attendance copy of beat meeting, 2020–21

The members of the executive committee are 6. Out of five FPCs/EDCs, there is no female representative in the two FPCs say Rabhabasti FPC and Jayanti FPC. In the other FPCs, it is observed that female members in the executive committee vary from 16 to 40% while in the general body, it varies from 41.67 to 45%. The participation rate of female and male members in the general body meetings in different FPCs in the Alipurduar forest division of North Bengal is shown in Table 6.18. It is found that the overall participation rate in the GB meeting is 93.83%, out of which female members’ participation is 91.47%, and that for male members’ participation is 95.60%. Out of five FPCs, three FPCs show that the female member participation rate in GB meetings is 100%. Table 6.19 presents meeting-related information on the FPCs in the Alipurduar forest division of North Bengal. It has been asked of the FPC official relating to election procedure, the number of meetings held per year, and maintaining resolution/ meeting books for the general body meeting. All FPCs except one replied that the election of the members was held by popular votes by raising hands. The number of

122

6 Analysis of Forest Protection Committee (FPC), Forest Beat Office …

Table 6.18 Participation in the general body meeting of Alipurduar FPCs Name of FPC

General body members Participation in the GB (nos) Meeting (nos)

Participation rate (%)

Female

Female

Male

Total

Male

Total

Female

Male

Total

Garopampu

118

142

260

78

117

195

66.10

82.39

74.25

Rabhabasti

85

108

193

55

85

140

64.71

78.70

71.70

Santrabari (EDC)

140

180

320

140

180

320

100

100

100

28 Basti (EDC)

228

312

540

228

312

540

100

100

100

Jayanti

250

350

600

250

350

600

100

100

100

All

821

1092

1913

751

1044

1795

91.47

95.60

93.83

Sources Attendance copy of beat meeting, 2020–21

meetings held per year lies between 2 and 5 days and agenda-wise resolutions are written in the meeting book and maintained such meeting book. The performance of each FPC is judged by the number of man days created for afforesting, formation of self-help groups especially among SC/ST women members, controlling of illegal timber logging, helping to implement poverty reduction measures, and training facilities for controlling forest fire. The result of their performance is shown in Table 6.20. In respect of man day creation for afforestation, out of five FPC, only two FPC offered more than 20 man-days per year while rest three FPC offered below 15 man-days yearly. All FPCs are successful in controlling illegal timber logging, in training facilities for forest fire prevention, and helping to implement poverty reduction measures in the study area. In respect of SHGs formation, especially among SC/ST women, three out of five FPC formed SHGs involving more than 60% SC/ST women for their empowerment. Figure 6.5 shows a comparative analysis of the participation rate of females and males in the General Body meeting across four forest divisions of West Bengal. It is found from this Fig. 6.5 that the participation rates of female and male members in the general body meetings are highest in the Alipurduar forest division of North Bengal compared to the other forest divisions of South Bengal. Table 6.19 Meeting related information of the FPCs in Alipurduar forest division, North Bengal Name of FPC

Election held by popular vote (Y/N)

Meetings held in FPC (year)

Meeting book is availability (Y/N)

Garopampu

1

3

1

Rabhabasti

1

3

1

Santrabari (EDC)

0

5

1

28 Basti (EDC)

1

2

1

Jayanti (JFMC)

1

3

1

Sources Attendance copy of beat meeting, 2020–21

6.5 Forest Beat Office Across South Bengal and North Bengal Forest Divisions

123

Table 6.20 Performance of the FPCs in Alipurduar forest division, North Bengal Name of FPC

Number of households involved in afforestation

SHG formation by FPC (SC/ST)

Garopampu

40

Rabhabasti

9

Santrabari (EDC)

20

Control of Illegal logging (Yes = 1, No = 0)

Helping to implement poverty reduction measures (Yes = 1, No = 0)

Training facilities for controlling forest fire (Yes = 1, No = 0)

34 (69.39) 1

1

1

5 (50)

1

1

1

13 (65)

1

1

1

28 Basti (EDC)

6

12 (92.31) 1

1

1

Jayanti (JFMC)

1

11 (44)

1

1

1

Source Field survey, 2020–21

Percentage of Participation

120.00 100.00 80.00 60.00 40.00 20.00 0.00 Purulia

Bankura Female

Paschim Medinipur

Alipurduar

Male

Fig. 6.5 Participation of female and male members in the GB meeting in South Bengal and North Bengal forest divisions, West Bengal

6.5 Forest Beat Office Across South Bengal and North Bengal Forest Divisions We have surveyed 12 beat offices, 3 each from four forest divisions of West Bengal purposively. A description of 12 forest beat offices in South and North Bengal forest divisions is shown in Table 6.21. In Table 6.21 we have described forest cover, the number of sample FPCs, and the number of meetings held per year for each beat office. The number of meetings held yearly is found to be highest in the beat office of Ranibundh under the Bankura (south) forest division of South Bengal while the

124

6 Analysis of Forest Protection Committee (FPC), Forest Beat Office …

Table 6.21 Description of forest beat office in South and North West Bengal Beat office

Forest cover (Ha)

Number of sample FPC

Number of meetings held (yearly)

Burda

3908

1

12

Bagmundi

8512

5

12

Kalimati

1537

3

12

Purulia forest division

Bankura (south) forest division Ambikanagar

1024

3

17

Ranibandh

2421

4

18

Punshiya

2600

3

12

Rupnarayan forest division, Paschim Medinipur Pathrisole

1740

4

5

Roshkundu

2125

3

5

Chandabila

1999

3

4

Alipurduar forest division West Rajabhatkawa

7500

2

4

Santrabari

5500

2

2

Jayanti

1200

1

2

Source Field survey

lowest meeting held in Santrabari and Jayanti beat offices under Alipurduar forest division of North Bengal (Table 6.21 and Fig. 6.6). The vacancy position of different beat offices is presented in Table 6.22 and is shown in Fig. 6.7. It is found from Table 6.22 that 40% of the sanctioned post remains vacant in the Purulia forest division and about 70% of the sanctioned post remains vacant in Bankura (south) forest division. In the Rupnarayan forest division, the vacancy position is about 46% of the sanctioned post while 33% remains vacant in the Alipurduar forest division of North Bengal.

6.6 Gram Panchayat Across South Bengal and North Bengal Forest Divisions We have surveyed ten (10) Gram Panchayats (GP), three from each forest division in South Bengal and one from the North Bengal forest division. We asked the Panchayat official whether their representatives were in forest protection committees (FPCs) or not, whether there has been illegal timber logging or not; and how price is determined by harvested timber to understand the issues of forest governance. Table 6.23 presents such information. It is found from Table 6.23 that there is no GP representative to the

6.6 Gram Panchayat Across South Bengal and North Bengal Forest Divisions

125

Purulia

Bankura

Paschim Medinipur

Jayanti

Santrabari

West Rajabhatkawa

Chandabila

Roshkundu

Pathrisole

Punshiya

Ranibandh

Ambikanagar

Kalimati

Bagmundi

10 9 8 7 6 5 4 3 2 1 0

Burda

Numbers

Number of meetings held (Yearly) by forest Beat Office in South and North West Bengal

Alipurduar

Fig. 6.6 Number of meetings held (yearly) across forest beat offices in South Bengal and North Bengal forest divisions Table 6.22 Vacancy position of different posts across different beat offices in South and North Bengal forest divisions Forest division Purulia

Bankura

Rupnarayan, Paschim Medinipur

Alipurduar

Beat office

Sanctioned posts (Nos)

Vacant posts (Nos)

Existing posts (Nos)

Burda

4 (40.0)

1 (10.0)

3 (30.0)

Bagmundi

3 (30.0)

3 (30.0)

0 (0.0)

Kalimati

3 (30.0)

–

3 (30.0)

Total

10 (100)

4 (40.0)

6 (60.0)

Ambikanagar

9 (39.1)

7 (30.4)

2 (8.7)

Ranibandh

11 (47.8)

9 (39.1)

2 (8.7)

Punshiya

3 (13.0)

–

3 (13.0)

Total

23 (100)

16 (69.6)

7 (30.4)

Pathrisole

8 (33.3)

3 (12.5)

5 (20.8)

Roshkundu

7 (29.2)

4 (16.7)

3 (12.5)

Chandabila

9 (37.5)

4 (16.7)

5 (20.8)

Total

24 (100)

11 (45.8)

13 (54.2)

West Rajabhatkawa

10 (37.0)

4 (14.8)

6 (22.2)

Santrabari

7 (25.9)

1 (3.7)

6 (22.2)

Jayanti

10 (37.0)

4 (14.8)

6 (22.2)

Total

27 (100)

9 (33.3)

18 (66.7)

Source Field survey

126

6 Analysis of Forest Protection Committee (FPC), Forest Beat Office …

Percentage

Percentage of Vacant and Existing Posts 100.00 90.00 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00

Purulia

Bankura Vacant Posts

Paschim Medinipur

Alipurduar

Existing Posts

Fig. 6.7 Percentage of vacant and existing posts across different forest divisions of South Bengal and North Bengal. Source Field survey

Table 6.23 Gram Panchayat of South and North West Bengal on role of FPC Gram Panchayat

Number of Gram Panchayat representative to FPC

Illegal feeling of timber (1 = increased, 2 = no change, 3 = decreased)

Timber price determination (1 = decided by traders, 2 = decided by negotiation b/w traders and FPC, 3 = decided by FPC)

Purulia forest division Perorgoria

1

3

2

Icchakota

2

1

1

Charida

1

3

2

Bankura (south) forest division Sonagara

1

3

1

Rudra

2

1

3

Ranibadh

4

2

1

Rupnarayan forest division Paschim Medinipur Garbeta

0

3

1

Godasole

0

3

2

Garanga

0

2

3

3

3

Alipurduar forest division Rajabhatkawa

0

Source Field survey, 2019–21

FPCs of the Rupnarayan Forest division of South Bengal and the Alipurduar forest division in North Bengal. In the case of illegal felling of timber, two Gram Panchayat out of ten has expressed that there has been an increase in illegal felling of timber while six GPs opined that illegal timber logging has decreased. In the case of price

6.6 Gram Panchayat Across South Bengal and North Bengal Forest Divisions

127

determination of harvested trees, four GPs out of 10 GPs pointed out that traders exclusively determine price while three GPs opined that FPC exclusively determines prices of harvested trees. On the other hand, 3 GPs expressed that both traders and FPCs are determining the prices of harvested trees. In North Bengal FPC plays a significant role in the price determination of harvested trees while in South Bengal traders, FPC, and both are responsible for the price determination of harvested trees. Summing Up In the Purulia forest division of South Bengal, the executive committee member comprises 5. 40% of female members are in the executive committee and that varies from 17 to 35% in the general body (GB) meetings. In this forest division, the overall participation rate in the GB meetings is 52.50%, out of which female members’ participation is 46.88% and male members’ participation is 54.62%. The number of GB meetings held per year lies between 10 and 15 days and agenda-wise resolutions are written in the meeting book and maintained such meeting book properly. The performance of each FPC in the Purulia forest division is a mix. All FPCs are successful in controlling illegal timber logging. In respect of SHGs formation, especially SC/ ST women, about 67% of FPCs formed SHGs involving more than 60% of SC/ST women for their empowerment. Only 44.44% of FPCs arranged training programs for forest fire prevention. In the Bankura (south) forest division of South Bengal, the executive committee members are 6, and female members in the executive committee vary from 16 to 40% while in the general body, it varies from 28 to 42%. The overall participation rate in the GB meeting is 66.40%, out of which female members’ participation is 62.21% and male members’ participation is 68.58%. The number of meetings held per year of FPC lies between 5 and 15 days. All FPCs are successful in controlling illegal timber logging and helping to implement poverty reduction measures. But in respect of SHGs formation, especially among SC/ST women, 30% of FPCs formed SHGs involving more than 60% SC/ST women for their empowerment and 70% of FPCs arranged training programs for forest fire prevention. In the Rupnarayan forest division of Paschim Medinipur in South Bengal, the executive committee member ranges from 6 to 14 and female members in the executive committee vary from 22 to 50% while in the general body meeting female members varies from 28 to 44%. It is found that the overall participation rate in the GB meeting is 85.22%, while female participation is 85.61%, and that for male members’ participation is 84.96%. The number of meetings held per year of FPC ranges from 1 to 7 days. All FPCs are successful in controlling illegal timber logging. In respect of SHGs formation, especially among SC/ST women, only 10% of FPCs formed SHGs involving more than 60% of SC/ST women for their empowerment. No FPCs arranged any training program for forest fire prevention in the Rupnarayan forest division of Paschim Medinipur. In the Alipurduar forest division of North Bengal, the executive committee members are 6, and female members in the executive committee vary from 16 to 40% while in the general body, such member varies from 41.67 to 45%. It is found

128

6 Analysis of Forest Protection Committee (FPC), Forest Beat Office …

that the overall participation rate in the GB meeting is 93.83%, while female participation is 91.47%, and that for male members’ participation is 95.60%. The numbers of GB meetings held per year are 2–5 days. All FPCs are successful in controlling illegal timber logging, training facilities for forest fire prevention, and helping to implement poverty reduction measures in the study area. In terms of SHGs formation, especially among SC/ST women, 60% of FPCs formed SHGs involving more than 60% SC/ST women. After studying the vacancy position of each beat office, it is revealed that 40% of the sanctioned post remains vacant in the Purulia forest division and about 70% vacant in Bankura (south) forest division. In the Rupnarayan forest division, the vacancy position is about 46% of the sanctioned post while 33% remains the vacant position in the Alipurduar forest division of North Bengal. It is also revealed from the survey on GP that there is no GP representative to the FPCs in the Rupnarayan Forest division of South Bengal and Alipurduar forest division in North Bengal. There has been a contrary result between FPC and GP studies on illegal timber logging. The study confirms the illegal timber logging present there. In North, Bengal FPC plays a significant role in the price determination of felling trees while in South Bengal the traders, FPCs, and both are responsible for the price determination of felling trees.

Chapter 7

Institutions and Enforcement at Local Level

Abstract This chapter examines the role of institutions and enforcement by formulating an institutional index and enforcement index involving forest-dependent communities at the local level in South Bengal and North Bengal Forest divisions. This chapter also examines the role played by formal institutions like West Bengal Forest Development Corporation Limited (WBFDCL) and Large-sized Adivasi Multipurpose Cooperative Societies (LAMPS) and Some Non-governmental organizations (NGO) in the study area.

7.1 Institutional Index and Enforcement Index in the Purulia Forest Division, South Bengal The objective of the section is to formulate an institutional index and enforcement index in the Purulia forest division of South Bengal. The local institution comprises individuals or households and forest protection committees in the study area. Here, enforcement means how the local people prevent illegal logging and they protect forests using forest patrolling, and help to maintain rules and regulations regarding the collection of forest products. The institutional index is based on three main indicators like participation index, monitoring index, and perception index. We have also divided the main indicators into sub-indicators. The details of each indicator of institutional and enforcement and how indices values are calculated are shown in Chap. 3 (Sects. 3.2.3, 3.2.4, and Table 3.2). The results of institutional and enforcement indices are presented in Tables 7.1 and 7.2 respectively. It is observed from Table 7.1 that the institutional index is found to be 0.701 (Table 7.1). The perception index is found to be the highest (0.905) and the monitoring index is the lowest. The high value of the perception index shows the indication of good forest governance. This also means that the monitoring system of the forest department with the community is successful in managing forest resources and to prevent deforestation. The value of enforcement index is found to be 0.717 (Table 7.2). In the enforcement index forest patrols index value (0.873) is the highest. The high value of forest patrols and low value of illegal logging are the indications of good forest governance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. P. Basu, Governance and Institution in the Indian Forest Sector, https://doi.org/10.1007/978-3-031-34746-7_7

129

130

7 Institutions and Enforcement at Local Level

Table 7.1 Institutional index at the local level in the Purulia forest division of South Bengal Main indicators

Sub-indicators

Index value

Participation index

Participation in afforestation

0.754

Presence in the meeting of forest protection committee

0.645 0.699

Monitoring index

Follow ups forest management by laws

0.462

Supervise forest management plan implementation

0.521

Forest boundary maintenance

0.511 0.498

Perception index

Existing any informal rule for the use of forest products

0.767

Government rules regulating forest use

0.983

Community member obeys government rules for forest use

0.966 0.905

Institutional index

0.701

Source Author’s calculation from primary data

Table 7.2 Enforcement index in Purulia forest division, South Bengal Main indicator

Sub-indicators

Index value

Enforcement index

Forest patrols

0.873

Preventing illegal logging activities

0.528

Rules for getting permission to collect forest product

0.767

Permissions issued by appropriate authority

0.698

Enforcement index

0.717

Source Author’s calculation from primary data

Figure 7.1 shows the institutional, enforcement and monitoring indices of the Purulia forest division. The result reveals that the enforcement index is found to be the highest (0.717) followed by the institutional index (0.701) and monitoring index (0.498).

7.2 Institutional Index and Enforcement Index in Bankura (South) Forest Division, South Bengal The results of the institutional and enforcement indices of the Bankura (south) forest division are presented in Tables 7.3 and 7.4 respectively. It is observed from Table 7.3 that the institutional index is found to be 0.67. The perception index is found to be highest (0.915) followed by participation index (0.859) and monitoring index is the lowest (0.208).

7.2 Institutional Index and Enforcement Index in Bankura (South) Forest … 0.800 0.700

131

0.717

0.701

0.600

0.498

0.500

Institutional Index

0.400

Enforcement Index

0.300

Monitoring Index

0.200 0.100 0.000 Puruliya

Fig. 7.1 Institutional, enforcement and monitoring index of Purulia forest division. Source Author’s calculation from primary data Table 7.3 Institutional index in the Bankura forest division, South Bengal Main indicators Participation index

Sub-indicators

Index value

Participation in afforestation

0.785

Presence in the meeting of forest protection committee

0.932 0.859

Monitoring index

Follow ups forest management by laws

0.184

Supervise forest management plan implementation

0.167

Forest boundary maintenance

0.272 0.208

Perception index

Existing any informal rule for the use of forest products

0.826

Government rules regulating forest use

0.963

Community member obeys government rules for forest use

0.956 0.915

Institutional index

0.660

Source Author’s calculation from primary data Table 7.4 Enforcement index in the Bankura (south) forest division, South Bengal Main indicator

Sub-indicators

Index value

Enforcement index

Forest patrols

0.252

Preventing illegal logging activities

0.616

Need of permission to collect/harvest forest product

0.412

Issuance of permit by the correct authority

0.524 0.451

Source Author’s calculation from primary data

132

0.700

7 Institutions and Enforcement at Local Level

0.660

0.600 0.500

0.451

0.400

Institutional Index Enforcement Index

0.300 0.208

Monitoring Index

0.200 0.100 0.000 Bankura Fig. 7.2 Institutional, enforcement and monitoring index of Bankura forest division. Source Author’s calculation from primary data

The value of the enforcement index is found to be 0.451. In the enforcement index preventing illegal activities index value (0.616) is the highest followed by issuance of permit (0.524) while forest patrols index values is the lowest (0.252). The high value of preventing illegal activities is the indication of good forest governance. Figure 7.2 shows the institutional, the enforcement and monitoring indices of the Bankura (south) forest division. Thus, the study reveals that the institutional index is the highest (0.660) followed by the enforcement index (0.451) and monitoring index (0.208).

7.3 Institutional Index and Enforcement Index in Rupnarayan Forest Division of Paschim Medinipur in South Bengal The results of the institutional and enforcement indices are presented in Tables 7.5 and 7.6 respectively. It is observed from Table 7.5 that the institutional index of Paschim Medinipur is found to be 0.39 (Table 7.5). The monitoring index is found to be the highest (0.621) and the perception index is the lowest (0.070). The high value of the monitoring index shows the indication of good forest governance. This also means that the monitoring system of the forest department with the community is successful in managing forest resources and to prevent deforestation. The value of the enforcement index is found to be 0.257. In the enforcement index forest patrols index value (0.569) is the highest followed by preventing illegal logging activities, issuance of a permit by the correct authority, and the need for permission to collect forest products. The high value of forest patrols is an indication of good forest governance.

7.4 Institutional Index, Enforcement Index and Monitoring Index …

133

Table 7.5 Institutional index in the Rupnarayan forest division (Paschim Medinipur), South Bengal Main indicators

Sub-indicators

Index value

Participation index

Participation in afforestation

0.798

Presence in the meeting of forest protection committee

0.148 0.473

Monitoring index

Follow ups forest management by laws

0.538

Supervise forest management plan implementation

0.707

Forest boundary maintenance

0.617 0.621

Perception index

Existing any informal rule for the use of forest products

0.172

Government rules regulating forest use

0.014

Community member obeys government rules for forest use

0.025 0.070

Institutional index

0.388

Source Author’s calculation from primary data

Table 7.6 Enforcement index in the Rupnarayan forest division (Paschim Medinipur), South Bengal Main indicator

Sub-indicators

Index value

Enforcement index

Forest patrols

0.568

Reporting of illegal activities

0.289

Need of permission to collect/harvest forest product

0.055

Issuance of permit by the correct authority

0.115 0.257

Source Author’s calculation from primary data

Figure 7.3 shows the institutional, enforcement and monitoring index of Rupnarayan forest division of Paschim Medinipur. The result reveals that monitoring index is the highest (0.621) followed by the institutional index (0.388) and the enforcement index (0.257).

7.4 Institutional Index, Enforcement Index and Monitoring Index in Alipurduar Forest Division in North Bengal The results of the institutional and the enforcement indices are presented in Tables 7.7 and 7.8 respectively. It is observed from Table 7.7 that the institutional index is found to be 0.786. The monitoring index is found to be the highest (0.957) and participation index is the lowest (0.597). The high value of monitoring index shows the indication

134

7 Institutions and Enforcement at Local Level

0.700 0.621 0.600 0.500 0.400

0.388

Institutional Index

0.300

0.257

Enforcement Index Monitoring Index

0.200 0.100 0.000 Medinipur

Fig. 7.3 Institutional, enforcement and monitoring index of Rupnarayan forest division of Paschim Medinipur. Source Author’s calculation from primary data

Table 7.7 Institutional index in the Alipurduar forest division, North Bengal Main indicators

Sub-indicators

Index value

Participation index

Participation in afforestation

0.214

Presence in the meeting of forest protection committee

0.980 0.597

Monitoring index

Follow ups forest management by-laws

0.964

Supervise forest management plan implementation

0.937

Forest boundary maintenance

0.970 0.957

Perception index

Existing any informal rule for the use of forest products

0.651

Government rules regulating forest use

0.927

Community member obeys government rules for forest use

0.838 0.805

Institutional index

0.786

Source Author’s calculation from primary data

of good forest governance. This also means that the monitoring system of the forest department with community is successful in managing forest resource and to prevent deforestation. The value of enforcement index is found to be 0.828 (Table 7.8). In the enforcement index forest patrols product index value (0.921) is the highest. The high value of forest patrols and need of permission to collect/harvest forest product are the indications of good forest governance.

7.5 Monitoring, Institutional, and Enforcement Indices in South and North …

135

Table 7.8 Enforcement index in the Alipurduar forest division, North Bengal Main indicator

Sub-indicators

Index value

Enforcement index

Forest patrolling

0.921

Preventing illegal logging activities

0.967

Need of permission to collect/harvest forest product

0.656

Issuance of permit by the correct authority

0.768

Enforcement index

0.828

Source Author’s calculation from primary data

1.200 1.000 0.800

0.957 0.786

0.828 Institutional Index

0.600

Enforcement Index

0.400

Monitoring Index

0.200 0.000 Alipurduar

Fig. 7.4 Institutional, enforcement and monitoring index in Alipurduar forest division in North Bengal. Source Author’s calculation from primary data

Figure 7.4 shows the institutional, the enforcement and the monitoring indices of the Alipurduar forest division. The result reveals that monitoring index is found to be the highest (0.957) followed by enforcement index (0.828) and institutional index (0.786) in the Alipurduar forest division.

7.5 Monitoring, Institutional, and Enforcement Indices in South and North Bengal Forest Divisions This section presents monitoring, institutional, and enforcement values at the local level across the South and the North Bengal forest divisions. In Table 7.9 and Fig. 7.5 it is found that the monitoring index, institutional index, and enforcement index are higher in the North Bengal forest division compared to the South Bengal forest division.

136

7 Institutions and Enforcement at Local Level

Table 7.9 Monitoring, institutional and enforcement indices in South and North Bengal forest divisions

Forest division

Monitoring index

Institutional index

Enforcement index

South Bengal

0.440

0.591

0.488

North Bengal

0.957

0.786

0.828

Source Author’s calculation

1.200 1.000 0.800 0.600 0.400 0.200 0.000 Monitoring Index

Institutional Index South Bengal

Enforcement Index

North Bengal

Fig. 7.5 Monitoring, institutional and enforcement indices in South and North Bengal forest divisions. Source Author’s calculation

7.6 Formal Institutions and NGOs in the Forest Sector in West Bengal In this section it is important to discuss the roles played by different formal institutions and NGOs in the forest sectors in West Bengal. First, we examine the roles of formal institutions of West Bengal Forest Development Corporation Limited (WBFDCL) and then Large-sized Adivasi Multipurpose Cooperative Societies (LAMPS).

7.6 Formal Institutions and NGOs in the Forest Sector in West Bengal

137

7.6.1 West Bengal Forest Development Corporation Limited (WBFDCL) West Bengal Forest Development Corporation Limited (WBFDCL) was formed in 1974 to provide support to the Forest Department and the Forest Directorate in implementing their plans, programs, and policies relating to forestry. The broad objectives of WBFDCL are (a) to offer timber and non-timber forest products at reasonable prices for the general public; (b) to enhance awareness for the conservation of nature and wildlife through eco-tourism and (c) to improve the socio-economic development of joint forest management committees (JFMCs); (d) to promote wood-based industries and to popularize the use of eco-friendly inputs like bio-fertilizers for enhancing the fertility of the soil. Lastly, it helps to mobilize local people by providing constant support in protecting and managing forest resources. It also involves e-auctioning of forest harvested produce, carpentry, NTFP production including Sundarbans honey production and marketing and eco-tourism.

7.6.2 Large-Sized Adivasi Multipurpose Cooperative Societies (LAMPS) LAMPS came from primary Agricultural Cooperative Societies for tribal areas in many states including West Bengal. It helps to generate more employment opportunities for the tribal people by collecting and marketing their minor forest products. It also provides interest-free short-term loan facilities to the tribal members for farming, agriculture, and purchase of cattle like pigs, sheep, goats, and plow bullocks and for any other activities like Bubai rope making, sal leaf plates making, etc. It also helps to empower tribal women to participate in economic activities for generating income and establishing social identity. In West Bengal, about 42,000 tribal families has been benefitted through LAMPS.

7.6.3 The Ramkrishna Mission Lok Shiksha Parishad (RKM-LP) (NGO) The Ramakrishna Mission Lokshiksha Parishad, a rural wing of Ramakrishna Mission Ashrama is working as a non-governmental organization (NGO) for a long time and engaged to develop socio-economic cultural issues of rural poor who are living in the forest fringes areas in West Bengal. In the late 1980s and early 1990s, the Lok Shiksha Parishad forested 1000 ha of privately owned wastelands in 60 villages of the Purulia district by planting 2,900,000 plants. The Lokshiksha Parishad

138

7 Institutions and Enforcement at Local Level

received a financial grant from the Ford Foundation to initiate a training and orientation program for both the JFMC members and the forest personnel. The Lok Shiksha Parishad has given more focus on the development of human resources in the districts of Bankura, Purulia, and Midnapore of South Bengal for strengthening the joint forest management program.

7.6.4 The Indian Institute for Bio-social Research and Development (IBRAD) IBRAD is another NGO that was established in 1987, especially for the health sector and later it extended its area to tribal development, joint irrigation management, and participatory forest management in West Bengal. The objectives are to strengthen institutionalization and empowerment among forest dwellers for the protection of forest resources. In addition to the two NGOs discussed above, we studied 49 NGOs, 168 NGOs, 37 NGOs, and 27 NGOs from the Purulia forest division, Bankura forest division, Paschim Medinipur forest division, and Alipurduar forest division respectively (Tables 7.10, 7.11, 7.12, and 7.13). Data have been collected from different websites for these districts. It is found from the study of 49 NGOs that only 6% of NGOs are involved in forestry and social forestry sectors in the Purulia forest division. Of the 168 NGOs it is observed that 7.15% of NGOs are involved in the environment and forestry sectors in the Bankura forest division. Of the 37 NGOs it is observed that 8% of NGOs are engaged in the environment and forests in Paschim Medinipur district. Lastly, of the 27 NGOs we find that there is no single NGO involved in the forest sector in the Alipurduar forest division of North Bengal.

7.6 Formal Institutions and NGOs in the Forest Sector in West Bengal

139

Table 7.10 List of NGOs in Purulia forest division in Purulia district S. No.

Area of work

Number of NGO

Name of NGOs

1

Self Help Groups

7 (14.29)

Dakakendu Society, Purulia Sabuj Sangha, Vivekbahini Bratachari Sakha, Pradan, Jana Sangati Kendra, Sarvik Gram Bikash Kendra, Sabyasachi Club

2

Rural Development and Poverty Alleviation, Watershed

2 (4.08)

Kalyan, Co-ordination, Liya

3

Non Formal Education, Vocational Training

6 (12.24)

Bhatbundh Mahila Samity, Institute of Training and Development, Majhihira National Basic Education Institution, Puncha Infirmary for the Defeated Human Race (PIDHR), Subhas Swapna Bikash Sangstha, Bamnia Bengal Tiger Club

4

Differently abled, Children and Children education

8 (16.33)

Liberal Association Movement of People, Ramkrishna Vivekananda Mission, Sarada Ashram Welfare Home, Gopalnagar Subhayan, Nirman Sangha, Purulia Pratibandhi Kalyan Samity, Pragatishil Krishak (Karshak) Seva Sangha, Mandra Lion’s Club

5

Forestry, Social Forestry

3 (6.12)

Manbhum Ananda Ashram Nityananda Trust, Panipathar Pally Bikash Seva Samity, Purulia Nirmal Seva Sangha

6

Agriculture, Soil Conservation

2 (4.08)

Marshal Dahar Gaunta, Nodal Research Centre

7

Primary 7 (14.29) Education, Education and Literacy

Ramkrishna Society for Rural Development, Purulia Pally Seva Sangha, Tapananda Rural Development Society, Vivekananda Rural Development Society, All Backward Classes. Relief and Development. Mission, Bundwan Sports Association, Vivekananda Bikash Kendra

8

Health and Family Welfare

8 (16.33)

Purulia Leprosy Home, Purulia and Hospital, Ramkrishna Mission Lokashiksha Parishad, Manipur Leprosy Rehabilitation Centre, Paschim Banga Kheria Sabar Kalyan Samity, Hara Parbati Club, Bhamuria Social Welfare Society, Indian Social Action and Research Association, Gandhi Memorial Leprosy Foundation

9

Green Field Programme

2 (4.08)

Tagore Society for Rural Development, Manbhum Jatiya Pally Seva Sangha

10

Awareness Programme

4 (8.16)

Nehru Yuba Kendra, Women Interlinked Foundation, Jamgoria Sevabrata, Purulia district Agragami Mahila O Sishu Mangal Samity

Total

49 (100)

Source http://purulia.gov.in/distAdmin/collectorate/dsw/ngo_list.html

140

7 Institutions and Enforcement at Local Level

Table 7.11 List of NGOs in Bankura forest division in Bankura district S. No.

Area of work

Number of NGO

Name of NGOs

1

Animal Husbandry

50 (29.76)

MGBK, AEARDS, BSAMS, BSUS, BGUS, BGUS, BHCS, BBLCKS, BMSWS, BBWS, BCWO, BTMASKS, CRDS, CAMVS, CGARDS, DSSGSA, DGKSMM, FAAG, GWACDS, GBNST, IIFSAMTT, IBSMKS, JAS, JISORM, KDS, KAMUS, KASS, KAYKS, KJAKS, KSSAL, KNGJS, KWACDS, KDS, KJF, MJKSN, MKUMUS, MPS, PVKS, PLCS, RSFRD, RDS, SLCS, SBYS, SBLSSAMSS, SPSP, SRDS, SCS, SSSBGS, SA, SA

2

Rural Development and Poverty Alleviation, Labour and Employment

7 (4.17)

AGUS, NCAWS, NRDS, PSMUS, PNTS, SASSDM, BDS

3

Human Rights, Tribal affairs, Differently abled

10 (5.95)

BATM, BBC, GASM, KWWS, WBAPA, AAMUS, BAUWS, BAA, MSM, UDBODHAN

4

Aged/Elderly

16 (9.52)

APUS, BSDASS, CMKS, DGIFRD, DSSAMWS, JSUS, JRWO, JRAVWO, JRPUS, KPUS, PSD, RDS, SSBSYA, SA, SOFIA, VAKS

5

Environment and Forests

12 (7.15)

ASKKS, BVKS, BAVGSS, BKAGSS, HNRWSS, JAKS, KRDS, MJSM, MSDS, RLJS, SSFIRD, TJS

6

Agriculture, Water Resources

16 (9.52)

BGMJS, BLCS, BZHJKSOSKS, GYS, JCWS, KSS, KSSS, KGUS, LMKPS, NAKS, ORWDS, PEARDS, SSAF, TBVC, TSS, MMKS

7

Education and Literacy

16 (9.52)

BDDGUS, BSOHM, BABGSS, BCAMI, CBURP, GAWS, KMTCES, KOGA, KNAKS, KASS, LC, LPP, ONSS, SSVPUS, SBS, SF

8

Health and Family Welfare, Vocational Training, Information Technology

12 (7.15)

BBRHAO, BKB, BRAFMAHA, KRAKS, MNSSWS, RBCC, USDMKS, BCRCLM, JSSB, KPKS, NPE, School of Computing and Information Systems Post Beliatore District Bankura State Wb Country India

9

Arts and Culture, Sports

17 (10.12)

BC, BENA, BPARIATI, BPS, BGPUSS, JRWO, KSC, KRDS, KSRDS, LKAVS, PRIOSCK, PS, SML, JKSSMSS, KVSS, LPPS, JKSSMSS

10

Children, Children Education

12 (7.15)

BUWS, CAMS, GJM, KIWWA, MJKSN, NYCSM, NYCAP, PSN, RSATWO, RVGUSS, SM, WAFRAD

Total

168 (100)

Source https://www.giveindia.org/all-ngos/west-bengal/bankura/

7.6 Formal Institutions and NGOs in the Forest Sector in West Bengal

141

Table 7.12 List of NGOs in Paschim Medinipur district S. No.

Area of work

Number of NGO

Name of NGOs

1

Animal Husbandry

6 (16.22)

Arindam Social And Animals Welfare Society, Dhamkuria Vivekananda Milan Sangha, Energetic Rural Urban And Development Society, Rural Development Association, Sakundiha Gouridevi V D and S W Society, Sanjiboni Unnyan Sanstha

2

Rural Development and – Poverty Alleviation, Labour and Employment, Vocational Training

3

SHG, Tribal affairs, Minority Issues

4

Aged/Elderly, Differently 11 (29.73) abled

Balakrout Rural Welfare Society, Berhballavpur Samaj Kalyan Samity, Child And Social Welfare Society, Ghatal Mahakuma Samajik Suraksha Kendra, Harisinghapur Satyanarayan Sangha, Jhargram Jetore Research Centre, Kamargeria Vivekananda Manab Kalyan Society, Manabik Samsthan, Mathurapur Gram Bikas Kendra, Midnapore Madhusudan Nagar Craft Centre, Ramdaspur Pally Unnayan Samity

5

Environment and Forests 3 (8.11)

Deshapranpalli Social Welfare Society, Environmental Service Group, Neelmadhab Chlid Welfare Trust

6

Agriculture, Water Resources

–

7

Health And Family Welfare

1 (2.70)

Thalassaemia Society Of Midnapore District

8

Arts & Culture, Sports

5 (13.51)

Kanandihi Uttaran, Medinipore Colonelgola Knowledge Development Society, Midnapore Dance And Music Circle, Nigam Anand Joyguru Yoga Ashram, Saranga

9

Information and Communication Technology, Science and Technology

1 (2.70)

Science Centre

10

Children, Children 9 (24.33) education, Education and Literacy, Non Formal Education

Total

1 (2.70)

Hadia Institute Of Management It And Educational Training

Baligeria Vivekananda Seva Sangha, Barchara Sabuj Sathi Sangha, Baruna Tarun Sangha, Organisation, Harisinghapur Satyanarayan Sangha, Midnapore Education And Services Society, Midnapore Vivekananda Sisu Bikash And Youth Development Society, Nahapar Jati Upajati Womance Welfare Organisation, Rural India Self Help Institute

37 (100)

Source https://www.giveindia.org/all-ngos/west-bengal/medinipur/

142

7 Institutions and Enforcement at Local Level

Table 7.13 List of NGOs in Alipurduar forest division in Alipurduar district, North Bengal S. No.

Area of work

Number of NGO

Name of NGOs

1

Animal Husbandry

4 (14.81)

Alipurduar Nabaday Nursari Sishu Vidyalala, Duars Alternative Medical Research Institute, Purba Birpara Shakti Sangha, Saontalpur Nagoric Adhikar Surokha Welfare Society

2

Rural Development and Poverty Alleviation, Labour and Employment

3 (11.11)

Bara Daldali Rural Welfare Association, Kumargram Duar Social Welfare Organization, Salsalabari Swayambhar Rural Development Society

3

Human Rights, Tribal affairs, Differently abled

4 (14.81)

Alipurduar Pratibimba, Alipurduar Rural And Tribal Development Kallyan Samiti, Alipurduar Consumers Protection Society, Dooars Jiban Suraksha Welfare Society

4

Aged/Elderly

1 (3.7)

Alipurduar Anubhab Foundation

5

Environment and Forests

–

6

Agriculture, Water Resources

2 (7.4)

Dooars Care Society, Glorious Computer And Educational Institute

7

Education and Literacy

7 (25.93)

Binapani Alipurduar Welfare Society, Damanpur Anubhav, Dooars Builts Relief Society Dooars Universal Progressive Society Duars Chaparerpar Nabachetana Welfare Organisation, Patkapara Soccial Welfare Org

8

Health And Family Welfare, Vocational Training, Information Technology

2 (7.4)

Alipurduar Shadow Welfare Organigation, Dakshinpanialguri gram bikash samity

9

Arts and Culture, Sports

1 (3.7)

Udayanbitan

10

Children, Children Education

3 (11.11)

Deb Adhikari Educational And Charitable Trust Duarser Apanjan, Madhya Para Swami Vivekananda Vocational Computer Training Center

Total

27 (100)

Source https://www.giveindia.org/all-ngos/west-bengal/alipurduar/

Summing Up In the Purulia forest division, the enforcement index is the highest (0.717) followed by the institutional index (0.701) and monitoring index (0.498). In the Bankura forest division, the institutional index is the highest (0.660) followed by the enforcement index (0.451) and monitoring index (0.208) whereas in the Paschim Medinipur forest division monitoring index is found to be the highest (0.621) followed by institutional index (0.388) and enforcement index (0.257). In the Alipurduar forest division, the

7.6 Formal Institutions and NGOs in the Forest Sector in West Bengal

143

monitoring index is found to be the highest (0.957) followed by the enforcement index (0.828) and institutional index (0.786). It has been observed that the monitoring index, institutional index, and enforcement index are higher in the North Bengal forest division compared to the South Bengal forest division. This means that monitoring, institutional and enforcement indices are very effective in the North Bengal forest division than in the South Bengal forest division. WBFDCL and LAMPs are two major formal institutions operating at the regional level in West Bengal and performed socio-economic development of tribal people especially tribal women for their upliftment and empowerment. WBFDCL performed in eco-tourism development in West Bengal. There are very few NGOs working in the forest and environmental sector in South Bengal and found no role played by NGOs in the forest sector in North Bengal.

Chapter 8

Measurement of Participation of Communities in the Planning, Monitoring and Implementation Stages

Abstract This chapter attempts to measure the participation of the communities by formulating a participation index in the planning stage, monitoring stage, and implementation stages across four forest divisions of West Bengal. This chapter examines the least participation, moderate and high participation in the forest management program across different forest divisions in South Bengal and North Bengal. In addition, this chapter also analyses the participation of different disadvantaged sections of communities across different forest divisions of South Bengal and North Bengal.

8.1 Forest Participation of Purulia Forest Division, South Bengal Forest Participation Index The participation index is used to measure the degree of forest participation (Basu 2021). We divide participation into three stages the planning stage, the implementation stage, and the monitoring stage (Tadesse et al. 2017). The participation indices for all three stages are presented in Table 8.1 and the overall index value for participation is found to be 0.64 (Table 8.1). The participation index in the planning stage is found to be the highest (0.68) compared to that in the implementation stage (0.67) and the monitoring stage (Table 8.1). The percentages of households involved in different stages of participation based on index values are presented in Table 8.2. The percentages of households that participated in the planning stage, implementation stage, and implementation stage are 72.6%, 49.6%, and 25% respectively (Table 8.2). In the case of overall participation for all three stages taken together, more than 50% of households (i.e., 52.4%) belong to moderate participation (Table 8.2 and Fig. 8.1). We have classified marginalized or disadvantaged classes of households in terms of gender, caste, education, landholdings, and age; and examine their participation in different stages like planning, implementation, and monitoring. The results of participation in terms of the participation index for all disadvantaged classes of households are shown in Table 8.3. Table 8.3 shows that there is no significant difference in participation of male-headed and female–headed households across © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. P. Basu, Governance and Institution in the Indian Forest Sector, https://doi.org/10.1007/978-3-031-34746-7_8

145

146

8 Measurement of Participation of Communities in the Planning …

Table 8.1 Participation index values in different stages of participation in Purulia Stages

Indicators

Participation index

Participation in planning stage

Forest boundary demarcation

0.57

Identifying forest users

0.87

Participatory forest resource assessment

0.57

Forest management committee election

0.78

Encouraging others to participate

0.63

Preparing forest management plan

0.57

Developing forest management by-laws

0.57

Approval of forest management agreement

0.85 0.68

Participation in implementation stage

Reforestation of degraded forest areas 0.72 Planting of fruit bearing trees

0.70

Planting trees and management

0.68

Nursery establishment

0.74

Bee keeping

0.47

Forest fire fighting

0.68

Attending meeting

0.86

Participation in knowledge and skill developing training

0.50 0.67

Participation in monitoring stage

Follow ups forest management bylaw 0.46 Forest Patrols

0.52

Reporting of illegal activities

0.87

Supervise forest management plan implementation

0.52

Forest boundary maintenance

0.51 0.58

Index value of participation

0.64

Source Basu (2021)

different stages of participation (Basu 2021). It is evident from Table 8.3 that the participation for SC/ST households seems to be higher than non-SC/ST households and such results are statistically significant (Basu 2021). The participation of no educated households (i.e., illiterate households) seems to be higher than educated households and it is significant.

8.1 Forest Participation of Purulia Forest Division, South Bengal

147

Table 8.2 Classification of households involved in different stages in Purulia Various stages

Range of score

Attribute

Mean participation

Household size (252)

Planning index

≤ 0.60

Least participation

0.393

59 (23.4)

0.61–0.70

Moderate participation

0.673

10 (4)

> 0.70

High participation

0.774

183 (72.6)

≤ 0.60

Least participation

0.473

68 (27)

0.61–0.70

Moderate participation

0.638

59 (23.4)

> 0.70

High participation

0.790

125 (49.6)

≤ 0.60

Least participation

0.441

111 (44.0)

0.61–0.70

Moderate participation

0.635

78 (31)

> 0.70

High participation

0.751

63 (25)

≤ 0.60

Least participation

0.486

53 (21.0)

0.61–0.70

Moderate participation

0.648

132 (52.4)

> 0.70

High participation

0.758

67 (26.6)

Implementation index

Monitoring index

Overall participatory index

Source Basu (2021) Note Figures in the bracket shows percentage of total households 0.7 0.68 0.66 0.64 0.62 0.6 0.58 0.56 0.54 0.52 Planning Stage

Implementation Stage

Fig. 8.1 Participation index in various stages. Source Basu (2021)

Monitoring Stage

0.84

0.84

0.668

0.87

0.581

0.779

0.64

0.569

0.577

0.859

0.68

Identifying forest users

Participatory forest resource assessment

Forest management committee election

Encouraging others to participate

Preparing forest management plan

Developing forest management bylaws

Approval of forest management agreement

0.573

0.587

0.626

0.773

0.56

0.547

Forest boundary 0.581 demarcation

Female

Participation in planning stage

Male

Indicators

Various stages

0.38 (− 0.353)

0.376 (0.355)

0.7

0.874

0.592

0.584

− 0.461 (0.324)

0.115 (0.455)

0.669

0.851

0.59

0.882

0.59

SC/ST

0.290 (0.387)

0.114 (0.455)

0.637 (0.264)

0.595 (0.278)

1.00 (0.161)

t-value

0.63

0.823

0.545

0.545

0.577

0.63

0.557

0.835

0.553

Non-SC/ ST

2.571 (− 0.006)

1.321 (0.095)

1.852 (0.033)

1.545 (0.062)

3.039 (0.001)

6.496 (0.000)

1.313 (0.096)

1.248 (0.107)

1.467 (0.072)

t-value

0.72

0.898

0.603

0.599

0.689

0.834

0.61

0.912

0.603

Illiterate

0.627

0.798

0.538

0.532

0.567

0.699

0.535

0.803

0.542

Formal education

Table 8.3 Marginalized classes of households in different stages of participation in Purulia

3.756 (0.000)

2.896 (0.002)

2.788 (0.003)

2.868 (0.002)

4.280 (0.000)

3.994 (0.000)

3.211 (0.001)

3.197 (0.001)

2.631 (0.005)

t-value

0.653

0.811

0.548

0.537

0.626

0.786

0.544

0.821

0.551

Age (20–40) years

0.699

0.886

0.595

0.593

0.647

0.775

0.602

0.896

0.595

Age (above 40) years

0.571

0.577

0.865

0.68

− 2.455 (− 0.008) − 2.105 (− 0.018)

− 2.23 (− 0.013)

− 1.925 (− 0.028)

0.628

− 0.721 (− 0.236)

0.583

− 2.555 (− 0.006)

0.773

0.868

− 2.248 (− 0.013)

0.316 (− 0.376)

0.573

− 1.961 (− 0.26)

0.68

0.843

0.577

0.573

0.659

0.79

0.573

0.865

0.588

Marginal Landless and small farmers farmers

t-value

(continued)

− 0.180 (0.429)

0.689 (0.246)

− 0.004 (0.498)

− 0.113 (0.455)

− 1.079 (0.141)

− 0.535 (0.296)

0.456 (0.324)

0.093 (0.463)

− 0.717 (0.237)

t value

148 8 Measurement of Participation of Communities in the Planning …

0.863

0.511

0.67

Attending meeting

Participation in knowledge and skill developing training

0.531

0.682

Forest fire fighting

Forest patrols

0.473

Bee keeping

0.459

0.645

0.74

Nursery establishment

Follow ups forest management bylaw

0.4

0.682

Planting trees and management

Participation in monitoring stage

0.68

0.702

Planting of fruit bearing trees

0.493

0.48

0.86

0.62

0.42

0.76

0.66

0.76

Reforestation of 0.716 degraded forest areas

Female

Participation in implementation stage

Male

Indicators

Various stages

Table 8.3 (continued)

0.531

0.441

− 0.5 (0.310)

0.928 (− 0.18)

0.69

0.465

0.909

0.738

0.776 (0.222)

1.37 (0.102)

0.06 (0.476)

0.905 (0.186)

0.476

0.785

− 0.267 (0.396)

1.38 (0.090)

0.691

0.343 (0.367)

0.724

0.743

− 0.910 (0.185)

0.309 (0.380)

SC/ST

t-value

0.52

0.504

0.62

0.573

0.768

0.549

0.451

0.652

0.659

0.652

0.675

Non-SC/ ST

0.425 (0.336)

− 2.479 (0.007) 0.533

0.451

0.68

0.469

− 2.244 (0.013) 3.016 (0.002)

0.881

0.701

0.48

0.799

0.67

0.701

0.723

Illiterate

3.667 (0.000)

5.002 (0.000)

1.017 (0.156)

2.790 (0.003)

0.786 (0.216)

1.691 (0.046)

2.274 (0.012)

t-value

0.521

0.476

0.656

0.543

0.838

0.643

0.452

0.662

0.695

0.7

0.717

Formal education

0.496 (0.310)

− 1.075 (0.142)

1.064 (0.144)

− 1.541 (0.062)

1.234 (0.109)

1.481 (0.070)

1.270 (0.103)

0.514

0.442

0.678

0.48

0.888

0.704

0.449

0.76

0.699

− 0.607 (0.272) 3.230 (0.001)

0.709

0.735

Age (20–40) years

0.016 (0.493)

0.199 (0.421)

t-value

0.537

0.474

0.663

0.513

0.847

0.659

0.481

0.731

0.669

0.695

0.712

Age (above 40) years

0.452

0.521

− 0.930 (0.177)

0.67

0.509

0.88

− 1.431 (0.077)

0.734 (− 0.232)

− 0.669 (− 0.252)

1.204 (− 0.115)

0.663

0.469

− 1.531 (− 0.064) 1.103 (− 0.136)

0.745

0.678

0.715

0.697

0.539

0.479

0.67

0.483

0.831

0.702

0.466

0.736

0.685

0.674

0.764

Marginal Landless and small farmers farmers

0.706 (− 0.24)

0.709 (− 0.239)

0.334 (− 0.369)

0.766 (− 0.222)

t-value

(continued)

− 0.677 (0.250)

− 1.153 (0.125)

0.082 (0.467)

0.535 (0.297)

1.355 (0.089)

− 0.976 (0.165)

0.140 (0.444)

0.218 (0.141)

− 0.182 (0.428)

0.929 (0.177)

− 2.297 (0.011)

t value

8.1 Forest Participation of Purulia Forest Division, South Bengal 149

0.52

0.576

Forest boundary 0.509 maintenance

0.58

0.63

0.493

Supervise forest 0.524 management plan implementation

0.64

0.893

0.87

Reporting of illegal activities

Female

Male

Indicators

0.592 (0.279)

0.66

0.58

0.504

− 0.231 (0.409)

0.105 (0.458)

0.522

0.904

− 0.462 (0.324)

0.842 (0.203)

SC/ST

t-value

Source Basu (2021). Figures in the bracket show p-values

Overall participation index

Various stages

Table 8.3 (continued)

0.61

0.576

0.524

0.52

0.809

Non-SC/ ST

2.802 (0.003)

0.226 (0.411)

− 0.791 (0.215)

0.048 (0.481)

2.691 (0.004)

t-value

0.66

0.58

0.503

0.517

0.871

Illiterate

0.623

0.584

0.521

0.527

0.876

Formal education

0.635

0.575

− 0.479 (0.316) 2.312 (0.011)

0.507

0.534

− 0.379 (0.353)

− 0.688 (0.246)

0.878

Age (20–40) years

− 0.175 (0.430)

t-value

0.648

0.581

0.513

0.513

0.87

Age (above 40) years

0.577 0.64

− 0.886 (0.188)

0.497

0.519

0.894

0.65

0.583

0.536

0.524

0.835

Marginal Landless and small farmers farmers

− 0.351 (0.363)

− 0.245 (0.403)

0.785 (0.217)

0.237 (0.407)

t-value

− 0.201 (0.420)

− 0.312 (0.378)

− 1.489 (0.069)

− 0.187 (0.426)

1.783 (0.038)

t value

150 8 Measurement of Participation of Communities in the Planning …

8.2 Forest Participation of Bankura (South), South Bengal

151

8.2 Forest Participation of Bankura (South), South Bengal Table 8.4 shows the participation indices of various stages of participation in the Bankura (south) forest division. The participation index is recorded as 0.47 (Table 8.4). The participation index in the implementation stage is found to be the highest (0.59) compared to that in the planning stage (0.48) and the monitoring stage (0.34) (Table 8.4). Table 8.4 Participation index values in different stages of participation in Bankura (south) Participation in different stages

Indicators

Index value

Participation in planning stage

Forest boundary demarcation

0.32

Identifying forest users

0.45

Participatory forest resource assessment

0.44

Forest management committee election

0.81

Encouraging others to participate

0.64

preparing forest management plan

0.37

Developing forest management by-laws

0.40

Approval of forest management agreement

0.39 0.48

Participation in implementation stage

Reforestation of degraded forest areas

0.44

Planting of fruit bearing trees such as mahua and mango

0.67

Planting trees and management

0.69

Nursery establishment

0.49

Beekeeping

0.46

Forest fire fighting

0.78

Attending meetings

0.91

Participations in knowledge and skill developing training

0.28 0.59

Participation in monitoring stage

Follow ups forest managements by-laws

0.18

Forest patrols

0.25

Reporting of illegal activities

0.83

Supervise forest management plan implementation

0.17

Forest boundary maintenance

0.27 0.34

Overall participation index Source Author’s calculation

0.47

152

8 Measurement of Participation of Communities in the Planning …

0.700 0.600 0.500 0.400 0.300 0.200 0.100 0.000 Planning Index

Implementation Index

Monitoring Index

Fig. 8.2 Participation index in various stage Table 8.5 Classification of households involved in different stages in Bankura (south) Various stages

Range of score

Attribute

Mean participation

Household size (228)

Planning index

≤ 0.60

Least participation

0.400

157 (68.9)

0.61–0.70

Moderate participation

0.634

62 (27.2)

> 0.70

High participation

0.778

9 (3.9)

≤ 0.60

Least participation

0.441

101 (44.3)

0.61–0.70

Moderate participation

0.649

75 (32.9)

> 0.70

High participation

0.790

52 (22.8)

≤ 0.60

Least participation

0.262

185 (81.1)

0.61–0.70

Moderate participation

0.657

35 (15.4)

> 0.70

High participation

0.8

8 (3.5)

Overall ≤ 0.60 participatory index

Least participation

0.42

186 (81.6)

0.61–0.70

Moderate participation

0.656

25 (10.1)

> 0.70

High participation

0.736

17 (7.5)

Implementation index

Monitoring index

Source Author’s calculation Note Figures in the bracket shows percentage of total hhs

0.386

0.409

0.494

0.456

0.437

0.818

0.626

0.369

0.400

0.393

0.477

Identifying forest users

Participatory forest resource assessment

Forest management committee election

Encouraging others to participate

Preparing forest management plan

Developing forest management bylaws

Approval of forest management agreement

0.432

0.409

0.727

0.773

0.455

0.364

Forest boundary 0.313 demarcation

Female

Participation in planning stage

Male

Indicators

Different stages

0.777

0.598

0.349

0.402

0.377

0.458

− 1.215 (0.118)

− 0.604 (0.276)

− 0.433 (0.334)

− 0.234 (0.408)

− 0.428 (0.336)

0.422

− 0.292 (0.386)

0.552 (0.293)

0.427

0.316

− 0.961 (0.172)

1.182 (0.124)

SC/ST

t-value

0.551

0.459

0.408

0.459

0.776

0.949

0.500

0.531

0.327

Non-SC/ ST

− 4.408 (0.000)

− 1.799 (0.037)

− 0.141 (0.444)

− 2.898 (0.002)

0.460

0.363

0.346

0.333

0.637

0.803

− 4.638 (0.000)

− 3.490 (0.000)

0.453

0.419

− 3.065 (0.001) − 2.026 (0.022)

0.325

Illiterate

− 0.267 (0.395)

t-value

0.498

0.428

0.464

0.414

0.635

0.824

0.423

0.482

0.311

Formal education

− 1.798 (0.037)

− 1.345 (0.090)

− 2.588 (0.005)

− 1.963 (0.025)

0.032 (0.487)

− 0.477 (0.317)

0.707 (0.240)

− 1.860 (0.032)

0.372 (0.355)

t-value

Table 8.6 Marginalized classes of households in different stages of participation, Bankura (south)

0.484

0.440

0.457

0.362

0.586

0.819

0.422

0.483

0.302

Age (20–40) years

0.476

0.379

0.385

0.376

0.653

0.812

0.444

0.438

0.324

Age (above 40) years

0.325 (0.373)

1.067 (0.144)

1.407 (0.081)

− 0.315 (0.377)

− 1.137 (0.129)

0.142 (0.444)

− 0.474 (0.318)

1.224 (0.112)

− 0.510 (0.306)

t-value

0.481

0.395

0.407

0.376

0.645

0.815

0.444

0.446

0.320

Marginal and small farmers

0.438

0.393

0.357

0.321

0.500

0.786

0.357

0.500

0.286

Landless farmers

(continued)

0.710 (0.245)

0.021 (0.492)

0.580 (0.285)

0.627 (0.270)

1.533 (0.073)

0.256 (0.401)

1.308 (0.105)

− 0.705 (0.246)

0.335 (0.371)

t value

8.2 Forest Participation of Bankura (South), South Bengal 153

0.917

0.272

0.589

Attending meeting

Participation in knowledge and skill developing training

0.252

0.777

Forest fire fighting

Forest patrols

0.461

Bee keeping

0.189

0.490

Nursery establishment

Follow ups forest management bylaw

0.588

0.689

Planting trees and management

Participation in monitoring stage

0.386

0.675

Planting of fruit bearing trees

0.250

0.136

0.818

0.795

0.409

0.455

0.682

0.614

0.545

0.432

Reforestation of degraded forest areas

Female

Participation in implementation stage

Male

Indicators

Different stages

Table 8.6 (continued)

0.036 (0.486)

0.843 (0.203)

0.209

0.156

0.580

0.279

− 1.498 (0.073)

0.021 (0.492)

0.908

0.791

− 0.285 (0.389)

1.253 (0.111)

0.450

0.480

0.679

0.956 (0.174)

0.619 (0.271)

0.086 (0.466)

0.651

0.402

− 1.055 (0.151)

0.800 (0.216)

SC/ST

t-value

0.408

0.286

0.622

0.296

0.908

0.735

0.480

0.510

0.724

0.735

0.592

Non-SC/ ST

0.436

− 1.246 (0.108)

0.291

0.584 0.184

0.222

− 0.361 (0.360)

− 1.455 (0.075) − 2.603 (0.006)

− 4.556 (0.000)

0.880

0.474

− 0.949 (0.173)

− 0.008 (0.497)

0.684

− 0.836 (0.203)

0.791

0.654

− 1.606 (0.056)

1.206 (0.116)

0.462

Illiterate

− 2.705 (0.004)

t-value

0.284

0.185

0.595

0.275

0.937

0.766

0.477

0.500

0.694

0.685

0.423

Formal education

− 1.556 (0.061)

− 0.022 (0.491)

− 0.505 (0.307)

0.367 (0.357)

− 1.705 (0.045)

0.674 (0.250)

− 1.944 (0.027)

− 0.956 (0.170)

− 0.222 (0.412)

− 0.721 (0.236)

0.678 (0.249)

t-value

0.294

0.159

0.603

0.310

0.948

0.776

0.474

0.517

0.647

0.707

0.448

Age (20–40) years

0.241

0.197

0.587

0.282

0.904

0.777

0.453

0.479

0.687

0.658

0.453

Age (above 40) years

1.138 (0.129)

− 0.878 (0.191)

0.764 (0.223)

0.547 (0.293)

1.464 (0.073)

− 0.035 (0.486)

0.914 (0.182)

1.461 (0.073)

− 0.811 (0.210)

1.040 (0.150)

− 0.080 (0.468)

t-value

0.245

0.187

0.592

0.283

0.916

0.785

0.456

0.486

0.696

0.671

0.444

Marginal and small farmers

0.357

0.143

0.545

0.286

0.786

0.679

0.464

0.500

0.571

0.643

0.429

Landless farmers

(continued)

− 1.328 (0.102)

0.664 (0.258)

0.803 (0.218)

− 0.034 (0.487)

1.272 (0.112)

1.228 (0.120)

− 0.232 (0.410)

− 0.258 (0.400)

1.364 (0.096)

0.327 (0.374)

0.129 (0.450)

t value

154 8 Measurement of Participation of Communities in the Planning …

0.364

0.336

Forest boundary 0.262 maintenance

0.342

0.469

0.114

0.172

Supervise forest management plan implementation

0.473

0.818

0.835

Reporting of illegal activities

Female

Male

Indicators

0.303 0.447

−0.084 (0.467)

0.209

−1.041 (0.154)

0.115 (0.455)

0.131

0.810

SC/ST

1.189 (0.122)

0.191 (0.425)

t-value

Source Author’s calculation. Figures in the bracket represent p-values

Over all participation index

Different stages

Table 8.6 (continued)

0.552

0.482

0.500

0.296

0.918

Non-SC/ ST

− 4.345 (0.000) 0.457

0.326

− 5.226 (0.000)

0.158

− 4.085 (0.000)

0.265

0.803

− 2.344 (0.010)

− 4.362 (0.000)

Illiterate

t-value

0.483

0.358

0.279

0.176

0.865

Formal education

− 1.428 (0.077)

− 1.093 (0.138)

− 0.289 (0.386)

− 0.504 (0.307)

− 1.312 (0.095)

t-value

0.473

0.341

0.286

0.159

0.810

Age (20–40) years

0.468

0.345

0.268

0.176

0.841

Age (above 40) years

0.233 (0.408)

− 0.103 (0.459)

0.331 (0.371)

− 0.467 (0.321)

− 0.583 (0.281)

t-value

0.471

0.339

0.264

0.164

0.836

Marginal and small farmers

0.454

0.379

0.393

0.214

0.786

Landless farmers

0.291 (0.388)

− 0.565 (0.291)

− 1.170 (0.131)

− 0.715 (0.243)

0.488 (0.316)

t value

8.2 Forest Participation of Bankura (South), South Bengal 155

156

8 Measurement of Participation of Communities in the Planning …

Table 8.5 shows the percentages of households that are involved in different stages of participation based on index values. The maximum percentage of households (68.9%) are least interested to participate in the planning stage. The same thing happens also in the case of the monitoring stage (81.1%). On the whole, the households’ participation is weakly reflected in Table 8.5 (Fig. 8.2). Table 8.6 shows the participation indices for all marginalized or disadvantaged classes of households in the planning, implementation, and monitoring stages in Bankura (south). The results from Table 8.6 shows that there is no difference in participation between SC/ST and non-SC/ST households, only significant result is found for differences in participation between illiterate and educated households. It is also noted that the participation of educated households for making planning is higher than that of illiterate households (Table 8.6).

8.3 Forest Participation in Rupnarayan Forest Division of Paschim Medinipur South Bengal Table 8.7 presents participation indices for various stages in the Rupnarayan forest division, Paschim Medinipore. The participation index is observed as 0.53 (Table 8.7). The participation index in the planning is found to be the highest (0.58) compared to that in the monitoring (0.54) and the implementation (0.46) (Table 8.7). Table 8.8 presents the percentages of households that are involved in different stages of participation based on the index. Table 8.8 indicates that 58.2%, 77.5%, and 55.4% of households belong to the least participation class in the planning stage, in the implementation, and in the monitoring stage respectively (Fig. 8.3). Table 8.9 shows that female participation is higher in the planning, implementation, and monitoring stage compared to male-headed households and is significant as indicated by t-values. The participation of SC/ST households is much higher compared to non-SC/ST households in the planning, implementation, and monitoring cases. Similarly, the participation of illiterate households in the implementation and monitoring of cases plays a key role compared to educated households. Table 8.9 further shows that elderly households participate more in monitoring and implementation cases compared to young households.

8.3 Forest Participation in Rupnarayan Forest Division of Paschim …

157

Table 8.7 Participation index values in different stages of participation in the Rupnarayan, Paschim Medinipore Participation in different stages

Indicators

Index value

Participation in planning stage

Forest boundary demarcation

0.533

Identifying forest users

0.594

Participatory forest resource assessment 0.627 Forest management committee election

0.326

Encouraging others to participate

0.575

Preparing forest management plan

0.662

Developing forest management by-laws

0.662

Approval of forest management agreement

0.674 0.582

Participation in implementation stage

Reforestation of degraded forest areas

0.415

Planting of fruit bearing trees

0.467

Planting trees and management

0.371

Nursery establishment

0.315

Bee keeping

0.577

Forest fire fighting

0.641

Attending meeting

0.298

Participation in knowledge and skill developing training

0.631 0.464

Participation in monitoring stage

Follow ups forest management by-laws

0.538

Forest patrols

0.568

Reporting of illegal activities

0.289

Supervise forest management plan implementation

0.707

Forest boundary maintenance

0.617 0.544

Overall participation index Source Author’s calculation

0.530

158

8 Measurement of Participation of Communities in the Planning …

Table 8.8 Classification of households involved in different stages in the Rupnarayan, Paschim Medinipore

Various stages

Range of score values

Attribute

Mean participation

Household size (213)

Planning index

≤ 0.60

Least participation Moderate participation High participation Least participation Moderate participation High participation Least participation Moderate participation High participation Least participation Moderate participation High participation

0.447

124 (58.2)

0.656

38 (17.8)

0.854

51 (23.9)

0.375

165 (77.5)

0.649

21 (9.9)

0.870

27 (12.7)

0.385

118 (55.4)

0.6

37 (17.4)

0.831

58 (27.2)

0.445

151 (70.9)

0.643

29 (13.6)

0.820

33 (15.5)

0.61–0.70 > 0.70 Implementation index

≤ 0.60 0.61–0.70 > 0.70

Monitoring index

≤ 0.60 0.61–0.70 > 0.70

Overall ≤ 0.60 participatory index 0.61–0.70 > 0.70

Source Author’s calculation Note Figures in the bracket shows percentage of total hhs 0.700 0.600 0.500 0.400 0.300 0.200 0.100 0.000 Planning

Implementation

Fig. 8.3 Participation index in various stages

Monitoring

0.589 0.647

0.620 0.706

0.314 0.471

0.574 0.588

0.658 0.706

0.658 0.706

Participatory forest resource assessment

Forest management committee election

Encouraging others to participate

Preparing forest management plan

Developing forest management bylaws

0.462 0.598 (0.322) 0.200 0.608 (0.421) 0.695 0.358 (0.244)

1.106 0.598 (0.136) 2.071 0.662 (0.021)

− 0.676 0.482 (0.315)

− 0.629 0.621 1.331 (0.100) − 0.338 0.298 1.509 (0.074) − 0.593 0.532 0.175 (0.431) − 0.692 0.589 0.610 (0.275) − 0.656 0.677 0.612 (0.274)

0.649

0.662

0.554

0.297

0.644

0.590

0.518

0.686 0.668 (0.247)

− 0.707 0.009 (0.496)

0.879 0.560 (0.190)

1.143 0.310 (0.127)

− 0.603 0.963 (0.168)

0.224 0.582 (0.411)

0.657

0.628

0.587

0.339

0.645

0.603

0.529

0.280 0.653 (0.390)

1.831 0.659 (0.034)

− 0.594 0.532 (0.298)

− 0.344 0.544 (0.293)

− 0.630 1.089 (0.139)

− 0.604 0.615 (0.270)

0.686

0.669

0.525

0.280

0.619

0.568

0.500

(continued)

− 0.709 (0.240)

− 0.222 (0.412)

1.184 (0.120)

1.055 (0.147)

0.261 (0.397)

0.856 (0.197)

0.832 (0.204)

Marginal Landless t value and farmers small farmers

0.189 0.545 (0.425)

Age Age t-value (20–40) (above years 40) years

0.642 0.538 (0.261)

Illiterate Formal t-value education

− 0.599 0.581 0.785 (0.221)

Non-SC/ t-value ST

1.838 0.549 (0.035)

SC/ ST

− 0.563 0.460 0.656 (0.260)

Female t-value

Identifying forest users

Male

0.528 0.588

Indicators

Participation in Forest planning stage boundary demarcation

Different stages

Table 8.9 Marginalized classes of households in different stages of participation in Rupnarayan, Paschim Medinipore

8.3 Forest Participation in Rupnarayan Forest Division of Paschim … 159

2.876 0.578 (0.002)

− 0.606 0.508 2.649 (0.008)

0.564 0.735

Bee keeping

2.856 0.358 (0.003)

− 0.358 0.210 0.960 (0.175)

0.306 0.412

Nursery establishment

0.380 0.397 (0.352)

− 0.377 0.355 0.963 (0.174)

0.362 0.471

Planting trees and management

0.375 0.490 (0.354)

− 0.474 0.452 1.932 (0.034)

0.454 0.618

1.449 0.422 (0.075)

− 0.444 0.347 2.076 (0.026)

Planting of fruit bearing trees

1.571 0.589 (0.059)

− 0.594 0.551 1.420 (0.086)

0.576 0.651

0.398 0.618

0.598 0.662 (0.276)

0.577

0.275

0.347

0.446

0.410

0.575

0.685

0.054 0.582 (0.478)

1.673 0.299 (0.048)

0.938 0.353 (0.175)

0.887 0.478 (0.188)

0.188 0.413 (0.425)

0.521 0.580 (0.302)

0.574

0.326

0.384

0.459

0.417

0.583

0.674

0.208 0.578 (0.418)

− 0.318 0.553 (0.290)

− 0.367 0.578 (0.282)

0.386 0.494 (0.350)

− 0.419 0.069 (0.473)

− 0.588 0.091 (0.464)

0.576

0.305

0.381

0.398

0.407

0.566

0.678

(continued)

0.044 (0.482)

0.232 (0.408)

− 0.235 (0.407)

1.806 (0.037)

0.173 (0.432)

0.665 (0.254)

− 0.131 (0.448)

Marginal Landless t value and farmers small farmers

0.009 0.672 (0.497)

Age Age t-value (20–40) (above years 40) years

− 0.674 0.558 (0.289)

Illiterate Formal t-value education

− 0.682 0.653 2.007 (0.029)

Non-SC/ t-value ST

0.663 0.794

SC/ ST

Approval of forest management agreement

Female t-value

Male

Indicators

Participation in Reforestation implementation of degraded stage forest areas

Different stages

Table 8.9 (continued)

160 8 Measurement of Participation of Communities in the Planning …

0.951 0.593 (0.172) 2.600 0.314 (0.005)

− 0.586 0.524 1.182 (0.126) − 0.331 0.185 0.651 (0.261)

Reporting of 0.283 0.353 illegal activities

1.495 0.544 (0.069)

− 0.566 0.468 2.001 (0.030)

0.559 0.676

1.989 0.485 (0.024)

− 0.481 0.425 1.803 (0.045)

0.454 0.581

Forest patrols

0.061 0.676 (0.476)

− 0.632 0.629 0.175 (0.432)

Participation in 0.630 0.647 knowledge and skill developing training

0.523 0.706

0.542 0.309 (0.294)

− 0.308 0.274 1.553 (0.069)

0.266

0.545

0.532

0.446

0.590

0.288

0.635

0.862 0.239 (0.200)

0.861 0.576 (0.195)

0.220 0.527 (0.413)

1.375 0.465 (0.085)

1.772 0.679 (0.039)

0.360 0.266 (0.359)

0.326

0.562

0.545

0.464

0.595

0.322

0.632

− 0.299 1.542 (0.062)

0.249 0.627 (0.402)

− 0.558 0.317 (0.376)

0.054 0.467 (0.478)

1.711 0.630 (0.044)

− 0.302 0.989 (0.162)

0.263

0.415

0.483

0.458

0.636

0.288

0.669

(continued)

0.572 (0.284)

3.371 (0.001)

1.186 (0.119)

0.298 (0.383)

− 0.107 (0.458)

0.217 (0.414)

− 0.840 (0.201)

Marginal Landless t value and farmers small farmers

0.459 0.630 (0.323)

Age Age t-value (20–40) (above years 40) years

0.275 0.652 (0.392)

Illiterate Formal t-value education

0.283 0.471

Attending meeting

Non-SC/ t-value ST

0.328 0.647 (0.372)

SC/ ST

− 0.646 0.629 0.503 (0.310)

0.638 0.676

Forest fire fighting

Female t-value

Male

Indicators

Participation in Follow ups monitoring forest stage management bylaw

Different stages

Table 8.9 (continued)

8.3 Forest Participation in Rupnarayan Forest Division of Paschim … 161

2.753 0.565 (0.003) 2.654 0.546 (0.004)

− 0.568 0.485 2.325 (0.016) − 0.547 0.487 2.092 (0.025)

0.533 0.671

0.521 0.634

1.489 0.667 (0.070)

− 0.642 0.556 1.764 (0.047)

0.515

0.524

0.572

0.707

1.319 0.528 (0.094)

1.366 0.538 (0.087)

1.916 0.620 (0.028)

0.531

0.548

0.616

0.690

− 0.539 0.154 (0.439)

− 0.561 0.331 (0.370)

0.076 0.607 (0.470)

0.507

0.498

0.644

0.686

1.113 (0.134)

1.855 (0.033)

− 0.667 (0.253)

0.585 (0.280)

Marginal Landless t value and farmers small farmers

0.874 0.714 (0.192)

Age Age t-value (20–40) (above years 40) years

− 0.728 0.030 (0.488)

Illiterate Formal t-value education

0.605 0.765

Non-SC/ t-value ST

Forest boundary maintenance

SC/ ST

0.374 0.706 (0.355)

Female t-value

− 0.712 0.694 2.590 (0.008)

Male

Supervise 0.694 0.853 forest management plan implementation

Indicators

Source Author’s calculation. Figures in the bracket show p-values

Overall participation index

Different stages

Table 8.9 (continued)

162 8 Measurement of Participation of Communities in the Planning …

8.4 Forest Participation of Alipurduar Forest Division, North Bengal

163

8.4 Forest Participation of Alipurduar Forest Division, North Bengal Table 8.10 shows the participation indices for different stages of participation. The participation index is calculated as 0.897. The participation index for the planning, monitoring, and implementation stages are 0.907, 0.952, and 0.833 respectively (Table 8.10).

Table 8.10 Participation in different stages in the Alipurdua Participation in different stages

Indicators

Index value

Participation in planning stage

Forest boundary demarcation

0.871

Identifying forest users

0.868

Participatory forest resource assessment 0.891 Forest management committee election

0.921

Encouraging others to participate

0.950

Preparing forest management plan

0.914

Developing forest management by laws

0.924

Approval of forest management agreement

0.921 0.907

Participation in implementation stage

Reforestation of degraded forest areas

0.858

Planting of fruit bearing trees

0.788

Planting trees and management

0.669

Nursery establishment

0.821

Bee keeping

0.639

Forest fire fighting

0.947

Attending meeting

0.970

Participation in knowledge and skill developing training

0.970 0.833

Participation in monitoring stage

Follow ups forest management by law

0.964

Forest patrols

0.921

Reporting of illegal activities

0.967

Supervise forest management plan implementation

0.937

Forest boundary maintenance

0.970 0.952

Overall participation index Source Author’s calculation

0.897

164

8 Measurement of Participation of Communities in the Planning …

Table 8.11 Classification of households involved in different stages in the Alipurduar Various stages

Range of score values

Attribute

Mean participation

Households size (151)

Planning index

≤ 0.60

Least participation

0.493

9 (5.96)

0.61–0.70

Moderate participation

0.642

11 (7.28)

> 0.70

High participation

0.958

131 (86.75)

≤ 0.60

Least participation

0.539

16 (10.60)

0.61–0.70

Moderate participation

0.67

32 (21.19)

> 0.70

High participation

0.928

103 (68.21)

≤ 0.60

Least participation

0.55

4 (2.65)

0.61–0.70

Moderate participation

0.7

3 (1.99)

> 0.70

High participation

0.968

144 (95.36)

Overall ≤ 0.60 participatory index

Least participation

0.531

2 (1.32)

0.61–0.70

Moderate participation

0.661

7 (4.64)

> 0.70

High participation

0.914

142 (94.04)

Implementation index

Monitoring index

Source Author’s calculation Note Figures in the bracket represent percentage of total hhs

Alipurduar forest division 0.980 0.960 0.940 0.920 0.900 0.880 0.860 0.840 0.820 0.800 0.780 0.760 Planning Index

Implementation Index

Monitoring Index

Fig. 8.4 Participation in different stages in Alipurduar forest division

0.875 0.840

0.883 0.780

0.899 0.840

0.919 0.920

0.948 0.960

0.923 0.860

0.931 0.880

Forest boundary demarcation

Identifying forest users

Participatory forest resource assessment

Forest management committee election

Encouraging others to participate

Preparing forest management plan

Developing forest management by laws

Participation in planning stage

− 0.894 0.570 (0.285) 1.258 0.962 (0.106)

− 0.912 0.933 0.012 (0.495) − 0.964 0.923 0.388 (0.350)

0.813 0.928 0.913 (0.211)

0.975 0.923 0.894 (0.169)

0.334 0.894 (0.370)

0.637 0.904 (0.263)

0.928

− 0.817 1.493 (0.069)

0.806 0.866 0.933 (0.213)

0.938

0.918

0.943

0.933

0.871

− 0.856 0.644 (0.261)

1.285 0.856 0.885 (0.105)

0.977

0.900

0.908

− 0.309 (0.379) − 0.965 (0.169)

0.923

0.915

0.877

0.838

0.935

0.923

0.929

0.917

0.869

0.857

0.893

− 0.922 0.642 (0.261)

− 0.907 0.528 (0.299)

1.848 0.951 (0.033)

0.168 0.914 (0.433)

0.991 0.881 (0.162)

0.424 0.854 (0.336)

0.933

1.000

1.000

1.000

1.000

1.000

0.833

(continued)

− 0.167 (0.435)

− 4.132 (0.000)

− 3.494 (0.000)

− 4.112 (0.000)

− 4.584 (0.000)

− 5.751 (0.000)

0.408 (0.344)

Marginal Landless t value and farmers small farmers

− 0.873 0.971 (0.167)

Age Age t-value (20–40) (above years 40) years

0.625 (0.267)

− 0.902 (0.185)

− 2.002 (0.024)

− 0.306 (0.380)

− 0.099 (0.461)

Illiterate Formal t-value education

0.871

Non-SC/ t-value ST

0.853 0.865 (0.198)

SC/ ST

0.436 0.887 0.837 (0.333)

Female t-value

Male

Different stages Indicators

Table 8.12 Marginalized classes of households in different stages of participation in Alipurduar

8.4 Forest Participation of Alipurduar Forest Division, North Bengal 165

0.985

0.387 0.817 (0.350) 0.108 0.644 (0.457) − 0.913 0.932 (0.177) − 0.942 0.833 (0.203)

− 0.825 0.808 0.969 (0.169) − 0.639 0.635 1.721 (0.047) − 0.938 0.962 1.631 (0.054)

0.810 0.860

0.625 0.720

0.940 0.980

0.980 0.940

Nursery establishment

Bee keeping

Forest fire fighting

Attending meeting

0.888 0.964 0.981 (0.191)

0.964

0.909 0.673 (0.183)

− 0.691 0.615 0.179 (0.429)

0.661 0.680

Planting trees and management

0.639

0.820

0.660

0.773

0.564 0.808 (0.287)

− 0.794 0.769 1.243 (0.111)

0.902

Planting of fruit 0.774 0.840 bearing trees

Participation in Reforestation implementation of degraded stage forest areas

− 0.769 1.419 (0.079)

0.917

0.340 0.886 (0.367)

1.257 0.830 0.904 (0.109)

0.980 0.909 0.900 (0.167)

0.933

− 1.525 (0.066)

− 1.705 (0.046)

0.118 (0.453)

− 0.054 (0.479)

0.163 (0.435)

0.800 (0.213)

0.992

0.962

0.623

0.862

0.738

0.823

0.877

0.910

− 1.126 (0.132) − 2.080 (0.020)

0.938

0.958

0.935

0.655

0.786

0.607

0.756

0.839

0.903

0.905

1.788 0.970 (0.038)

1.085 0.948 (0.140)

− 0.657 0.762 (0.224)

1.901 0.862 (0.030)

1.711 0.746 (0.045)

1.597 0.821 (0.056)

0.686 0.851 (0.247)

0.259 0.903 (0.398)

0.967

0.933

0.500

0.500

0.000

0.500

0.900

0.963

0.933

(continued)

0.099 (0.461)

0.305 (0.382)

7.089 (0.000)

18.673 (0.000)

19.778 (0.000)

14.959 (0.000)

− 0.804 (0.215)

− 2.058 (0.026)

− 0.168 (0.434)

Marginal Landless t value and farmers small farmers

0.861 0.922 (0.195)

Age Age t-value (20–40) (above years 40) years

− 0.859 (0.196)

Illiterate Formal t-value education

1.218 0.894 (0.113)

Non-SC/ t-value ST

0.912 0.875

SC/ ST

− 0.938 0.885 0.012 (0.495)

Female t-value

0.919 0.920

Male

0.875 0.760

Approval of forest management agreement

Different stages Indicators

Table 8.12 (continued)

166 8 Measurement of Participation of Communities in the Planning …

Participation in monitoring stage 0.938

0.959

0.954

0.954

0.958

− 0.885 1.230 (0.110) 1.038 0.981 (0.151) − 0.904 0.746 (0.229)

0.526 1.000 (0.300) − 0.938 0.072 (0.471)

1.524 0.902 0.952 (0.069) − 0.979 0.942 2.273 (0.012) 1.799 0.928 0.952 (0.042)

− 0.974 0.962 2.551 (0.006) 1.763 0.951 0.952 (0.044)

Reporting of 0.960 1.000 illegal activities

Supervise 0.956 0.840 forest management plan implementation

0.964 1.000

0.958 0.916

Forest boundary maintenance

0.940 0.820

Forest patrols

0.840

0.985

1.055 0.969 0.952 (0.150)

0.160 0.816 (0.437) 0.580 0.923 (0.282)

0.972 0.920

0.515 0.832 0.828 (0.305)

0.974

1.000

− 1.717 (0.046)

0.977

0.954

0.985

−1.055 0.972 (0.147)

2.567 (0.006)

− 1.325 (0.095)

0.787 (0.216)

0.946

0.858

− 0.851 (0.199)

− 1.101 (0.137)

0.985

0.935

0.964

0.923

0.952

0.899

0.935

0.812

0.958

2.394 0.952 (0.009)

0.532 0.974 (0.298)

0.995 0.933 (0.161)

1.152 0.970 (0.126)

1.140 0.922 (0.128)

2.772 0.963 (0.003)

1.811 0.854 (0.036)

0.980

0.967

1.000

1.000

0.933

1.000

0.654

0.933

(continued)

− 1.669 (0.054)

0.203 (0.421)

− 3.860 (0.000)

− 2.023 (0.023)

− 0.166 (0.435)

− 2.551 (0.006)

9.700 (0.000)

0.873 (0.198)

Marginal Landless t value and farmers small farmers

1.412 0.974 (0.080)

Age Age t-value (20–40) (above years 40) years

− 0.582 (0.281)

Illiterate Formal t-value education

1.194 0.962 (0.118)

Non-SC/ t-value ST

0.829 0.845

SC/ ST

− 0.979 0.952 0.536 (0.297)

Female t-value

0.968 0.980

Male

Follow ups forest management by law

Participation in knowledge and skill developing training

Different stages Indicators

Table 8.12 (continued)

8.4 Forest Participation of Alipurduar Forest Division, North Bengal 167

0.905

− 1.361 (0.088)

Illiterate Formal t-value education

0.214 0.880 (0.415)

Non-SC/ t-value ST

0.977 0.897 0.893 (0.168)

SC/ ST

Source Author’s calculation. Figures in the bracket show p-values

0.900 0.879

Over all participation index

Female t-value

Male

Different stages Indicators

Table 8.12 (continued)

0.913

0.883

0.866

2.329 (0.014)

Marginal Landless t value and farmers small farmers

1.830 0.903 (0.035)

Age Age t-value (20–40) (above years 40) years

168 8 Measurement of Participation of Communities in the Planning …

8.5 Comparative Analysis of Forest Participation Index Across South …

169

Table 8.11 shows the percentages of households that are involved in different stages of participation based on the index. This table further shows that there has been a large percentage of households participating in the monitoring and implementation stages compared to the planning stage (Fig. 8.4). The participation of all marginalized classes of households in different stages is shown in Table 8.12. The participation of marginal and small farmers is higher than the landless farmers and, it is significant. On the other hand, the young households’ participation is higher in the implementation stage and significant compared to the elderly households.

8.5 Comparative Analysis of Forest Participation Index Across South Bengal and North Bengal Forest Division The comparative analysis of the forest participation index in the South and North Bengal forest divisions is presented in Table 8.13 and Fig. 8.5. It is found that the overall participation of the households in the North Bengal forest division is higher than that of the South Bengal forest division because of the higher contribution of participation, planning, and implementation indices.

Table 8.13 Overall participation index of South and North Bengal forest divisions Forest division

Planning index

Implementation index

Monitoring index

Overall participation index

South Bengal

0.584

0.580

0.490

0.551

North Bengal

0.907

0.833

0.952

0.897

Source Author’s calculation from field survey

170

8 Measurement of Participation of Communities in the Planning …

1.000 0.900 0.800 0.700 0.600 0.500 0.400 0.300 0.200 0.100 0.000 Planning

Implementation South Bengal

Monitoring

Overall Participation Index

North Bengal

Fig. 8.5 Overall participation index in South and North Bengal forest divisions. Source Author’s calculation from field survey

Summing Up The participation index of households in the Alipurduar forest division is the highest (0.897) followed by the Purulia forest division (0.64), Rupnarayan forest division of PaschimMedinipur (0.53), and Bankura forest division (0.47). In the Purulia division, the percentages of households that participated in the planning stage, implementation stage, and implementation stage are 72.6%, 49.6%, and 25% respectively. The participation for SC/ST households is higher than that of non-SC/ST households and the participation of illiterate households seems to be higher than educated households. In the Bankura (south), there is no difference in participation between SC/ST and non-SC/ST households, only significant result is found for differences in participation between illiterate and educated households. In the Rupnarayan forest division of Paschim Medinipore, it is evident that female participation is higher in the planning, implementation, and monitoring stages compared to male-headed households. The participation of SC/ST households is much higher compared to non-SC/ST households in the planning, implementation, and monitoring cases. On the other hand, the participation of illiterate households in the implementation and monitoring of cases plays a key role compared to educated households. Elderly households participate more in monitoring and implementation cases compared to young households. The participation of marginal and small farmers is higher than landless households in the monitoring stage and is significant in the Rupnarayan forest division. In the Alipurduar forest division, the overall participation index for marginal and small farmers is higher than the landless farmers and is significant. The participation of marginal and small farmers is higher than that of landless farmers and, it is

References

171

significant. On the other hand, the young households’ participation is higher in the implementation stage and significant compared to the elderly households. Lastly, the overall participation of households in the North Bengal forest division is higher than that of the South Bengal forest division. It is observed that the higher participation in North Bengal is explained by the higher contribution of participation, planning, and implementation indices.

References Basu JP (2021) Forest participation of local communities: a study of a tribal dominated region in India. J Soc Econ Dev 23(4):1–22 Tadesse S, Woldetsadik M, Senbeta F (2017) Forest users’ level of participation in a participatory forest management program in Southwestern Ethiopia. Forest Sci Technol 13(4):164–173

Chapter 9

Measurement of Forest Governance in South Bengal and North Bengal Forest Divisions at Household Level

Abstract This chapter depicts the formulation of forest governance indices at the household level and village level across three forest divisions of South Bengal say Purulia forest division, Bankura forest division and Paschim Medinipore forest division, and Aliporeduar forest division in North Bengal. In addition, this chapter classifies which villages are performing good forest governance, medium forest governance, excellent forest governance, and weak forest governance in South Bengal as well as in North Bengal villages under different forest divisions. Besides, this chapter also identifies the factors which are responsible for forest governance in selected forest divisions of South Bengal and North Bengal. In addition, a comparative analysis of forest governance is done in this chapter.

9.1 Forest Governance of the Purulia Forest Division, South Bengal Forest governance is measured by the forest governance index (FGI). According to FAO, we have taken six main indicators of forest governance, namely, Rule of Law (RL), Transparency (T), Accountability (A), Participation (P), Inclusive and Equitable (IE), and Efficient and Effective (EE). Each main indicator is divided into sub-indicators. Forest governance is said to be good when it is characterized by the mentoring of the rule of law; transparency and low levels of corruption; stakeholder participation in decision-making; accountability and a low regulatory burden within the forest sector. The selection of indicators is done in line with some literature and local conditions prevailing in the study areas. The indicators and sub-indicators have been discussed in Chap. 3, Sect. 3.2.6, and Table 3.4. The results of the indices of the main indicators and sub-indicators are presented in Table 9.1. It is observed that the forest governance index (FGI) at the household level in the forest division of Purulia is found to be 0.446 (Table 9.1). It is revealed that the contribution of the participation index is found to be the highest (0.643), followed by the inclusive and equitable index (0.579), efficient and effective index © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. P. Basu, Governance and Institution in the Indian Forest Sector, https://doi.org/10.1007/978-3-031-34746-7_9

173

174

9 Measurement of Forest Governance in South Bengal and North Bengal …

Table 9.1 Forest governance index (FGI) in the Purulia forest division Main index

Sub index

Value

Total

Rule of law

Are there any formal rules regulating forest use?

0.017

0.263

Is there any informal rule for the use of forest products?

0.233

Do you know the timber brokers helping with deforestation due to leakage of forest laws?

0.391

Is there any weak forest administration?

0.044

Is there political intervention for illegal encroachment and illegal logging?

0.619

Is there any strong administration which helps to save the reserve forests?

0.276

Do you know permission to be taken from the forest protection committees beyond the specified level of forest product use?

0.233

Is there any money involved in getting permission for extra collection of forest products?

0.585

Do you know any decisions taken from the meeting of the executive committee?

0.452

Do you know the agenda of the meeting placed before the general body meeting?

0.202

Are you regularly present in the general body meeting?

0.355

Do you have any experience of tackling conflict if any?

0.563

Do you know that community members obey government rules?

0.034

Planning index

Forest boundary demarcation

0.578

Identifying forest users

0.867

Participatory forest resource assessment

0.579

Forest management committee election

0.779

Encouraging others to participate

0.639

Preparing a forest management plan

0.571

Developing forest management bye-laws

0.577

Approval of forest management agreement

0.857

Transparency

Accountability

Participation

0.368

0.317

0.681

(continued)

9.1 Forest Governance of the Purulia Forest Division, South Bengal

175

Table 9.1 (continued) Main index

Sub index

Value

Total

Reforestation of degraded forest areas

0.721

0.669

Planting of fruit-bearing trees such as mahua and mango

0.7

Planting trees and management

0.681

Nursery establishment

0.742

Beekeeping

0.468

Forest fire fighting

0.677

Attending meetings

0.863

Participation in knowledge and skill developing training

0.5

Follow up forest management by law

0.462

Helps forest patrolling

0.528

Preventing illegal timber logging

0.873

Supervise forest management plan implementation

0.521

Forest boundary maintenance

0.511

Inclusive and equitable

Do you know the female members form a Self-Help Group?

0.579

0.579

Efficient and effective

Do you know that there has been an increase in the 0.536 availability of wood and non-timber forest products in last 5 years?

0.504

Implementation index

Monitoring index

0.579

0.643

Do you know the dependency on forest resources 0.472 goes down due to the successful implementation of poverty eradication programs by the government? Forest governance index

0.446

Source Author’s calculation

(0.504), transparency index (0.386), accountability index (0.317) and rule of law index (0.263). It is also found that the contribution of rule of law index is the lowest. Rule of law, transparency, and accountability indices are relatively lagging behind the indices of participation, inclusive and equitable, and efficiency and effective (Table 9.1).

176

9 Measurement of Forest Governance in South Bengal and North Bengal …

The results of the forest governance index across nine villages are shown in Table 9.2. From Table 9.2 it is found that the village Bandhghutu has experienced the highest forest governance (0.509) and the village Lawadi has experienced the lowest forest governance (0.383) in the forest division of the Purulia district. The sample villages are said to follow poor, medium, good, and excellent forest governance based on the values of sub-indices and the values of forest governance indices. Excellent, good, medium, and poor governance are classified as follows. Poor governance is classified where the forest governance index is ≤ 0.245, the medium where the forest governance index is between 0.246 and 0.498, good governance is where the forest governance index lies between 0.499 and 0.733, and excellent governance where the forest governance index is ≥ 0.734. The overall forest governance of the Purulia forest division is medium. Rule of law is poor for three villages (out of nine villages) while the other six villages follow a medium rule of law. Transparency is good for two villages out of nine villages while the villages follow medium transparency. Accountability is good for one village; it is poor for the other two villages while it is medium for six villages. Forest participation is good for all nine villages. In the case of inclusive and equitable one village holds excellent governance while it holds good for the other six villages and it is medium for two villages. Efficient and effective management is good for five villages while it is medium for the rest four villages. It is revealed from Table 9.3 that good forest governance holds for two villages while the other seven villages follow medium forest governance. The overall forest governance of Purulia forest division is medium while it is good for participation, inclusive and equitable, efficient and effective. It is observed from Table 9.3 that the villages such as Tarpenia, Lawadi, and Nischintapur villages have poor rule of law index, while other villages have a medium rule of law index. The villages like Tarpenia and Nischintapur have been experiencing a good the transparency index while other villages follow medium transparency. The accountability index is poor for the villages say Lawadi and Nischintapur while the village of Rabidi follows well in accountability. It is also observed from Table 9.3 that the forest participation indexes are good for all villages. To identify the factors affecting forest governance we take the forest governance index (FGI), as a dependent variable. The independent variables are taken as enforcement index, educational index of the household, the caste of sample households, lands holding, and percentage of forest income to total income and trust between communities and forest department. The basic statistics of the independent variables are presented in Table 9.4. We have applied a linear multiple regression model to estimate the factors responsible for forest governance. The results of the estimated model are presented in Table 9.5. Among six explanatory variables included in the model, only four variables are showing significant results. The regression is run by adjusting heteroscedasticity. The model is overall significant as indicated by the value of F statistic (F = 34.62) which means that the model has strong explanatory power (Table 9.5). The value of adj R2 is 0.446 for the linear model showing that 44% of the variations of the forest governance are explained by the variables considered in the model.

0.342

0.127

0.316

0.256

0.344

0.182

0.284

0.363

0.219

0.263

Bandhghutu

Tarpenia

Perorgoria

Rabidi

Charida

Lawadi

Bagti

Ichakota

Nischintapur

Puruliya forest division

Source Author’s calculation

Rule of law

Village name

0.368

0.519

0.324

0.259

0.370

0.360

0.360

0.271

0.562

0.417

Transparency

0.317

0.205

0.278

0.303

0.202

0.360

0.568

0.344

0.326

0.423

Accountability

0.643

0.693

0.667

0.679

0.651

0.602

0.644

0.558

0.663

0.629

Participation

Table 9.2 Forest governance index (FGI) of the villages in the Purulia forest division

0.579

0.731

0.722

0.386

0.442

0.710

0.636

0.625

0.522

0.769

Inclusive and equitable

0.504

0.378

0.537

0.523

0.450

0.645

0.504

0.547

0.453

0.474

Efficient and effective

0.446

0.458

0.482

0.406

0.383

0.503

0.495

0.443

0.442

0.509

Index

Governance

5

4

8

9

2

3

6

7

1

Rank

9.1 Forest Governance of the Purulia Forest Division, South Bengal 177

Medium

Medium

Medium

Medium

Poor

Medium

Medium

Poor

Medium

Perorgoria

Rabidi

Charida

Lawadi

Bagti

Ichakota

Nischintapur

Purulia forest division

Source Author’s calculation

Good

Poor

Tarpenia

Medium

Medium

Medium

Medium

Medium

Medium

Good

Medium

Medium

Bandhghutu

Transparency

Rule of law

Village name

Medium

Poor

Medium

Medium

Poor

Medium

Good

Medium

Medium

Medium

Accountability

Good

Good

Good

Good

Good

Good

Good

Good

Good

Good

Participation

Good

Good

Good

Medium

Medium

Good

Good

Good

Good

Excellent

Inclusive and equitable

Table 9.3 Categorization of forest governance index across villages in Purulia forest division

Good

Medium

Good

Good

Medium

Good

Good

Good

Medium

Medium

Efficient and effective

Medium

Medium

Medium

Medium

Medium

Good

Medium

Medium

Medium

Good

Forest governance

178 9 Measurement of Forest Governance in South Bengal and North Bengal …

9.1 Forest Governance of the Purulia Forest Division, South Bengal

179

Table 9.4 Basic statistics of variables Mean

Std. Dev.

Min

Max

Dependent variable Governance index

0.446

0.118 0.144 0.816

Enforcement index

0.717

0.185 0.25

1

Educational index

0.166

0.148 0

0.683

Caste (SC = 1, ST = 2, GEN = 3, OTH = 4)

2.409

1.113 1

4

2.534

3.145 0

20.04

Independent variable

Total land holding (in acre) % of forest income to total income

24.394 15.102 0

Trust between communities and Forest Dept. (If No = 1, Yes = 2, Don’t know = 3)

2.127

0.550 1

100 3

Source Author’s calculation

Table 9.5 Estimates of multiple linear regression model in Purulia forest division Linear regression model Coeff

Std. error

t

P > |t|

Enforcement index

0.172

0.036

4.80

0.000

Educational index

0.053

0.058

0.91

0.362

Caste (SC = 1, ST = 2, GEN = 3, OTH = 4)

0.025

0.007

3.46

0.001

Total land holding (in Acre)

0.002

0.003

0.67

0.503

% of forest income to total income

0.001

0.000

2.75

0.006

Trust between communities and 0.055 Forest Dept. (If No = 1, Yes = 2, Don’t know = 3)

0.012

4.57

0.000

Constant

0.001

3.73

0.000

0.002

Observations = 252 F (6245) = 34.62 Prob > F = 0.000 R2 = 0.4588 Adj R2 = 0.4456

It is observed from Table 9.5 that the coefficient of the enforcement index is positive and significant. If enforcement is properly and efficiently applied, the illegal logging of timber will be reduced and its consequent effect will be on good forest governance and vice-versa. The caste of the household is positive and significant. The reason is that the general caste and OBC classes of households are relatively less forest-dependent and lead to good forest governance. On the other hand, the high forest dependency among SC and ST households leads to poor forest governance. The coefficient of forest income to total income is positive and significant. It also implies that forest governance is positively related to income from forest. This means that with higher income from the forest for livelihood generation the forest-dependent communities are assumed to protect and manage the forest effectively. Further, there

180

9 Measurement of Forest Governance in South Bengal and North Bengal …

is a positive and significant relationship between trust and forest governance. This means that the trust between forest communities and forest departments creates a congenial environment for forest protection which helps for good forest governance.

9.2 Forest Governance of the Bankura (South), Forest Division, South Bengal The results of the forest governance indices of the main indicators and sub-indicators in the Bankura (south) forest division are presented in Table 9.6. It is observed that the Forest Governance Index (FGI) of the households in the forest division of Bankura (south) is found to be 0.397 (Table 9.6). It is revealed that the contribution of the transparency index is found to be highest (0.703), followed by the inclusive and equitable index (0.509), participation index (0.470), efficient and effective index (0.298), accountability index (0.231) and rule of law index (0.170). The results of the forest governance indices of the households indicated by Table 9.6 shows that the rule of law, accountability, efficiency, and effective indices are relatively lagging behind the transparency, inclusive and equitable, and participation indices. The results of the forest governance index across ten villages are shown in Table 9.7. From Table 9.7 it is found that the villages say Makhnu has experienced the highest forest governance (0.453) while the village says Kamo has experienced the lowest forest governance (0.340) in the Bankura (south) forest division. The sample villages are classified into poor, medium, good, and excellent forest governance based on the values of sub-indices and the values of forest governance indices. Poor governance means the forest governance index value is ≤ 0.245, medium governance means the forest governance index value is between 0.246 and 0.498, good governance is where forest governance index value lies between 0.499 and 0.733, and excellent governance means the forest governance index value exceeds 0.734. It is observed from Table 9.8 that rule of law is poor for all villages except the village Makhnu. Transparency is good for nine villages out of ten villages while Makhnu follows excellent transparency. Accountability is poor for six villages while it is medium for four villages. Forest participation is good for two villages and other villages follow medium. Two villages say Kadmagarh and Dhankura hold excellent in the inclusive and equitable index. The efficiency and Effectiveness index is medium for seven villages while it is poor for rest three villages. It is revealed from Table 9.8 that all ten villages follow medium forest governance in the Bankura (south) forest division and the overall forest governance of the Bankura (south) forest division is medium. It is observed from Table 9.8 that all villages are performing poor rule of law index except Makhnu village. The villages like Kamo, Bagdiha, Mitham, Jamgeria, Barapatcha, Mahadebsinan, Kadmagarh, Kalabani and Dhankura have been experiencing good transparency while Makhnu village has experienced excellent transparency. The accountability index is poor for the villages Kamo, Mitham,

9.2 Forest Governance of the Bankura (South), Forest Division, South Bengal

181

Table 9.6 Forest governance index (FGI) in Bankura (south) forest division Main index

Sub index

Value

Total

Rule of law

Is there any formal rule regulating forest use?

0.037

0.170

Is there any informal rule for the use of forest products?

0.173

Do you know the timber brokers helping with deforestation due to leakage of forest laws?

0.171

Is there any weak forest administration?

0.092

Is there political intervention for illegal encroachment and illegal logging?

0.447

Is there any strong administration which helps to save reserve forest?

0.101

Do you know permission to be taken from the forest protection committees beyond their specified level of forest product use?

0.589

Is there any money involvement for getting permission for extra collection of forest product?

0.796

Transparency

0.703

Do you know any decisions taken from meeting 0.476 of executive committee?

Accountability

Do you know the agenda of meeting placed before the general body meeting?

0.952

Are you regularly present in the general body meeting?

0.059

0.231

Do you have any experience of tackling conflict 0.601 if any?

Participation

Do you know the community members obey government rules?

0.033

Planning index

Forest boundary demarcation

0.318

Identifying forest users

0.450

Participatory forest resource assessment

0.439

Forest management committee election

0.814

0.478

Encouraging others to 0.636 participate Preparing forest management plan

0.373

Developing forest 0.404 management bye-laws (continued)

182

9 Measurement of Forest Governance in South Bengal and North Bengal …

Table 9.6 (continued) Main index

Sub index

Implementation index

Monitoring index

Value Approval of forest management agreement

0.395

Reforestation of degraded forest areas

0.443

Planting of fruit bearing trees such as mahua and mango

0.669

Planting trees and management

0.689

Nursery establishment

0.487

Beekeeping

0.456

Forest fire fighting

0.779

Attending meetings

0.908

Participations in knowledge and skill developing training

0.283

Follow up forest 0.184 managements bye-law

Total

0.589

0.342

Helps forest patrolling 0.252 Preventing illegal timber logging

0.833

Supervise forest management plan implementation

0.167

Forest boundary maintenance

0.272 0.470

Inclusive and equitable

Do you know the female members forms Self-Help Group?

0.509

0.509

Efficient and effective

Do you know that there has been an increased in 0.446 availability of Wood and non-timber forest products in last 5 years?

0.298

Do you know the dependency of forest resources go down due to the successful implementation of poverty eradication programmes of the government? Forest governance index Source Author’s calculation

0.149

0.397

0.143

0.265

0.226

0.175

0.216

0.136

0.214

0.139

0.095

0.134

0.170

Kamo

Makhnu

Bagdiha

Mitham

Jamgeria

Barapatcha

Mahadebsinan

Kadmagarh

Kalabani

Dhankura

Bankura

Source Author’s calculation

Rule of law

Village name

0.703

0.695

0.844

0.533

0.726

0.713

0.732

0.672

0.708

0.814

0.664

Transparency

0.231

0.205

0.340

0.170

0.231

0.217

0.253

0.213

0.281

0.275

0.187

Accountability

0.470

0.549

0.369

0.593

0.422

0.458

0.435

0.467

0.413

0.484

0.479

Participation

0.509

0.773

0.208

0.875

0.346

0.700

0.538

0.526

0.375

0.529

0.333

Inclusive and equitable

Table 9.7 Forest governance index (FGI) of the villages in Bankura (south) forest division

0.298

0.258

0.378

0.167

0.304

0.383

0.391

0.311

0.208

0.353

0.232

Efficient and effective

0.397

0.435

0.372

0.413

0.374

0.434

0.428

0.394

0.369

0.453

0.340

Index

2

8

5

7

3

4

6

9

1

10

Ranking

Forest governance

9.2 Forest Governance of the Bankura (South), Forest Division, South Bengal 183

184

9 Measurement of Forest Governance in South Bengal and North Bengal …

Barapatcha, Mahadebsinan, Kadmagarh and Dhankura, the rest of the villages like Makhnu, Bagdiha, Jamgeria, and Kalabani follow medium accountability. It is also observed from Table 9.8 that the forest transparency index is good for all villages. To identify the factors affecting forest governance we take the forest governance index (FGI), as the dependent variable. The independent variables are taken as enforcement index, educational index of the household, the caste of sample households, lands holding, and percentage of forest income to total income and trust between communities and forest department. The basic statistics of the independent variables are presented in Table 9.9. We have applied a linear multiple regression model which helps to estimate the factors affecting forest governance. The results of the estimation model are presented in Table 9.10. Among six explanatory variables included in the model, only four variables are showing significant results. The model is overall significant as indicated by the value of F statistic (F = 31.84) which means that the model has strong explanatory power (Table 9.10). The value of adj R2 is 0.463 for the linear model showing that 46% of the variations of the forest governance are explained by the variables considered in the model. It is observed from Table 9.10 that the coefficient of enforcement index is negative and significant. This means that the forest governance has been responding badly with strictly application of enforcement. The caste of household has positive and significant impact on forest governance. The reason is that the general caste and OBC classes of households are relatively less forest dependent and leading to good governance. On the other hand, the high forest dependency among SC and ST households leads to poor forest governance. The coefficient of forest income to total income is positive and significant. It also implies that the forest governance is positively related to income from forest. This means that higher the income from forest for livelihood generation the forest dependent communities are assumed to protect and manage forest effectively. Further there is a positive and significant relation between trust and forest governance. This means that the trust between forest communities and forest department creates a congenial environment on forest protection which helps for good governance.

9.3 Forest Governance of Rupnarayan Forest Division of Paschim Medinipur, South Bengal The results of the indices of the main indicators and sub-indicators are presented in Table 9.11. It is observed that the Forest Governance Index (FGI) at the household level in the forest division of Rupnarayan, Paschim Medinipur is found to be 0.302 (Table 9.11). It is revealed that the contribution of participation index is found to be highest (0.530), followed by inclusive and equitable index (0.376), efficient and effective index (0.310), accountability index (0.220), transparency index (0.218) and rule of law index (0.159).

Good

Poor

Poor

Poor

Poor

Poor

Poor

Barapatcha

Mahadebsinan

Kadmagarh

Kalabani

Dhankura

Bankura (south) forest Poor division

Source Author’s calculation

Good

Poor

Jamgeria

Good

Good

Good

Good

Good

Good

Poor

Poor

Medium

Poor

Poor

Poor

Medium

Poor

Medium

Medium

Good

Medium

Good

Medium

Medium

Medium

Medium

Medium

Good

Excellent

Poor

Excellent

Medium

Good

Good

Good

Medium

Good

Medium

Medium

Medium

Poor

Medium

Medium

Medium

Medium

Poor

Medium

Medium

Medium

Medium

Medium

Medium

Medium

Medium

Medium

Medium

Medium

Medium

Mitham

Good

Medium

Poor

Poor

Medium

Medium

Bagdiha

Excellent

Medium

Medium

Makhnu

Poor

Poor

Kamo

Good

Rule of law Transparency Accountability Participation Inclusive and equitable Efficient and effective Forest governance

Village name

Table 9.8 Categorization of forest governance index of the villages in Bankura (south) forest division

9.3 Forest Governance of Rupnarayan Forest Division of Paschim … 185

186

9 Measurement of Forest Governance in South Bengal and North Bengal …

Table 9.9 Basic statistics of variables Mean

Std. Dev.

Min

Max

Dependent variable Governance index

0.397

0.114 0.160 0.715

Enforcement index

0.451

0.363 0

1

Educational index

0.249

0.145 0

0.678

Caste (SC = 1, ST = 2, GEN = 3, OTH = 4)

2.289

0.873 1

4

4.761

4.87

0

42

25.763 13.801 0

60

Independent variable

Total land holding (in acre) % of forest income to total income Trust between communities and Forest Dept. (If No = 1, Yes = 2, Don’t know = 3)

2.202

0.424 1

3

Source Author’s calculation

Table 9.10 Estimates of multiple linear regression model in Bankura (south) forest division Multiple regression model Coeff

Std. error

t

P > |t|

− 0.104

0.022

− 4.62

0.000

Educational index

0.031

0.054

0.58

0.560

Caste (SC = 1, ST = 2, GEN = 3, OTH = 4)

0.047

0.009

5.27

0.000

Total land holding (in acre)

− 0.001

0.002

− 0.41

0.682

% of forest income to total income

0.002

0.001

3.16

0.002

Trust between communities and Forest Dept. (If No = 1, Yes = 2, Don’t know = 3)

0.079

0.013

6.26

0.000

Constant

0.002

0.001

4.20

0.000

Enforcement index

Observations = 228 F (6221) = 31.84 Prob > F = 0.000 R2 = 0.4636 Adj R2 = 0.4491

It is also found that the contribution of rule of law index is lowest, of the six indicators. Rule of law, transparency and accountability are relatively lagging behind participation, inclusive and equitable and efficient and effective index in terms of their index values. The results of forest governance index across ten villages are shown in Table 9.12. From Table 9.12 it is found that the villages say Godasole has experienced the highest forest governance (0.274) and the village says Ichalkoda has experienced the lowest forest governance (0.239) in the forest division of Rupnarayan. The sample villages are classified into poor, medium, good, and excellent forest governance based on the values of forest governance indices as described above. Rule of law is poor for all ten villages of Rupnarayan forest division of Paschim

9.3 Forest Governance of Rupnarayan Forest Division of Paschim …

187

Table 9.11 Forest governance index (FGI) in Rupnarayan forest division of Paschim Medinipur, South Bengal Main index

Sub index

Value

Total

Rule of law

Is there any formal rule regulating for forest use?

0.014

0.159

Is there any informal rule for the use of forest product?

0.172

Do you know the timber brokers helping for Deforestation due to leakage of forest laws?

0.258

Is there any weak forest administration?

0.035

Is there political intervention for illegal encroachment and illegal logging?

0.366

Is there any strong administration which helps to save reserve forest?

0.106

Do you know permission to be taken from the forest protection committees beyond specified level of forest product use?

0.055

Is there any money involvement for getting permission for extra collection of forest product?

0.415

Transparency

0.218

Do you know any decisions taken from meeting 0.115 of executive committee?

Accountability

Do you know the agenda of meeting placed before the general body meeting?

0.288

Are you regularly present in the general body meeting?

0.148

0.220

Do you have any experience of tackling conflict 0.488 if any?

Participation

Do you know the community members obey government rules?

0.025

Planning index

Forest boundary demarcation

0.533

Identifying forest users

0.594

Participatory forest resource assessment

0.627

Forest management committee election

0.326

0.582

Encouraging others to 0.575 participate Preparing forest management plan

0.662

Developing forest 0.662 management bye-laws (continued)

188

9 Measurement of Forest Governance in South Bengal and North Bengal …

Table 9.11 (continued) Main index

Sub index

Implementation index

Monitoring index

Value Approval of forest management agreement

0.674

Reforestation of degraded forest areas

0.415

Planting of fruit bearing trees such as mahua and mango

0.467

Planting trees and management

0.371

Nursery establishment

0.315

Beekeeping

0.577

Forest fire fighting

0.641

Attending meetings

0.298

Participations in knowledge and skill developing training

0.631

Follow ups forest managements by law

0.538

Total

0.464

0.544

Helps forest patrolling 0.568 Preventing illegal timber logging

0.289

Supervise forest management plan implementation

0.707

Forest boundary maintenance

0.617 0.530

Inclusive and equitable

Do you know the female members forms Self-Help Group?

0.376

0.376

Efficient and effective

Do you know that there has been an increased in 0.119 availability of wood and non-timber forest products in last 5 years?

0.310

Do you know the dependency of forest resources go down due to the successful implementation of poverty eradication programmes of the Government? Forest governance index Source Author’s calculation

0.063

0.302

0.096

0.189

0.099

0.104

0.218

0.236

0.206

0.161

0.111

0.061

0.159

Upper Patrisole

Lower Patrisole

Kastokura

Dhabani

Aushbandi

Godasole

Fulsason

Dharnadihi

Chandabila

Ichalkoda

Paschim Medinipur forest division

Source Author’s calculation

Rule of law

Village name

0.218

0.182

0.175

0.183

0.190

0.435

0.202

0.224

0.185

0.165

0.181

Transparency

0.220

0.182

0.157

0.167

0.167

0.557

0.151

0.167

0.167

0.173

0.188

Accountability

0.530

0.482

0.522

0.510

0.510

0.751

0.490

0.472

0.470

0.473

0.494

Participation

Table 9.12 Forest governance index (FGI) of the villages in Rupnarayan forest division

0.376

0.063

0.529

0.450

0.000

0.483

0.323

0.125

0.556

0.250

0.625

Inclusive and equitable

0.310

0.063

0.022

0.079

0.060

0.351

0.108

0.000

0.046

0.030

0.021

Efficient and effective

0.302

0.239

0.245

0.253

0.264

0.274

0.265

0.267

0.267

0.267

0.267

Index

10

9

8

7

1

6

5

3

4

2

Ranking

Forest governance

9.3 Forest Governance of Rupnarayan Forest Division of Paschim … 189

190

9 Measurement of Forest Governance in South Bengal and North Bengal …

Medinipur district. Transparency is poor for nine villages out of ten villages while one village follows medium transparency. Accountability is good for one village and the remaining nine villages follow poor accountability. Forest participation is good for three villages while medium for six villages and excellent for one village. Efficient and effective management is poor for nine villages while it is medium for the remaining one village. It is revealed from Table 9.13 that medium forest governance holds for eight villages while the other two villages follow poor forest governance. It is observed from Table 9.13 that rule of law is poor for all villages. It is also found that the transparency index and accountability index is poor in all villages except Godasole. It is also observed from Table 9.12 that Upper Pathrisole, Lower Pathrisole, Kastokura, Dhabani, and Aushbandi follow medium forest participation while Godasole follows excellent participation and the remaining three villages have good participation in the Rupnarayan forest division of Paschim Medinipur district. Upper Patrhrisole, Kastokura, and Chandabila followed a good inclusive and equitable index whereas Lower Pathrisole, Aushbandi, Godasole, and Dharnadihi villages followed a medium and the remaining two villages followed a good inclusive and equitable index. Godasole has experienced medium in efficient and effective forest index and other villages have experienced poor in efficient and effective forest index. For Paschim Medinipur forest division forest participation is good and inclusive and the equitable index is medium whereas other indices have poor values. Thus, the overall forest governance of the Rupnarayan forest division of Paschim Medinipur is poor. To identify the factors affecting the forest governance we take forest governance index (FGI) as dependent variable and the independent variables are taken to be the Table 9.13 Categorization of forest governance index of the villages in Rupnarayan forest division Village name

Rule Transparency Accountability Participation Inclusive Efficient Forest of and and governance law equitable effective

Upper Patrisole

Poor Poor

Poor

Medium

Good

Poor

Medium

Lower Patrisole

Poor Poor

Poor

Medium

Medium

Poor

Medium

Kastokura

Poor Poor

Poor

Medium

Good

Poor

Medium

Dhabani

Poor Poor

Poor

Medium

Poor

Poor

Medium

Aushbandi

Poor Poor

Poor

Medium

Medium

Poor

Medium

Godasole

Poor Medium

Good

Excellent

Medium

Medium Medium

Fulsason

Poor Poor

Poor

Good

Poor

Poor

Medium

Dharnadihi Poor Poor

Poor

Good

Medium

Poor

Medium

Chandabila Poor Poor

Poor

Good

Good

Poor

Poor

Ichalkoda

Poor Poor

Poor

Medium

Poor

Poor

Poor

Medinipur

Poor Poor

Poor

Good

Medium

Poor

Poor

Source Author’s calculation

9.3 Forest Governance of Rupnarayan Forest Division of Paschim …

191

enforcement index, educational index of the household, caste of sample households, lands holding and percentage of forest income to total income, and trust between communities and forest department. The basic statistics of the independent variables are presented in Table 9.14. We have applied a linear multiple regression model. The results of estimation model are presented in Table 9.15. Among six explanatory variables included in the model, only three variables are showing significant results. The model is overall significant as indicated by the value of F statistic (F = 29.16) which means that the model has strong explanatory power (Table 9.15). The value of adj R2 is 0.443 for the linear model showing that 44% of the variations of the forest governance are explained by the variables considered in the model. It is observed from Table 9.15 that the coefficient of the enforcement index is positive and significant. If enforcement is properly and efficiently applied the illegal logging of timber will be reduced and its consequent and positive effect on forest governance and vice-versa. The coefficient of land-holding is negative and significant. This means that the low-holding farms can maintain good forest governance than that of large holding farms. Further, there is a positive and significant relationship between trust and forest governance. This means that the trust between forest communities and forest departments creates a congenial environment for forest protection which helps for good governance. Table 9.14 Basic statistics of variables Mean Std. Dev.

Min

Max

Dependent variable Governance index

0.266 0.139 0.035 0.903

Independent variable Enforcement index

0.257 0.218 0

1

Educational index

0.264 0.201 0

0.917

Caste (SC = 1, ST = 2, GEN = 3, OTH = 4)

2.136 0.774 1

4

Total land holding (in acre)

2.327 3.923 0

42.00

% of forest income to total income

0.194 0.111 0

0.519

Trust between communities and Forest Dept. (If No = 1, Yes = 1.977 0.205 1 2, Don’t know = 3) Source Author’s calculation

3

192

9 Measurement of Forest Governance in South Bengal and North Bengal …

Table 9.15 Estimates of multiple linear regression model in Rupnarayan forest division Multiple regression model Coeff

Std. error

t

P > |t|

Enforcement index

0.352

0.040

8.87

0.000

Educational index

0.030

0.051

0.58

0.563

Caste (SC = 1, ST = 2, GEN − 0.006 = 3, OTH = 4)

0.011

− 0.57

0.567

Total land holding (in acre)

− 0.006

0.003

− 1.96

0.051

% of forest income to total income

− 0.080

0.073

− 1.10

0.275

Trust between communities and Forest Dept. (If No = 1, Yes = 2, Don’t know = 3)

0.089

0.018

4.94

0.000

Constant

0.001

0.001

1.1

0.271

Observations = 213 F (6206) = 29.16 Prob > F = 0.000 R2 = 0.4592 Adj R2 = 0.4435

9.4 Forest Governance of Alipurduar Forest Division of North Bengal It is observed that the Forest Governance Index (FGI) at the household level in the forest division of Alipurduar is found to be 0.483 (Table 9.16). It is revealed that the contribution of participation index is found to be the highest (0.897) followed by inclusive and equitable index (0.775), transparency index (0.545), accountability index (0.385), rule of law index (0.172) and so on. The results of the forest governance index across six villages are shown in Table 9.17. From Table 9.17 it is found that the villages say Pampubasti has experienced the highest forest governance (0.517) and village 24 Basti has experienced the lowest forest governance (0.422) in the Alipurduar forest division of North Bengal (Table 9.18). The sample villages are classified into poor, medium, good and excellent forest governance based on the values of forest governance indices. The overall forest governance of Alipurduar district is medium. Rule of law is poor for all six villages. Forest participation is excellent for all six villages. Accountability is medium for all six villages of Alipurduar. Again forest participation is poor for all six villages. The inclusive and equitable index shows an excellent for four villages, and good for two villages. The Efficient and Effectiveness index value is poor for all six villages.

9.4 Forest Governance of Alipurduar Forest Division of North Bengal

193

Table 9.16 Forest governance index (FGI) in Alipurduar forest division in North Bengal Main index

Sub index

Value

Total

Rule of law

Is there any formal rule regulating for forest use?

0.073

0.172

Is there any informal rule for the use of forest product?

0.349

Do you know the timber brokers helping for Deforestation due to leakage of forest laws?

0.149

Is there any weak forest administration?

0.036

Is there political intervention for illegal encroachment and illegal logging?

0.288

Is there any strong administration which helps to save reserve forest?

0.139

Do you know permission to be taken from the forest protection committees beyond specified level of forest product use?

0.344

Is there any money involvement for getting permission for the extra collection of forest product?

0.629

Do you know any decisions taken from meeting of executive committee?

0.232

Do you know the agenda of meeting placed before the general body meeting?

0.974

Are you regularly present in the general body meeting?

0.019868

Do you have any experience of tackling conflict if any?

0.97351

Transparency

Accountability

0.545

0.385

Do you know the community members obey government 0.162252 rules? Participation

Planning index

Implementation index

Forest boundary demarcation

0.871

Identifying forest users

0.868

Participatory forest resource assessment

0.891

Forest management committee election

0.921

Encouraging others to participate

0.950

Preparing forest management plan

0.914

Developing forest management bye-laws

0.924

Approval of forest management agreement

0.921

Reforestation of degraded forest areas 0.858 Planting of fruit bearing trees such as mahua and mango

0.788

Planting trees and management

0.669

Nursery establishment

0.821

Beekeeping

0.639

0.907

0. 833

(continued)

194

9 Measurement of Forest Governance in South Bengal and North Bengal …

Table 9.16 (continued) Main index

Sub index

Monitoring index

Value Forest fire fighting

0.947

Attending meetings

0.970

Participations in knowledge and skill developing training

0.970

Follow ups forest managements bye-laws

0.964

Helps forest patrolling

0.921

Preventing illegal timber logging

0.967

Supervise forest management plan implementation

0.937

Forest boundary maintenance

0.970

Total

0.952

0.897 Inclusive and equitable

Do you know the female members form Self-Help Group?

0.775

0.775

Efficient and effective

Do you know that there has been an increased in availability of wood and non-timber forest products in last 5 years?

0.185

0.126

Do you know the dependency of forest resources go down due to the successful implementation of poverty eradication programmes of the Government?

0.066

Forest governance index

0.483

Source Author’s calculation

To identify the factors affecting forest governance we take the forest governance index (FGI) as a dependent variable. The independent variables are taken as the enforcement index, educational index of the household, the caste of sample households, lands holding, and percentage of forest income to total income and trust between communities and forest department. The basic statistics of the independent variables are presented in Table 9.19. We have applied a linear multiple regression model. The results of estimation model are presented in Table 9.20. Among six explanatory variables included in the model, only three variables are showing significant results. The coefficient of enforcement index is negative and significant. The total landholding is positively associated with forest governance. The trust has a positive and significant impact on forest governance in the Alipurduar forest division of North Bengal.

0.156

0.224

0.189

0.153

0.186

0.134

0.172

Garobasti

Pampubasti

Rabhabasti

Santrabari

28 Basti

Jayanti

Alipurduar

Source Author’s calculation

Rule of law

Village name

0.545

0.567

0.483

0.622

0.597

0.507

0.514

Transparency

0.385

0.323

0.387

0.427

0.456

0.385

0.378

Accountability

0.897

0.841

0.836

0.959

0.896

0.924

0.933

Participation

0.775

0.806

0.520

0.800

0.667

0.897

0.885

Inclusive and equitable

Table 9.17 Forest governance index (FGI) of the villages in Alipurduar forest division

0.126

0.070

0.120

0.127

0.189

0.167

0.115

Efficient and effective

0.483

0.457

0.422

0.515

0.499

0.517

0.497

Index

5

6

2

3

1

4

Rank

Forest governance

9.4 Forest Governance of Alipurduar Forest Division of North Bengal 195

196

9 Measurement of Forest Governance in South Bengal and North Bengal …

Table 9.18 Categorization of forest governance index of the villages in Alipurduar forest division Village name

Rule Transparency Accountability Participation Inclusive Efficient Governance of and and law equitable effective

Garobasti

Poor Good

Medium

Excellent

Excellent Poor

Medium

Pampubasti Poor Good

Medium

Excellent

Excellent Poor

Medium

Rabhabasti

Poor Good

Medium

Excellent

Good

Poor

Medium

Santrabari

Poor Good

Medium

Excellent

Excellent Poor

Medium

28 Basti

Poor Medium

Medium

Excellent

Good

Poor

Medium

Jayanti

Poor Good

Medium

Excellent

Excellent Poor

Medium

Alipurduar

Poor Good

Medium

Excellent

Excellent Poor

Medium

Source Author’s calculation

Table 9.19 Basic statistics of variables Mean

Std. Dev.

Min

Max

Dependent variable Governance index

0.483

0.101 0.175 0.695

Independent variable Enforcement index

44.596 12.148 24

80

Educational index

0.828

0.189 0.25

1

Caste (SC = 1, ST = 2, GEN = 3, OTH = 4)

0.283

0.157 0

0.636

Total land holding (in acre)

2.232

0.647 1

3

% of forest income to total income

0.430

0.480 0

3.06

Trust between communities and Forest Dept. (If No = 1, Yes = 2, Don’t know = 3)

7.839

7.100 0

34.717

Source Author’s calculation Table 9.20 Estimates of multiple linear regression model in Alipurduar forest division Linear regression model Coeff

Std. error

t

P > |t|

− 0.211

0.040

− 5.23

0.000

Educational index

0.012

0.051

0.24

0.809

Caste (SC = 1, ST = 2, GEN = 3, OTH = 4)

0.002

0.012

0.16

0.874

Total land holding (in acre)

0.041

0.016

2.52

0.013

% of forest income to total income

0.005

0.001

0.50

0.621

Trust between communities and Forest Dept. (If No = 1, Yes = 2, Don’t know = 3)

0.231

0.023

10.05

0.000

Constant

0.001

0.001

1.27

0.208

Enforcement index

Source Author’s calculation

Number of obs = 151 F (6144) = 53.47 Prob > F = 0.000 R-squared = 0.69 Adj R-square = 0.67

9.5 Comparative Analysis of Forest Governance Across Different Forest …

197

9.5 Comparative Analysis of Forest Governance Across Different Forest Divisions of South Bengal and North Bengal The forest governance indices across four forest divisions along with t-statistic are presented in Table 9.21. It is observed that the mean forest governance index for the Purulia forest division is higher than that of the Bankura forest division, and such mean difference is significant (Table 9.21). The mean forest governance index for the Purulia forest division is also higher than that of the Rupnarayan forest division and their mean difference is significant while the mean forest governance index for the Purulia forest division is higher than that of the Alipurduar forest division and the mean difference is significant as per t-values. On the other hand, the mean forest governance index for the Bankura forest division is higher than that of the Rupnarayan forest division and Alipurduar forest division and their mean difference is significant. It is also observed from Table 9.21 the mean forest governance index is lower than that of the Alipurduar forest division and the mean difference is significant. Figure 9.1 shows the forest governance in different forest divisions of South Bengal and North Bengal. It is found that the forest governance index for South Bengal is lower than that of North Bengal (Table 9.21). The forest governance index is highest in the Alipurduar forest division followed by the Purulia, Bankura, and Paschim Medinipur. The rule of law index is highest for the Purulia forest division and lowest for the Paschim Medinipur forest division (Fig. 9.2). In terms of transparency index Bankura forest division is found to be Table 9.21 Forest governance index across four forest divisions in South and North Bengal

Forest divisions

Mean forest governance index

Purulia

0.446

Bankura

0.397

Purulia

0.446

Rupnarayan

0.266

Purulia

0.446

Alipurduar

0.482

Bankura

0.397

Rupnarayan

0.266

Bankura

0.397

Alipurduar

0.482

Rupnarayan

0.266

Alipurduar

0.482

South Bengal

0.374

North Bengal

0.483

Source Author’s calculation

t-value

P value

4.586

0.000

14.769

0.000

3.308

0.000

10.677

0.000

7.685

0.000

17.221

0.000

11.033

0.000

198

9 Measurement of Forest Governance in South Bengal and North Bengal …

Forest Governence Index 0.600 0.500 0.400 0.300 0.200 0.100 0.000 Purulia

Bankura

Paschim Medinipur

Alipurduar

Fig. 9.1 Forest governance indices for four forest divisions

Rule of Law 0.300 0.250 0.200 0.150 0.100 0.050 0.000 Purulia

Bankura

Paschim Medinipur

Alipurduar

Fig. 9.2 Rule of law indices for four forest divisions

the highest followed by the Alipurduar forest division, the Purulia forest division and Paschim Medinipur forest division (Fig. 9.3). In terms of accountability index Alipurduar forest division is highest followed by Purulia forest division, Bankura forest division and Paschim Medinipur forest division (Fig. 9.4). The participation index is highest for Alipurduar forest division and lowest for Bankura forest division (Fig. 9.5). The inclusive and equitable index is highest for Alipurduar forest division followed by Purulia forest division, Bankur forest division and Paschim Medinipur forest division (Fig. 9.6). In terms of effective and efficient index, Purulia forest division seems to be highest and Alipurduar forest division is lowest (Fig. 9.7).

9.5 Comparative Analysis of Forest Governance Across Different Forest …

199

Transparency 0.800 0.700 0.600 0.500 0.400 0.300 0.200 0.100 0.000 Purulia

Bankura

Paschim Medinipur

Alipurduar

Fig. 9.3 Transparency indices for four forest divisions

Accountability 0.450 0.400 0.350 0.300 0.250 0.200 0.150 0.100 0.050 0.000 Purulia

Bankura

Paschim Medinipur

Alipurduar

Fig. 9.4 Accountability indices for four forest divisions

Participation Index 1.000 0.900 0.800 0.700 0.600 0.500 0.400 0.300 0.200 0.100 0.000 Purulia

Bankura

Paschim Medinipur

Fig. 9.5 Participation indices for four forest divisions

Alipurduar

200

9 Measurement of Forest Governance in South Bengal and North Bengal …

Inclusive and Equitable 0.900 0.800 0.700 0.600 0.500 0.400 0.300 0.200 0.100 0.000 Purulia

Bankura

Paschim Medinipur

Alipurduar

Fig. 9.6 Inclusive and equitable indices for four forest divisions

Efficient and Effective 0.600 0.500 0.400 0.300 0.200 0.100 0.000 Purulia

Bankura

Paschim Medinipur

Alipurduar

Fig. 9.7 Efficient and effective indices for four forest divisions

The forest governance with different indicators in South and North Bengal forest divisions is presented in Table 9.22 and in Fig. 9.8. This shows that the forest governance in North Bengal is higher than in South Bengal forest division. On the other hand, the transparency, accountability, participation and inclusive and equitable index values in North Bengal forest division are higher compared to the South Bengal forest division. It is also observed from Table 9.23 that the forest governance is medium for both South Bengal and North Bengal forest divisions. Rule of law is poor for both the forest divisions; transparency is good for North Bengal forest division and participation is excellent for North Bengal forest division (Table 9.23).

9.5 Comparative Analysis of Forest Governance Across Different Forest …

201

Table 9.22 Forest governance index for South Bengal and North Bengal forest divisions Forest Rule division of law

Transparency Accountability Participation Inclusive Efficient Governence and and equitable effective

South Bengal

0.200 0.432

0.259

0.551

0.494

0.309

0.374

North Bengal

0.172 0.545

0.385

0.897

0.775

0.126

0.483

Source Author’s calculation from field survey

South Bengal

ce ve rn en Go

Effi Eff cien ec t a tiv nd e

Pa rti

cip

ati

on In c Eq lusiv ui e ta an bl d e

y bi lit un ta co Ac

Tr

an

Ru

le

sp

of

ar e

La

nc

w

y

1.000 0.800 0.600 0.400 0.200 0.000

North Bengal

Fig. 9.8 Forest governance and its indicators in South and North Bengal forest divisions

Table 9.23 Categorization of forest governance index in South Bengal and North Bengal forest divisions Forest Rule Transparency Accountability Participation Inclusive Efficient Governence division of and and law equitable effective South Bengal

Poor Medium

Medium

Good

Medium

Medium Medium

North Bengal

Poor Good

Medium

Excellent

Excellent Poor

Medium

Source Author’s calculation

Summing Up First, the Forest Governance Index (FGI) at the household level in the forest division of Alipurduar is found to be the highest (0.483) followed by Purulia (0.446), Bankura (0.397) and Rupnarayan of Paschim Medinipur (0.302). The forest governance of the Purulia forest division, Bankura (south) forest division, and Alipurduar forest division is found to be medium while poor forest governance of the Rupnarayan forest division is observed. Second, in terms of different indicators of forest governance, the rule of law index is the highest for the Purulia forest division and lowest for the Rupnarayan

202

9 Measurement of Forest Governance in South Bengal and North Bengal …

forest division of Paschim Medinipur. In terms of the transparency index Bankura forest division is found to be the highest followed by the Alipurduar forest division, Purulia forest division, and Paschim Medinipur forest division. In terms of the accountability index Alipurduar forest division is found to be the highest followed by the Purulia forest division, Bankura forest division, and the Rupnarayan forest division of Paschim Medinipur. The participation index is highest for the Alipurduar forest division and lowest for the Bankura forest division. The inclusive and equitable index is highest for the Alipurduar forest division followed by the Purulia forest division, Bankura forest division, and Rupnarayan forest division of Paschim Medinipur. In terms of effective and efficient index, the Purulia forest division seems to be the highest and the Alipurduar forest division is the lowest. Third, the factors which affect forest governance in Purulia and Bankura (south) forest divisions are enforcement index, caste, forest income to total income, and trust between the forest department and local people. Fourth, enforcement index, trust, and landholdings are determining factors of forest governance in the Rupnarayan forest division of Paschim Medinipur and Alipurduar forest division. Fifth, the forest governance index in the North Bengal forest division is higher than in the South Bengal forest division because of the good maintenance of transparency, accountability, participation, and inclusive and equity the North Bengal forest division compared to the South Bengal forest division.

Chapter 10

Forest Dependency and Forest Governance in South Bengal and North Bengal Forest Divisions

Abstract This chapter examines the forest dependency of households measured by the forest dependency index across three forest divisions of South Bengal say Purulia forest division, Bankura (south) forest division, and Rupnarayan forest division in Paschim Medinipore, and one forest division say Aliporeduar forest division in North Bengal. Besides, this chapter examines the impact of forest governance on forest dependency with the help of a step-wise regression model across different forest divisions in South Bengal and North Bengal.

10.1 Forest Dependency Index (FDI) in Purulia Forest Division The Forest dependency of the sample households is measured by the forest dependency index. The indicators used for measuring the forest dependency index have been discussed in Chap. 3 (Table 3.3). The results of the forest dependency indices of the main indicators and subindicators are presented in Table 10.1. It is observed that the Forest Dependence Index (FDI) of the households as a whole in the district of Purulia is found to be 0.38 (Table 10.1). It is revealed that the forest collection importance index is found to be the highest (0.655), followed by the physical asset index (0.490), wealth index (0.287), and non-forest livelihood strategy index (0.089). The number of households is classified into less forest dependence, moderate dependence, and high dependence based on the values of forest dependence indices. Less forest-dependent households are those who belong to the forest dependence index values less than equal to 0.2. Moderate forest-dependent households are in between forest dependency index of 0.21 and 0.4 and high forest-dependent households are those who belong to more than forest dependent values of 0.40 (Table 10.2). It is observed from Table 10.2 that about 13% of households are less forestdependent, 25% of households are moderate forest dependent and 62% of households are high forest-dependent. Figure 10.1 indicates the percentage of forestdependent households. The study reveals that more than 60% of households are highly forest-dependent (Fig. 10.1). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. P. Basu, Governance and Institution in the Indian Forest Sector, https://doi.org/10.1007/978-3-031-34746-7_10

203

204

10 Forest Dependency and Forest Governance in South Bengal and North …

Table 10.1 Forest dependence index (FDI) in Purulia forest division Main index

Sub index

Value

Forest collection importance

Collected forest products

0.445

Household dependent on forest

0.865 0.655

Physical asset

Distance from home to forest

0.341

Avg. Time spent by HHs for collecting NTFP

0.482

Households engage in the collection of NTFP

0.494

Gender engages in the collection of NTFP

0.642 0.490

Wealth

Total land holding including forest land

0.126

Livestock

0.790

Type of house

0.223 0.655

Non-forest livelihood strategies

Agricultural income

0.176

Business income

0.048

Service income

0.036

Monthly wage

0.157

Handicraft income

0.029 0.089

FDI index

0.472

Source Author’s calculation from primary data

Table 10.2 Distribution of forest dependent households in the district of Purulia Forest dependency index

Assigned attribute

≤ 0.20

Less forest dependence

0.21–0.40

Moderate forest dependence

> 0.40

High forest dependence

Households Number

%

34

13.49

63

25.00

155

61.51

Source Author’s calculation from primary data

10.2 Relation Between Forest Dependency and Forest Governance in Purulia Forest Division The objective of this section is to examine the impact of forest governance on forest dependency in the Purulia forest division in South West Bengal. We have taken six indicators of governance rule of law, transparency, accountability, participation, inclusive and equitable, and efficiency and effectiveness. In addition to governance, we have also taken four socio-economic variables like education, caste, land holdings,

10.2 Relation Between Forest Dependency and Forest Governance in Purulia …

205

Table 10.3 Basic statistics of the stepwise regression model Mean

Std. Dev.

Min

Max

0.380

0.113

0.066

0.569

Rule of law index (RLI)

0.263

0.199

0

1

Transparency index (TI)

0.368

0.170

0.083

0.75

Accountability index (AI)

0.317

0.178

0

0.667

Participation index (PI)

0.643

0.111

0.146

0.838

Inclusive and equitable index (IEI)

0.579

0.495

0

1

Efficient and effective index (EEI)

0.503

0.255

0

1

Educational index (EDUI)

0.166

0.148

0

0.683

Caste (C) (SC = 1, ST = 2, GEN = 3, OTH = 4)

2.409

1.113

1

4

Landholding (in acre) (LH)

2.534

3.145

0

20.04

24.000

15.102

0

100

Dependent variable FDI index (FDI) Independent variable

Forest income to total income (FI) Source Author’s calculation

Purulia forest division 70 60 50 40 30 20 10 0 Less Dependency

Moderate

High Dependency

Fig. 10.1 Percentage of forest-dependent households in the Purulia forest division

and forest income. Thus, we have ten explanatory variables and forest dependency index (FDI) as the dependent variable. To avoid the multi-collinearity problems we have applied a step-wise regression model. The basic statistics of the dependent and explanatory variables are presented in Table 10.3. In Table 10.4 the included and excluded variables are in the step-wise regression model. In the first step we have one explanatory variable say forest income (FI) has been included as indicated by Table 10.4 while the rest nine variables are excluded. In the second step, we have included two variables like forest income (FI) and land holding (LH) while the rest eight are excluded (Table 10.4). In the third step, we have included three explanatory variables like forest income (FI), land holding (LH), and Caste (C) while the rest seven variables are excluded. In the fourth step, we have included four explanatory variables like forest income (FI), land holding (LH), Caste (C), and education (EDU),

206

10 Forest Dependency and Forest Governance in South Bengal and North …

Table 10.4 Included and excluded variables in the stepwise regression

Number of expandable variable = 10 Included variables

Excluded variables

1st step

01 (FI)

9

2nd step

02 (FI, LH)

8

3rd step

03 (FI, LH, C)

7

4th step

04 (FI, LH, C, EDU)

6

5th step

05 (FI, LH, C, EDU, TI)

5

6th step

06 (FI, LH, C, EDU, TI, RLI)

4

Source Author’s calculation

and the rest six explanatory variables are excluded. In the fifth, we have included five explanatory variables like forest income (FI), land holding (LH), Caste (C), education (EDU), and transparency index (T) while the other five variables are excluded. In the sixth step, we have included six explanatory variables like forest income (FI), land holding (LH), Caste (C), education (EDU), transparency index (T), and rule of law (RL) while other four variables are excluded (Table 10.4). In step 1 there is a positive and significant impact of forest income on forest dependency. This means that forest dependency will rise with the rise in income and vice-versa. In this step R2 = 0.30 and F = 110. The nature of the tolerance value and VIF value shows that there is no multi-collinearity (Table 10.4). In step 2, there is a positive and significant effect of land holdings on forest dependency. The households with high land holdings are dependent on forestry. It seems to the fact that the study area is drought-prone and agriculture is not remunerating and the people are compelled to depend on forestry as an alternative source of livelihood. In step 2, R2 = 0.42 which is higher than that under step 1. The values of Tolerance and VIF show that there is no multi-collinearity. In step 3, forest income and landholdings have a positive impact on forest dependency. But caste is negatively associated with forest dependency and it is significant. This means that the schedule caste and schedule tribes are dependent on forest compared to OBC and general caste households. The value of R2 = 0.48 implies the improvement of R2 over R2 in step 2. In step 4, we have four explanatory variables included. We find that the coefficient of education is negative and significant. This means that the households that have less education are more dependent on forestry and vice-versa. Here R2 = 0.49 and F = 61. In step 5, five explanatory variables are significant. Out of five explanatory variables, two variables like forest income and land holdings have positive and significant while the other three variables have a negative and significant impacts on forest dependency. The coefficient of the transparency index is negative and significant. This shows that forest dependency goes down if decisions are taken in higher transparent ways and vice-versa. Here R2 = 0.499 which is an improvement over R2 in step 4.

10.2 Relation Between Forest Dependency and Forest Governance in Purulia …

207

In step 6, six explanatory variables are significant. In step 6, the coefficient of the rule of law is negative and significant. This means that forest dependency rises with the existence of a weak rule of law while it goes down with maintaining a strong rule of law. Here R2 = 0.50 and F = 44 (Table 10.5).

Table 10.5 Estimates the stepwise regression model in the Purulia forest division Step 1: FDI = t=

0.280* + 0.004FI* (24.836) (10.502)

Tolerance = R2

=

F=

1.0 0.30 110

VIF = Step 2: FDI = t=

1.0 0.238* + 0.005FI* + 0.013LH* (20.200) (12.539)

(7.285)

(0.976)

(0.976)

Tolerance = R2 =

0.42

F=

93

VIF = Step 3: FDI = t=

(1.025)

0.313* + 0.004FI* + 0.014LH* − 0.026C* (17.067) (10.551)

(8.462)

(− 5.181)

(0.858)

(0.944)

(0.825)

Tolerance = R2 =

0.41

F=

77

VIF = Step 4: FDI = t=

(1.166) (17.185) (9.471)

=

(1.059)

(1.197)

0.330* + 0.004FI* + 0.015LH* − 0.024C* − 0.103EDU*

Tolerance = R2

(1.025)

(0.788)

(8.836)

(− 4.756)

(0.93)

(0.815)

(1.269) (1.075) (1.217) (1.212)

F=

61

t=

0.354* + 0.004FI* + 0.014LH* − 0.025C* − 0.103EDU* − 0.068 T* (16.186) (9.794)

(8.684)

(− 4.927)

(0.757)

(0.923)

(0.811)

(0.825)

(0.91)

(1.321)

(1.083)

(1.232)

(1.222)

(1.088)

Tolerance = R2 = VIF =

(0.825)

0.48

VIF = Step 5: FDI =

(− 2.714)

(− 2.744)

(− 2.210)

0.494 (continued)

208

10 Forest Dependency and Forest Governance in South Bengal and North …

Table 10.5 (continued) F= Step 6: FDI = t=

50 0.381*+0.004FI*+0.015LH*−0.024C*−0.101EDU*−0.099 T*−0.069RL* (15.730) (9.854) (8.832) (− 4.792) (− 2.70)

Tolerance =

(0.737)

R2 =

0.509

F=

44

VIF =

(− 3.005) (− 2.490)

(0.923)

(0.807)

(0.814)

(0.789)

(0.825)

(1.321) (1.024)

(1.238)

(1.203)

(1.267)

(1.212)

Note *Implies significance at 1% level; Source Author’s calculation

10.3 Forest Dependence Index (FDI) in Bankura (South) Forest Division It is observed that the Forest Dependence Index (FDI) of the households as a whole in the Bankura forest division is found to be 0.53 (Table 10.6). It is revealed that the forest collection importance index is found to be the highest (0.793), followed by the wealth index (0.673), physical asset index (0.549), and non-forest livelihood strategy index (0.092). The less forest dependence, moderate forest dependence, and high forestdependence households are classified based on the forest dependence index values as described above. It is observed from Table 10.7 that nearly 9% of households are less forest dependent, 10% of households are moderate forest dependent and 89% of households are high forest dependent. Figure 10.2 shows the categorization of forest-dependent households. Thus, the study reveals that nearly 90% of households are highly forest dependent.

Table 10.6 Forest dependence index (FDI) in Bankura (south) forest division Main index

Sub index

Value

Forest collection importance

Collected forest products

0.607

Household dependent on forest

0.978 0.793

Physical asset

Distance from home to forest

0.402

Avg. Time spent by HHs for collecting NTFP

0.447

Households engaged in the collection of NTFP

0.516

Gender engages in the collection of NTFP

0.833 0.549 (continued)

10.3 Forest Dependence Index (FDI) in Bankura (South) Forest Division

209

Table 10.6 (continued) Main index

Sub index

Value

Wealth

Total land holding including forest land

0.904

Livestock

0.908

Type of house

0.208 0.673

Non-forest livelihood strategies

Agricultural income

0.201

Business income

0.024

Service income

0.022

Monthly wage

0.206

Handicraft income

0.004 0.092

Forest dependence index (FDI)

0.527

Source Author’s calculation from primary data Table 10.7 Distribution of forest dependent households in Bankura (south) forest division

Forest dependency index

Assigned attribute

Households

≤ 0.20

Less forest dependence

0.21–0.40

Moderate forest dependence

> 0.40

High forest dependence

Number 2

0.877

23

10.088

203

89.035

Source Author’s calculation from primary data

Bankura(South) 100 80 60 40 20 0 Less Dependency

Moderate

%

High Dependency

Fig. 10.2 Percent of forest-dependent households in Bankura (south) forest division

210

10 Forest Dependency and Forest Governance in South Bengal and North …

10.4 Relation Between Forest Dependence and Forest Governance in Bankura (South) Forest Division The objective of this section is to examine the impact of forest governance on forest dependency in the Bankura (south) forest division in South West Bengal. We have taken six indicators of governance rule of law, transparency, accountability, participation, inclusive and equitable, and efficiency and effectiveness. In addition, we have also taken four socio-economic variables like education, caste, land holdings, and forest income. Thus, we have ten (10) explanatory variables and forest dependency index (FDI) as dependent variable. To avoid the multi-collinearity problem we have applied a step-wise regression model. The basic statistics of the dependent and explanatory variables are presented in Table 10.8. In Table 10.8 we have included and excluded variables in the step-wise regression model. From Table 10.8 in the first step we have one explanatory variable say forest income (FI) has been included while the rest nine are excluded. In the second step, we have included two variables like forest income (FI) and efficient and effective index (EE) while the rest eight are excluded. In the third step, we have included three explanatory variables like forest income (FI), efficient and effective index (EE), and rule of law (RL) while the rest seven variables are excluded. In the fourth step, we have included four explanatory variables like forest income (FI), efficient and effective index (EE), rule of law (RL), and transparency (T), and the other six explanatory variables are excluded. In the fifth, we have included five explanatory variables like forest income (FI), efficient and effective index (EE), rule of law (RL), transparency (T), and land holding (LH) while the other five variables are excluded (Table 10.9). Table 10.8 Basic statistics of the stepwise regression model Mean

Std. Dev.

Min

Max

0.527

0.086

0.066

0.676

Rule of law index (RLI)

0.170

0.160

0

0.833

Transparency index (TI)

0.703

0.239

0.25

1

Accountability index (AI)

0.231

0.120

0

0.667

Participation index (PI)

0.470

0.141

0.021

0.808

Inclusive and equitable index (IEI)

0.509

0.501

0

1

Efficient and effective index (EEI)

0.298

0.222

0

1

Educational index (EDUI)

0.247

0.145

0

0.678

Caste (SC = 1, ST = 2, GEN = 3, OTH = 4) (C)

0.096

0.296

0

1

Land holding (in acre) (LH)

4.761

4.87

0

42

25.706

13.780

0

60

Dependent variable FDI index (FDI) Independent variable

% of forest income to total income (FI) Source Author’s calculation

10.4 Relation Between Forest Dependence and Forest Governance … Table 10.9 Included and excluded variables in the stepwise regression model

211

Number of expandable variable = 10 Included variables

Excluded variables

1st step

01 (FI)

9

2nd step

02 (FI, EE)

8

3rd step

03 (FI, EE, RL)

7

4th step

04 (FI, EE, RL, T)

6

5th step

05 (FI, EE, RL, T, LH)

5

Source Author’s calculation

Estimates of stepwise regression model are presented in Table 10.10. We have presented R2 , F value, and Tolerance and VIF values in Table 10.10. Tolerance and VIF values reflect the nature of multi-collinearity present in the model. In step 1 there is a positive and significant impact of forest income on forest dependency. This means that forest dependency rises with the rise in the income and vice-versa. In this step R2 = 0.14 and F = 37. The nature of tolerance and VIF shows that there is no multi-collinearity. In step 2, there is a negative and significant impact of effective and efficient on forest dependency. The high effective and efficient forest governance leads to less forest dependency. In step 2, R2 = 0.18 which is higher than that under step 1. The values of Tolerance and VIF show that there is no multi-collinearity. In step 3, forest income and rule of law have positive impact on forest dependency. But effective and efficient management is negatively associated with forest dependency and it is significant. The value of R2 = 0.23 which implies the improvement of R2 over R2 in the step 2. In step 4, we have four explanatory variables included the coefficient of transparency is negative and significant. This means that high transparency in forest governance leads to less forest dependency. Here R2 = 0.25 and F = 20. In step 5, five explanatory variables are significant. Out of five explanatory variables, three variables like forest income, rule of law and land holding have positive and significant impact on forest dependency while other two variables have negative and significant impact on forest dependency. The coefficient of transparency index is negative and significant it shows that forest dependency goes down if decisions are taken with higher transparent ways and vice-versa. Here R2 = 0.27 which is improvement over R2 in step 4.

212

10 Forest Dependency and Forest Governance in South Bengal and North …

Table 10.10 Estimates of stepwise regression model in the Bankura (south) forest division Step 1:

FDI =

0.466* + 0.002FI*

t=

(41.418) (6.082)

Tolerance =

1.0

R2 =

0.14

F=

37

VIF = Step 2:

1.0

FDI =

0.495* + 0.002FI* − 0.086EE*

t=

(36.687) (5.894) (− 3.640)

Tolerance =

(0.992) (0.992)

R2 =

0.18

F=

26

VIF = Step 3:

(1.008) (1.008)

FDI =

0.487 + 0.002FI* − 0.126EE* − 0.135RL*

t=

(36.773) (5.751) (− 5.029)

Tolerance = R2 =

0.23

F=

24

VIF = Step 4:

(1.014)

(0.83)

(1.213)

(1.205)

FDI =

0.522* + 0.002FI* − 0.100EE* + 0.140RL* − 0.062T*

t=

(28.516) (5.841)

Tolerance =

(0.986)

R2 =

0.25

F=

20

VIF = Step 5:

(3.909)

(0.986) (0.824)

(1.014)

(− 3.808)

(4.128)

(0.72)

(− 2.707)

(0.826)

(1.388)

(0.831)

(1.21)

(1.203)

FDI =

0.502 + 0.002FI* − 0.098EE* + 0.142RL* − 0.64T* + 0.003LH*

t=

(25.819) (6.465) (− 3.771)

Tolerance =

(0.909) (0.72) (0.826) (0.829) (0.919)

R2 =

0.27

F=

18

VIF =

(1.101) (1.389) (1.21) (1.206) (1.088)

(4.224)

(− 2.874)

(2.768)

Note * implies significance at 1% level; Source Author’s calculation

10.5 Forest Dependence Index (FDI) of the Households in Rupnarayan Forest Division of Paschim Medinipur The forest dependence indices of the households are presented in Table 10.11. It is observed that the Forest Dependence Index (FDI) of the households as a whole in the forest division of Paschim Medinipur is found to be 0.59 (Table 10.11). It is

10.5 Forest Dependence Index (FDI) of the Households in Rupnarayan Forest …

213

revealed that the non-forest livelihood strategy index is found to be highest (0.698), followed by forest collection importance index (0.670), wealth index (0.554) and physical asset index (0.424). The households are classified into less forest dependence, moderate dependence and high dependence on the basis of the values of forest dependence indices as described above. It is observed from Table 10.12 that about 0.5% of households are less forest dependent, 6.5% of households are moderate forest dependent and remaining 93% of households are highly forest dependent. Figure 10.3 shows the percentage of forest dependent households. Thus, the study reveals that more than 90% of households are highly forest dependent. Table 10.11 Forest dependence index (FDI) in Rupnarayan forest division of Paschim Medinipur Main index

Sub index

Value

Forest collection importance

Collected forest products

0.410

Household dependent on forest

0.930 0.670

Physical asset

Distance from home to forest

0.114

Avg. Time spend by HHs for collecting NTFP

0.368

Household engage in collection NTFP

0.375

Gender engage in collection NTFP

0.840 0.424

Wealth

Total land holding including forest land

0.943

Livestock

0.103

Type of house

0.614 0.554

Non forest livelihood strategies

Agricultural income

0.898

Business income

0.985

Service income

0.968

Monthly wage

0.640

Handicraft income

0.000 0.698

Forest dependency index (FDI) Source Author’s calculation from primary data

0.558

214

10 Forest Dependency and Forest Governance in South Bengal and North …

Table 10.12 Distribution of forest dependent households in the forest division of Paschim Medinipur

Forest dependency index

Assigned attribute

Households

≤ 0.20

Less forest dependence

0.21–0.40

Moderate forest dependence

> 0.40

High forest dependence

Number

%

1

0.469

14

6.573

198

92.958

Source Author’s calculation from primary data

Paschim Medinipur 25 20 15 10 5 0 Less Dependency

Moderate

High Dependency

Fig. 10.3 Percentage of forest dependent households in Rupnarayan forest division

10.6 Relation Between Forest Dependence and Forest Governance in Rupnarayan Forest Division of Paschim Medinipore This section attempts to examine the impact of forest governance on forest dependency in the Rupnarayan forest division in South West Bengal. We have taken six indicators of governance like Rule of law, transparency, accountability, participation, inclusive and equitable and efficiency and effectiveness. In addition we have also taken four socio-economic variables like education, caste, land holding and forest income and the impact on forest dependency. Thus, we have ten explanatory variables and forest dependency index (FDI) as dependent variable. In order to avoid multi-collinearity problem we have applied stepwise regression model. The basic statistics of the dependent and explanatory variables are presented in Table 10.13. The included and excluded variables are presented in Table 10.14. The estimates of the stepwise regression model are provided in Table 10.13. In the first step we have one explanatory variable say forest income (FI) has been included while the rest nine variables are excluded. In the second step we have included two variables like forest income (FI) and rule of law (RL) while the rest eight are excluded. In the third step

10.6 Relation Between Forest Dependence and Forest Governance …

215

we have included three explanatory variables like forest income (FI), rule of law (RL) and land holding (LH) while the rest seven variables are excluded. Estimates of a stepwise regression model are presented in Table 10.15. In step 1 there is a positive and significant impact of forest income on forest dependency. This means that forest dependency increases with the increase in income and vice-versa. In this step R2 = 0.31 and F = 93.014. The nature of tolerance and VIF shows that there is no multi-collinearity. In step 2, there is a positive and significant effect of rule of law on forest dependency. This means that forest dependency rises with maintaining of a strong rule of law while it goes down with maintaining a weak rule of law. In step 2, R2 = 0.33 which is higher than that of step 1. The values of Tolerance and VIF show that there is no multi-collinearity. In step 3, forest income and rule of law have a positive impact on forest dependency. But land holding is negatively associated with forest dependency and it is significant. This means that higher landholding leads to less dependency on the forest. The value of R2 = 0.35 implies the improvement of R2 over R2 in step 2. Table 10.13 Basic statistics of the stepwise regression model Mean

Std. Dev.

Min

Max

0.586

0.091

0.162

0.793

Rule of law index (RLI)

0.159

0.163

0

0.833

Transparency index (TI)

0.218

0.141

0

0.75

Accountability index (AI)

0.220

0.149

0

0.833

Participation index (PI)

0.530

0.171

0.042

1

Inclusive and equitable index (IEI)

0.376

0.485

0

1

Efficient and effective index (EEI)

0.091

0.195

0

1

Educational index (EDUI)

0.264

0.201

0

0.917

Caste (SC = 1, ST = 2, GEN = 3, OTH = 4) (C)

2.136

0.774

1

4

Land holding (in acre) (LH)

2.327

3.923

0

42

% of forest income to total income (FI)

0.194

0.111

0

0.519

Dependent variable FDI index (FDI) Independent variable

Source Author’s calculation

Table 10.14 Included and excluded variables in the stepwise regression model

Number of expandable variable = 10 Included variables

Excluded variables

1st step

01 (FI)

9

2nd step

02 (FI, RL)

8

3rd step

03 (FI, RL, LH)

7

Source Author’s calculation

216

10 Forest Dependency and Forest Governance in South Bengal and North …

Table 10.15 Estimates of the stepwise regression model in Rupnarayan forest division Step 1:

FDI =

0.499 + 0.451FI*

t=

(47.674) (9.644)

Tolerance =

Step 2:

1.0

R2

0.31

F

93.014

VIF =

1.0

FDI =

0.484 + 0.454FI* + 0.089RL*

t=

(42.108)

Tolerance = R2

0.33

F

52.059

VIF = Step 3:

(9.850)

(2.831)

1.0

1.02

1.0

1.0

FDI =

0.500 + 0.417FI* + 0.082RL* − 0.003LH*

t=

(38.172) (8.714)

(2.632)

1.0

1.0

1.0

1.01

1.0

Tolerance = R2

0.35

F

37.471

VIF =

1.0

(− 2.424)

Note * implies significance at 1% level; Source Author’s calculation

10.7 Forest Dependence Index (FDI) of the Households in Alipurduar Forest Division, North Bengal It is observed that the Forest Dependence Index (FDI) of the households as a whole in the forest division of Alipurduar stands to be 0.539 (Table 10.16). It is revealed that the non-forest livelihood strategy index is found to be the highest (0.673), followed by the wealth index (0.538), forest collection importance index (0.516), and physical asset index (0.429). The households are classified into less forest dependence, moderate dependence, and high dependence based on the values of forest dependence indices as described above. It is observed from Table 10.17 that about 0.662% of households are less forest dependent, 19.20% of households are moderately forest-dependent and the remaining 80% of households are highly forest dependent. Figure 10.4 shows the percentage of forest-dependent households. Thus, the study reveals that about 80% of households are highly forest dependent.

10.7 Forest Dependence Index (FDI) of the Households in Alipurduar Forest …

217

Table 10.16 Forest dependence index (FDI) in Alipurduar forest division Main index

Sub index

Value

Forest collection importance

Collected forest products

0.243

Household dependent on forest

0.788 0.516

Physical asset

Distance from home to forest

0.278

Avg. Time spent by HHs for collecting NTFP

0.334

Households engages in the collection of NTFP

0.430

Gender engages in the collection of NTFP

0.673 0.429

Wealth

Total land holding including forest land

0.860

Livestock

0.715

Type of house

0.038 0.538

Non-forest livelihood strategies

Agricultural income

0.861

Business income

0.895

Service income

0.963

Monthly wage

0.648

Handicraft income

0.000 0.673

FDI

0.539

Source Author’s calculation from primary data

Table 10.17 Distribution of forest dependent households in Alipurduar forest division, North Bengal

Forest dependency index

Assigned attribute

≤ 0.20

Less forest dependence

0.21–0.40

Moderate forest dependence

> 0.40

High forest dependence

Households Number

%

1

0.662

29

19.205

121

80.132

Source Author’s calculation from primary data

218

10 Forest Dependency and Forest Governance in South Bengal and North …

Alipurduar 90 80 70 60 50 40 30 20 10 0 Less Dependency

Moderate

High Dependency

Fig. 10.4 Percentage of forest-dependent households in Alipurduar forest division

10.8 Relation Between Forest Dependence and Forest Governance in Alipurduar Forest Division, North Bengal This section attempts to examine the impact of forest governance on forest dependency in the Alipurduar forest division in North Bengal. We have taken six indicators of governance like Rule of law, transparency, accountability, participation, inclusive and equitable, and efficiency and effectiveness. In addition, we have also taken four socio-economic variables like education, caste, land holdings, and forest income. Thus, we have taken these ten explanatory variables and the forest dependence index (FDI) as the dependent variable. The multi-collinearity problem has been avoided by using a stepwise regression model. The basic statistics of the dependent and explanatory variables are presented in Table 10.18. The included and excluded variables are presented in Table 10.19. The estimates of the stepwise regression model are provided in Table 10.20. In the first step we have one explanatory variable say forest income (FI) has been included while the rest nine variables are excluded. In the second step we have included two variables like forest income (FI) and land holdings (LH) while the rest eight are excluded. In the third step we have included three explanatory variables like forest income (FI), land holding (LH) and education index (EDUI) while the rest seven variables are excluded. Estimates of stepwise regression model are presented in Table 10.20. In the step 1 there is a positive and significant impact of forest income on forest dependency. This means that forest dependency increases with the increase in the forest income and vice-versa. In this step R2 = 0.39 and F = 96.329. The nature of tolerance and VIF shows that there is no multi-collinearity.

10.8 Relation Between Forest Dependence and Forest Governance …

219

Table 10.18 Basic statistics of the model Mean

Std. Dev.

Min

Max

0.539

0.118

0.182

0.745

Rule of law index (RLI)

0.172

0.174

0

0.722

Transparency index (TI)

0.897

0.103

0.529

1

Accountability index (AI)

0.385

0.109

0

0.833

Participation index (PI)

0.103

0.103

0

0.471

Inclusive and equitable index (IEI)

0.775

0.419

0

1

Efficient and effective index (EEI)

0.126

0.171

0

0.667

Educational index (EDUI)

0.283

0.157

0

0.636

Caste (SC = 1, ST = 2, GEN = 3, OTH = 4) (C)

2.232

0.647

1

3

Land holding (in acre) (LH)

0.43

0.48

0

3.06

% of forest income to total income (FI)

7.839

7.1

0

34.717

Dependent variable FDI index (FDI) Independent variable

Source Author’s calculation

Table 10.19 Included and excluded variables in the stepwise regression model Number of expandable variable = 10 Included variables

Excluded variables

1st step

1 (FI)

9

2nd step

2 (FI, LH)

8

3rd step

3 (FI, LH, EDUI)

7

Source Author’s calculation

In step 2, there is a negative and significant effect of landholdings on forest dependency. This means that the forest dependency rises with fall in landholdings. This means that the small and marginal farms are highly forest dependence. In step 2, R2 = 0.44 which is higher than that of step 1. The values of Tolerance and VIF show that there is no multi-collinearity. In step 3, forest income has positive impact on forest dependency. But a land holding is negatively associated with forest dependency and it is significant. This means that higher land holding leads to less dependency on forest. Education index has negative impact on forest dependency. This means that the forest dependency increases among the households whose education is not sufficient. The value of R2 = 0.44 which implies the improvement of R2 over R2 in the step 2.

220

10 Forest Dependency and Forest Governance in South Bengal and North …

Table 10.20 Estimates of stepwise regression model in Alipurduar forest division Step 1:

Step 2:

FDI =

0.457 + 0.0101FI*

t=

(40.787) (9.815)

Tolerance =

1.0

R2

0.39

F

96.32

VIF =

1.0

FDI =

0.480 + 0.011FI* − 0.056LH*

t=

(38.77) (10.388) (− 3.686)

Tolerance =

0.998

R2

0.44

F

59.024

VIF = Step 3:

1.002

0.998

1.002

FDI =

0.507 + 0.011FI* − 0.005LH* − 0.103EDUI*

t=

(29.291) (10.65) (− 3.69)

(− 2.262)

0.993

0.997

0.995

1.166

1.059

Tolerance = R2

0.46

F

42.148

VIF =

1.197

Source Author’s calculation

Summing Up The Forest Dependence Index (FDI) of the households as a whole in the forest division of Purulia is found to be 0.38. In this forest division, about 62% of households are highly forest dependent. The relation between forest dependency and forest governance has been analyzed with the help of a stepwise regression model. The determining factors responsible for forest dependency have been identified which are forest income (FI), land holdings (LH), caste (C), education (EDU), transparency index, and rule of law (RL). Similarly, the Forest Dependence Index (FDI) of the households as a whole in the forest division of Bankura (south) is found to be 0.53. In this forest division, about 89% of households are highly forest dependent. The factors affecting forest dependence are identified using a stepwise regression model. These factors are forest income (FI), efficient and effective index (EE), rule of law (RL), transparency (T), and land holdings (LH). Again, the Forest Dependence Index (FDI) of the households as a whole in the Rupnarayan forest division of Paschim Medinipur is found to be 0.59. In this division, about 93% of households are highly forest dependent. For the Rupnarayan forest division of PaschimMedinipur, the factors affecting forest dependence are forest income (FI), rule of law (RL), and land holdings (LH).

10.8 Relation Between Forest Dependence and Forest Governance …

221

The Forest Dependence Index (FDI) of the households in the Alipurduar forest division in North Bengal is found to be 0.539. About 80% of households are highly forest dependent. In this forest division, the determinants of forest dependence are forest income, landholdings, and educational index. Forest dependency is not influenced by governance indicators in the Alipurduar forest division of North Bengal.

Chapter 11

Conclusions and Policy Recommendations

Abstract This chapter concludes the findings of the study, governmental policies, and recommendations.

11.1 Conclusions The following are the conclusions of the study. • The forest cover in the state of West Bengal is 19.04% of the state’s geographical area. In terms of forest density classes, the state of West Bengal contributes 10.95% of the geographical area under open forest. The forest cover in India as well as West Bengal is increasing. India’s forest policy of 1988 and forest conservation act of 1980 is in favor of the rise in forest cover. Different countries like Indonesia, Myanmar, Thailand, Costa Rica, Brazil, Ghana, and Ecuador have banned logging by formulating forest policies and acts. • The macroeconomic impact on forest cover in India showed that forest cover increases with the betterment of governance and vice versa. This means that good governance has a positive impact on forest cover in India. On the other hand, economic growth reflected by the increase in GDP per capita has a negative impact on forest cover in India. • The socio-economic conditions of the sample households are weak across Purulia, Bankura, Paschim Medinipur, and Alipurduar Forest divisions of South and North Bengal respectively. Most of the households comprise landless, marginal and small landholdings, scheduled tribes, and scheduled castes. There has been high illiteracy rate across these forest divisions. • Most households can access infrastructural facilities like facilities for sanitation, health care facility, and banking facilities, etc. The households residing in kaccha houses. The occupational structure shows that more than 50% of households have three occupations in Purulia, Bankura, and Rupnarayan of Paschim Medinipur forest divisions whereas more than 25% of households have double occupation for subsistence in these forest divisions. On the other hand, the occupational structure shows that more than 52% of households have two occupations in the Alipurduar forest division in North Bengal. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. P. Basu, Governance and Institution in the Indian Forest Sector, https://doi.org/10.1007/978-3-031-34746-7_11

223

224

11 Conclusions and Policy Recommendations

• Major sources of livelihood of the households in the forest divisions of Purulia, Bankura, and Rupnarayan of Paschim Medinipur are forest product collection, casual labor, and agriculture. Monthly income per household differs substantially across four forest divisions of West Bengal. In the Purulia forest division the monthly income per household is found to be Rs. 8714.02, in Bankura (South) forest division it is Rs. 7467.69; in the Rupnarayan forest division of Paschim Medinipur it is Rs. 8853.02. In the Alipurduar forest division the monthly income per household is found to be Rs. 9496.31. More than 56% of households in the Purulia, Bankura, and Rupnarayan forest divisions have derived an income Rs. 5001 to Rs. 10,000. More than 66% of households in Alipurduar forest division have an income of Rs. 5001 to Rs. 10,000. On the other hand, there is a substantial variation in income derived from forest products across different forest divisions. Households in the Purulia forest division derived 24% of their income from forest products, 23.3% income in the Bankura (South) forest division, and 15.48% of their income in the Rupnarayan forest division while in Alipurduar it is 6.92% from income from forest products. Income from forest products is highest in the South Bengal forest division compared to the North Bengal forest division. • The collection of Non-Timber Forest Products (NTFP) is one of the major sources of livelihood of the households of Purulia, Bankura, and Rupnarayan of Paschim Medinipur forest divisions. More than 86% of households of Purulia, Bankura, and Rupnarayan of Paschim Medinipur forest division collect fuelwood. In addition, they also collect sal leaves, mahua flowers, mushrooms, and others. More than 78% of households of the Alipurduar forest division of North Bengal collect fuelwood. In addition, they also collect mushrooms, honey, fodder, and others from forest. • Households have derived many other benefits from forests like soil protection, climate protection, livestock grazing, tourism, etc. It is observed that the households put soil protection as 1st preference, climate protection as 2nd preference, and livestock grazing a preference across Purulia, Bankura (South), and Rupnarayan of Paschim Medinipur of forest divisions. The majority of the households of South West Bengal have expressed that they are not getting benefits from the tourism sector. On the other hand, households in Alipurduar forest division, North Bengal, have given 1st preference to tourism, 2nd preference to climate protection, and 3rd preference to soil protection. • In the Purulia forest division the overall participation rate in the general body (GB) meeting is 52.50%, out of which female member’s participation rate is 46.88% and for male members’ participation is 54.62%. The number of GB meetings held per year lies between 10 and 15 days and agenda-wise resolutions are written in the meeting book and maintained such meeting book. All FPCs are successful in controlling illegal timber logging. In respect of SHGs formation, especially SC/ST women, about 67% of FPCs have formed SHGs involving more than 60% of SC/ST women for empowerment. Only 44.44% of FPCs arranged training programs for forest fire prevention.

11.1 Conclusions

225

• In Bankura (South) the overall participation rate in the GB meeting is 66.40%, out of which female member’s participation is 62.21% and for male members’ participation is 68.58%. The number of meetings held per year lies between 5 and 15 days. All FPCs are successful in controlling illegal timber logging and helping to implement poverty reduction measures. But in respect of SHGs formation, especially among SC/ST women, 30% of FPCs formed SHGs involving more than 60% SC/ST women for their empowerment and 70% of FPCs arranged training programs for forest fire prevention. • In the Rupnarayan forest division of Paschim Medinipur the overall participation rate in the GB meeting is 85.22%, out of which female members participation is 85.61% and that for male members’ participation is 84.96%. The number of meetings held per year lies between 1 and 7 days. All FPCs are successful in controlling illegal timber logging. In respect of SHGs formation, especially among SC/ST women, only 10% FPC formed SHGs involving more than 60% SC/ST women for their empowerment. No FPCs arranged any training program for forest fire prevention in Rupnarayan forest division of Paschim Medinipur. • In the Alipurduar forest division of North Bengal, the overall participation rate in the GB meeting is 93.83%, out of which female member’s participation is 91.47%, and that for male members’ participation is 95.60%. The numbers of GB meetings held per year are 2–5 days. All FPCs are successful in controlling illegal timber logging, in training facilities for forest fire prevention, and helping to implement poverty reduction measures in the study area. In terms of SHGs formation, especially among SC/ST women, 60% of FPCs formed SHGs involving more than 60% SC/ST women. • The vacancy position of different staff for each forest beat office across different forest divisions it is revealed that 40% of the sanctioned post remains vacant in the Purulia forest division and about 70% remains vacant in Bankura (South) forest division. In the Rupnarayan forest division the vacancy position is about 46% while 33% of vacant position is observed in the Alipurduar forest division of North Bengal. • It is also revealed from Gram Panchayat (GP) survey that there is no GP representative to the FPCs in the Rupnarayan Forest division of South Bengal and the Alipurduar forest division in North Bengal. The study confirms that illegal timber logging is present. In the North Bengal forest division FPC plays a significant role in the price determination of felling trees while in the South Bengal trader, FPC and both are responsible for the price determination of felling trees. • In the Purulia forest division the enforcement index is found to be the highest (0.717) followed by the institutional index (0.701) and the monitoring index (0.498). In Bankura (South) forest division institutional index is the highest (0.660) followed by the enforcement index (0.451) and monitoring index (0.208) whereas in the Rupnarayan forest division of Paschim Medinipur monitoring index is found to be the highest (0.621) followed by institutional index (0.388) and enforcement index (0.257). In the Alipurduar forest division the monitoring index is found to be the highest (0.957) followed by the enforcement index (0.828) and institutional index (0.786).

226

11 Conclusions and Policy Recommendations

• It has been observed that the monitoring index, institutional index, and enforcement index are higher in the North Bengal forest division compared to the South Bengal forest division. This means that monitoring, institutional and enforcement indices are very effective in North Bengal forest division than South Bengal forest division. • WBFDCL and LAMPs are two major formal institutions operating at the regional level in West Bengal and performed socio-economic development of tribal people especially tribal women for their upliftment and empowerment. WBFDCL performed in eco-tourism development in West Bengal. • There are very few NGOs working in the forest and environmental sector in South Bengal and found no role played by NGOs in the North Bengal forest division. • The participation index of households in the Alipurduar forest division is the highest (0.897) followed by the Purulia forest division (0.64), Rupnarayan forest division of Paschim Medinipur (0.53), and Bankura forest division (0.47). In the Purulia forest division there is no exclusionary women participation in the participatory forest management program. The level of participation is higher for SC/ST households than for non-SC/ST households. In the planning and implementation stage, the participation of SC/ST households is higher than that of non-SC/ST households. The overall participation index for illiterate households is higher than the formally educated households and significant. In the planning stage, the elderly households’ participation is higher than the young households and it is statistically significant. • In Bankura (South) forest division there is also no exclusionary women participation in the participatory forest management program. The level of participation is lower for SC/ST households than for non-SC/ST households and is significant. In the implementation stage, planning, and monitoring stage the participation of SC/ST households is lower than that of non-SC/ST households and significant. In the planning stage the participation index for illiterate households is lower than the formally educated households and significant. • In the Rupnarayan forest division of Paschim Medinipur there exit women participate in the participatory forest management program. Women’s participation is found to be significant in the planning, implementation, and monitoring stages. The level of participation is higher for SC/ST households than for non-SC/ST households. In the planning and implementation stage, the participation of SC/ST households is higher than that of non-SC/ST households. The overall participation index for illiterate households is higher than the formally educated households and significant. In the implementation and monitoring stage, the participation of illiterate-headed households is higher than that of formally educated-headed households. The participation of marginal and small farmers is higher than landless households in the monitoring stage and is significant in the Rupnarayan forest division. • In the Alipurduar forest division the overall participation index for marginal and small farmers is higher than the landless farmers and is significant. The participation index for elderly households (whose age is greater than 40 years) in the

11.1 Conclusions

•

•

•

• •

•

•

227

implementation stage is lower than for the young households (whose age lies between 20 and 40 years) is statistically significant. The Forest Governance Index (FGI) at the household level in the forest division of Alipurduar is found to be the highest (0.483) followed by the Purulia forest division (0.446) Bankura (South) forest division (0.397), and Rupnarayan of Paschim Medinipur (0.302). The overall forest governance of the Purulia forest division, Bankura (South) forest division, and Alipurduar forest division is found to be medium/moderate while the overall forest governance of the Rupnarayan forest division of Paschim Medinipur is poor. In terms of different indicators of forest governance, the rule of law index is highest for the Purulia forest division and lowest for the Paschim Medinipur forest division. In terms of the transparency index Bankura forest division is found to be the highest followed by the Alipurduar forest division, Purulia forest division, and Paschim Medinipur forest division. In terms of the accountability index Alipurduar forest division is found to be the highest followed by the Purulia forest division, the Bankura forest division, and the Paschim Medinipur forest division. The participation index is highest for the Purulia forest division and lowest for the Alipurduar forest division. The inclusive and equitable index is highest for the Alipurduar forest division followed by the Purulia forest division, Bankur forest division, and Paschim Medinipur forest division. In terms of effective and efficient index, Purulia forest division seems to be highest and Alipurduar forest division is lowest. The factors which affect forest governance in the Purulia forest division are enforcement index, caste, forest income to total income, and trust between forest department and local people. On the other hand, caste, forest income to total income, enforcement index and trust are the determinants of forest governance in the Bankura (South) forest division. Enforcement index, trust, and landholdings are the determinants of forest governance in the Rupnarayan forest division and Alipurduar forest division. The forest governance index in North Bengal is higher than South Bengal forest division because of higher values of transparency, accountability, and participation and inclusive and equitable in the North Bengal forest division compared to the South Bengal forest division. The Forest Dependence Index (FDI) of the households as a whole in the forest division of Purulia is found to be 0.38. In this forest division, about 62% of households are highly forest dependent. The relation between forest dependency and forest governance has been analyzed with the help of a stepwise regression model. The determining factors responsible for forest dependency have been identified as forest income (FI), land holdings (LH), caste (C), education (EDU), transparency index, and rule of law (RL). The Forest Dependence Index (FDI) of the households as a whole in the forest division of Bankura (South) is found to be 0.53. In this forest division, about 89% of households are highly forest dependent. The factors affecting forest dependence are identified using a stepwise regression model. These factors are forest income

228

11 Conclusions and Policy Recommendations

(FI), efficient and effective index (EE), rule of law (RL), transparency (T), and land holdings (LH). • Forest Dependence Index (FDI) of the households as a whole in the forest division of Paschim Medinipur is found to be 0.59. In this division, about 93% of households are highly forest dependent. For the Rupnarayan forest division the factors affecting forest dependency are identified as forest income (FI), rule of law (RL), and land holdings (LH). • The forest dependence Index (FDI) of the households in the Alipurduar forest division in North Bengal is found to be 0.539. About 80% of households are highly forest dependent. In this forest division, the factors affecting forest dependency are identified as forest income, landholdings, and educational index. The above results confirm that the first hypothesis is rejected. This means that the forest governance for the North Bengal forest division and the South Bengal forest division is medium or moderate. The second hypothesis is accepted. This implies that the participation index is more prevalent in the North Bengal forest division compared to the South Bengal forest division. The third hypothesis is accepted. This means that the monitoring index, institutional index, and enforcement index are higher in the North Bengal forest division than the South Bengal forest division. The fourth hypothesis is accepted. This means that good governance has a positive impact on the rise of forest cover in India at the macro level.

11.2 Government Policy India has enacted more than 200 laws for protecting the environment with significant provisions in the constitution (Sandhu and Sidhu 2015). Local institutions have a significant role in forest conservation and its sustainable use. The institutions at the local level are Joint Forest Management Committees (JFMC), Community Forest Management groups (functioning in Orissa), Van Panchayats (functioning in Uttarakhand), traditional village-level institutions or Village Councils (schedule VI area) and Biodiversity Management Committees, Forest protection committee (in West Bengal), etc. Self Help Groups promote forest-based livelihood activities. There are some governmental initiatives like Forest Conservation Act 1980, Wildlife Protection Act 1972, Joint Forest Management 1990, and Green India Mission 2011 for sustainable forest management. Over the last 33 years, the National Forest Policy of 1988 has been the guiding document for forest management in India.

11.3 Recommendations

229

11.3 Recommendations The general recommendations are made as follows. First, there should be socio-economic development of the local communities that are involved in forest protection and management by providing better health infrastructure, educational facilities, and food security. Second, the attitude of the forest department should be cooperative and trustworthy so that there has been a congenial environment for forest conservation. In addition, there should be proper functioning of local forest institutions like forest protection committees at the village level to reduce the gap between communities and forest departments. Third, agriculture is not profitable although higher percentages of the populations depend on it in the study area. Special importance should be given to the development of agriculture through proper management of water and irrigation facilities and setting up the agro-forestry industry. Fourth, the focus should be given to petty business and non-farm employment, setting up the handicraft industry, and formation of a self-help group for diversification of livelihoods of the local communities. Fifth, importance should be given to alternative income opportunities through the MGNREGA employment program and local petty businesses and the development of eco-tourism. The high dependency on forests causes deforestation and degradation of the forest resources which further deteriorates the quality of the forest. The proper functioning of institutions and effective use enforcement helps to reduce forest degradation. This further means that good forest governance has a positive impact on the quality of the forest and forest conservation through the efficient functioning of institutions and enforcement that can prevent the high dependency on the forest. Sixth, the Large-Sized Adivasi Multi-Purpose Co-operative Societies (LAMPS) should be spread at the ground level where economically poor tribal are living. LAMPS should provide agricultural inputs like fertilizers, pesticides, seeds, and loan at subsidized rates to the tribal and other scheduled caste families. In addition, LAMPS should provide goats, pigs, hens, and cows to the tribal for their livelihood generation. Seventh, the role of NGOs in forest and tribal development is assuming special importance because of their unique qualities like innovativeness, committed agency workers for effective implementation, flexibility in approach to suit local conditions, closer contact with local tribes, high level of motivation, and minimum procedural practices. Eight, priority should be given to research on silviculture, especially in the region of the North Bengal forest division. Nine, there are various schemes of central government like Pradhan Mantri Awas Yojana (Gramin), Antyaday Anna Yojana, and Pradhan Mantri Ujjwala Yojana operating in the study areas. Priority should be given to the implementation of these schemes properly so that the benefits can reach SC/ST household beneficiaries.

230

11 Conclusions and Policy Recommendations

Then, the study indicates that there are some cases of illegal timber logging present. To prevent such illegal timber logging forest department should fill up all vacant positions immediately for the betterment of enforcement of rules and regulations. Eleven, the study has indicated that the rule of law is poor for each forest division. Special emphasis should be given to the improvement of rule of law. Lastly, importance should be given to cross-sectoral cooperation. This means that there should be cooperation among various sectors like the forest sector, rural development sector, agricultural sector, and irrigation sector. The specific recommendations are made across four forest divisions of West Bengal. First, for the Purulia forest division of South West Bengal, specific recommendations are made. 1. Government should emphasize social sector development like education, health, housing, and sanitation and the development of infrastructures at the household and village level. 2. Importance should be given to afforestation in degraded or barren land through MGNREGA/SHGs. 3. Emphasis should be given to the alternative employment policies of the government (MGNREGA, MSME) so that the forest dependency will reduce. 4. The government of West Bengal should give priority to the development of the tourism sector in Bagmundi block in the Purulia forest division. Second, for the Bankura (South) forest division of South West Bengal, the specific recommendations are as follows. 1. In Bankura (South) forest division, forest participation of the households is low. Because of the low wages offered by the forest department compared to the wages offered by NREGA. Therefore, the forest department should fix up wage rates equal to the wage rate offered by MGNREGA. 2. Democratic decentralization of governance requires operational autonomy for the village-level entity within a transparent regulatory framework. There should be proper monitoring of the sustainable use of resources and enforcement norms by the government to conserve these resources. 3. Importance should be given to alternative sources of livelihood such as agricultural development, and extension of irrigation to reduce the flow of migration from this region. 4. The focus should be given to setting up agri-business industries for providing more employment and income (like tomato juice, sugar industry, wine industry from mahua fruits, bamboo basket, and toy industry, Biri Industry from kendu). 5. Third, for the Rupnarayan forest division of Paschim Medinipur of South West Bengal, the recommendations are as follows. (a) Priority should be given by the government of West Bengal to social sector development like health, housing, and sanitation and the development of infrastructure such as roads, and safe drinking.

Reference

231

(b) JFM program has increased the protection of forests and increased the availability of minor forest produce and fuel wood in many places, but in some other villages there is lacking due to the JFM institution not functioning well. (c) Importance should be given to the attendance of the communities in the general body meetings and informing them of formal and informal rules for forest harvesting by the community members. (d) The focus should be given to the village industry like forest-based handicraft industries say mat, rope, swing, and bamboo products to increase employment and income. (e) Poor forest governance should be improved by implementing rule of law, transparency, and accountability. (f) Importance should be given to the formation of SHGs through FPCs to ensure women’s empowerment. (g) The government of West Bengal should give more priority to the development of the tourism sector in the Garhbeta block for the improvement of Gongoni (Grand Canyon of Bengal), Raikota, and Jhalda in the Paschim Medinipur district. Fourth, for the Alipurduar forest division of North Bengal, specific recommendations are made. 1. Extension of irrigation is urgently needed in the Alipurduar forest division to develop multiple cropping systems. 2. Various Policies like afforestation should be implemented properly by the forest department and the demands of stakeholders should be addressed in advance. Low forest participation of the community should be increased through proper identification of forest users and their involvement in the discussion of the forest plans. In addition, forest fire equipment, knowledge, and skill development; training should be properly implemented by the forest department. 3. For a sustainable increase in income of households, importance should be given to the diversification of livelihoods. 4. Agroforestry industry should be set up in the Alipurduar forest division. 5. The bureaucratic attitude of the forest department should be avoided and in place, trust should be created between the forest department and local communities.

Reference Sandhu EV, Sidhu AS (2015) Environmental governance in India: A systematic review of the initiatives. Pac Bus Rev Int 8(4):49–57