Objectives: To assess values of carbon and other than carbon, specifically timber, firewood, grasses in community and collaborative forests; to evaluate the projected contribution of carbon credit in the net value of forest using the discounted cash flow method for next ten years.
Methods: Stratified random sampling was applied setting the randomized block experimental design. A total of 176 samples were collected from three collaborative forest managements and three community forests of Tarai, Nepal, using GPS coordinate navigation in the field. Diameter at breast height (DBH) and height of plants (DBH >5 cm) were recorded and samples of plants (DBH< 5 cm) were collected. Soil samples were collected from 0–10, 10–30 and 30–60 cm depths. Biomass was estimated using allometric equation that was converted into carbon while soil carbon was analyzed in the lab. Samples were collected for three consecutive years and carbon stock credit was determined using the stock difference method. The carbon credit was evaluated using benefit and loss, net present value (NPV), profitability index (PI) and benefit-cost (B/C) ratio.
Results: The expected total value of carbon and non-carbon product between 2011 and 2013 was 62,260 USD and 125,100 USD, respectively, and it was a loss. The projected mean NPV, PI and B/C ratio were found to be 136 USD ha-1 year-1, 10 and 3, respectively, including the value of carbon for next ten years. The decision matrix showed the income would vary in these forests and bundling is good enough.
Conclusions: The higher the carbon sequestration is, the higher the NPV, PI and B/C ratio would be.
Keywords: carbon, community, collaborative, forests, credit
1 Central Department of Botany, Tribhuvan University, Kirtipur, Kathmandu
2 Tribhuvan University Commission, Kirtipur, Kathmandu
3 Tri-Chandra College, Ghantaghar, Kathmandu
RecievedApr 10 2015 Accepted: Aug 21 2015 Published: Nov 27 2015
CitationMandal RA, Dutta IC, Jha PK, Karmacharya SB (2015) Potential contribution of value of carbon in net income of community managed forests, Tarai, Nepal. Science Postprint 1(2): e00053. doi:10.14340/spp.2015.11A0001.
Copyright©2014 The Authors. Science Postprint published by General Healthcare Inc. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 2.1 Japan (CC BY-NC-ND 2.1 JP) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Funding There is no any fund available for this research work.
Competing interest There is no competing interest.
Ethics The community and collaborative forests users were agreed to carry out the research work.
Corresponding author: Ram Asheshwar Mandal
Address: REDD Implementation Centre, Babarmahal Kathmandu, Nepal
Khalid Mahmood1, Anup KC2, and Reviewer A
1 Nuclear Institute for Agriculture and Biology (NIAB), Faisalabad, Pakistan
2 Amrit Science Campus, Tribhuvan University, Nepal
Forests have been functioning to capture CO2 from the atmosphere 1 and produce non-carbon products like timber, firewood and grasses, so their values are very significant 2. Forestry experts claim that 6.7×1012 t of CO2 could be reduced annually from the atmosphere by the photosynthesis of the world’s forests 3. For this, avoided deforestation, forest management and afforestation could contribute about 42%, 31% and 27%, respectively, in the next 30–50 years period 4, 5.
Carbon sequestration through photosynthesis is one of the unique functions of green plants, which is probably not the function of other living beings. The ecological benefits of forests are production of oxygen, soil fertility, erosion control, water recycling, humidity control and provision of wildlife habitats 6. The value of a typical tree is worth about 196,250 USD of ecological benefits during its lifetime 7, whereas when it is sold as timber, the same tree is worth about 590 USD 8. In fact the value of all public trees would be 75.5 million USD in total in Santa Monica urban forest 6. Despite these benefits, people destroy the trees to meet their instant interest 9.
Forest management is considered to be the key measure to face the impacts of climate change. The elevating forest loss, industrialization, transportation and luxurious lifestyle are the main causes of increasing CO2 emission in the world 8. Globally, approximately 13 million ha of forests are deforested annually, which releases 1.4×109 t of carbon to the atmosphere 9, 10. Especially, the estimates indicate that about 1.1×107 ha of forests is lost each year in tropical regions due to deforestation and conversion to the agriculture land 11. The forest cover loss was found to be about 32,000 ha in the last 19 years from 1991 to 2010 with a rate of 0.40% in Tarai, Nepal 12. However, community based forest management is a successful example of participatory management in Nepal. There are 18,133 community forests and 20 collaborative forests in Nepal that are managing 165,265 ha and 54,072 ha, respectively. Community forests are generally small patches of forest that are managed, conserved and utilized by instant users to produce basic forest products 13, while collaborative forests are managed and conserved by not only users but also forest management units and local governments to meet the demands of users including people who also live in remote areas 14.
The carbon credit under reducing emission from deforestation and forest degradation (REDD+) is becoming a popular and possible option. The context of carbon credits under REDD+ mechanism is like a banking platform but it requires the carbon stock change above the reference level. The setting of reference level depends upon the choice of sub national level then any positive change in next year will be the carbon credit under REDD+ 15. Simply, monitoring the periodic change in forest carbon stock is one way to evaluate the carbon credits and their monetary value 16. It is essential to remark that the evaluation of carbon credits is another important part 17. In fact, evaluating carbon credits and the monetary value is a very novel idea in REDD+ in Nepal 18. However, the REDD+ demonstration project handed over about 95,000 USD to community forest users directly for carbon credit under REDD+ in watersheds of Chitwan, Gorkha and Dolkha districts, Nepal. So what was the contribution of values of carbon in net income of community forests and what would be the appropriate options to evaluate the forest carbon project? Here, this article tries to answer these questions. Therefore we first assess benefit and loss of carbon credit based on carbon stock change in community and collaborative forests for ten years. Second we evaluate the projected contribution of carbon credit in the net value of forest using the discounted cash flow method for next ten years.
In total, 6 community and collaborative forests were selected as research sites since they all are natural forests under different management modalities. Specifically, 3 community forests (CFs) and 3 collaborative forest managements (CFMs) were selected for this study in Mahottary district in Tarai, Nepal. Selected CFs were Baudh (70 ha), Chure Parwati (442 ha) and Chyandanda (41 ha) while collaborative forests are Banke-Maraha (2,006 ha), Tuteshwarnath (1,334 ha) and Gadhanta- Bardibash (1,450 ha). The geographical location of Mahottary district is 26°36'–28°10'N and 85°41'–85°57'E (Figure 1) 19. Minimum temperature is 5.6°C and maximum is 40°C and average annual rainfall records are 1,100–3,500 mm. These forests are dominantly covered with natural vegetation like Shorea robusta (sal), Terminalia tomentosa (saj), Lagerstroemia parviflora (botdhairo), Terminalia chebula (harro) and Terminalia belerica (Barro).
Reprinted from “Evaluating sustainability in community and collaborative forests for carbon stocks,” by Ram Asheshwar Mandal, Iswar Chandra Dutta, Pramod Kumar Jha, Sidhi Bir Karmacharya (2013) The Proceedings of the International Academy of Ecology and Environmental Science 3(2): pp. 76–86.
The sampling methods and experimental design were set according to forest strata: mature tree, pole, regenerating sapling, seedling, and litter, herb and grass. The forests were surveyed using GPS Garmin eTrex and their maps were prepared using coordinates in ArcGIS 10. Then, the participatory map of each stratum was prepared by preliminary survey and participatory rural appraisal (PRA) technique. The stratification was done on GIS map using participatory rural appraisal (PRA) maps and Google earth map. Here, each stratum of the forest was considered as a block and sample plots were selected randomly, thus randomized block design (RBD) was set for the research experiment and the stratified random sampling was carried out to gather the biophysical data.
Pilot sampling was done to determine the required number of sample plots. For this, 10–15 samples were taken from each stratum of mature tree, pole, and regeneration (sapling and seedling) strata of CFs and CFMs 20. Then, diameter at breast height (DBH), height of trees in the sample plots and the biomass were estimated which were later used to determine co-efficient of variance.
where, X is the biomass and overlined X represents the mean.
Coefficient of variation,
Required number of sample plots (N) = CV2t2/E2,
where t = value of student’s t-distribution table at n-1 degree of freedom (df) at 10% probability but in (n − 1), n denotes number of sample plots taken as pilot sample, i.e., 10–15, and E is the standard error at 10% and n, total number of samples.
A total of 173 samples were collected from the forests. These included 32, 33, and 31 samples from Banke-Maraha, Tuteshwarnath and Gadhanta-Bardibas CFMs, respectively, and 77 from CFs (30, 25 and 22 samples from Chure Parwati, Baudh and Chyandanda CFs, respectively).
Data were collected following major steps. Firstly, the sample plots were distributed randomly on each stratum in the map and the coordinates of sample plots were uploaded in GPS. Secondly, sample plots were permanently established in the field 21 by navigating the uploaded GPS coordinates. Then, sample plots were established according to the nature of the stratum (for mature tree stratum, plots of 20 m × 25 m were laid with nested plots of 10 m × 10 m for poles, 5 m × 5 m for saplings, 5 m × 2 m for seedlings and 1 m × 1 m for litter, herbs and grasses). Meanwhile, the location for soil sample was fixed at the centre of the plot. The height and diameter of trees, poles, saplings were measured in the sample plots. Saplings (1 cm< DBH <5 cm), seedlings, herbs and shrubs were counted within the sample plots and fresh weights of some of them were recorded to find the moisture content. Soil samples were collected from three different depths: 0–10 cm, 10–30 cm and 30–60 cm in order to determine the soil carbon 22, 23. The measurements were taken for three consecutive years in order to identify the change in carbon stock. The current market price of carbon was explored from the web search.
Collected data were analyzed to estimate the carbon stock, current annual carbon increment and carbon sequestration. Then economic value was calculated and analysis was done to evaluate the potentiality of carbon credit.
1. Estimation of carbon stocks and current annual carbon increments (CACI):
a) Estimation of carbon stocks:
First of all, the above ground tree and pole dry biomass (AGTB) was calculated by using following allometric equation 24 for mature tree and poles.
AGTV = 0.0509 × ρD2H,
where ρ represents wood density (g ml−1), D represents diameter at breast height (cm), H represents height (m). This equation does not return precise results for DBH <5cm 24. So, samples of saplings (DBH <5cm), seedlings, leaf litter, herbs and grasses together were brought back to the laboratory to dry and their dry biomass was calculated using unitary method. The root biomass was calculated by using root shoot ratio 0.1 20. The dry biomass was converted into carbon multiplied by 0.47 22.
Soil carbon content was analyzed by Walkley-Black method 22.
Soil organic carbon
= Organic carbon content (%) × BD (g/ml) × Soil depth,
where bulk density (BD) is oven-dried weight of the soil per volume of the soil in the core.
Carbon stock = Total biomass carbon + Soil organic carbon.
b) Estimation of current annual carbon increment (CACI):
CACI = Carbon stockn year − Carbon stock (n-1) year
CO2 removal (carbon sequestration) = CACI × 44/12
2. Calculation of value of carbon sequestration: The market value of carbon sequestration was calculated assuming a ton of CO2 removal worth 5 USD 25.
3. Economic evaluation of carbon credit: The discount cash flow method is an important method for the evaluation of any project which focuses on conversion of the future monetary value into present value. Thus, the projected carbon credit was evaluated using net present value (NPV), profitability index (PI) and benefit cost ratio (B/C) 26.
The formulae used for the calculation of benefit or loss, NPV, PI and B/C are given below:
Benefit = Total returns – Total management cost;
Loss = Total management cost – Total returns.
where, r represents required rate of return (it is fixed 8% for this project because this is interest rate of Nepal Rastra Bank), and CF represents cash flow after tax.
PI = Total present value – Net cash outlay.
B/C = Benefit/Total management cost.
The benefit and loss depend on the monetary value of carbon, non-carbon products and investment. The value of carbon was negative in Banke-Maraha CFM in first period. There was no sale of non-carbon products like timber and firewood, so there was loss in 2011–2012. However non-carbon products were sold in this CFM in 2012–2013; that is why there was no loss in the total amount. The records of these two periods showed that the overall value of carbon was 62,260 USD and that of non-carbon products was 125,100 USD.
The value of carbon varied among forests both in CFMs and CFs. The monetary values were 15.2 USD and 11.9 USD in 2011–2012 which decreased to 8.8 USD and increased to 28.1 USD in Tuteshwarnath and Gadhanta-Badibash CFMs, respectively. In case of CFs, the estimated values of carbon were 31.2 USD, 23.9 USD and 42.2 USD in 2011–2012, and increased to 38.7 USD, 42.2 USD and 52.3 USD in Buddha, Chure and Chyandanda CFs, respectively, in 2012–2013. Similarly, the values of non carbon products also varied depending on the forests.
The benefit and loss depend on the total expected value. The total expected value is the compilation of carbon and non carbon values. The difference between total expected value and management costs were used to determine the benefit and loss. Expenditure mainly includes the cost of plantation farming, regeneration management, protection and silvicultural operations. In total, there was a loss of about 55,035 USD in 2011–2012 but it was followed by a benefit of 54,790 USD in 2012–2013 from the expected values of carbon and non-carbon products. The value of carbon would be about 66.5% whereas value of non carbon would be 33.5% from the forests (Table 1). The study done in the community forest of Ilam showed that the monetary value of carbon was 40,207 USD and it depended on the annual increment of carbon and rate of carbon 27. Other study done in Mahottary, Tarai Nepal showed the income from carbon might be nearly 37.4%, 57.3% and 75.3% of that in Indrakali, Newardanda and Kamidanda, and Kalidamar CFs, respectively, due to positive change in carbon sequestration 28. The income from forest carbon depends on CACI in the forests 29.
Note: non-carbon products include timber, fire wood, and grasses.
The expected annual average value of carbon and non-carbon products calculated based on the expected value of 10-year project period showed that the estimated average annual NPV would be higher in case where the value of carbon is included than excluding it. However, the exception was found in Banke–Maraha CFM where the value including carbon was 41.8 USD ha-1 and that of excluding carbon was 42.2 USD ha-1 (Table 2). One reason might be negative value of carbon in this CFM. The other reason might be the higher expected value of timber than that of carbon. However, overall projected mean of carbon-included NPV would be 136.1 USD ha-1 year-1 whereas carbon-excluded NPV would be 39.8 USD. The highest estimated carbon-included NPV would be 253.6 USD in Chyandanda CF because the annual carbon sequestration rate was the highest in 2011–2013. It is essential to remark that the carbon-excluded NPV would only be 0.9 USD but 139.5 USD in case where the value of carbon was included in Chure Parwati CF (Table 2). It might be because of the minimum scope of the sale of non carbon products. A study done in the CF of Ilam showed that if the forest carbon project is planned for 15 years, the NPV would be 441 USD ha-1 30. The value for Chyandanda CF may be close to this value in Ilam, if the carbon-included NPV was calculated for 15 years. If 4 t CO2 ha-1 is sequestered, the price of carbon sequestration per ha would be 20 USD, and the net value would be 80 USD. So, these are the motivating factors to increase the net income through carbon credit 31 under REDD+ project in Nepal.
Note: EC stands for excluding carbon and IC for including carbon.
Simultaneously, profitability index (PI) was applied to evaluate the carbon credit for 10 years. Principally if the value of PI >1, the project is accepted, where the higher the PI value is, the greater the chance of being accepted is. Our study showed both carbon included and excluded PIs would be greater than 1 (Table 2). The projected highest PI was 28.5 in which the value of carbon was included for Chyandanda CF. This might be because of the higher value of carbon in the community forest. On the other hand, the projected carbon-excluded PIs were found to be less than 1 in case of Chure-Parwati and Buddha CFs. The value of services and goods is affected by the types of products and existing stocks and this might have caused the value to be less than one 32. However, no study has been done from this perspective in Nepal so far.
The higher the carbon sequestration, the higher would the NPV, PI and B/C ratio be. This will be a basis to evaluate carbon credit under REDD+ project in the future. The carbon-excluded B/C ratio was less than 1 in all CFs and CFMs, except for Banke-Marha CFM of which the value was 1.9, while it was greater than 1 when the value of carbon was included in every forest. Specifically, the estimated average carbon-included B/C ratio was 2.6 but only 0.8 when the value of carbon was excluded. The highest carbon-included B/C ratio was found in Chyandanda CF with a value of 7.5. On the other hand, the carbon-included B/C ratio was found to be low with a value of 1.3 in Banke-Maraha CFM. Thus, theory and practice of forest economics can be referred to when designing a project of carbon credit, and it is indeed essential 33, 34.
The evaluation for the decision making was based on the income from CFs and CFMs in both cases in which carbon was included and excluded (Table 3). However, the decision based on average annual income might create confusion. Thus it would be better to evaluate forestry projects applying NPV, PI and B/C ratio. Since CFs are small scale forests and CFMs are large forest blocks, bundling them for REDD+ project may be highly beneficial 35.
Carbon sequestration will obviously be an additional income source in addition to other sources such as forest products (i.e., timber and firewood). The NPV, PI and B/C ratio provide explicit basis to determine carbon credits under the REDD+ mechanism, but evaluation based only on benefit and loss may create confusion. The bundling approach for the community and collaborative forests may reduce the cost of monitoring, reporting and verification under REDD+. Therefore, it is recommended to carry out intensive economic analysis of carbon credit for other REDD+ demonstration projects.
Conceived and designed the work: Mandal RA, Dutta IC, Jha PK, Karmacharya SB
Acquired the data: Mandal RA, Dutta IC, Jha PK, Karmacharya SB
Analyzed and/or interpreted the data: Mandal RA
Drafted the work: Mandal RA, Dutta IC, Jha PK, Karmacharya SB
Revised and approved the work: Mandal RA, Dutta IC, Jha PK, Karmacharya SB
We acknowledge Mr. Ram Ashish Yadav, Ram Jeeban Yadav and Ram Bahadur Lungeli for their support during field data collection. In addition, we respectfully acknowledge Dr. Ram Kailash P. Yadav, Dr. Bharat Babu Shrestha, Assistant Professor of Tribhuvan Univeristy and Dr. Bishnu Hari Pandit, Chairman of Kathmandu Forestry College for their encouragements.