EN中文

Regional Sustainability >

2024 , Vol. 5 >Issue 2: 100141

DOI: https://doi.org/10.1016/j.regsus.2024.100141

Full Length Article

Assessing the role of forest resources in improving rural livelihoods in West Bengal of India

  • BISUI Soumen a ,
  • SHIT Pravat Kumar , a, b, *
Expand
  • aResearch Center for Natural and Applied Science, Raja Narendra Lal Khan Women’s College (Autonomous), Vidyasagar University, Midnapore, 721102, India
  • bPostgraduate Department of Geography, Raja Narendra Lal Khan Women’s College (Autonomous), Vidyasagar University, Midnapore, 721102, India
*E-mail address: (Pravat Kumar SHIT).

Received date: 2023-06-20

  Revised date: 2024-03-08

  Accepted date: 2024-06-11

  Online published: 2025-08-12

Fold

Abstract

Forest resources play a vital role in supporting the livelihoods of rural communities residing in forest-rich areas. In India, a forest-rich country, a significant proportion of non-timber forest products (NTFPs) is consumed locally, supporting numerous rural communities relying on forests for essential resources, such as firewood, timber, and NTFPs. This study focuses on two forest-dominant districts in West Bengal of India, namely, Jhargram District and Paschim Medinipur District. Furthermore, this study aims to enhance the understanding of forest-dependent communities by comparing the standard of living among different village classes. Thus, we categorized villages into three classes based on the distance from home to forests, including inner villages, fringe villages, and outer villages. Through focus group discussions and household surveys, we explored the sources of local economy, income sources of household, and reasons for economic diversification in different village classes. The study findings confirm that substantial variations existed in the income sources and the standard of living in these villages. Forest income varied dramatically among the three village classes, with inner villages having greater forest income than fringe villages and outer villages. Meanwhile, households in outer villages depended on forests and engaged in diverse economic activities for their livelihoods. Compared with inner and fringe villages, households in outer villages derived a significant portion of their income from livestock. This discrepancy can be attributed to challenges, such as inadequate transportation, communication, and underdeveloped market chains in inner villages. Moreover, these findings emphasize the need to develop sustainable forest management practices, create alternative income-generation opportunities, and improve infrastructure and market access in inner villages, as well as promote economic diversification in outer villages. Through targeted policy measures, these forest-rich regions can achieve improved livelihoods, enhanced standard of living, and increased resilience for their communities.

Key words： Non-timber forest products (NTFPs); Forest resources; Standard of living index; Livelihood dependency index; Income sources; India

Cite this article

BISUI Soumen , SHIT Pravat Kumar . Assessing the role of forest resources in improving rural livelihoods in West Bengal of India[J]. Regional Sustainability, 2024 , 5(2) : 100141 . DOI: 10.1016/j.regsus.2024.100141

1. Introduction

Forest resources play a dynamic role in improving rural economies, particularly for communities situated in close proximity to forests (Belcher et al., 2015; Makhubele et al., 2022). Moreover, forests offer a range of economic benefits, including timber production, non-timber forest products (NTFPs), and ecosystem services, such as carbon sequestration, water filtration, and wildlife habitat (Dash et al., 2016; Walle and Nayak., 2022). These resources provide households with supplementary sources of income during periods of shortage and contribute to maintaining agricultural productivity (Babulo et al., 2009; Kar and Jacobson, 2012; Jagger et al., 2014; Mukul et al., 2016). Global studies have demonstrated that forest-based income plays a crucial role in sustaining the livelihoods of rural populations living in forest fringe areas (Kamanga et al., 2009; Jagger et al., 2014; Lax and Köthke, 2017; Vaughan, 2020). The assessment of forest-based income across 17 developing nations using 51 case studies revealed that forest resources provided 22.00% of the average family income (Nerfa et al., 2020; Mendako et al., 2022). Despite experiencing rapid deforestation and degradation, primarily owing to agricultural development, forests remain crucial for ensuring food security (Cavendish, 2000; Vedeld et al., 2007; Cocks et al., 2011; Pradhan and Singh, 2019). However, inadequate policies and market conditions sometimes lead forest fringe communities to turn toward agriculture, resulting in the conversion of forest land for agricultural purposes. Hence, effective policies and management programs are necessary to meet the needs of the local population, ensure high benefits from forests, and promote sustainable practices (Mahapatra and Shackleton, 2011; Wunder et al., 2014).
In India, forests play a vital role in supporting rural communities, with approximately 68.00% of rural people depending on forests for their fuelwood, timber, and non-timber forest product (NTFP) needs. Forest ecosystems are extremely crucial for the daily livelihoods of forest fringe communities, and approximately 57.00% of income for rural people in India is derived from forests (Sukhdev, 2009). West Bengal State, being one of India’s forest-rich states and having a forest cover of approximately 13.38%, relies heavily on forests to sustain agricultural livelihoods (Sharma et al., 2015; Bisui et al., 2021a). Forests in West Bengal contribute to preventing soil erosion, retaining water, and providing shade for crops, thereby leading to increased agricultural yields and improved livelihoods for farmers (Jana et al., 2017; Adhikary et al., 2021). Furthermore, forests are lifelines for rural communities, providing essential fuelwood and fodder for livestock. However, the distribution of forest benefits in West Bengal remains unequal due to the disparities in market access, communication, and the impacts of past land tenure systems, and several forest-dependent communities face challenges in accessing and benefiting from forest resources (Sahoo et al., 2022). Rural communities in West Bengal are facing a decline in available forest resources due to widespread deforestation and degradation, driven by factors such as agricultural expansion, infrastructure development, and illegal logging (Bisui et al., 2021b; Sahoo et al., 2022). Majority of forest-based dwellers legally collect NTFPs; however, to fulfill their basic needs, some dwellers engage in illegal collection of few forest resources like timber and immature green leaves and branches for fodder, further exacerbating the deforestation and degradation of forests (Shit et al., 2020). While the government has initiated various programs for forest conservation, overexploitation remains a challenge. Recently, joint forest management programs involving local communities have shown positive impacts in controlling deforestation and promoting social forestry initiatives (Mukherjee et al., 2017; Jana et al., 2019; Bisui et al., 2022).
This study intends to enhance the understanding of the livelihood patterns of forest-dependent communities through assessing the role of forest resources, analyzing economic dynamics, conducting a comparative analysis of forest dependency, and quantifying the livelihood dependence and standards of living. This approach is relatively understudied, with only a few studies exploring the socioeconomic conditions and forest dependency ratios among forest dwellers and surrounding communities (Jana et al., 2017, 2019).

2. Study area

The study was conducted in two forest-dominated districts in West Bengal State of India, namely, Jhargram District and Paschim Medinipur District. The two districts encompass a total forest cover area of 1700 km2, accounting for approximately 14.31% of the state’s overall forest cover. The predominant vegetation in this area is Sal (Shorea robusta C. F. Gaertner), which covers around 60.00% of the region, with the remaining portion comprising plantations, scrub jungles, and bushes. According to the classification of forest types by Champion and Seth’s (1968), the Sal forests in this region fall under categories such as dry tropical forests, tropical dry deciduous forests, dry Sal-bearing forests, and dry peninsular Sal forests (Das and Das, 2016; Bisui and Shit, 2023).
These forests comprise patches of varying sizes interspersed among cultivated fields and human settlements. The common species found in these forests, along with Sal, include Indian Kino (Pterocarpus marsupium Roxb.), Kendu (Diospyros melanoxylon Roxb.), Mahua (Madhuca latifolia (L.) J. F. Macbr.), Kusum (Schleichera oleosa (Lour.) Oken), Arjun (Terminalia arjuna (Roxb. ex DC.) Wight & Arn.), Bahera (Terminalia bellirica (Gaertn.) Roxb.), and Simul (Bombax ceiba L.). The study area falls within a subtropical and sub-humid climatic zone characterized by hot-moist summer and cool dry winter. Furthermore, the region receives an average annual precipitation of 1400 mm, with the rainy season spanning from June to September due to the influence of the southwest monsoon. The highest precipitation occurs during July and August. Precipitation gradually decreases from October, and a dry season persists until May. Moreover, the soil in this region is predominantly lateritic, and paddy is the dominant crop (Bisui and Shit, 2023). Considering the challenges related to agriculture, limited transportation and communication infrastructure, and low literacy levels, forest-based income constitutes a major livelihood activity in this region. The percentage of food-insecure people in this region decreased from 33.70% in 2004 to 25.00% in 2012 (Basar and Das, 2018). Therefore, we chose this study area to understand the significance of forests in improving rural livelihoods and assess the degree of forest dependency based on the distance from the forest.

3. Materials and methods

3.1. Data collection

This study is entirely based on field survey data, as we aim to gather comprehensive information about forest dependency. To achieve a profound collection of data, we employed various techniques tailored to the specific aspects of this study. Further, to ensure a comprehensive understanding of forest dependency, we implemented a combination of techniques that allow for a thorough exploration of the subject matter. Field observation serves as a valuable tool to directly observe and record pertinent information on the socioeconomic impact and forest product collection and usage, as well as the current field conditions. By being physically present in the field, we can closely observe and record relevant activities and their implications. The field survey was conducted from 2021 to 2023.

3.1.1. Selection and classification of villages for household survey

For household survey, we specifically targeted two districts in West Bengal, namely, Jhargram District and Paschim Medinipur District, which are predominantly covered by forests. We carefully chose villages located near the forests to ensure a representative sample. For delineating the boundaries of the villages, we utilized specific techniques. The process comprised the following two steps: (1) we utilized high-resolution satellite imagery from Google Earth to locate and identify villages within the forest-dominated regions of Jhargram and Paschim Medinipur districts, and by carefully examining the imagery, we pinpointed areas with human settlements; and (2) we conducted a pilot survey to verify the accuracy of the identified villages from the satellite imagery. Ground truthing involved physically visiting the areas indicated by the satellite imagery to confirm the existence and boundaries of the villages. This step ensured that the villages selected for this study can be accurately identified and surveyed. After delineating the village boundaries in the forest-dominated regions, we selected 30 villages for this study. We divided these villages into three classes based on their proximity to the forest (Belcher et al., 2015; Bisui and Shit, 2023). The first class comprised inner villages situated within 0-1 km from the forest. These villages are closest to the forest and have immediate access to forest resources. Therefore, the residents of inner villages are more likely to rely heavily on these resources. The second class, fringe villages, located 1-2 km away from the forest, also benefited from significant proximity but might exhibit different resource utilization patterns. The residents of fringe villages may have some access to forest resources but to a lesser extent than those in inner villages. The third class comprised outer villages, situated 2-5 km away from the forest. These villages are located at a greater distance from the forest, resulting in limited direct access to forest resources. The residents of outer villages may have minimal reliance on forest-dependent activities.
We selected 9 villages in fringe and outer villages, respectively, and 12 villages in inner villages due to their limited population. Subsequently, we employed a random selection method to choose approximately 10 households from each village, resulting in a total of 120 households from each village class. Consequently, household survey concluded with a total sample size of 360 households, representing more than 30.00% of the total households in these villages. To ensure the quality of the data collected, we employed local language assistants who were fluent in the dialect spoken in the study area.
During the household survey, we used a structured questionnaire to collect relevant information from each participating household. This questionnaire covered various aspects, including the sources of local economy, the extent of dependency on forest resources, and the household infrastructure. Thus, by employing a structured questionnaire, we ensured a standardized approach to data collection, making it easier to compare and analyze the data.
In summary, our survey employed a rigorous approach by using random household selection, local language assistants, and a structured questionnaire, enabling us to collect reliable and comprehensive data on the socioeconomic aspects, income from forest resources (e.g., fuelwood, medicinal plants, Sal leaves, Sal seeds, honey, Kendu leaves, and Mahua flowers), and household infrastructure within the surveyed villages. Such a meticulous approach enhances the validity and reliability of the data, thereby facilitating the meaningful analysis and interpretation of the study findings.

3.1.2. Focus group discussion (FGD)

In addition to the household survey, we conducted 20 focus group discussions (FGDs) with 8-10 participants per group, and they were randomly selected with no specific population composition. The discussions lasted 20-30 min and took place within government buildings, such as primary schools and Integrated Child Development Services Center, with a member of the gram panchayat (a regional level administrative unit in India) serving as the moderator. The FGD primarily aims to gain deeper insights into the sources of local economy, the previous sources of household income, and the reasons for income diversification in different village classes. In addition, to ensure a comprehensive understanding of local economy and income diversification, we carefully selected participants for the FGDs. We specifically chose village leaders and forest committee members, as they possess valuable knowledge and experience regarding the economic activities and forest resources in their respective villages. Moreover, during the FGDs, we facilitated an open and structured conversation where the participants shared their perspectives and insights. The FGDs were focused on understanding the various sources of local economy, exploring the previous sources of household income, and examining the factors that led to income diversification in various village classes. Through the FGDs, we explored the participants’ experiences, opinions, and observations regarding local economy, the collection of forest resources and their market values, and income diversification. Moreover, we encouraged the participants to share their individual and collective insights, allowing for a comprehensive understanding of the dynamics and factors influencing economic activities.

3.2. Data analysis

The field data collected during this study were compiled and analyzed using IBM SPSS software version 20 (International Business Machines Corporation, Armonk, the United States) and Microsoft Office Excel 2010 (Microsoft Corporation, Redmond, the United States). These statistical tools are widely used in social science research for data analysis and interpretation. The exchange rate of 83.5 INR per 1.0 USD was used in this study for income estimation.

3.2.1. Household income

The total household income was calculated by summing the income from different sources, including agriculture, forest-based activities, and wages earned by the household members. This approach allows for a comprehensive assessment of the overall income generated by a household (Langat et al., 2016).
${{Y}_{h}}=\sum\limits_{i=1}^{n}{{{S}_{i}}}$,
where Yh is the total household income (INR); i is the income source; Si is the summation of different income sources (INR); and n is the upper limit of the summation of the income sources.

3.2.2. Forest income

Forest income was calculated by totaling the income from various forest resources (Vedeld et al., 2007; Langat et al., 2016).
${{Y}_{f}}=\sum\limits_{i=1}^{n}{({{Q}_{i}}\times {{P}_{i}})}$,
where Yf is the total forest income (INR); Qi is the quantity of collected forest products (kg); and Pi is the market price of NTFPs (INR/kg).

3.2.3. Agriculture income

Agriculture income was calculated by summing the value of all agricultural products produced by households. This calculation considered the market prices of these products, which were determined on the basis of the local market conditions. The agriculture income represents the earnings derived from various agricultural activities, such as crop cultivation and other agricultural outputs (Langat et al., 2016; Bisui and Shit, 2023).
${{Y}_{a}}=\sum\limits_{i=1}^{n}{({{A}_{c}}\times {{R}_{i}})}$,
where Ya is the agriculture income (INR); Ac represents the production of crops (kg); and Ri is the market price of crops (INR/kg).

3.2.4. Livestock income

Livestock income was calculated by assessing the value of the household’s domestic animals and birds. This valuation involves assigning a monetary value to the livestock based on their market worth or prevailing prices in the local area. Livestock income represents the economic contribution of the household’s livestock, which includes various animals, such as cattle, goats, sheep, pigs, poultry, and other domesticated animals. These animals serve multiple purposes, including milk, meat, and egg production, as well as providing other by-products (Langat et al., 2016; Bisui and Shit, 2023).
${{Y}_{l}}=\sum\limits_{i=1}^{n}{\left( {{N}_{i}}{{T}_{i}}-{{K}_{i}} \right)}+\sum\limits_{i=1}^{n}{\left( {{O}_{i}}{{T}_{i}}-{{K}_{i}} \right)}$,
where Yl is the livestock income (INR); Ni represents the number of livestock in category i; Ti is the market value of livestock i (INR/head); Ki represents the cost for raising livestock i (INR/head); and Oi is the quantity of products from livestock i (kg/head).

3.2.5. Measuring forest dependency

Forest dependency was measured by calculating the proportion of the total income and forest income, represented by the following equation (Langat et al., 2016; Bisui et al., 2022):
$\text{RFI}=\frac{{{Y}_{f}}}{{{Y}_{h}}}$,
where RFI is the relative forest income.

3.2.6. Standard of living index (SLI)

We collected data on various household assets to assess their standard of living. Specifically, we adopted the formula for calculating the SLI as provided by the National Family Health Survey (IIPS and ORC Macro, 2000). The index comprised 27 items that encompass different aspects of living conditions, including housing conditions, agricultural machinery, access to various services, such as water, electricity, and fuel, as well as ownership of consumer durables. The calculation of the SLI involved assigning weights to each of these 27 items. These weights were developed by the International Institute of Population Sciences and indicated the relative importance of each item in determining the overall standard of living. Subsequently, by summing the weights assigned to these 27 items, we obtained the SLI for each household (IIPS and ORC Macro, 2000).

3.2.7. Livelihood dependency index (LDI)

We conducted an analysis of forest dependency using the LDI, which measures the significance of forest products in sustaining livelihoods. We calculated this index using a modified formula (Eq. 6) based on the work of Schmerbeck et al. (2015). The LDI considers the importance of different forest products in meeting the basic needs and income generation of households. The equation used to calculate the LDI incorporates various factors related to forest dependency. Calculating the LDI allows us to quantitatively assess the degree to which households rely on forest products and to compare their dependency levels. This information provides insights into the role of forests in supporting local livelihoods and the potential impacts of changes in forest availability or accessibility on these communities.
$\text{LDI}=(m{{\text{D}}_{1}}\times 1.00)+(m{{\text{D}}_{2}}\times 0.75)+(m{{\text{D}}_{3}}\times 0.50)+(m{{\text{D}}_{4}}\times 0.25)+(m{{\text{D}}_{5}}\times 0.00)$,
where m represents the number of households; and D1, D2, D3, D4, and D5 represent product categories.

3.3. Statistical analysis

For this study, various statistical methods were used for data analysis. Descriptive statistics were employed to summarize the sociodemographic information gathered from the survey. To assess the differences of socioeconomic and demographic characteristics in the three village classes (i.e., inner, fringe, and outer villages), we used one-way analysis of variance (ANOVA), allowing us to determine if there were significant variations among the three village classes in terms of these characteristics. To further examine the specific group differences, we employed the Tukey’s post hoc honestly significant difference test. Further, multivariate analysis was conducted to explore how different factors contribute to variations in income sources and forest dependency patterns among households within different village classes.

4. Results

4.1. Socioeconomic and demographic characteristics

The caste distribution within all the three village classes revealed that most households belong to the scheduled tribe category (mean of 39.5%). Educational levels varied significantly (P≤0.001) across the three village classes, with households in inner villages exhibiting lower educational levels compared with outer villages. Additionally, 20.8% of households in outer villages had higher education level, whereas only 0.8% in inner villages and 5.8% in fringe villages possessed the same educational level (Table 1). There were no significant differences in the distribution of gender and household size among the three village classes, with most households classified as medium sized. However, a significant difference (P≤0.001) existed in the standard of living among the three village classes.
Table 1 Socioeconomic and demographic characteristics of households in inner, fringe, and outer villages.
Variable Percentage of households (%) P-value
Inner villages Fringe villages Outer villages
Education Less educated 37.5 40.0 7.5 0.000
Moderately educated 61.7 54.2 71.7
Highly educated 0.8 5.8 20.8
Gender Male 67.5 60.0 56.7 0.212
Female 32.5 40.0 43.3
Cast General 4.2 7.5 5.0 0.408
Scheduled cast 5.0 5.8 6.7
Scheduled tribe 40.0 41.7 36.7
Other 50.8 45.0 51.7
Household size Small 3.3 3.3 2.5 0.151
Medium 67.5 68.3 58.3
Large 29.2 28.3 39.2
Standard of living Low 19.2 2.5 0.0 0.000
Medium 75.0 97.5 80.8
High 5.8 0.0 19.2
Figure 1 illustrates that income sources varied significantly among the three village classes. Livestock income of outer villages was higher than those of inner and fringe villages. Moreover, forest income was higher in inner villages than in other two village classes (P≤0.050). These findings underscored the sociodemographic disparities and differences in income sources among the three village classes, indicating the varying levels of forest dependency and livelihood patterns.
Fig. 1. Percentages of livestock, agriculture, and forest income in inner, fringe, and outer villages.

4.2. SLI in different village classes

Table 2 reveals that households in inner villages predominantly fell within the low (19.16%) and medium (75.00%) categories of the SLI, with a smaller percentage belonging to the high category (5.83%). Conversely, households in fringe villages were primarily classified under the medium category (97.50%), suggesting a more balanced livelihood strategy that incorporates agriculture, wage labor, and forest resource use. Furthermore, households in outer villages exhibited a higher SLI due to the diversity of economic activities. Notably, some households in inner villages demonstrated a high SLI, suggesting that achieving a good standard of living solely through income derived from the forest is possible, and that there are appropriate market access and technology to diversify forest products. This finding was supported by 90.00% of the respondents in this area. Furthermore, we observed a significant difference in the SLI among these three village classes (P≤0.001). These findings emphasized the variation in the standard of living across different village classes and highlighted the potential for improving livelihoods through forest-based income, market development, and technological advancements, underscoring the importance of addressing socioeconomic disparities and implementing strategies to enhance the standard of living in forest-dependent communities, particularly in inner and fringe villages.
Table 2 Standard of living index (SLI) in inner, fringe, and outer villages.
Village class SLI (weighted score)
Low Medium High
Inner villages 23 90 7
Fringe villages 3 117 0
Outer villages 0 97 23
Total 26 304 30

4.3. LDI in different village classes

In this study, the village class analysis revealed that compared with outer villages, households in inner villages benefited the most from forest resources and relied predominantly on the forest for their subsistence livelihood (Fig. 2). Further, households in fringe villages depended on the forest, although to a lesser extent, as they also generated income from other economic activities. Households in inner and fringe villages heavily depended on forest products, such as fuelwood, timber wood, Sal leaves, Sal seed, and Mahua. Contrarily, households in outer villages relied solely on the forest for fuelwood and timber. Figure 2 shows that households in inner and fringe villages depended on various forest products for their livelihood, whereas households in outer villages relied solely on fuelwood. Minor forest products, such as honey, Mahua flowers, and Kendu leaves, were also significant income sources for households in inner villages. Approximately 80.00% of the respondents confirmed that the implementation of proper market channels and technology would improve their livelihoods and contribute to the protection of forest resources. Moreover, the one-way ANOVA analysis demonstrated that the LDI significantly differed among different village classes (P≤0.001). These findings highlighted the varying forest dependency levels among different village classes, with inner villages displaying the highest dependence on diverse forest products for their livelihoods.
Fig. 2. Livelihood dependency index (LDI) of various forest products in inner, fringe, and outer villages.

4.4. Livelihood activities of households

Households in forest-dominant villages primarily relied on forest resources for their subsistence. The indigenous population followed a traditional practice of grazing cows and goats. They directed their goats to vegetation-rich areas where they may chew various plants and tree leaves. Many families ran small farms of poultry, which is a popular agricultural activity among the households. During the blossoming season from February to April, the indigenous people also concentrated on harvesting Mahul flowers, a vivid and fragrant blossom peculiar to the area. These flowers were picked for various purposes, including religious ceremonies, traditional adornments, and the manufacturing of essential oils. The collection supported the lives of the local community, preserved their cultural past, and fostered a close bond with the natural environment (Fig. 3). Table 3 depicts that households in inner villages were entirely dependent on forest resources and engaged in the collection of a wide variety of forest products. Contrarily, households in outer village relied on the forest solely for fuelwood, while their economic activities were diversified. Households in outer villages engaged in agriculture, labor work, and livestock rearing to sustain their livelihoods. It is important to note that the income of households in inner villages was solely derived from forest products, leading to continuous forest degradation as the land is increasingly converted for agricultural purposes. This conversion has resulted in a decline in the availability of forest resources. However, Table 3 indicates that households in inner and fringe villages not only depended on fuelwood but also heavily relied on Sal leaves and Mahua flowers.
Table 3 Income of households in inner, fringe, and outer villages.
Income source Income in inner villages (INR) Income in fringe villages (INR) Income in outer villages (INR)
Minimum Maximum Mean Minimum Maximum Mean Minimum Maximum Mean
Timber 5000.0 24,000.0 13,800.0 4000.0 15,000.0 8441.7 0.0 8000.0 2658.3
Fuelwood 5400.0 48,600.0 26,748.0 5400.0 32,400.0 16,565.0 1000.0 20,000.0 9458.3
Medicine plant 300.0 1500.0 1028.3 0.0 800.0 298.3 0.0 0.0 0.0
Sal leaves 8734.6 2000.0 15,300.0 2000.0 14,400.0 5819.2 0.0 4000.0 372.5
Sal seed 150.0 3000.0 1270.8 0.0 3000.0 680.0 0.0 0.0 0.0
Honey 0.0 700.0 245.0 0.0 500.0 204.2 0.0 0.0 0.0
Kendu 800.0 6000.0 3066.7 0.0 2000.0 283.3 0.0 0.0 0.0
Mahua 0.0 1100.0 629.3 0.0 500.0 171.9 0.0 0.0 0.0
Mahua seed 0.0 2100.0 630.8 0.0 500.0 267.9 0.0 0.0 0.0
Forest income 22,900.0 87,450.0 56,153.5 17,450.0 54,200.0 32,722.5 2400.0 27,000.0 12,492.5
Agriculture income 5000.0 22,000.0 13,193.8 5000.0 50,000.0 17,041.7 12,000.0 45,000.0 22,933.3
Total income 38,900.0 100,850.0 70,914.8 28,800.0 104,400.0 53,828.3 33,400.0 90,000.0 53,100.8

Note: Exchange rate: 83.5 INR=1.0 USD.

Fig. 3. Photos showing the livelihood activities of households. (a), collection of Mustard seed; (b), agricultural labor in a rice field; (c), Babui collection; (d), grazing cows; (e), grazing goats; (f), poultry farming; (g), indigenous households’ activities revolve around the collection of Mahul flowers; (h), non-timber forest products; (i), collection of dry firewood from the surrounding forests; (j), collection of Sal leaves; (k), rearing pig; (l), selling of the firewood.
Despite their heavy reliance on forest resources, households in inner villages had a lower standard of living due to the lack of proper markets and scientific innovations. Notably, approximately 85.00% of the respondents from outer villages confirmed that they were previously residents of inner villages. However, they migrated to outer villages due to forest degradation resulting from the conversion of land for agriculture. The total income of households in inner villages was lower than that in outer villages; however, their income derived from forest resources was significantly higher. These results emphasized the distinct differences in forest dependency and livelihood strategies between inner and outer villages. Households in inner villages primarily relied on various forest products for their livelihoods, while households in outer villages had diversified their income sources and engaged in various economic activities. Households in inner villages faced challenges, such as forest degradation and limited market access. These issues highlighted the need for sustainable forest management practices, improved market opportunities, and the promotion of scientific innovations to enhance their livelihoods and conserve the forest resources they rely on

4.5. Income disparities among different village classes

We also conducted multivariate analysis, with village class as the independent variable and forest income, agriculture income, livestock income, and SLI as the dependent variables. The results, as shown in Table 4, confirmed that significant differences emerged in income sources and the standard of living among different village classes (P≤0.001). Specifically, forest income significantly differed among the three village classes, with inner villages having higher forest income compared with fringe villages, and fringe villages having higher forest income compared with outer villages.
Table 4 Results of multivariate analysis.
Statistic Sum of squares Degree of freedom Mean square F P-value
Forest income 114,581,903,280.0 2 57,290,951,640.0 618.1 0.000
Agriculture income 5,775,107,291.6 2 2,887,553,645.8 48.2 0.000
Livestock income 133,811,669,791.6 2 66,905,834,895.8 162.6 0.000
SLI 3782.1 2 1891.0 49.0 0.000
Figure 4 illustrates that livestock income was relatively lower in inner villages, which could be attributed to the lack of proper livestock product markets in such areas. Conversely, the forest income in outer villages was considerably lower compared with the other two village classes. This is due to the fact that households in outer villages have adopted diverse livelihood activities, such as agriculture, small-scale trade, livestock keeping, and remittances, which can contribute to their income and reduce their reliance on forest resources.
Fig. 4. Variations in income sources and relative forest income in different village classes. (a), forest income; (b), agriculture income; (c), livestock income (d), relative forest income. The boxes represent the range from the lower quantile (Q25) to the upper quantile (Q75). The horizontal lines inside the boxes represent medians. The dots outside the boxes represent outliers. The upper and lower whiskers indicate the maximum and minimum values, respectively. Exchange rate: 83.5 INR=1.0 USD.

5. Discussion and recommendations

5.1. Contribution of forest resources in improving livelihoods

Indigenous communities living in forested areas are generally closely connected to the forest and rely heavily on forest resources for their livelihoods (Shit and Pati, 2012; Delgado et al., 2016). This study focused on comparing forest-based livelihoods among different village classes. The study findings revealed that households in inner and fringe villages highly depend on forest resources, while households in outer villages depend on forest resources and engage in various activities. Regarding the distance from home to the forest, Mukul et al. (2010) found that income from NTFPs declined as the distance of household to the forest increased in northeastern Bangladesh. Similarly, Tanzania and Suleiman et al. (2017) and Mushi et al. (2020) identified that the distance between home and forest had a substantial and negative influence on the use of NTFPs. These findings are consistent with the studies conducted by Rahut et al. (2016), Garekae et al. (2017), Shit et al. (2021), and Makhubele et al. (2022). In this study, we observed that households living closer to the forest are increasingly inclined to engage in other activities after witnessing the higher standard of living among the residents in outer villages. Nevertheless, they maintain an emotional attachment to the forest and express the desire for proper forest management, increased forest productivity, and improved market chains to obtain fair prices for forest products. Several respondents from fringe villages mentioned that elephant migration poses a significant hindrance to easily accessing various forest products. Moreover, this study revealed a significant relationship between education and forest dependency. Higher educational levels tend to lessen the dependence on forest resources, while lower educational levels correspond to a higher dependency on forest resources. This finding aligns with other research conducted by Ofoegbu et al. (2017). Regarding livestock income, we found that households in outer villages earn more compared with households in inner and fringe villages due to the lack of proper transportation, communication, and developed market chains. Forest products are typically sold through middlemen, resulting in lower prices for the villagers. Further, households in inner and fringe villages mentioned that they can only collect forest products as directed by middlemen, which limits their ability to collect Sal seeds, Mahua seeds, and Kendu leaves.

5.2. Sustainable management of forest resources

Based on the data analysis and our observations, we concluded that forest resources are vital for the sustainable livelihoods of households living near the forest and for environmental conservation. A similar pattern was observed in northeastern Bangladesh, as demonstrated by Fardusi et al. (2011). According to Kar and Jacobson (2012), forest-adjacent families made a significant income from bamboo, wild vegetables, and cotton wool grass in Bangladesh. Other studies have reported that various wild edible NTFPs, such as honey, palm fruits, vegetables, fruits, mushrooms, orchids, and spices (de Sousa et al., 2018; Matias et al., 2018; Mahonya et al., 2019; Ndumbe et al., 2019), were used to meet nutritional needs and served as an income source for local communities. Therefore, we strongly emphasize the need for proper forest management and establishment of direct market channels without middlemen, which can enhance livelihoods by generating higher income while maintaining environmental control. Moreover, we propose several strategies for sustainable forest management to increase forest production.
(1) Enhancing educational opportunities. Considering the notable variations in educational levels among various village classes, focusing on improving educational opportunities in inner villages is crucial. Access to quality education can empower individuals and open up diverse livelihood options beyond forest-dependent activities (Zikargae et al., 2022).
(2) Strengthening market channels. The findings validate that proper market channels are essential for enhancing livelihoods in forest-dependent communities. Efforts should be made to establish robust market networks that connect villagers with buyers and offer fair prices for forest products, thereby maximizing income generation and promoting sustainable forest resource management (Belcher, 2005).
(3) Integrating technology. Introducing technological advancements can play a crucial role in diversifying forest-based income sources. Innovations, such as value addition, processing techniques, and efficient utilization of forest products, can enhance the economic viability of forest-dependent livelihoods. The use of precision forestry techniques incorporating data-gathering technology, automation, and digital networks enhances production efficiency by enabling plantation forest managers to make informed decisions regarding genetics, planting, silvicultural management, and harvesting (Fardusi et al., 2017; Singh et al., 2022).
(4) Addressing socioeconomic disparities. The sociodemographic disparities observed among distinct village classes underscore the necessity for targeted interventions to address inequities. Initiatives focusing on skill development, capacity building, and empowerment of marginalized communities can reduce disparities and ensure inclusive growth in forest-surrounding villages (Thorbecke and Charumilind, 2002).
(5) Promoting sustainable forest management. Forest degradation and overreliance on forest resources pose significant challenges to livelihoods in inner villages. The implementation of sustainable forest management practices, including reforestation, conservation measures, and community-based initiatives, can maintain the ecological balance and ensure the long-term availability of forest resources.
(6) Diversifying livelihood. Encouraging livelihood diversification among forest-dependent communities can reduce their overdependence on forest resources. Further, promoting alternative income-generating activities, such as agriculture, livestock rearing, and small-scale trade, can offer additional income sources and reduce the pressure on forests.
(7) Strengthening livestock product markets. Outer villages exhibit lower livestock income, suggesting the need for improved livestock product markets in those areas. Establishing market linkages, providing training on livestock management and product marketing, and enhancing the value chain for livestock-related activities are also crucial (Nambiar, 2021).
(8) Promoting collaboration and partnerships. Addressing the complex challenges faced by forest-dependent communities requires collaborative efforts involving local communities, government agencies, non-governmental organizations, and other stakeholders. Building partnerships and fostering cooperation can facilitate the implementation of integrated solutions for sustainable livelihood development (Edwards et al., 2019).
(9) Fostering awareness and empowerment. Raising awareness within the local population regarding the value of forest resources and sustainable practices, as well as the importance of preserving ecosystems is crucial. Moreover, empowering communities with knowledge and decision-making capabilities can enable them to actively participate in conservation efforts and sustainable livelihood practices (Chou, 2019).
(10) Advancing policy support. Policy frameworks need to be strengthened to bolster sustainable livelihoods and forest management. These policies must carefully address the distinctive requirements of forest-dependent communities, facilitate access to resources, promote market linkages, and provide incentives for sustainable practices (Saxena, 2016).
Implementing these recommendations can lead to significant improvements in the standard of living, the reduction of socioeconomic disparities, and the promotion of sustainable livelihoods in villages located in and around forests. This is especially crucial for inner and fringe villages that heavily rely on forest resources.

6. Conclusions

This study aims to compare the livelihood strategies and the standard of living in three different village classes. The findings indicate that inner and fringe villages heavily rely on forest resources for their livelihood, highlighting the crucial role of forests in livelihood diversification. Furthermore, the results reveal that outer villages have a lower forest dependency level but still rely on forests for fuelwood and fodder, which is likely due to higher educational levels and diversified livelihood activities in such areas. Moreover, the study findings exhibit significant implications for local-level forest management and conservation strategies aimed at ensuring the sustainability of forests. The results underscore the need to establish proper market channels and implement eco-friendly forest management practices. The public and private sectors should actively engage in addressing these challenges and finding appropriate solutions to promote healthier livelihoods in the forest areas. Furthermore, this study offers valuable insights into the relationship between people and forests, laying the foundation for future in-depth studies on the livelihoods of forest fringe communities.

Authorship contribution statement

Soumen BISUI: conceptualization, data curation, formal analysis, investigation, methodology, and writing - original draft; and Pravat Kumar SHIT: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, supervision, visualization, and writing - review & editing. All authors approved this manuscript.

Ethics statement

Ethics approval was obtained from the Ethics Committee of Raja Narendralal Khan Women’s College (Autonomous), West Bengal, India. In addition, the participants provided their informed consent to participate in this study.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

This work was supported by the Department of Science and Technology and Biotechnology, West Bengal, India (1433(Sanc.)/STBT-11012(20)/8/2021-ST SEC). The authors are extremely grateful to all those participating in the research, especially the community members of the villagers in Jhargram District and Paschim Medinipur District, West Bengal, India. We are grateful to the Postgraduate Department of Geography, Raja Narendra Lal Khan Women’s College (Autonomous), Vidyasagar University, West Bengal, India for supporting this research.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Adhikary P.P., Shit P.K., Bhunia G.S., 2021. NTFPs for socioeconomic security of rural households along the forest ecotone of Paschim Medinipur forest division, India. Forest Resources Resilience and Conflicts. doi: 10.1016/B978-0-12-822931-6.00018-6.

[2]
Babulo B., Muys B., Nega F., 2009. The economic contribution of forest resource use to rural livelihoods in Tigray, Northern Ethiopia. For. Policy Econ. 11(2), 109-117.

[3]
Basar S.M.A., Das P., 2018. Status of food security in West Bengal: A study based on NSSO unit level data. International Journal of Inclusive Development. 4(1), 1-7.

[4]
Belcher B.M., 2005. Forest product markets, forests and poverty reduction. International Forestry Review. 7(2), 82-89.

[5]
Belcher B., Achdiawan R., Dewi S., 2015. Forest-based livelihoods strategies conditioned by market remoteness and forest proximity in Jharkhand, India. World Dev. 66, 269-279.

[6]
Bisui S., Roy S., Sengupta D., et al., 2021a. Conversion of land use land cover and its impact on ecosystem services in a tropical forest. In: ShitP.K., PourghasemiH.R., DasP., et al., (eds.). Spatial Modeling in Forest Resources Management. Environmental Science and Engineering. Cham: Springer.

[7]
Bisui S., Roy S., Sengupta D., et al., 2021b. Assessment of ecosystem services values in response to land use/land cover change in tropical forest. Forest Resources Resilience and Conflicts. doi: 10.1016/B978-0-12-822931-6.00031-9.

[8]
Bisui S., Roy S., Bera B., et al., 2022. Economic and ecological realization of Joint Forest Management (JFM) for sustainable rural livelihood: A case study. Trop. Ecol. 64, 296-306.

[9]
Bisui S., Shit P.K., 2023. Assessing the dependence of livelihoods in rural communities on tropical forests: Insights from the Midnapore Forest Division in West Bengal, India. Socio Ecol. Pract. Res. 5, 423-437.

[10]
Cavendish W., 2000. Empirical regularities in the poverty-environment relationship of rural households: Evidence from Zimbabwe. World Dev. 28(11), 1979-2003.

[11]
Champion H.G., Seth S.K., 1968. A Revised Survey of the Forest Types of India. [2023-12-30]. https://books.google.co.in/books?hl=en&lr=&id=hIjwAAAAMAAJ&oi=fnd&pg=PR15&dq=Champion,+H.G.,+Seth,+S.K.,+1968.+A+Revised+Survey+of+the+Forest+Types+of+India.+XX:+Manag.+Publ.&ots=lEtV3Ho_y2&sig=VLMeVgOQvw3dNFuU-aAHwf5EcOA&redir_esc= y#v=onepage&q&f=false.

[12]
Chou T.C., 2019. Design for empowerment:Bolstering knowledge and skills to aid sustainable awareness and actions. PhD Dissertation. Pittsburgh: Carnegie Mellon University.

[13]
Cocks M., López C., Dold T., 2011. Cultural importance of non-timber forest products: Opportunities they pose for bio-cultural diversity in dynamic societies. [2023-12-30]. https://link.springer.com/chapter/10.1007/978-3-642-17983-9_5.

[14]
Das G.K., Das R., 2016. Mapping of the forest cover based on multi-criteria analysis: A case study on Jhargram sector in Paschim Medinipur District. Int. J. Sci. Res. 5(8), 492-499.

[15]
Dash M., Behera B., 2016. Determinants of household collection of non-timber forest products (NTFPs) and alternative livelihood activities in Similipal Tiger Reserve, India. For. Policy Econ. 73, 215-228.

[16]
Delgado T.S., McCall M.K., López-Binqüist C., 2016. Recognized but not supported: Assessing the incorporation of non-timber forest products into Mexican forest policy. For. Policy Econ. 71, 36-42.

[17]
de Sousa F.F., Vieira-da-Silva C., Barros F.B., 2018. The (in)visible market of miriti (Mauritia flexuosa L.f.) fruits, the “winter acai”, in Amazonian riverine communities of Abaetetuba, Northern Brazil. Glob. Ecol. Conserv. 14, e00393, doi: 10.1016/j.gecco.2018.e00393.

[18]
Edwards P., Sharma‐Wallace L., Barnard T., et al., 2019. Sustainable livelihoods approaches to inform government‐local partnerships and decision‐making in vulnerable environments. New Zealand Geographer. 75(2), 63-73.

[19]
Fardusi M.J., Rahman M.H., Roy B., 2011. Improving livelihood status through collection and management of forest resources: An experience from Sylhet Forest Division, Bangladesh. Int. J. For. Usufructs Manag. 12(2), 59-76.

[20]
Fardusi M.J., Chianucci F., Barbati A., 2017. Concept to practice of geospatial-information tools to assist forest management and planning under precision forestry framework: A review. Ann. Silvic. Res. 41(1), 3-14.

[21]
Garekae H., Thakadu O.T., Lepetu J., 2017. Socio-economic factors influencing household forest dependency in Chobe enclave, Botswana. Ecol. Processes. 6(1), 1-10.

[22]
IIPS(International Institute for Population Sciences), ORC Macro, 2000. National Family Health Survey (NFHS-2), 1998-99: India. [2023-12-30]. https://www.dhsprogram.com/pubs/pdf/FRIND2/FRIND2.pdf.

[23]
Jagger P., Luckert M.M.K., Duchelle A.E., et al., 2014. Tenure and forest income: Observations from a global study on forests and poverty. World Dev. 64, 43-55.

[24]
Jana S.K., Ahmed M.U., Heubach K., 2017. Dependency of rural households on non-timber forest products (NTFPs): A study in dryland areas of West Bengal, India. Int. J. Sustain. Econom. Manag. 6(2), 37-50.

[25]
Jana S.K., Payra T., Manna S.S., et al., 2019. Forest dependency and rural livelihood—a study in Paschim Medinipur District, West Bengal. Asian Journal of Multidimensional Research. 8(3), 164-178.

[26]
Kamanga P., Vedeld P., Sjaastad E., 2009. Forest incomes and rural livelihoods in Chiradzulu District, Malawi. Ecol. Econ. 68(3), 613-624.

[27]
Kar S.P., Jacobson M.G., 2012. NTFP income contribution to household economy and related socio-economic factors: Lessons from Bangladesh. For. Policy Econ. 14(1), 136-142.

[28]
Langat D.K., Maranga E.K., Aboud A.A., et al., 2016. Role of forest resources to local livelihoods: The case of East Mau forest ecosystem, Kenya. Int. J. For. Res. 2016, 4537354, doi: 10.1155/2016/4537354.

[29]
Lax J., Köthke M., 2017. Livelihood strategies and forest product utilisation of rural households in Nepal. Small-scale For. 16(4), 505-520.

[30]
Mahapatra A.K., Shackleton C.M., 2011. Has deregulation of non-timber forest product controls and marketing in Orissa state (India) affected local patterns of use and marketing. For. Policy Econ. 13(8), 622-629.

[31]
Mahonya S., Shackleton C.M., Schreckenberg K., 2019. Non-timber forest product use and market chains along a deforestation gradient in southwest Malawi. Front. For. Glob. Change. 2, doi: 10.3389/ffgc.2019.00071.

[32]
Makhubele L., Chirwa P.W., Araia M.G., 2022. The influence of forest proximity to harvesting and use of provisioning ecosystem services from tree species in traditional agroforestry landscapes. Int. J. Sustain. Dev. World Ecol. 29(8), 812-826.

[33]
Matias D.M.S., Tambo J.A., Stellmacher T., et al., 2018. Commercializing traditional non-timber forest products: An integrated value chain analysis of honey from giant honey bees in Palawan, Philippines. For. Policy Econ. 97, 223-231.

[34]
Mendako R.K., Tian G., Ullah S., et al., 2022. Assessing the economic contribution of forest use to rural livelihoods in the Rubi-Tele Hunting Domain, DR Congo. Forests. 13(1), 130, doi: 10.3390/f13010130.

[35]
Mukherjee P., Ray B., Bhattacharya R.N., 2017. Status differences in collective action and forest benefits: Evidence from joint forest management in India. Environ. Dev. Sustain. 19(5), 1831-1854.

[36]
Mukul S.A., Uddin M.B., Rashid A.Z.M., et al., 2010. Integrating livelihoods and conservation in protected areas: Understanding the role and stakeholder views on prospects for non-timber forest products, a Bangladesh case study. Int. J. Sustain. Dev. World Ecol. 17(2), 180-188.

[37]
Mukul S.A., Rashid A.M., Uddin M.B., et al., 2016. Role of non-timber forest products in sustaining forest-based livelihoods and rural households’ resilience capacity in and around protected area: A Bangladesh study. J. Environ. Plann. Manag. 59(4), 628-642.

[38]
Mushi H., Yanda P.Z., Kleyer M., 2020. Socioeconomic factors determining extraction of non-timber forest products on the slopes of Mt. Kilimanjaro, Tanzania. Hum. Ecol. 48, 695-707.

[39]
Nambiar E.K.S., 2021. Strengthening Vietnam’s forestry sectors and rural development: Higher productivity, value, and access to fairer markets are needed to support small forest growers. Trees, Forests and People. 3, 100052, doi: 10.1016/j.tfp.2020.100052.

[40]
Ndumbe L.N., Ingram V., Tchamba M., et al., 2019. From trees to money: The contribution of Njansang (Ricinodendron heudelotii) products to value chain stakeholders’ financial assets in the South West Region of Cameroon. Forests, Trees and Livelihoods. 28(1), 52-67.

[41]
Nerfa L., Rhemtulla J.M., Zerriffi H., 2020. Forest dependence is more than forest income: Development of a new index of forest product collection and livelihood resources. World Dev. 125, 104689, doi: 10.1016/j.worlddev.2019.104689.

[42]
Ofoegbu C., Chirwa P.W., Francis J., et al., 2017. Socio-economic factors influencing household dependence on forests and its implication for forest-based climate change interventions. South. For. J. For. Sci. 79(2), 109-116.

[43]
Pradhan P., Singh M., 2019. Role of non-timber forest products (NTFPs) in sustaining forest-based livelihoods: A case study of Ribdi village of West Sikkim, India. [2023-06-01]. https://www.cabidigitallibrary.org/doi/full/10.5555/20193414773.

[44]
Rahut D.B., Behera B., Ali A., 2016. Do forest resources help increase rural household income and alleviate rural poverty? Empirical evidence from Bhutan. For. Trees Livelihoods. 25(3), 187-198.

[45]
Sahoo K.P., Roy A., Mandal M.H., et al., 2022. Appraisal of coexistence and interdependence of forest and tribes in Jhargram District of West Bengal, India using SWOT-AHP analysis. GeoJournal. 88, 1493-1513.

[46]
Saxena A., Guneralp B., Bailis R., et al., 2016. Evaluating the resilience of forest dependent communities in Central India by combining the sustainable livelihoods framework and the cross scale resilience analysis. Current Science. 110(7), 1195-1207.

[47]
Schmerbeck J., Kohli A., Seeland K., 2015. Ecosystem services and forest fires in India—Context and policy implications from a case study in Andhra Pradesh. For. Policy Econ. 50, 337-346.

[48]
Sharma D., Tiwari B.K., Chaturvedi S.S., et al., 2015. Status, utilization and economic valuation of non-timber forest products of Arunachal Pradesh, India. J. For. Environ. Sci. 31(1), 24-37.

[49]
Shit P.K., Pati C.K., 2012. Non-timber forest products for livelihood security of tribal communities: A case study in Paschim Medinipur district, West Bengal. J. Hum. Ecol. 40(2), 149-156.

[50]
Shit P.K., Pourghasemi H.R., Das P., et al., 2020. Spatial Modeling in Forest Resources Management. Switzerland: Springer.

[51]
Shit P.K., Pourghasemi H.R., Adhikary P.P., et al., 2021. Forest Resources Resilience and Conflicts. Amsterdam: Elsevier.

[52]
Singh R., Gehlot A., Akram S.V., et al., 2022. Forest 4.0: Digitalization of forest using the Internet of Things (IoT). J. King Saud Univ. Comput. Inf. Sci. 34(8), 5587-5601.

[53]
Sukhdev P., 2009. Costing the Earth. Nature. 462, 277, doi: 10.1038/462277a.

[54]
Suleiman M.S., Wasonga V.O., Mbau J.S., et al., 2017. Non-timber forest products and their contribution to households’ income around Falgore Game Reserve in Kano, Nigeria. Ecol. Processes. 6(1), 1-14.

[55]
Thorbecke E., Charumilind C., 2002. Economic inequality and its socioeconomic impact. World Dev. 30(9), 1477-1495.

[56]
Vaughan A., 2020. World braces for economic impact. New Sci. 245(3272), 10, doi: 10.1016/S0262-4079(20)30477-2.

[57]
Vedeld P., Angelsen A., Bojö J., et al., 2007. Forest environmental incomes and the rural poor. For. Policy Econ. 9(7), 869-879.

[58]
Walle Y., Nayak D., 2022. Analyzing households’ dependency on non-timber forest products, poverty alleviation potential, and socioeconomic drivers: Evidence from Metema and Quara districts in the dry Forests of Amhara Region, Ethiopia. J. Sustain. For. 41(8), 678-705.

[59]
Wunder S., Börner J., Shively G., et al., 2014. Safety nets, gap filling and forests: A global-comparative perspective. World Dev. 64, 29-42.

[60]
Zikargae M.H., Woldearegay A.G., Skjerdal T., 2022. Empowering rural society through non-formal environmental education: An empirical study of environment and forest development community projects in Ethiopia. Heliyon. 8(3), e09127, doi: 10.1016/j. heliyon.2022.e09127.

Options
Outlines

/

〈 〉