




































Ecology, Economy, and Society–the INSEE Journal 8(1): 99-131, January 2025 

RESEARCH PAPER 
 

Methodological Accounting of the Ecosystem Services 
Benefits of Community-based Restoration: A 
Systematic Review 
 

Sony Baral, Aayoush Raj Regmi, Khagendra P. Joshi, Kishor 
Atreya**** 
 
Abstract: Ecosystem services are important for human well-being; yet, their 
valuation poses significant challenges, particularly in the context of benefit–cost 
analyses (BCA) for ecosystem restoration. This is because most ecosystem services 
are not bought or sold directly on the market. This study systematically reviews the 
cost and benefit parameters used in BCA and highlights the limitations in 
accounting for the social benefits of community-based forest restoration 
programmes. Of a total of 500 research articles, 41 met our inclusion criteria, with 
only 11 focusing on community-managed projects. The analysis reveals that 
transaction costs are often underrepresented, while timber resources and carbon 
sequestration are prioritized. Common valuation methods include the direct market 
price approach (46%) and benefit transfer (63%). The economic analysis shows a 
higher median benefit–cost ratio for general forest management projects (2.24) 
compared to community-managed programmes (1.65). These findings underscore 
the need to refine BCA variables and enhance economic efficiency in community 
forest restoration efforts. 
 
Keywords: Benefit Cost Analysis, Restoration, Community Managed Forests,  
Valuation Techniques 

 
 

                                                      
 Institute of Forestry, Tribhuvan University, Kathmandu, Nepal 44600 (TU); 

sbaral@iofpc.edu.np  

 School of Forestry & NRM and Institute of Forestry, TU; aayoushrajregmi@gmail.com 

 Institute of Forestry, TU; Joshikp01@gmail.com  

**** School of Forestry & NRM, Balkhu Kathmandu and Institute of Forestry, Pokhara 
Campus, TU; katreya@iofpc.edu.np 

Copyright © Baral, Regmi, Joshi, Atreya 2025. Released under Creative Commons 
Attribution © NonCommercial 4.0 International licence (CC BY-NC 4.0) by the author.  

Published by Indian Society for Ecological Economics (INSEE), c/o Institute of Economic 
Growth, University Enclave, North Campus, Delhi 110007.  

ISSN: 2581–6152 (print); 2581–6101 (web).  

DOI: https://doi.org/10.37773/ees.v8i1.1169  

mailto:sbaral@iofpc.edu.np
mailto:aayoushrajregmi@gmail.com
mailto:Joshikp01@gmail.com
mailto:katreya@iofpc.edu.np
https://doi.org/10.37773/ees.v8i1.1169


Ecology, Economy and Society–the INSEE Journal [100] 

1. INTRODUCTION 

Ecosystems all over the world have undergone substantial changes in recent 
decades. For example, approximately 24% of the Earth’s land is being used 
for cultivation, causing widespread alterations. The destruction of 
ecosystems has had profound impacts, with approximately 35% of the 
world’s mangroves and 20% of the world’s coral reefs having been lost over 
the past three decades (Reid et al. 2005). These changes have resulted in 
significant consequences for both human well-being and the environment. 
To minimize such impacts to the benefit of the environment, much effort 
has gone into restoring deforested and degraded land worldwide through 
various restoration projects, such as the Bonn Challenge 2011, a global 
initiative that aims to restore 350 million ha of degraded and deforested 
land by 2030, among many others (IUCN n.d.). 

Forest ecosystems offer both use and non-use values. Use values 
encompass direct benefits (DBs) provided by the ecosystem, such as 
timber, firewood, recreation, fishing, carbon storage capacity, habitats, 
impressive views, and so on, whereas the non-use values include option, 
bequest, and existence values (Krieger 2001). Despite its numerous benefits, 
undertaking an economic evaluation of forest restoration remains a 
significant challenge because the costs of forest restoration are paid 
upfront, whereas the benefits accrue over time (Daily 1995). Moreover, 
many of these values are not traded in the market, and even though they are 
critical to consider in the trade-off, they are often overlooked in ecosystem 
restoration projects (Robbins and Daniels 2012). The appropriate valuation 
of ecosystem restoration projects is crucial because it would help assess the 
multiple benefits of ecosystem services, thus contributing to effective 
decision-making, planning, and implementation. It would also improve 
project transparency by enabling the optimal use of resources (Reid et al. 
2005; Robbins and Daniels 2012). 

Among the various methods of economic valuation, benefit–cost analysis 
(BCA) is a popular tool used by decision-makers to calculate the cost and 
possible benefits of a project or a policy. It facilitates decision-making 
process by incorporating all the costs and benefits of the project for society, 
including both market and non-market values. In the case of non-market 
values, a BCA helps provide information to decision-makers on the 
environmental changes resulting from a policy or project that would benefit 
society (Atkinson and Mourato 2008), whereas in the case of direct market 
values, it provides an evaluation of the environmental goods that are 
directly traded in the market. It is particularly relevant for valuing the 
provisioning services of the ecosystem, such as timber, firewood, and 
fodder. 



[101] Baral, Regmi, Joshi, Atreya 

Several approaches have been developed to calculate the non-market value 
in monetary terms: stated preference, revealed preference, and the value 
transfer method. Stated preference is captured using survey questions that 
ask respondents to choose between the scenario or status quo and about 
their willingness to pay to obtain a specified good (Atkinson and Mourato 
2008; Van Zanten et al. 2023). Contingent valuation and choice experiments 
are some of the techniques used in the stated preference method, which 
uses a hypothetical market scenario to help estimate all the components of a 
total economic valuation. In contrast, revealed preference is based on the 
actual preferences of people for environmental goods (Saarikoski et al. 
2016). It utilizes information from the actual market to determine the value 
of non-market goods. The hedonic pricing method and the travel cost 
method are notable examples of the revealed preference method and are 
used in restoration projects. In the hedonic method, the pricing is based on 
the attributes of the services (Chee 2004) and is commonly used to evaluate 
projects in urban areas, whereas the travel cost method is used to evaluate 
the direct use value of recreational sites (Cheng et al. 2019) and the 
behaviours of recreational users. The value transfer method, another 
method of calculating ecosystem services, makes inferences regarding the 
economic value of environmental goods and services at one place based on 
the existing primary valuation studies in other locations (Hu et al. 2020). 
Finally, the cost of avoided damage is a market-based valuation approach 
that estimates the benefits of avoiding the damage that would have 
occurred in the absence of a particular service (Gerner et al. 2018). 

The BCA also estimates both direct and indirect costs, with the latter 
subdivided into opportunity costs and other indirect costs. Direct costs in 
forest restoration projects are mostly the capital expenditures associated 
with nursery operation, silviculture operation, and protection of the forest, 
as well as recurrent expenditures, including the costs associated with forest 
user group (FUG) institution management, such as meetings and travels 
(Walton et al. 2006). Opportunity costs refer to the profits from alternative 
land uses forgone by maintaining the land under forest cover because 
keeping land under forest cover excludes the possibility of other land uses. 
Evaluating the benefits of the next best option to forest cover, or the most 
likely alternative land use, is a way to assess the opportunity cost. Direct and 
opportunity costs must be considered to assess the total cost of forest 
restoration. 

Costs are hardly discussed in the restoration literature (Robbins and Daniels 
2012). Of the around 2,000 studies conducted on restoration, only 95 
provide insightful information on cost data (TEEB 2009). The socio-
economic aspects of restoration are often overlooked (Aronson et al. 2010; 



Ecology, Economy and Society–the INSEE Journal [102] 

Wortley, Hero, and Howes 2013). Usually, although communities are 
reluctant to make monetary contributions, they are willing to play a role in 
restoration through other means (such as labour contribution) (Rai and 
Scarborough 2015). Even if direct and indirect costs are taken into account, 
the opportunity cost forgone may not be included in studies (Rai, Neupane, 
and Dhakal 2016). To examine the effectiveness of a project, it is imperative 
to take into account all cost parameters, including the costs associated with 
monitoring the forest, volunteer time, lost revenue, and other non-
monetary benefits. This review provides an overview of the cost and benefit 
parameters as well as the limitations in accounting for social benefits in the 
existing literature on restoration projects. Estimating the cost and benefit 
also helps decision-makers understand the efficiency of the restoration 
investment. 

 

2. METHODOLOGY 

The study systematically assessed the literature using the Google Scholar 
database, a scientific citation indexing service that offers the ability to 
search across multiple databases, enabling thorough exploration of the 
academic and scientific literature. 

2.1 Searching for Articles and Language 

The search was performed in English using the keywords section of the 
Publish or Perish 8 software, in a one-time operation, without interrupting 
the results. The Boolean operator AND was used between the search terms. 
Only articles published in the English language or translated into English 
were included as they could be understood by the whole review team. 

2.1.1 Search String 

We conducted a single search of the literature on 10 May 2023, using the 
following keyword combination: cost and benefit AND benefit and cost analysis 
AND benefit and cost analysis methodological framework AND economic of benefit cost 
analysis AND economic of forest restoration AND benefit cost analysis forestry 
programme  

2.1.2 Software and Search Setting 

We used the Publish or Perish 8 software to perform a systematic review of 
studies on BCA of restoration programmes. We set up the environment of 
the software in such a way that it listed up to 500 articles between 2005 to 
2022. For this, we used the Google Scholar search engine due to its 
comprehensive search capabilities (Gusenbauer 2019). 



[103] Baral, Regmi, Joshi, Atreya 

2.2 Article Screening and Inclusion Criteria 

We screened and included articles based on their relevance to the research 
topic, publication within a specified timeframe, and peer-review status. We 
only considered studies published in the English language with accessible 
full texts and clear methodological rigor. Opinion pieces and studies lacking 
empirical data were excluded, ensuring that our review comprises only high-
quality, pertinent research. 

2.2.1 Screening Process 

Out of 500 research articles, we retrieved 128 articles manually by reading 
the titles and abstracts. Manual evaluation enabled a contextual 
understanding of the subject, flexible interpretation, and subjective 
judgement of the article. We then adopted a hierarchical approach for an in-
depth analysis of the 128 articles in the following sequence: title, abstract, 
methodology, and result. This later helped us develop a review protocol. 
When we could not individually decide whether to include or exclude an 
article, we discussed it with the review team to arrive at a decision. We also 
manually evaluated the 128 articles by inspecting the full text of each article 
and finally selected 41 research articles (n = 41) for systematic review by 
removing irrelevant and duplicate articles. 

2.2.2 Inclusion Criteria 

To ensure quality, articles from inaccessible sources, review articles, books, 
reports, and working articles were not included in the review (Table 1); we 
only included articles published in Q1 journals (Table 2). 

We further narrowed our selection by considering articles published in 
English, journals with primary data only, and articles containing the 
keyword benefit–cost analysis (or cost–benefit analysis) along with any of 
the other keywords in the title or abstract (Figure 1). 

Table 1: Inclusion and Exclusion Criteria for Article Selection 

Inclusion criteria Exclusion criteria 

Q1 journal Other than Q1 journals 

Primary research article Books, reports, and working articles 

Article in English Review article 

Keywords included in title and abstract Inaccessible journal article 

Source: Authors’ analysis 

2.3 Developing a Review Protocol  

A review protocol was designed in MS Excel. The protocol was developed 
based on information obtained from the initial screening of articles. We 
classified the articles under different headings and subheadings. Articles 



Ecology, Economy and Society–the INSEE Journal [104] 

with similar research themes were included under the same heading. There 
were five main themes (restoration types, cost parameters, benefits 
parameters, valuation technique, and economic analysis) and three research 
designs (qualitative, quantitative, and mixed methods) in the protocol. 

Figure 1: Article Selection Stages, Including the Initial Search and Screening Steps 
to the Final Articles Included in the Review 

Source: Authors’ framework adopted for review 

2.4 Data Coding 

The studies selected for the review contained many types of relevant data, 
including bibliographic information, study characteristics, and other data. 
Reviewers extracted the data from the articles included and coded them in 
the worksheet. Furthermore, we held regular discussions on how to refine 
the protocol and address questionable data that emerged during the coding 
process. 

2.5 Limitations 

Although the review process was systematic, there were some limitations. 
We used only one search engine, Google Scholar, to search for articles. 



[105] Baral, Regmi, Joshi, Atreya 

Thus, other relevant articles may have been excluded from the search. Also, 
even though some local journals and grey literature provided invaluable 
insights regarding our subject matter, we did not include those articles due 
to uncertainties regarding the peer review process, which could result in 
questionable data. The findings of this article are solely based on the search 
keywords, and the results of this review article align with the findings of the 
selected articles alone. Data extraction and coding were challenging because 
of the diverse nature of the methods and indicators in the included studies 
as well as the different interpretation approaches adopted in each article. 

 

3. RESULTS 

3.1 Spatial and Temporal Distribution of the Publications  

The articles reviewed were from 24 different countries, spanning six 
continents (Figure 2), with the highest number of articles from Asia (n = 
10, 24.3%), followed by Europe (n = 7, 17%). All other continents 
(Australia, South America, and North America) had six articles each. 
Country-wise, the highest number of articles was from Brazil (n = 5, 
12.1%), followed by New Zealand, Australia, Indonesia, Mexico, and the 
USA, with three (7.3%) articles each. Most of the articles only focused on a 
single country; only two (4.8%) articles (Meyerhoff and Dehnhardt 2007; 
Stafford et al. 2017) covered multiple countries. 

Figure 2: Spatial Distribution of the Selected Studies 

 

Source: Authors’ analysis 

As stated above, we searched for articles published between 2005 and 2022, 
but most of the studies were from the last decade of the 2010s (n = 29, 



Ecology, Economy and Society–the INSEE Journal [106] 

70.7%) (Figure 3). There were no articles published in 2009 and 2022; the 
latest articles included in our review are from the year 2021, from Nepal 
(Paudel, Bhusal, and Kimengsi 2021) and Brazil (Bechara et al. 2021). 

Figure 3: Temporal Distribution of the Selected Studies 

 

Source: Authors’ analysis 

3.2 Restoration Types and Data Collection 

To assess the restoration types, the literature was classified into four main 
restoration categories (landscape, forest, wetland, and agroforestry), and the 
management was divided into community management or “others”, as 
shown in Figure 4. The “others” class represents various other management 
regimes, for instance, managed by non-governmental organizations/ 
international non-governmental organizations (NGOs/INGOs) or 
government or private projects. Among them, the majority of articles 
focused on the landscape category (Almansa, Calatrava, and Martínez-Paz 
2012; Balana et al. 2012; Crossman et al. 2010; Garcia-Quijano et al. 2005; 
Pistorius, Carodenuto, and Wathum 2017) (n = 18, 44%) and on forest 
ecosystems (Aheto et al. 2016; Bechara et al. 2021; Keefe, Alavalapati, and 
Pinheiro 2012; McPherson et al. 2017; Moriizumi, Matsui, and Hondo 2010; 
Paudel, Bhusal, and Kimengsi 2021) (n = 20, 49%). Wetlands were covered 
by eight articles (Crossman et al. 2010; Daigneault, Eppink, and Lee 2017; 
Jerath et al. 2016; Johnson et al. 2016; Polizzi et al. 2015), whereas 
agroforestry was covered by ten articles (Djamhuri 2008; Siregar et al. 2007; 
Torres et al. 2010; van der Horst 2007). Among the 41 articles reviewed, 
around 26% (n = 11) covered community-managed restoration projects. 
However, there was a lack of articles on community-managed landscape 
restoration projects, with only one publication (Gunawardena and Rowan 
2005) being found. In contrast, there were more articles on community-run 
agroforestry projects (n = 4, 9.75%) than other projects (n = 2, 4.87%). 



[107] Baral, Regmi, Joshi, Atreya 

 

Figure 4: Coverage of Different Restoration Types in the Articles 

Source: Authors’ analysis 

Figure 5: Data Collection Techniques 

Source: Authors’ analysis 

For the BCA, various methodologies were used to acquire data sets. The 
data sources used in the studies were generated mostly through secondary 
data set sources (n = 31, 75%), and approximately 43% (n = 17) of the 
articles utilized spatial techniques. Household surveys, key informant 



Ecology, Economy and Society–the INSEE Journal [108] 

interviews (KIIs), and biophysical surveys were each conducted in nine 
articles, while focus group discussions (FGDs) were conducted in two of 
the studies (Figure 5). As BCA requires various types of data and 
parameters, a large number of studies (n = 32, 78%) used more than one 
data collection technique. 

3.3 Cost and Benefit Types  

In our analysis, we categorized the studies into two cost types and two 
benefit types. This resulted in four categorizations: direct implementation 
cost (DIC), transaction cost (TC), direct benefit (DB), and indirect benefit 
(IB). DBs are those benefits that are directly enjoyed or utilized by the users 
or the management. Among the various categories, IB was the one most 
often used by studies (n = 31, 75%) followed by DIC (n = 30, 73%), 
whereas TC was the least used (n = 10, 24%). Among the 41 articles 
studied, only 4 (9.7%) used the four types of costs and benefits. 
Furthermore, 15 (36.5%) of the articles used both benefit categories, while 
only 10 (24.3%) included all cost categories (Figure 6). 
 
Figure 6: Percentage of Articles Covering Various Cost and Benefit Types 

 
Source: Authors’ analysis 

 

3.3.1 Cost Parameters 

The two cost types, DIC and TC, were further divided into various cost 
parameters (Figure 7). The DIC consisted of six parameters: seedling cost 
incurred during the establishment of forests or plantations, fencing costs, 



[109] Baral, Regmi, Joshi, Atreya 

silvicultural operation costs (thinning, pruning, harvesting, etc.) for the 
management of forests or other vegetation, cost of fire line preparation, 
costs incurred for patrolling, and the management cost of staff, meetings, 
and so on. Among these parameters, seedling and silvicultural operation 
costs were calculated in the highest number of studies: 22 (53.6%) and 21 
(51.2%) studies, respectively. The articles incorporating almost all of the 
cost parameters were the following: Barry et al. (2014); Bechara et al. (2021); 
Keefe, Alavalapati, and Pinheiro (2012); Kroeger et al. (2019); Paudel, 
Bhusal, and Kimengsi (2021); Pistorius, Carodenuto, and Wathum (2017); 
Rai, Neupane, and Dhakal (2016); and Wiskerke et al. (2010). The articles 
calculating fewer cost parameters—one or two only—include Borrego and 
Skutsch (2014); Daigneault, Eppink, and Lee (2017); Moriizumi, Matsui, 
and Hondo (2010); Siregar et al. (2007); and van der Horst (2007). We 
found similar trends among these articles, with most of them incorporating 
seedling costs alone in their study. Fire line construction was the least-
utilized parameter in DIC, with nine studies (21.9%) covering it, and its cost 
was calculated only by those articles that calculated all the other cost 
parameters in their study (Barry et al. 2014; Djamhuri 2008; Verdone and 
Seidl 2017). Also, 11 articles (26.8%) in the study solely focused on 
calculating benefits without considering DICs and TCs (Almansa, Calatrava, 
and Martínez-Paz 2012; Haglund et al. 2011; Jerath et al. 2016; Jim and Chen 
2006; Johnson et al. 2016; McPherson et al. 2017; Morri et al. 2014; Polizzi et 
al. 2015; Strassburg et al. 2016). 
 
Figure 7: Number of Reviewed Articles Based on Cost Parameters 

 

Source: Authors’ analysis 

3.3.2 Benefit Parameters 

For the benefits, various IB and DB parameters were also analysed (Figure 
8). Six DB parameters were analysed: firewood, timber, fodder, litter, non-



Ecology, Economy and Society–the INSEE Journal [110] 

timber forest products (NTFPs), and horticultural benefits. Among these, 
timber was the most studied DB parameter, by around 53.6% (n = 22) of 
the studies (Naime et al. 2020; Olschewski and Benitez 2005; Warren-
Thomas et al. 2018), followed by firewood (Morri et al. 2014; Stafford et al. 
2017; Verdone and Seidl 2017) and horticulture (Borrego and Skutsch 2014; 
Flugge and Abadi 2006; Siregar et al. 2007), which were both studied by 
31.7% (n = 13) of the articles. The benefit of litter was the least-used DB 
parameter, with only 9.7% (n = 4) of the studies calculating it (Djamhuri 
2008; Flugge and Abadi 2006; Wiskerke et al. 2010). Additionally, only eight 
articles (19.5%) covered the additional DBs offered by NTFPs (Flugge and 
Abadi 2006; Irawan, Tacconi, and Ring 2013). 

Figure 8: Number of Reviewed Articles Based on Benefit Parameters 

Source: Authors’ analysis 

 
IBs are those benefits that are not directly enjoyed by users. These include 
recreational activities, carbon sequestration, water purification, increased 
biodiversity, and reduced soil erosion. Carbon sequestration was the IB 
most evaluated in the publications we studied, with 25 (60.9%) of the 
studies calculating it (Jerath et al. 2016; Naime et al. 2020; Olschewski and 
Benitez 2005; Verdone and Seidl 2017). The other IBs included water 
(Kroeger et al. 2019; Lee et al. 2018), biodiversity (Garcia-Quijano et al. 2005; 
Ndebele and Forgie 2017; Wiskerke et al. 2010), erosion reduction (Balana et 
al. 2012; Lee et al. 2018; Morri et al. 2014), and recreational benefits (Jim and 
Chen 2006; Logar, Brouwer, and Paillex 2019), which were calculated by 15 
(36.5%), 14 (34.15%), 13 (31.7%), and 11 (26.8%) studies, respectively. 
Finally, there were seven articles (14.6%) that did not include any IB 
parameters in their study (Aheto et al. 2016; Bechara et al. 2021; Djamhuri, 
2008; Haglund et al., 2011; Paudel, Bhusal, and Kimengsi, 2021; Rai, 
Neupane, and Dhakal 2016; Siregar et al. 2007). The various cost and 



[111] Baral, Regmi, Joshi, Atreya 

benefit parameters utilized by the articles are visually represented in Figure 
9. 

Figure 9: Word Cloud of the Cost and Benefit Parameters Utilized in the Study 
According to the Frequency of Use 

 
Source: Authors’ analysis 

Figure 10: Utilization of Different Valuation Techniques for Various Cost–Benefit 

Parameters 

 
Source: Authors’ Analysis  



Ecology, Economy, and Society–the INSEE Journal 8(1): 99-131, January 2025 

Table 2: Cost and Benefit Parameters and Valuation Techniques in the 41 Articles Selected 

Article 

Direct 
Implementation 

Cost 

Transaction 
Cost 

Direct Benefits Indirect 
Benefits 

Valuation Technique 
Used 

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Balana et al. (2012) 

    ✔      ✔  ✔      ✔  ✔ 

Direct market price 
(DMP), avoided cost 
(AC), value transfer 
(VT) 

Wiskerke et al. (2010) ✔ ✔  ✔    ✔   ✔ ✔ ✔ ✔ ✔ ✔  ✔  ✔  VT 

Pistorius, Carodenuto, and Wathum 
(2017) 

✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔   ✔      ✔ ✔ ✔ ✔ 
DMP, VT 

Siregar et al. (2007)  ✔  ✔         ✔    ✔      DMP, VT 

Irawan, Tacconi, and Ring (2013)  ✔          ✔   ✔   ✔    VT 

Lee et al. (2018) ✔ ✔ ✔   ✔            ✔ ✔  ✔ DMP, VT 

Stafford et al. (2017)    ✔        ✔ ✔ ✔     ✔ ✔   AC and VT 

Djamhuri (2008)  ✔  ✔ ✔ ✔   ✔   ✔ ✔ ✔ ✔  ✔      DMP 



[113] Baral, Regmi, Joshi, Atreya 

Torres et al. (2010)  ✔ ✔  ✔   ✔ ✔ ✔         ✔    DMP 

Ndebele and Forgie (2017)                 ✔   ✔  
Contingent valuation 
(CV) 

Evans et al. (2015) ✔ ✔ ✔  ✔             ✔    DMP and VT 

Kroeger et al. (2019)  ✔ ✔ ✔  ✔ ✔ ✔ ✔           ✔  ✔ AC and VT 

Verdone and Seidl (2017)  ✔ ✔ ✔ ✔ ✔      ✔ ✔   ✔ ✔ ✔ ✔  ✔  VT 

Logar, Brouwer, and Paillex (2019)   ✔    ✔           ✔  ✔ ✔  Choice model and VT 

Gunawardena and Rowan (2005)      ✔ ✔     ✔ ✔    ✔    ✔ ✔ DMP 

Meyerhoff and Dehnhardt (2007)                  ✔  ✔ ✔ ✔ 
AC and contingent 
valuation 

Daigneault, Eppink, and Lee (2017)  ✔ ✔                ✔ ✔ ✔ ✔ VT 

Naime et al. (2020)  ✔    ✔  ✔   ✔ ✔ ✔     ✔    DMP and CV 

Garcia-Quijano et al. (2005) ✔ ✔ ✔         ✔     ✔ ✔ ✔ ✔ ✔ 

DMP, AC, travel cost, 
contingent valuation, 
and VT 

Almansa, Calatrava, and Martínez-Paz 
(2012)  

          ✔ ✔    ✔ ✔ ✔  ✔ ✔ 
CV 

Olschewski and Benitez (2005)  ✔ ✔ ✔         ✔    ✔  ✔    
DMP and value 
transfer 

Jim and Chen (2006)                  ✔     CV 

Polizzi et al. (2015)                  ✔  ✔   CV 

Crossman et al. (2010) ✔ ✔ ✔             ✔ ✔ ✔ ✔ ✔  DMP, CV, and VT 

McPherson et al. (2017)                 ✔ ✔    DMP and VT 

Warren-Thomas et al. (2018)  ✔  ✔   ✔      ✔   ✔   ✔    DMP, hedonic 



Ecology, Economy and Society–the INSEE Journal [114] 

pricing, and VT 

Aheto et al. (2016) ✔  ✔   ✔      ✔          DMP and CV 

Jerath et al. (2016)                   ✔    DMP 

Johnson et al. (2016)                   ✔ ✔  ✔ AC and VT 

Borrego and Skutsch (2014)  ✔          ✔ ✔ ✔ ✔ ✔ ✔  ✔    
DMP and hedonic 
pricing 

Flugge and Abadi (2006)  ✔ ✔ ✔  ✔        ✔ ✔ ✔ ✔  ✔ ✔   DMP and VT 

Moriizumi, Matsui, and Hondo (2010)  ✔  ✔        ✔ ✔   ✔   ✔    
DMP, hedonic 
pricing, and VT 

Haglund et al. (2011)   ✔ ✔  ✔   ✔    ✔   ✔ ✔      DMP and CV 

Barry et al. (2014)  ✔ ✔ ✔ ✔ ✔   ✔ ✔ ✔  ✔      ✔  ✔ ✔ CV and VT 

van der Horst (2007)  ✔  ✔              ✔   ✔  VT 

Morri et al. (2014)            ✔ ✔      ✔ ✔ ✔ ✔ AC and VT 

Strassburg et al. (2016)                   ✔ ✔  ✔ AC and VT 

Rai, Neupane, and Dhakal (2016)    ✔ ✔ ✔ ✔ ✔  ✔  ✔ ✔ ✔         DMP 

Paudel, Bhusal, and Kimengsi (2021)   ✔ ✔ ✔ ✔ ✔ ✔  ✔  ✔ ✔ ✔         DMP 

Keefe, Alavalapati, and Pinheiro (2012)  ✔ ✔ ✔     ✔ ✔ ✔  ✔   ✔ ✔  ✔    VT 

Bechara et al. (2021)  ✔ ✔ ✔ ✔    ✔              VT 

Source: Authors’ analysis 



Ecology, Economy, and Society–the INSEE Journal 8(1): 99-131, January 2025 

3.4 Valuation Techniques 

Economic valuation in the case of forest restoration or natural resources is 
highly complex due to the presence of both DBs and IBs, which require 
different methods of valuation. As mentioned earlier, the valuation 
technique can be market-based, non-market-based, or secondary data 
valuation. In the literature reviewed, market-based valuations were based on 
direct market prices, and non-market valuations were based on avoided 
costs, travel costs, the hedonic pricing method, contingent valuation, choice 
experiments, and value transfer. Value transfer was used to estimate all cost 
and benefit parameters. Similarly, direct price was used to estimate all the 
cost and benefit categories (DIC, TC, DB, and IB) except for some 
parameters. Refer to Figure 10 for a complete description of the different 
valuation techniques used in the cost–benefit parameters. Among the 
studies, 58.5% (n = 24) used more than one valuation method. The most 
widely used valuation techniques were value transfer (n = 26, 63%), 
followed by direct market price (n = 22, 53.6%). The contingent valuation 
method was used by 26.8% (n = 11) of the articles, while the avoided cost 
method was used by around 19.5% (n = 8), most of which calculated IBs. 
The most underutilized valuation techniques were travel cost (Garcia-
Quijano et al. 2005) and choice experiment, used in just one study each 
(2.43%) (Logar, Brouwer, and Paillex 2019). The use of multiple valuation 
methods was common in the reviewed articles. 

Figure 11: Box Plot Showing the Time Frame Used to Calculate the Net Present 
Value (Values in Bold Indicates the Medians) 

 
Source: Authors’ analysis 



Ecology, Economy and Society–the INSEE Journal [116] 

3.5. Economic Analysis 

3.5.1 Time Period 

The calculation of net present value (NPV) was performed in 28 of the 41 
studies. The time period used in the study for the calculation of the NPV 
ranged from 1 to 200 years (Figure 11). The time frames for community-
managed projects were comparatively shorter than for those managed 
through other interventions. The longest period for community-based 
management was 30 years (Olschewski and Benitez 2005), with a median 
value of only three years. In contrast, in the case of the “other” projects, the 
maximum duration calculated was 200 years (Verdone and Seidl 2017), and 
the median value was also much larger, at 20 years. 

Figure 12: Discount Rate Applied by Different Articles 

Source: Authors’ analysis 

3.5.2 Discount Rate 

When evaluating the feasibility of an environmental project—that is, the 
investment interest in the project in comparison to the cash flow it can 
generate—one of the most essential parameters to be determined is the 
discount rate (Weitzman 1994). A high discount rate indicates low 
investment interest in the project, which could result in the failure of the 
project to be implemented, while a lower discount rate implies lower risk 
and an increased present value of future cash flows (Pálinkó and Szabó 
2012). In the case of community-managed restoration projects, most of the 
articles (Djamhuri 2008; Moriizumi, Matsui, and Hondo 2010; Ndebele and 



[117] Baral, Regmi, Joshi, Atreya 

Forgie 2017; Torres et al. 2010) used a discount rate between 5% and 9.9% 
(Figure 12). For restoration projects other than those managed by the 
community, a similar number of articles (n = 13, 43.7%) used discount rates 
between 0% and 5% (Evans et al. 2015; Kroeger et al. 2019) as well as 5% to 
9.9% (Balana et al. 2012; Pistorius, Carodenuto, and Wathum 2017). 

3.5.3 Benefit–Cost Ratio (BCR) 

Among the reviewed articles, 31.7% (n = 13) calculated the benefit–cost 
ratio (BCR), and in 29.26% (n = 12), the BCR was calculated for different 
scenarios. For calculating the BCR, we used only the maximum and 
minimum BCR values that were reported in the studies. The BCR values 
ranged from 0.09 to 30.92 (Figure 13). The median values of the BCR 
reported in the “other” projects were slightly higher as compared to those 
in the community-managed projects (2.24 vs 1.6) (Figure 13). The highest 
and lowest values reported in the community-managed projects were 8.57 
and 0.09, respectively, whereas in the case of the “other” projects, it ranged 
from 0.3 to 30.92. Overall, the BCR reported in the “other” projects was 
slightly higher than that in the community-managed projects. 
 
Figure 13: Boxplot of BCRs Reported in the Community-Managed and Other 
Projects (the Marker Indicates Mean Values, while the Values in Bold Are the 
Medians) 

Source: Authors’ analysis 

 
 



Ecology, Economy and Society–the INSEE Journal [118] 

3.5.4 Sensitivity Analysis 

For any project evaluation, there are various externalities that can change 
the outcome of the BCA analysis. For instance, using lower discount rates 
can yield a higher NPV than higher discount rates. Hence, it is vital to 
perform a sensitivity analysis by changing various parameters. Similar to the 
sensitivity analysis, the Monte Carlo simulation also evaluates changes in the 
economic valuation of a project as a function of changes in various inputs 
(Mooney 1997). Only 54% (n = 22) of the articles we reviewed had 
conducted sensitivity analyses (Johnson et al. 2016; Warren-Thomas et al. 
2018; Wiskerke et al. 2010), and the techniques used were different. In 
14.6% (n = 6) of the articles, the sensitivity analysis was performed by 
varying different types of discount rates, while in 17% (n = 7), different 
discount rates, along with other variables such as land use changes, were 
used. In 20% (n = 8) of the studies, various other parameters were used to 
perform the sensitivity analyses. For example, in Daigneault, Eppink, and 
Lee (2017), 72 different modelled scenarios were tested to evaluate the 
economic feasibility of the national riparian restoration programme in New 
Zealand. Furthermore, the Monte Carlo simulation was utilized in just one 
of the studies (Meyerhoff and Dehnhardt 2007). 

Figure 14: Various Methods Were Used for Sensitivity Analyses in the Articles 
Reviewed 

 
Source: Authors’ analysis  



[119] Baral, Regmi, Joshi, Atreya 

4. DISCUSSION 

Among the articles included in the review, there is a slight predominance of 
articles from Asia, whereas other continents have an almost equal share in 
the rest. This may be due to the fact that many countries from Asia have 
made official commitments to support the Bonn Challenge through 
restoration projects; for example, Bangladesh, India, Mongolia, Pakistan, 
and Sri Lanka have made formal agreements to restore an almost 22.65 
million ha in total to support the initiative. Likewise, other land restoration 
projects have also been implemented in the Middle East and the East and 
South Asian regions (Stanturf and Mansourian 2020). The pledges made to 
restore degraded land in the Bonn Challenge also emanate from countries in 
Latin America, Asia, and Africa, which could account for the representation 
from these continents. Regarding the methods used, many studies used 
spatial techniques and secondary data sets rather than primary methods 
such as biophysical surveys, KIIs, and FGDs. This can be attributed to the 
popularity of value transfer methods because they are time-efficient and 
cost-effective and can be applied easily when primary valuation studies are 
not viable (Van Zanten et al. 2023). 

4.1 Parameters Used in the Benefit–Cost Analysis 

Various methods have been used for the valuation of ecosystem services: 
monetary, non-monetary, and mixed methods (Farber, Costanza, and 
Wilson 2002; Turner et al. 2016). DBs such as timber, firewood, fodder, and 
the other regulating services offered by the ecosystem have metrics for 
assessment (Mandle et al. 2021) and are often traded in the market at a 
certain price, which is also in line with the results of our study. 

In contrast, the IBs offered by the ecosystem generally include services such 
as fresh air, habitats for wild animals, scenic views, and many others, which 
are difficult to assess monetarily. These types of value are of great 
importance, even more so if the forests are managed by locals, because their 
traditional knowledge is put to use, their cultural identity is preserved, and 
the general well-being of the communities involved in managing the forest 
is improved (Chan et al. 2016; MA 2005). Although these kinds of value are 
significant, unlike in the case of DBs, assessing them is difficult because 
they are not bought or sold directly in the market and are difficult to trade. 
They are also based on the preferences of people, whose choices and 
perceptions play an important role in evaluating such ecosystem services 
(Milcu et al. 2013). Therefore, economists have developed techniques such 
as benefits transfer, contingent valuation, and willingness to pay to measure 
such benefits (Brander 2013). Most of the articles in our study used such 
techniques to estimate IBs. For example, in a study conducted in two 



Ecology, Economy and Society–the INSEE Journal [120] 

districts in Uganda, 90% of the population ascribed value to the existence 
of the forest and gave high priority to other non-use values of the 
ecosystem (Bamwesigye et al. 2020). Such results indicate the importance of 
the forest for the locals, and these techniques play an important role in 
capturing all the social benefits and value associated with non-market 
services. 

The other cost parameters, such as the TCs associated with the social BCA, 
are also important. In the articles we reviewed, the direct cost of seedlings, 
fencing, and silvicultural management was accounted for by most of the 
studies, but the TC was not well documented. It is necessary to determine 
the cost of managing forest resources (TC) in order to prevent conflicts in 
management (Adhikari and Lovett 2006). The time and money spent on 
meetings, monitoring the forest, and training are of great significance but 
are not well represented in the articles we reviewed. Excluding these cost 
parameters may put the feasibility of the project in question (Phan et al. 
2017). Therefore, all such costs should be considered while conducting the 
social BCA to support effective decision-making. 

Timber, firewood, and fodder are some of the DBs offered by the 
ecosystem. Most studies (Anup, Koirala, and Adhikari 2015; Parajuli, 
Lamichhane, and Joshi 2015) have given a lot of importance to such 
benefits while estimating the cost–benefit relationship, as evident in the 
results of our study, because people value this benefit as a direct source of 
income from the forest. For example, in a study conducted in the 
Ayeyarwaddy region of Myanmar, it was found that 43% of household 
incomes come from selling timber and firewood (Aye et al. 2019). Similarly, 
another study conducted in Bangladesh showed that such forest products 
have been supporting the livelihoods of forest-based communities and are a 
major source of income for local communities (Miah et al. 2012). This may 
be the reason for including such benefits in most studies. However, other 
benefits offered by the forest have been overlooked, such as NTFPs, litter, 
and other agricultural products. These values are not considered when 
calculating the BCA of the forest programme. NTFPs are a source of 
income and also an important source of food for forest-dependent 
communities when no other source is available (Sunderlin et al. 2005). 
Therefore, these should be well incorporated in studies. A similar situation 
is seen when calculating IBs. Most articles prioritized carbon sequestration 
while measuring IBs. However, the other benefits offered by the ecosystem, 
such as the preservation of biodiversity and the watershed, are also of high 
value and should be included when measuring the social benefits of the 
project. 

 



[121] Baral, Regmi, Joshi, Atreya 

4.2 Economic Analysis of Ecosystem Restoration Projects 

Economic valuations help decision-makers justify the implementation of 
projects. In our study, we reviewed articles to obtain data on the calculation 
time period, discount rate used, reported BCRs, and sensitivity analysis. We 
found that the BCA of community-managed forests is often for a shorter 
period as compared to other management programmes. This may be due to 
the fact that the costs and benefits of community-managed forests are 
usually calculated within the operational plan time frame; for example, in 
Nepal’s community forest management practice, the costs and benefits are 
usually calculated for five years, which aligns with the duration of the 
operational plan (Rai, Neupane, and Dhakal 2016). This may also be the 
case for other participatory forest management programmes. Community-
based restoration helps to rehabilitate deforested and degraded land, as 
reported by Gautam, Shivakoti, and Webb (2004). However, because a 
short period of time is used to calculate the NPV of community-managed 
forests, there are fewer studies that have evaluated the costs and benefits of 
the project after its inception, making it more difficult to assess the 
efficiency of these management practices (Wortley, Hero, and Howes 
2013). Assessment is essential as, otherwise, it may seem that the 
government is shifting the burden of conservation to the community, as 
such management practices are facilitated by the government, and almost 
67% of the total forest land in Asia is owned by the state. Although the 
policy arrangements of the participatory forest management programme are 
intended to empower locals and ensure equal sharing of benefits, the 
implementation of these policies in developing countries leaves much to be 
desired (De Royer, Van Noordwijk, and Roshetko 2018; Pathak, Yi, and 
Bohara 2017). Therefore, it is imperative to calculate the costs and benefits 
of such management projects regularly to ensure equal benefit-sharing 
among the FUGs. 

The BCRs in our reviewed articles ranged from 0.09 to a maximum of 
30.92. The BCR is an indicator of whether the project will be profitable or 
not. A value of 1 or above represents a positive NPV, whereas a value 
below 1 indicates a negative NPV. The results of our study show a lower 
BCR for community-managed projects. However, due to the small sample 
size of our study, the findings are not conclusive. Furthermore, in the case 
of government-managed projects, various national-level (Daigneault, 
Eppink, and Lee 2017) and global-level projects (Verdone and Seidl 2017) 
were analysed, and they had much higher BCRs (22.2 and 30.92, 
respectively) than small projects. The results are in line with those of 
Lovelock, Barbier, and Duarte (2022), who reported that the benefits from 



Ecology, Economy and Society–the INSEE Journal [122] 

small restoration projects are lesser as there are limited biodiversity benefits 
and a higher implementation cost per ha.  

In the case of discount rates, we found that most articles used discount 
rates of <10%. In contrast, Browne, Fraser, and Snowball (2018) reported 
in their review article on wetland restoration that most of the articles used a 
<5% discount rate. This is a consequence of there being no guidelines for 
the choice of discount rates. Rather, the choice is subjective and ethical. 
Fundamentally, applying a higher discount rate in certain cases can result in 
long-term damage to biodiversity and ecosystems. Conversely, a lower 
discount rate for the broader economy might boost investment and growth, 
which could, in turn, cause more environmental degradation (Gowdy et al. 
2012). Furthermore, the selection of discount rates is a topic of debate 
among environmental economists (Browne, Fraser, and Snowball 2018). To 
overcome this issue, the best way is to combine various discount rates 
through sensitivity analysis (Georgiou and Turner 2012). We also found 
that most of the sensitivity analyses were performed by combining different 
discount rates or by combining various other parameters with discount 
rates. Sensitivity analysis is a necessity for BCA, as the various assumptions 
made for the BCA may not be true. Among the articles we reviewed, only 
54% had performed a sensitivity analysis. Furthermore, only one article had 
conducted the more robust Monte Carlo simulation (Meyerhoff and 
Dehnhardt 2007). Our results are in line with the remarks of Wainaina et al. 
(2020), who were critical of the sensitivity analyses done in landscape 
restoration BCA studies and recommended a more thorough methodology, 
such as the Monte Carlo simulation, to be implemented in future studies. 

 

5. CONCLUSION 

This article provides a systematic review of the methodologies used in the 
literature for the economic evaluation of community-based ecosystem 
restoration projects. The review clearly demonstrates that, worldwide, 
considerable effort has gone into assessing ecosystem restoration at the 
landscape level through cost and benefit analyses, with minimal focus on 
the assessment of community-managed restoration projects, in general. For 
economic evaluation, the DIC has been used much more than the TC. In 
contrast, many of the studies calculated IBs rather than DBs. There is a 
definite bias towards the calculation of certain cost–benefit parameters. For 
example, the TC was calculated in only 10 of the studies. Similarly, there is a 
lot of focus on certain benefit parameters, such as timber and carbon 
sequestration. While these are definitely the major benefits worldwide, in 
the case of community-managed restoration projects, other benefits, such 



[123] Baral, Regmi, Joshi, Atreya 

as NTFPs and horticultural and leaf litter for manure production, are 
equally valuable and should not be ignored. 

Economic analyses of community-managed restoration programmes were 
carried out for a shorter time frame and generated comparatively fewer 
BCRs than government-managed projects. However, it’s important to note 
that these two kinds of projects cannot really be compared due to various 
reasons like the scale of projects, the different discount rates utilized, and 
differences in the cost–benefit parameters used. Our results imply that there 
is room for improvement in the calculation of the economic efficiency of 
community-based restoration programmes. Furthermore, sensitivity 
analyses were not performed in 46% of the articles, which is another area 
that needs to be improved while carrying out BCA analyses on restoration 
types in the future. 

ACKOWLEDGEMENTS 

We would like to extend our gratitude to the South Asian Network for 
Development and Environmental Economics (SANDEE) for their 
financial support. A special word of thanks is due to our advisors Mani 
Nepal, ICIMOD, and Francois Libois, Paris School of Economics, for their 
guidance. 

Ethics Statement: We hereby confirm that this study complies with 
requirements of ethical approvals from the institutional ethics committee 
for the conduct of this research.   

Data Availability statement: This paper is based on a systematic review of 
literature, so no primary data is collected. 

Conflict of Interest Statement: No potential conflict of interest was 
reported by the author. 

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