Assessing the Impact of Leasehold Forestry in Nepal: Enhancing Livelihoods and Preventing Degradation

Abstract

Global forests, valued at over USD 150 trillion and supporting the livelihoods of 25% of the world’s population, are threatened by degradation and deforestation, particularly in developing nations. Several forest management systems are in practice, but leasehold forestry (LF) is considered the best for achieving the dual goals of preventing degradation and alleviating poverty. Nepal is a pioneer in LF, prioritizing it since 1978. It is now practiced in 39 districts, covering 43,994 hectares of forests. Using DFID’s sustainable livelihood assessment guidelines (a framework never before applied to LF) and incorporating seven additional indicators identified through extensive literature review and expert consultation, as well as semi-structured interviews with key informants (n = 14) and LF users (n = 228), this study quantifies the contribution of LF in Nepal to 5 core assets and 21 livelihood indicators across three categories: “successful”, “moderately successful”, and “not successful”. The results reveal that LF significantly contributes to natural and financial capital, with a lesser impact on social and physical capital. Among the key indicators, LF has the greatest influence on savings and investment, but the least on infrastructure. This study offers targeted lessons and recommendations for less successful LF initiatives, which can help improve their outcomes. These insights are also valuable for policymakers and stakeholders to refine policies and programs and to optimize livelihood and restoration benefits from LF. Additionally, the baseline data provided will serve as a reference for monitoring and evaluating LF initiatives.

1. Introduction

Globally, forests have been an important source of subsistence livelihood for rural households [1,2]. The total value of global forests is estimated at USD 150 trillion, which is twice the value of global stock markets, and around 1.6 billion people (25 percent of the global population) are directly linked to them for subsistence needs, income, and other livelihood activities as per the Global Forest Goals Report, 2023 [3]. One-fifth of the world’s population, especially from agrarian societies, rely on forest-related activities to sustain their livelihood [4,5,6]. Forests contribute to local livelihoods through subsistence use of products, revenue from product sales, and indirect ecological benefits like improved agricultural productivity [7]. Overcoming environment degradation and poverty alleviation are the two major challenges of sustainable development that participatory forest management aims to reduce [8,9]. With these challenges, there is a need for empirical evidence and a better understanding of how participatory forest management contributes to improving different livelihood assets, including human, natural, financial, physical, and social capital, to identify its role in enhancing sustainable livelihood outcomes [8].

Forest areas cover 44.74 percent of Nepal, playing a crucial role in facilitating the livelihood of 76 percent of its total population [10]. The Nationally Determined Contribution (NDC) report for Nepal for the year 2020 identified livelihood and governance as direct influencers that ensure fair and equitable benefits from sustainable forest management among local communities. Community-based forest management in Nepal has been recognized for its significant contribution to sustainable forest management and livelihood improvement among rural communities [11]. Various participatory forest management initiatives were initiated by the government of Nepal in the 1970s and cover community, leasehold, collaborative, religious, protected, and buffer zone community forests [12,13]. Among these, pro-poor leasehold forestry was identified as a priority program in 1978, which aims to improve the livelihood of poor people or households and restore degraded forest land [14]. Leasehold forests are national forests, mostly in degraded condition with a crown cover of less than 20 percent, which are handed over to a small group of poor households for forty years with the rights and responsibilities for better management and utilization of the forest [15,16]. The program has been expanded in 39 districts, where around 7622 leasehold forests cover around 43,994 hectares [17]. A leasehold forest user group (LFUG) may contain five to fifteen of the poorest households [18]. The LFUGs prepare the forest management plan, specifying their objectives for the management of the degraded forest and deciding on livelihood improvement activities [15] with the technical support of project implementation agencies.

Nepal is one of the five major countries globally for the study of forest-based livelihoods; however, most studies focus primarily on community forests [5]. However, there is a shortage of longitudinal studies [19] and scarce comparative analysis between communities dependent on forests versus others [20] or comprehensive assessment of the contributions of forests in the different dimensions of livelihoods [21]. Despite the significant success of community forestry in Nepal, there is a noticeable lack of comprehensive studies evaluating the performance of community forest user groups in sustaining community forests [22]. Few studies have utilized livelihood indicators from DFID, and even fewer have examined other types of participatory forest management programs [18,23,24]. Similarly, there have been few studies undertaken on LF sector livelihoods, with only small numbers of LF individuals [14,18], hindering the clear understanding of their contribution to livelihood assets in different LF levels. Designing and applying context-specific forest management practices with stakeholder participation is crucial for achieving sustainable forest management outcomes [25]. Existing studies primarily focus on financial gains from forest-based products; however, holistic evaluations that consider all forms of capital are needed [26,27,28,29]. Community forests benefit a broader spectrum of local residents [30], which is quite different from an LF program that is specifically designed to address the needs of marginalized and economically disadvantaged groups, particularly the poorest households [31]. Therefore, livelihood indicators designed for CFs may not adequately reflect the clear contribution of LF in the livelihoods of the poorest people. Therefore, there is a need for better understanding of additional livelihood indicators in LF programs that specifically cover degraded forest restoration and livelihood improvement of the marginalized and poor households. This gap has led to insufficient understanding of leasehold forestry practices and their roles in supporting livelihoods, resulting in limited attention at the policy level.

With the handing over of degraded forest areas, it is a big challenge to improve the forest condition and enhance the income generation for the poorest people in the community [32], and the need to acknowledge and quantify the contribution of forest goods and services to people’s livelihood is crucial to driving policies and programs that can incentivize and motivate local communities in forest management [33,34,35]. Forest-based ecosystem services are crucial for improving livelihoods, environmental sustainability, and the economy [36]. Therefore, this paper aims to bridge these gaps by explicitly assessing the relationship between LF management and the livelihood of rural people in the mid-hills of Nepal by comparing three different categories of LFUGs, i.e., successful, medium and not successful, based on income, plantation success, and people’s participation in management criteria. We conducted an assessment based on the DFID Sustainable Livelihood Framework (SLF), a framework used commonly used in community forestry and many other forest management schemes but never before utilized before in the leasehold forestry scheme. We also incorporated seven additional indicators, identified through an extensive literature review and district-level expert consultations for a better understanding of LF impacts on livelihoods. On the backbone of its objectives, this study aims to test the hypotheses such as the establishment of LF having a significant impact on natural capital through forest resource availability, biodiversity conservation, and plantation success. We hypothesized that LF enhances financial capital through greater employment opportunities, income, and access to savings and loans. LF further contributes to human capital through by improving skills, leadership capabilities, and responsibility for forest management by the users. It also contributes to social capital. The user group relationships established within a social network can lead to improved decision-making capacity. These hypotheses provide a framework for analyzing the effectiveness of leasehold forestry in enhancing livelihood outcomes.

For this, the key questions examined in this study are as follows: (a) Whether the successful, medium successful and not successful LFUGs have differential outcomes from leasehold forests? (b) Whether the LF program has strengthen capacity and developed leadership? (c) Whether LF program contributed the physical infrastructure development and equipment availability at the local level? (d) Whether LF program strengthened social cohesion and increased food security in the community? (e) Whether the income generated promoted savings and loan provisions? and (f) Whether the LF program increased tree cover? This paper evaluates the socio-economic status of the local households and analyzes the extent to which the leasehold program has been successful in achieving the goal of enhancing livelihood capitals of the poor community managing three different categories of leasehold capital in mid-hills of Nepal. The findings of this research can be helpful for forest managers, development practitioners, and local-, provincial-, and national-level policymakers in enhancing the effectiveness of the leasehold forestry program to improve the livelihood of poor communities.

2. Materials and Methods

2.1. Research/Analytical Framework

Sustainable Livelihood Framework (SLF)

This study uses the SLF (Figure 1) to study and examine the complex interactions between LF and rural livelihoods in the mid-hills of Nepal [37,38,39]. The SLF was developed initially in the mid-1980s and refined by DFID in 1999 [40]. A sustainable livelihood refers to the combination of capabilities, assets, and activities that people rely on to make a living. A livelihood is considered sustainable when it can withstand and bounce back from stresses and shocks, while also preserving its capabilities and assets for the future without depleting the natural resource base [41,42].

SLF offers a structured way to identify the multifaced dynamics under five major assets: human, social, physical, natural, and financial capitals [43,44,45], which contribute to sustainable livelihood strategies, outcomes, vulnerability, and resilience [46]. The human capital under SLF constitutes the knowledge, skills, health, and capacity of the users that identify how individuals engage in different livelihood strategies and how they achieve their livelihood [47]. On the other hand, social capital encompasses the access to different social networks, linkages, resources, and relationships that play a vital role in resource sharing, collective action, and cooperation within the communities [48]. Natural capital signifies how the community has access to essential natural resources such as forests and land which are pivotal to environmental sustainability and livelihood activities [49]. Similarly, financial capital encompasses access to different financial-related activities such as loans, expenses, and income-generating activities that are crucial for economic resilience and investment. Lastly, physical capital represents the basic assets and infrastructure that are necessary for functioning in different livelihood-based activities including shelter, transportation, and other productive assets [50].

Utilizing an analysis based on the SLF framework, this study aims to identify and interpret how activities that are carried out in LF influence the dynamics of major livelihood assets among different LFUGs [51,52]. Livelihood assets structurally represent the use of capacity, opportunities, and resources that are available to individuals or communities while pursuing and sustaining their livelihoods. By using SLF, this study attempts to understand how LF interventions have impacted crucial livelihood assets in different success-based categories of LFUGs. By examining the degree to which LF improves different livelihood opportunities, enhances social relations, and fosters ecological conditions among forest-dependent communities, this study attempts to offer important insights into the sustainability and efficacy of LF as a poverty reduction tool to the rural areas of the mid-hills of Nepal. By integrating SLF, we aim to provide evidence-based results that can be integrated into sustainable development practices and recommend policy formulation that prioritizes and improves the well-being of different forest-dependent communities and the overall conservation of the natural resources.

2.2. Study Area

This study was conducted in Palpa District of western Nepal. Palpa was selected as the pilot project area for the leasehold forest program in 2010 [2]. The district covers an area of 1373 km², and is situated at 83°53′ N and 27°83′ E. Palpa is a diverse district in terms of the availability of natural resources, with a majority of many spectacular natural areas exhibiting immense contrast in altitude and ecology within a small landmass [53]. The average maximum temperature is 28 °C during May and June, and the minimum is 10 °C in January. Palpa District experiences an average annual rainfall of less than 1000 mm. Until 2018, a total of 143 LFs were handed over in the district, including 82 LFs in the Nisdi Rural Municipality, 24 LFs in the Ribdikot Rural Municipality, 18 LFs in the Purbakhola Rural Municipality, 15 LFs in the Mathagadi Rural Municipality, and 4 LFs in the Rainadevi Chahara Rural Municipality. Figure 2 shows the map of the study area, including Palpa District and the location of the sampled 18 LFs. Among the study areas, Nisdi Rural Municipality is the largest in terms of LF number (82), forest area (407.8 ha), and number of households (965), followed by Ribdikot (24 LFs; 125.5 ha; 387 HHs) and Purbakhola (15LFs; 152.6 ha; 182) (Figure 2). The LFs are mostly covered with Amriso (Thysanolaena maxima) and Tejpat (Cinnamomum tamala). Amriso is a highly promoted and popular species in leasehold forests, used for multiple purposes (e.g., as grass, broom material, and fuel). The species has high economic potential for local and national markets.

2.3. Data Collection

This study primarily relies on first-hand data collected through household questionnaire surveys with LF users. Secondary information on the distribution of LFUGs in various rural municipalities, including their area coverage, date of establishment, and number of households in each group, was collected from the Division Forest Office (DFO), Palpa, and related sub-division forest offices (SDFOs).

Before the start of field work (September 2023), a series of discussions were held with key leasehold stakeholders including DFO, SDFOs, LF rangers, LFUG mobilizers, local leaders, and social mobilizers. Additionally, the discussions helped in identifying three LF categories, namely, successful (S), medium successful (M), and not successful (NS). Also, discussion was useful for developing three selection criteria for LF categories; these were (1) annual income of LF, (2) plantation success status, and (3) users’ participation in LF management (

Table 1. Criteria of selection to differentiate three LF categories based on income, plantation, and participation.

Criteria/Category	Successful (S)	Medium Successful (M)	Not Successful (NS)
Annual income	>USD 750	USD 350 to USD 750	<USD 350
Plantation success status	>90%	50% to 90%	<50%
Users’ participation	>90%	50% to 90%	<50%

). The successful LF category had the highest annual income, the greatest user participation in LF management, and the highest plantation success rate, followed by medium successful and not successful categories (Table 1). A probability proportional to size (PPS) sampling method was used for the selection of the rural municipalities that were the primary sampling units (PSUs). Three rural municipalities, namely, Nisdi with 82 LFs (S = 21, M = 32, NS = 29), Ribdikot with 24 LFs (S = 5, M = 12, NS = 7), and Purbakhola (S = 4, M = 5, NS = 6), were chosen. A simple random sampling method was used for the selection of LF groups within the selected rural municipalities, and, finally, a complete census of all member households in the selected LF group was conducted. Based on these criteria, in total, 18 LFs (6 LFs in each category) were randomly selected for the questionnaire survey (

Table 2. Details of sampled leasehold forests including three major criteria for LF categorization, i.e., income, plantation success, and participation in management.

Category	Rural Municipality	LF Name	Area (ha.)	Household (No.)	Estd. Year (A.D.)	Annual Income (USD)	Plantation Success (%)	Participation in Mgmt (%)
Successful	Nisdi	Bhorma	10.0	12	2012	1015	100	100
	Nisdi	Ripkhoriya	8.0	11	2013	898	100	100
	Purbakhola	Bendada	15.0	12	2012	846	100	100
	Purbakhola	Dholimara	15.3	15	2012	918	100	100
	Ribdikot	Ghungradada	8.2	20	2017	1335	100	100
	Ribdikot	Thulochaur	6.6	14	2017	1050	100	100
Medium successful	Nisdi	Maulathar	12.0	11	2012	682	60	72
	Nisdi	Kafalkharka	5.5	9	2015	432	65	68
	Purbakhola	Golkhadal	10.8	14	2017	456	72	79
	Purbakhola	Bisdada	12.3	11	2016	378	60	77
	Ribdikot	Kalopahara	4.7	20	2015	419	68	69
	Ribdikot	Pakhapani	1.3	14	2015	500	71	62
Not successful	Nisdi	Phapardada	9.1	10	2013	267	15	18
	Nisdi	Katusghari	7.8	11	2015	82	13	24
	Purbakhola	Digaira	8.5	11	2012	0	0	0
	Purbakhola	Khiluwa Bhanghang	7.9	9	2013	0	0	0
	Ribdikot	Basdada	5.8	16	2015	53	18	33
	Ribdikot	Saibangaira	4.2	8	2013	163	12	31

). The LF users were the units of analysis. In total, 228 households participated, and all of them were individually interviewed (Figure 3).

Table 2 shows the names of the selected LFUGs and their key attributes related to LF status. This study employs a mixed-methods approach, combining both qualitative and quantitative research techniques. Primary data were collected from October to November 2023 through key informant interviews (n = 14), field observations, and an individual questionnaire survey (n = 228) with all users of selected LFs, achieving a 100 percent response rate. Key informant interviews (KIIs) were conducted to identify existing and potential indicators of leasehold forests’ contribution to livelihood across five different livelihood capital assets. Following a preliminary discussion with experts and government authorities at the district and local levels, three broad stakeholder groups (i.e., government officials, community leaders, and LF mobilizers) were identified, resulting in fourteen KIIs. We then held discussions with each of these stakeholders (nine LF mobilizers, one LF ranger, three SDFO staff members, and one DFO staff member). Informants were purposively selected to ensure the collection of relevant information and knowledge on the subject. While acknowledging that leasehold forests provide multiple benefits to livelihoods, only 21 indicators were considered to access contribution to livelihood. Field observations were carried out to visually assess the conditions of leasehold forests and their resource distribution, as well as to cross-verify the information gathered from interviews. Data triangulation was further achieved by comparing responses from individual questionnaire surveys with insights from KIIs. After finalizing 21 indicators, an individual household questionnaire survey was carried out to identify the demographic status of the community and the users’ perspectives on the contribution of leasehold forests.

Data on the socio-economic status and contribution of LF to five different capitals were collected from respective LFUGs. To identify the LF categories and criteria for LF types, five livelihood assets, namely, human, physical, social, financial, and natural capitals, were used [18,42]. Six LFUGs of each category were purposively selected based on the criteria of 3 different categories, and in total, 21 indicators were identified for 5 different livelihood capitals, from among which 14 indicators were adopted [42]. An additional seven indicators were developed through district-level LF expert discussion that fit leasehold forests’ impacts on livelihood, as in

Table 3. List of 21 livelihood indicators for five different capitals of LF.

Capital Assets	Indicators
Human capital	State of skill development on LF management (H1) Leadership capacity enhancement (H2) Responsibility bearing among users (H3) Gender equality in LF management * (H4)
Physical capital	Infrastructure construction (P1) Infrastructure construction effectiveness (P2) Enhancement of knowledge on community development (P3) Equipment and tools availability * (P4)
Social capital	Relation among user groups after LF handover (S1) Decision-making ability enhancement (S2) Outward migration control * (S3) Food security improvement * (S4)
Financial capital	Employment opportunities (F1) Time and cost of product collection (F2) Loan provision for IGAs (F3) Saving and investment * (F4)
Natural capital	Forest resource availability (N1) Water sources increment (N2) Greenery and landscape beauty improvement (N3) Biodiversity conservation enhancement * (N4) Plantation success * (N5)

Indicators adopted from [3]. *—Indicators identified from district-level LF expert discussion.

.

Individual face-to-face interviews were carried out, using a semi-structured questionnaire with all 228 users, achieving a 100% response rate. At first, ethical approval was received from the Division Forest Office, Palpa. Respondents were briefed on the study’s objectives, and verbal consent was secured before proceeding. After that, the questionnaire was translated into the Nepalese language to allow better understanding. All individuals were requested to answer questions related to the socio-economic context and LF impacts across all indicators of five different capital assets, regarding whether each indicator had increased or decreased after the handover of the LF, using a five-point Likert scale, as suggested by [54]. Additionally, to assess the contribution of leasehold forests to their livelihoods, respondents were asked to rate their perceptions on a 5-point Likert scale (where 5 = very high, 4 = high, 3 = neutral, 2 = low, and 1 = very low) across 21 specific indicators (see Appendix A for details).

2.4. Data Analysis

Data analysis was performed using R statistical programming version 4.1.2. The analysis involved evaluating the mean and percentage coverage of socio-economic statistics, as well as assessing the impacts of livelihood factors on various indicators of livelihood capital. To visualize the distribution of access to capital assets, the total response values for each capital were plotted in a spider-web diagram. The shape of the resulting pentagon illustrates the variation in leasehold forest (LF) users’ access to each capital [55]. In this diagram, the center of the pentagon represents no access to any capital, while the outer boundary signifies maximum access [18]. Additionally, the Pearson’s chi-square test at a five percent level of significance was carried out to examine the degree of association between leasehold forest and categorical independent livelihood indicators [56].

3. Results

3.1. Communities Involved in LFUG

Among the households involved in the leasehold forest, the households of Indigenous Janajati (Magar), Dalit, and Chettri communities were 221, 4, and 3, respectively. Among the groups, the Magar are known for their warrior heritage, Dalits face discrimination and are often engaged in low-status jobs, while Chettris historically hold power. The Magar was found to be the predominant indigenous community who has benefited the most from the LF in Palpa District.

3.2. Status of Leasehold Forest Management

As per the guidelines of leasehold forest user group formation, the forest was handed over to the LFUGs mostly with members ranging from 5 to 15 households. All sampled LFUGs were handed over before 2018. Each household received an equal proportion of the land area of the LF to manage and execute a wide range of activities of their choice. To strengthen and regulate the user group, the DFO Palpa approved the short-term management plan for 5 years and provided training and other technical guidance as needed. The groups were also given basic inputs such as tools, seeds, and seedlings of multi-purpose tree species (MPTs), as well as goats (as seed animals), as a livelihood upliftment program, and microcredit opportunities to start income-generating businesses. The non-timber forest species such as Amriso (Thysanoleana maxima) and tejpat (Cinnamomum tamala) were planted. Goat and cattle farming, and cultivation of vegetables (cauliflower, cucumber, cabbage, etc.) and fruits (kiwi, guava, orange, lemon, etc.) were carried out as major income-generating activities (IGAs) in the LFs. The demographic details of the sampled LFs are illustrated in

Table 4. Table showing demographic details of respondents of three categories.

Particulars	Category Wise
	Successful	Medium Successful	Not Successful
Average user’s age	46	46	45
Average family members
Total	7	6	6
Male	3	3	3
Female	3	3	3
LF users
Female	47	25	45
Male	37	54	20
Education
Illiterate	20	22	12
School	58	49	51
College	5	5	6
Occupation
Agriculture	64	75	44
Animal husbandry	3	2	1
Foreign employment	16	3	20
Land holding	9	7	14
Food sufficiency	4	5	8
Livestock population
Buffalo	2	2	2
Cow	2	3	2
Goat	9	10	10
Chicken	7	9	13
Pig	1	1	2
Annual income per HH (USD)	1081.3	541.5	130.2
Annual employment (MD)	23	17	9

.

Figure 4 shows that the average annual income per household and average annual employment created were highest in the successful category, followed by the medium successful and the not successful LFs. The average age of LF users in all categories of LF is nearly the same, i.e., 45 years. On the contrary, the average livestock number was the highest in the not successful category, followed by the medium successful and the successful categories.

Figure 5 shows that the average number of family members were nearly equal in all categories. In addition, it was found that the land holding ratio per family is highest in the not successful category, whereas it is lesser and nearly equal in the successful and medium successful categories. It also revealed that food sufficiency lasted less than six months in all three LF categories.

3.3. Contribution of LF to Five Different Livelihood Capitals

Table 5. Table showing the contribution of LF in 5 different capitals and 21 different indicators in three different LF categories. Here, H average, P average, S average, F average, and N average represent the average response values obtained by capitals from all the indicators of physical, human, financial, social, and natural capitals, respectively.

Capitals	Indicators	LF Category/Response Values
		Successful	Medium Successful	Not Successful	Average
Human capital	H1	3.99	2.99	2.43	3.14
	H2	4.01	3.53	2.97	3.50
	H3	4.06	3.78	2.83	3.56
	H4	2.44	2.41	2.32	2.39
	H average	3.63	3.18	2.64	3.15
Physical capital	P1	2.70	2.61	2.58	2.63
	P2	2.64	2.64	2.63	2.64
	P3	4.50	3.97	3.20	3.89
	P4	2.08	2.50	1.15	1.91
	P average	2.98	2.93	2.39	2.77
Social capital	S1	3.98	3.23	3.94	3.72
	S2	2.77	1.82	4.29	2.96
	S3	2.50	2.43	2.37	2.43
	S4	2.69	2.63	2.66	2.66
	S average	2.99	2.53	3.32	2.94
Financial capital	F1	4.07	3.34	2.72	3.38
	F2	3.98	3.79	2.75	3.51
	F3	3.62	3.24	4.51	3.79
	F4	3.93	3.53	4.54	4.00
	F average	3.90	3.48	3.63	3.67
Natural capital	N1	4.48	4.67	2.49	3.88
	N2	3.79	4.07	2.92	3.59
	N3	3.86	4.32	3.02	3.73
	N4	4.50	4.59	2.37	3.82
	N5	4.89	4.71	2.29	3.96
	N average	4.26	4.42	2.65	3.78

shows that the three major contributors to livelihood were saving and investment (F4), plantation success (N5), and enhancement of knowledge on community development (P3) indicators, whereas the three minor contributors to livelihood were equipment and tools availability (P4), gender equality in LF management (H4), and outward migration control (S3). In the successful LF category, the three major contributors to livelihood were plantation success (N5), biodiversity conservation enhancement (N4), and enhancement of knowledge on community development (P3), whereas the three minor contributors to livelihood were equipment and tools availability (P4), gender equality in LF management (H4), and outward migration control (S3). In the medium successful LF category, the three major contributors to livelihood were plantation success (N5), forest resource availability (N1), and biodiversity conservation enhancement (N4) indicators, whereas the three minor contributors to livelihood were decision-making ability improvement (S2), gender equality in LF management (H4), and outward migration control (S3). In the not successful LF category, the three major contributors to livelihood were saving and investment (F4), loan provision for IGAs (F3), and decision-making ability improvement (S2), whereas the three minor contributions to livelihood indicators were equipment and tools availability (P4), plantation success (N5), and gender equality in LF management (H4).

Figure 6 shows that the successful LF category has the greatest contribution to human, physical, social, and financial capital, which are the highest among all categories, but its contribution was lower than that of the medium successful category in terms of natural capital. In the medium successful category, the contribution of LF to all capitals was in the middle range compared to the successful and not successful categories, except for natural and social capital. In the not successful category, the contribution of LF to all capitals was the lowest among all categories except for social capital.

3.3.1. Contribution to Human Capital

Figure 7a shows that the contribution of LF to responsibility bearing among users (H3) was the highest, followed by the state of skill development in LF management (H1), leadership capacity enhancement (H2), and gender equality in LF management (H4), with the response values of 3.56, 3.50, 3.14, and 2.39, respectively. The average response value for human capital was 3.15 (Table 5). The chi-square test at a five percent level of significance revealed that the impact of LF on H1, H2, and H3 indicators was significant, whereas the H4 indicator was not significant (

Table 6. Table showing different indicators and their significance, chi-square statistics, and p-value resulting from chi-square test within the significance level of five percentage.

S.N.	Capitals	Indicators	X2 Statistics	p-Value	Result
1	Human	H1	155.07	1.711 × 10−29 **	Dependent
2		H2	118.97	5.412 × 10−22 **	Dependent
3		H3	129.40	1.719 × 10−25 **	Dependent
4		H4	2.33	0.6761	Independent
5	Physical	P1	2.88	0.2367	Independent
6		P2	0.09	0.9561	Independent
7		P3	134.99	1.145 × 10−26 **	Dependent
8		P4	134.54	4.167 × 10−28 **	Dependent
9	Social	S1	115.04	1.791 × 10−22 **	Dependent
10		S2	183.02	2.389 × 10−35 **	Dependent
11		S3	2.81	0.5903	Independent
12		S4	0.90	0.6384	Independent
13	Financial	F1	193.93	3.72 × 10−39 **	Dependent
14		F2	139.31	1.399 × 10−27 **	Dependent
15		F3	133.23	6.052 × 10−25 **	Dependent
16		F4	97.83	7.112 × 10−19 **	Dependent
17	Natural	N1	214.69	1.41 × 10−43 **	Dependent
18		N2	117.48	5.508 × 10−23 **	Dependent
19		N3	224.50	4.303 × 10−44 **	Dependent
20		N4	213.06	3.137 × 10−43 **	Dependent
21		N5	225.12	8.42 × 10−46 **	Dependent

**—Highly significant at a five percent level of significance.

).

3.3.2. Contribution on Physical Capital

Figure 7b shows that the contribution of LF on the enhancement of knowledge on community development (P3) was the highest, followed by infrastructure construction effectiveness (P2), infrastructure construction (P1), and equipment and tools availability (P4), with the response values of 3.89, 2.64, 2.63, and 1.91, respectively. The average response value for physical capital was 2.77 (Table 5). The chi-square test at a five percent level of significance revealed that the impact of LF on P3 and P4 indicators was significant, whereas its impact on P1 and P2 indicators was not significant (Table 6).

3.3.3. Contribution on Social Capital

Figure 7c shows that the contribution of LF to relations among user groups after the handover of LF (S1) was the highest, followed by decision-making ability improvement (S2), food security improvement (S4), and outward migration control (S3), with the response values of 3.72, 2.96, 2.66, and 2.43, respectively. The average response value for social capital was 2.94 (Table 5). The chi-square test at a five percent level of significance revealed that the impact of LF on S1 and S2 indicators was significant, whereas S3 and S4 indicators were not significant (Table 6).

3.3.4. Contribution on Financial Capital

Figure 7d shows that the contribution of LF on saving and investment (F4) was highest, followed by loan provision for IGAs (F3), time and cost of product collection (F2), and employment opportunities (F1), with the response values of 4.00, 3.79, 3.51, and 3.37, respectively. The average response value obtained by financial capital was 3.67 (Table 5). The chi-square test at five percent level of significance revealed that the impact of LF on F1, F2, F3, and F4 indicators was significant (Table 6).

3.3.5. Contribution on Natural Capital

Figure 7e shows that the contribution of LF on plantation success (N5) was the highest, followed by forest resource availability (N1), biodiversity conservation enhancement (N4), greenery and landscape beauty improvement (N3), and water sources increment (N2), with the response values of 3.97, 3.88, 3.82, 3.73, and 3.59, respectively. The average response value obtained by natural capital was 3.80 (Table 5). The chi-square test at a five percent level of significance revealed that the impact of LF on N1, N2, N3, N4, and N5 indicators was significant (Table 6).

3.4. Dependency of LF on Different Livelihood Indicators

Table 6 shows that among 21 indicators, LF had no significant contribution to five livelihood indicators, namely, gender equality in LF management (H4), infrastructure construction (P1), infrastructure construction effectiveness (P2), outward migration control (S3), and food security improvement. However, LF had a significant contribution to 16 livelihood capital indicators. Among all significant indicators, LF made the greatest contribution to plantation success (N5) and the least contribution to infrastructure construction effectiveness (P2).

4. Discussion

4.1. Contribution of Leasehold Forest on Human Capital Indicators

Our study showed significant contribution of the LF program to three indicators of human capital (p-value < 0.05), namely, skill development in LF management, increased responsibility among users, and enhanced leadership capacity. Similar to our study, [18] found a response value of 0.74 out of 1 in LF, and [23] found 2.59 out of 3 in CF, reflecting an enhancement of human capital from participatory forest management by local people. Reference [18] also mentioned that leasehold forestry has a positive contribution in human capital indicators. Pro-poor households involved in leasehold programs are exposed to skill development activities through various training opportunities [52]. The leasehold forest program has resulted in increased responsibility-bearing and decision-making capacity among local marginalized groups of the community [57,58,59]. Studies from other regions, such as India and Vietnam, also highlighted the role of community-based forest management programs in skill enhancement and capacity building among marginalized groups [60,61]. These findings support our results and indicate that participatory forest management is a globally relevant tool for promoting rural development and improving livelihood.

On the other hand, this study revealed that gender equality was not significantly enhanced. Despite focusing on the restoration of forest and income-generating activities that target the poor regardless of gender or caste, gender sensitivity in the LF program remains unclear [62]. Reference [57] also supported this statement, claiming that even though the LF program has created many positive outcomes, particularly in biodiversity conservation and community development, the inclusion of marginalized women and other disadvantaged groups remains insufficient. Similarly, studies from Sub-Saharan Africa and Latin America indicate that women’s participation in forestry management often remains minimal due to socio-cultural constraints, despite gender inclusion policies [63,64]. Despite gender mainstreaming efforts, transformative changes remain elusive in forest management [65]. This highlights the urgent need for effective gender mainstreaming that can address inequalities and can act as a driver for meaningful transformative change in forest governance.

4.2. Contribution of Leasehold Forest on Physical Capital Indicators

Our study shows that two indicators on physical capital, i.e., the enhancement of knowledge on community development and availability of equipment and tools, have significantly increased (p-value < 0.05) after handover of LF, whereas infrastructure construction and its effectiveness were found to be not significantly improved. Poor communities that rely on forests for their livelihood have been involved in various knowledge-sharing programs and are aware of community development through better forest management [14,66]. Major inputs, including training, tools, and seed and seedlings of desired species, are provided by respective forest offices for the establishment and success of leasehold forests [67], which has resulted in the availability of tools and equipment to forest users. The main focus of LF is the degraded forest land restoration with economically valuable species and to improve the livelihood of poor users through income-generation activities [14,68]. Findings from Nepal align with studies in Ethiopia and Indonesia, where community-based forest programs provide essential tools and seedlings to local people, increasing their technical capacity to manage forests sustainably for sustainable forest management [69,70]. As a result, LF does not prioritize infrastructure development or its strengthening. Reference [18] found a response value of 0.95 out of 1 in LF (high contribution), and [23] found 1.43 out of 3 (noticeable contribution) in CF, both indicating an enhancement of physical capital through forest management by local people. Contrastingly, [67] found that leasehold forestry played a major role in physical capital by focusing more on construction and its effectiveness. This could be due to the specific selection of the study area, where 60 percent of stone roof replacement activities were carried out at that time. In contrast, studies in China indicate that integrating forestry programs with local infrastructure projects can lead to enhanced economic benefits, suggesting a potential area for future improvement in Nepal’s LF approach [71].

4.3. Contribution of Leasehold Forest on Social Capital Indicators

Our study identified significant growth in the relationship among user groups and decision-making ability (p-value < 0.05) after the intervention of the LF program. However, food security and outward migration control were not significantly enhanced. Reference [21] found 2.57 out of 3 in CF, which also showed enhancement of social capital from forest management by local users. A similar study by [18] also concluded that collaboration among users for forest management and equitable benefit sharing within users enhances the social relationship and the spirit of cooperation within the community. Comparable results were found in participatory forestry projects in Uganda and Brazil, where increased cooperation among users and collective decision making led to stronger social cohesion [72,73]. Also, [74] found that significant representation of poor and marginalized groups, direct involvement in decision-making process, and motivation for income generation enhanced users’ decision-making ability. These factors were also observed in leasehold forest planning and management activities. Reference [75] concluded that there was 37.5 percent increase in the decision making of minority groups of the community. Out-migration, especially in Nepal’s mid-hills, is altering community engagement in forest management, and this can pose challenges to sustainability [76,77]. Furthermore, rural out-migration can affect different indicators of other forms of capital, such as labor availability (physical capital), departure of skilled individuals (human capital), participation in forest management activities (natural capital) [76], and increased burdens to women in forest management activities [78]. This challenge is not unique to Nepal, as similar patterns of out-migration affecting local forestry projects have been observed in Bolivia and Myanmar, highlighting the need to integrate migration policies with forest conservation strategies [79,80]. Hence, as argued by [81], all the capitals in SLF are inter-related with one another, and it can form a complex web of dependencies. In this case, rural out-migration, for example, not only impacts social capital but also has ripple effects on physical, human, and natural capital dynamics. References [21,82] argued that rural out-migration can increase natural capital because it leaves private land abandoned.

Contrastingly, [21,83] concluded that LF plays an important role not only in ensuring food security but also in fulfilling food diversity and nutritional requirements. This could be because [83] studied the combination of both community and leasehold forests which did not specifically focus on LF, where the users mostly cultivate medicinal plants and broom grass species for their income. Additionally, the research conducted by [84] among the indigenous Chepang community showed that they usually relied on wild food as their main diet. In contrast, [84] highlighted LF’s role in safeguarding food security and diversity, whereas [18] emphasized LF’s stronger impact on social capital, though their focus and study population may differ from ours. Furthermore, they are exploring alternative applications for remittance income, which undermines the sustainability of forest utilization [76]. Additionally, [18] found that LF has a higher contribution to social capital than our study (0.78 out of 1), which could be due to the exclusion of food security and outward migration control in livelihood indicators, leading to a higher response value for social capital.

4.4. Contribution of Leasehold Forest on Financial Capital Indicators

Our study shows that the LF has a significant contribution on all livelihood financial indicators (p-value < 0.05). All indicators, including saving and investment, loan provision for IGAs, less time and cost of products collection, and employment opportunities, were significantly increased with the handover of the LF. Many of the leasehold forests have already achieved financial returns for the community and generated income individually through restoration on degraded forest land in the mid-hills of Nepal. Reference [18] found a response value of 0.72 out of 1 in LF (high contribution), which also showed a higher increment of financial capital from the LF program. Our finding aligns with several other studies who highlight the forest’s significant benefits in financial capital indicators [85,86]. LF helps in the economic upliftment through mandatory saving and investment activities [87]. Soft loan provision for higher income and investment for local users is one of the major benefits of handing over the management of forest area to poor local people [88]. There is clear evidence that the availability of forest resources has increased and there has been a decrease in collection time after the participation of local people in forest management activities [89,90]. Similarly, international studies show that reducing dependency on external forest products through local management increases household income and financial stability, as demonstrated in Vietnam and India [91,92]. Reference [75] identified that fodder collection time was reduced by 45 percent and there was an increase in forest resources production due to the LF handover. An increase in employment opportunities results from the restoration of degraded land with economically valuable species [18,74]. On the contrary, a study conducted by [23,66,93,94] on community forests reported a lower response value (0.21 out of 1) in financial capital indicators. The reason behind the low contribution could be the exclusion of a saving and investment indicator in their study and that the sampled community forests were mostly prioritized for infrastructure development compared to others.

4.5. Contribution of Leasehold Forest on Natural Capital Indicators

Our study showed that the contribution of LF was significant in all the indicators of natural capital (p-value < 0.05). All indicators, including plantation success, forest resources availability, biodiversity enhancement, greenery and landscape beauty, and water sources increase, significantly increased with the handover of the leasehold forest. This aligns with similar findings from forest restoration programs in Mexico and Thailand, where participatory forest management contributed to increased biodiversity and water conservation [95,96]. In the study area, local people were mostly involved in various plantation activities leading to the restoration of degraded land. Several studies, including [23,66] in Nepal, [97] in Vietnam, and [98] in India, found similar results. Similar to our results, the response value to natural capital was 2.7 out of 3 in [23], and the response value was 0.6 out of 1 in [98], which showed a positive contribution to natural capital. Reference [68] identified that there was an increase in vegetation cover from 32%–90% with the implementation of the LF program. Not only leasehold forest, but also community forest programs in hilly areas also showed the highest contribution to be in natural capital [23,99]. Similar findings were observed by [100], who concluded that the leasehold forestry significantly resulted in the plantation success. In addition, handing the forest to the local communities led to increased access and use of local forest products [68,101], whereas there was notable enhancement of biodiversity conservation and greenery in degraded lands [102,103,104]. Moreover, forests managed by local people were found to contribute to the increased frequency and quantity of available water sources, as evidenced by studies [105,106]. Similar concerns have been raised in studies from Cambodia, where degraded land rehabilitation requires long-term investment before yielding tangible livelihood benefits [107]. However, [108] argued that providing forest areas below 0–20 percent crown cover does not give instant benefit to the ultra-poor; rather, it worsens their livelihood. Similarly, our result differs to the result of [18], as their natural capital was found to be lower (0.59 out of 1) than other capitals. This could be because the authors included disturbance indicators such as forest fire incidence, grazing, and encroachment in their study.

This study relies solely on self-reported data from all LF users, which are distributed within a limited spatial area, and may not be sufficient basis for generalization. Moreover, the study’s cross-sectional design hinders the ability for cause-and-effect associations to be drawn between LF program intervention and the outcomes. Also, the current research mainly focuses on the social, economic, and environmental dimensions of the outcomes and, therefore, other aspects, such as political, cultural, or legal, are not considered. However, the inclusion of multiple livelihood indicators and a high response rate enhances the robustness of the study findings. Future studies should focus on establishing criteria to differentiate various LF categories. Exploring longitudinal research tracking the environmental impacts of LF programs, considering demographic changes and the impacts of migration, are of future need.

5. Conclusions

This paper assesses the contribution of leasehold forests to the livelihood of poor forest users in the mountainous regions of Nepal. The promotion of pro-poor leasehold forestry reduces forest degradation and enhances the contribution of the forest sector to the long-term improvement of local livelihoods. In the context of the UN Decade on Ecosystem Restoration, the Sustainable Development Agenda, and the Post-2020 Global Biodiversity Framework, the effective implementation of the leasehold forestry program could be considered a proven measure for constituting national planning and action. This study documents several specific sets of lessons from leasehold forestry practices along the continuum of success. Overall, LF has a major contribution to natural- and financial-capital-related indicators, whereas it has a minor contribution to social- and physical-capital-related indicators. Among all significant indicators, LF contributes most to saving and investment, whereas it contributes least to the effectiveness of infrastructure construction. Comparing indicators of different capitals, LF makes the greatest contribution to responsibility bearing among users in human capital, enhancement of knowledge on community development in physical capital, relations among user groups after the LF handover in social capital, saving and investment in financial capital, and plantation success in natural capital. However, this study has certain limitations. The reliance on self-reported data may introduce response bias, and the study’s focus on a specific geographic region limits the generalizability of the findings. Despite these limitations, the inclusion of multiple livelihood indicators and a high response rate enhances the robustness of the findings. These lessons can be instrumental in improving less successful LFs. Forest offices can prepare detailed leasehold forest management plans, and central/provincial government can formulate pro-poor leasehold forest management guidelines. The baseline data provided by this study serve as a valuable resource for LF processes and future research. Overall, this study can assist policymakers and LF stakeholders in optimizing livelihood outcomes from LF and other community-based forest management. Future research could further develop these outcomes by establishing standardized criteria for differentiating LF categories and identifying relevant livelihood indicators that would contribute to more refined assessments and policy recommendations.