Assessing the mitigation potential of forestry activities in a changing climate: A case study for Karnataka

Forest Policy and Economics 12 (2010) 277–286

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Forest Policy and Economics j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / f o r p o l

Assessing the mitigation potential of forestry activities in a changing climate: A case study for Karnataka Kaysara Khatun a,⁎, Paul J. Valdes a, Wolfgang Knorr b, Rajiv Kumar Chaturvedi c a b c

School of Geographical Sciences, Bristol University, University Road, BS8 1SS, Bristol, UK Earth Sciences, Bristol University, University Road BS8 1SS, Bristol, UK Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India

a r t i c l e

i n f o

Article history: Received 20 February 2009 Received in revised form 5 November 2009 Accepted 17 December 2009 Keywords: Afforestation Carbon price Clean development mechanism Reforestation Land use Sustainable development

a b s t r a c t The Clean Development Mechanism (CDM), Article 12 of the Kyoto Protocol allows Afforestation and Reforestation (A/R) projects as mitigation activities to offset the CO2 in the atmosphere whilst simultaneously seeking to ensure sustainable development for the host country. The Kyoto Protocol was ratified by the Government of India in August 2002 and one of India's objectives in acceding to the Protocol was to fulfil the prerequisites for implementation of projects under the CDM in accordance with national sustainable priorities. The objective of this paper is to assess the effectiveness of using large-scale forestry projects under the CDM in achieving its twin goals using Karnataka State as a case study. The Generalized Comprehensive Mitigation Assessment Process (GCOMAP) Model is used to observe the effect of varying carbon prices on the land available for A/R projects. The model is coupled with outputs from the Lund–Potsdam–Jena (LPJ) Dynamic Global Vegetation Model to incorporate the impacts of temperature rise due to climate change under the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2, A1B and B1. With rising temperatures and CO2, vegetation productivity is increased under A2 and A1B scenarios and reduced under B1. Results indicate that higher carbon price paths produce higher gains in carbon credits and accelerate the rate at which available land hits maximum capacity thus acting as either an incentive or disincentive for landowners to commit their lands to forestry mitigation projects. © 2009 Elsevier B.V. All rights reserved.

1. Introduction Under Article 12 of the Kyoto Protocol, namely the Clean Development Mechanism (CDM) developed countries are able to implement greenhouse gas (GHG) reduction activities in developing countries, where the costs of such projects are usually much lower. These projects are to be carried out with the purpose of assisting developing country Parties in moving forward with their sustainable development goals, whilst simultaneously allowing developed country Parties in achieving compliance with their quantified emissions limitation and reduction commitments. The CDM has no specific reference to sinks, but it has been decided that afforestation and reforestation (A/R) will be allowed. The Kyoto Protocol stands to be revised in Copenhagen 2009, and afforestation, reforestation and deforestation (ARD) activities are expected to feature prominently as continuing mitigation strategies for subsequent commitment periods. The carbon sequestration by sinks approach as a mitigation strategy is appealing to policymakers because it can be equated directly with carbon emissions and is considered a relatively inexpensive strategy

⁎ Corresponding author. Tel.: +34 944 014 690. E-mail address: [email protected] (K. Khatun). 1389-9341/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.forpol.2009.12.001

(Kolshus et al., 2001). The forestry sector is fairly unique in that not only does it contribute significantly to global CO2 emissions through deforestation, pests and fire, but can also provide opportunities to lessen the levels of CO2 in the atmosphere by sequestering it in soils and vegetation as well as in wood products. In this way the forestry sector can play a critical role in stabilizing global CO2 concentrations (IPCC, 2007). Global studies (Sohngen and Sedjo, 2004; Sathaye et al., 2005) have analyzed the sensitivity of the forest sector's mitigation potential to carbon price variation using ARD activities and by region. Regional studies in India deal with methodologies (Ravindranath et al., 2007b; Sudha et al., 2007) and only one by Ravindranath et al. (2007a) examines the impact on available land from on carbon price for A/R sequestration activities. The study uses the Generalized Comprehensive Mitigation Assessment Process (GCOMAP) Model (Sathaye et al., 2005) for the whole of India based on two carbon prices $50 and $100 respectively and aims at estimating India's forestry mitigation potential at a regional level based on two systems of land classification. The authors conclude that investment capital barriers pose the main limitation for A/R projects in India. GCOMAP is a dynamic partial equilibrium economic model built to simulate the response of forestry land users to changes in prices in forest land and products and prices emerging in the carbon market

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(Sathaye et al., 2005). Partial equilibrium models have been used to examine the effects of carbon prices on afforestation and forest management options in an integrated framework of global demand and supply of timber (Sohngen and Sedjo, 2004) as well as to assess the demand for agricultural products over time, by region, and competition between agricultural production of crops and biofuels and forestlands for tree planting. The GCOMAP model has been employed as a tool to make policy recommendations using forestry projects by a number of authors, avoided deforestation by Kindermann and Obersteiner (2008), all forestry mitigation options by Sathaye et al. (2005) and A/R activities under the CDM by Ravindranath et al. (2007a). Our study complements the latter work by focussing on plantation projects in the four agro-ecological zones corresponding to Karnataka based on changing the carbon price and adds to it by factoring climate variability under a number of mitigation scenarios by coupling GCOMAP with data from the Lund–Potsdam– Jena (LPJ) dynamic global vegetation model (Sitch et al., 2003). The aim of this study is to look at the impact of the price of carbon credits for forestry on land availability and hence the policy implications should “wastelands” be offered up for mitigation purposes by the government of India. We also attempt to consider the implications and the usefulness of using the GCOMAP model as a policy tool for India and its usefulness in practical implementation. Two important aspects of forest plantation development will be looked at namely: the current and future status by exploring short (2020), medium (2050) and long term (2100) trends in forest plantation establishment and the economic and development issues associated with these forestry projects. This will be achieved by: • Quantification of biomass change by using LPJ outputs for the Karnataka Region as inputs to GCOMAP to offer insights into the effect on land availability and the significance to carbon stock and hence potential credits during a mitigation period for large-scale A/R projects. • Examination of the economic controlling factors by changing the carbon price and observation of the subsequent effects on available land produced by the use of short rotation (SR) and long rotation (LR) species for the IPCC scenarios A1B, A2 and B1. • Using both enhancements to observe: -The difference from base case on land availability and carbon stock in the short, medium and long term for SR and LR -The change in available land. Economics play a significant role in social development whether they are made explicit or just perceived by stakeholders. Hence it is “good practice” to calculate the costs for more than one rate to provide guidance for policymakers on how sensitive the impacts are to a given carbon price path and thus provide a glimpse of the overall picture. 2. Study area Karnataka has a geographic area of 19.18 million ha which constitutes 5.83% of the total area of the country with a range of climates varying from the very moist monsoon climate on the coastal Table 1 Area of available wasteland and amounts allocated for SR and LR plantations in Karnataka. Source: Ravindranath et al. (2007a). AEZ

Area (ha)

SR %

LR %

SR (ha)

LR (ha)

AEZ 3 AEZ 6 AEZ 8 AEZ 19 Total

260000 408740 472430 212460 1353630

67 67 67 67

33 33 33 33

174656 274572 317356 142721

85344 134168 155074 69739

Table 2 Breakdown of wasteland area. Source: Ravindranath et al. (2007a). By use

Area

Industrial roundwood Fuelwood Other purposes SR LR

34% 21% 45% 67% 33%

and hilly areas to the semi-arid climate of the northern districts (Forest Survey of India, 2005). The state is endowed with diverse and dense forests in the county ranging from evergreen forests of the Western Ghats to the scrub jungles of the plains (Fig. 1). The Western Ghats of Karnataka is one of the 25 global priority hotspots for conservation and one of two on the Indian subcontinent (Ministry of Environment and Forests, MoEF, 2004). An increase in temperature due to climate change will potentially impact on the vegetation and subsequently land use and resources. Due to the vast forests, Karnataka has a large rural population who depend on the forests for their livelihoods and energy requirements. The classification system of the zones used in the GCOMAP model for India have been categorized into 20 Agro-Ecological Regions on a 1:4 million scale. The mapping and classification of the various parts of the country for generation of agro-ecological regions involved the superimposition of four base maps, namely physiography, soils, bioclimate and length of growing period and have been used for resource planning at national level (Forest Survey of India, 2005). Zones 3, 6, 8 and 19 correspond to Karnataka as shown in Fig. 2. We have selected Karnataka to observe the impacts of the four somewhat different zones to rising carbon prices on land availability under the SRES scenarios should wastelands in the state be used for A/R projects under the CDM. The State has a variety of land uses (Tables 1 and 2). The selection of lands available for CDM projects is a key driver of mitigation potential. This appraisal is confined to lands only under the control of state forest and land revenue departments as these may be able to directly benefit local communities as under national state laws, they have rights to the resources of that land. The appraisal is also concerned with land that does not jeopardize food and livelihood security and hence the analysis is limited to land classified as “wastelands” as reported by National Remote Sensing Agency (NRSA). Degraded lands in India called wasteland, have been assessed by Ravindranath and Hall (1995) to be technically suitable for growing trees and can be regarded as a promising land type to be used for A/R activities under the CDM. Approximately 23% (75 million ha) of Indian land area is classified as wasteland and according to Sathaye et al. (2001) about 40% of this amount is considered available for forestation. This value includes degraded forestland as well as pasture land, marginal cropland and other privately owned non-crop land categories. These are the lands that are most likely to meet the additionality criteria required for eligibility under the CDM and such an effort would also help to offset the increase in atmospheric CO2. 3. Methodology The GCOMAP model includes four of the five carbon pools defined by the UNFCCC (2001) Marrakech Accord: these are aboveground biomass, belowground biomass through an expansion factor, litter and soil organic carbon. Dead organic matter however, is not included in this study. To estimate the future investment necessary for plantation implementation and the effect of those investments on the plantation rate, the linear model establishes a baseline scenario with no financial revenues from carbon (Ravindranath et al., 2007a). From this baseline the areas under plantation activities for carbon mitigation and also the overall mitigation activity and potential for the

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Fig. 1. Forest cover map of Karnataka. Source Forest Survey of India (2005).

period 2005–2104 are assessed.1 GCOMAP simulates the response of uses of forest and wasteland to changes in carbon price at different rates and estimates additional land brought under the mitigation activity above the baseline level. The model can also estimate net changes in carbon stocks while meeting the annual demand for timber and non-timber products (Sathaye et al., 2005; Ravindranath et al., 2007a). In this study GCOMAP is applied to get plantation rate scenarios for the future under different carbon prices and management systems for a number of time lines. The model does not however, take into account the changes in climate and the subsequent impact of CO2 concentrations on CO2 fertilization or changes in the carbon cycle and its consequence on the biomass growth. We therefore, decided to enhance GCOMAP to incorporate contributions of CO2 by adjusting the biomass values based on the IPCC SRES scenarios (Nakicenovic and Swart, 2000) from outputs obtained from the Lund–Potsdam–Jena (LPJ) dynamic Global Vegetation Model (Sitch et al., 2003). The GCOMAP Model takes into account not only the localized species of the trees but takes a more general approach in terms of short rotation and long rotation

1

The mitigation range used by GCOMAP for India (Ravindranath et al., 2007a).

plantations, 7 and 40 years respectively. Natural regeneration, while the best option due to specific biodiversity aspects cannot be included due to the time factors that would be required. LPJ extends from BIOME and is a terrestrial biosphere model, that has been implemented globally and like the BIOME family of models (Prentice et al., 1992; Haxeltine and Prentice, 1996; Haxeltine and Prentice, 1997) is used to predict the distribution of vegetation cover as it varies with climate, CO2 and time. LPJ also accounts for vegetation types that are sensitive to climate and CO2 amounts in the atmosphere; its main drivers are temperature and precipitation along with soil type information and annual global CO2 concentrations. LPJ is run with CO2 varying and therefore “sees” both the effect of climate change and the effect of CO2 on vegetation. The model simulates vegetation dynamics at a global, regional or a single site scale and processes within LPJ are simulated on a daily, monthly or annual time steps as appropriate. The inputs into the model are baseline climate data from the Climate Research Unit (CRU), representing the period 1960–1990 (New et al., 1999), plus the predicted changes in climate and CO2 annually from 2004 to 2100 at a spatial resolution of 0.5 × 0.5. The model predicts the changes in vegetation and biomass changes for India at each grid point in kg C/m2/yr for the three SRES scenarios A2, A1B, and B1.

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Fig. 2. Map of the 4 AEZ's that comprise Karnataka; Nb: they may not necessarily coincide with Karnataka's state boundaries. Source: Sehgal et al. (1992).

The LPJ grid points relating to the latitude and longitude points for Karnataka was extracted from LPJ outputs for the whole of India representing the biomass values over 100 years under the three scenarios (Scholze et al., 2006). The 15 grid points representing Karnataka were averaged to get a value for each of the scenarios and used as input into GCOMAP by changing the biomass factor using the mean annual increment (MAI) value in GCOMAP. The MAI refers to the average rate of biomass carbon growth over the life of an afforestation option and they vary depending on species, site productivity and management regime (Makundi and Sathaye, 2003). The output data is then separated out to see behaviour patterns in yearly and 10 year intervals and finally for three specific time periods. This is useful in providing “snapshots” on percentage differences on the price accrued and land gained between the climate change scenarios A1B, A2 and B1 compared to a base case that assumes similar circumstance as to those of today. The three time periods that are selected for illustrative purposes in this study are short term, namely 2020, the medium term 2050 and long term 2100. It is worth noting that if practices are sustainable and provide the “right incentives” from the onset, then these practices can be maintained well past the time scales that are noted here. The values related with the “no CC” scenarios from GCOMAP are outputs without the LPJ enhancement and those associated with SRES scenarios include LPJ input. The data corresponding to “no CC”, deals with the climate being much the same as it is today and is not to be confused with the business as usual (BAU) scenario represented by A2 which represents rapid increase in CO2 based on current rates of emissions. Time preferences are fundamental in understanding decisionmaking in any studies of the environment and certainly applicable to the climate. The generation of income for back loaded projects such as A/R activities, where much of the costs occur at the beginning of the project and the benefits at a much later stage, requires that the selection

of activities need to consider the environmental circumstances for the region over time in the selection of project type. 3.1. The impacts of the price of carbon For developing countries such as India to participate in any greenhouse gas reduction scheme there has to be incentives that have a robust financial element. Alongside the economic development of the country, a clear focus is required to cater for the needs of those that climate change impacts will directly affect, and to categorize it in a way that climate policy becomes more fully integrated with the country's core objective of poverty alleviation under the overarching theme of sustainable development (Ministry of Environment and Forests, MoEF, 2004)). The “problem” of delivering sustainable development is constructed as a set of objectives by the Indian Government that must be met and work specifically with the Millennium Development Goals. In forestry projects there is the traditional capital flow that can be gained by the values of timber and wood products but those alone are not sufficient for developing countries' participation in the global abatement of greenhouse gases strategies. The CDM rewards the reduction of greenhouse gases by trading carbon and thus offering a secondary incentive for income generation and participation in projects as those under the Mechanism. These prices refer to the carbon removals in a forestry project until the end of the Kyoto Protocol's second commitment period in 2017. However, the market price of carbon creates its own set of problems, as they will have other influence on livelihoods than just a matter of economic transactions within the carbon markets, as Smith and Scherr (2003) point out, socially beneficial projects are less costeffective because of their higher transaction costs. For afforestation

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projects to take place, the land required will only be offered if the “correct” incentives are in place. The price of carbon needs to integrate and account for real benefits after the initial set up costs, monitoring, verification etc. For that to happen, carbon in the forestry sector will have to be valued substantially higher than at those currently traded (at approx $3, World Bank, 2008) to make it a worthwhile venture for land already in high demand from other modes of agriculture. Current values of temporary credits make forestry a cheap abatement prospect and do not encourage project developers to consider the impacts on the communities that will be affected due to these projects and the land they occupy. The price of carbon offsets will have an effect on the long term success of any project undertaken under the CDM. The World Bank's BioCarbon Fund will pay on delivery of the carbon credits at a negotiated price usually within the range of US$3 to US$4 per tonne CO2e (http://www.BioCarbonFund.org, accessed Sept, 2008). The World Bank is among the few buyers of CDM forestry credits. However, their prices provide an indication of attainable prices, but cannot be related directly to the prices of temporary credits because the World Bank buys carbon removals under its own particular system that differs from the Kyoto credits. As an alternative, prices for CDM forestry projects can also be related to those for projects in other technology sectors. A number of possible values are used to evaluate the mitigation potential of the A/R projects in this study. A baseline of $0 is used for appraisal as this represents the project without financing and four other mitigation scenarios are considered for assessment using GCOMAP. The baseline scenario represents the current rate of forestation in different zones which is projected to follow a pattern similar to the present development. The first carbon price scenario of $5 is an indication of the current value for a temporary carbon credit. The second price of $15 is more aligned with the value of a permanent credit.2 The third price scenario of $50 represents a value that is in line with European Union Emission Trading level of approximately 33US$ (World Bank and IETA, 2008). It also correlates with those that are predicted for the future by Pointcarbon in their report “Carbon 2008: Post-2012”, which draws upon the world's largest ever carbon market survey in conjunction with Point Carbon's extensive models, databases and analyses of the global carbon markets. Pointcarbon's results conclude that there will be a global reference carbon price in 2020, the most frequently chosen reply in their survey, and the median, is 30–50 Euros or 50–70 US3 dollars. Finally the last value of $100 is chosen as a hypothetical value and is one that has been used in a number of other studies (Sathaye et al., 2005; Ravindranath et al., 2007a; Pointcarbon, 2008). For the lower value a nominal percentage increase per annum is included as without it the price ceases to have any significance within a relatively short timeframe.

4. Results The analyses and interpretation of the results are presented by examining the difference in losses and gains to available land and carbon stocks compared to the “no CC” scenario and the impact on land availability based on carbon price under the three SRES climate scenarios. Fig. 3a and b illustrate the change in the chosen carbon price paths over the mitigation period. The values used in this study for carbon prices $5 + 5% and $15 + 5% in the short term are $6.70 and $20.1 for the medium term are $28.0 and

2 Permanent credits closed at $15.63 for the December 2008 delivery the price of the right to emit a tonne of carbon dioxide on the European Climate Exchange has fallen from €30 in mid-2008 to about €14.75 and CER 13.76 in 21/10/2009 (www. Pointcarbon.com). 3 The analysis for this study was carried out prior to the “credit crunch” 2008/2009 — the exchange rate is very likely to be different.

281

Fig. 3. Fig. 3a: The change in carbon price (input for GCOMAP) over the mitigation period 2004–2104. Fig. 3b: close up of Fig. 3a.

$86.9 and long term are $332.1 and $996.3 respectively. For ease of plotting and presentation value, the amounts are left as they are ($5 + 5% and $15+ 5%). Tables 3a and 3b display the biomass values following the LPJ input representing the SRES scenarios, the increases were 23%, 32% and a decrease of 9% under A1B, A2 and B1 respectively. GCOMAP does not allow outputs for SR under the SRES scenarios; it displays the outputs as errors. Therefore there are no results for SR under B1 in the subsequent sections. The GCOMAP model assumes financial stability the short term, as the project will cease to exist but this is not the case for the long term as costs and benefits, as well as discount rates are more likely to vary over a greater number of years. Currently this is set at 12%, in the model, typical for developing countries (IPCC, 1996). Table 3a MAI values under the SRES scenarios for the short and long term respectively. MAI values increased by 23%, 32% under A1B and A2 respectively and decreased by 9% under B1. The change in Mass Annual Increment (MAI) Zone\SRES

No CC (mm)

A1B (mm)

A2 (mm)

3 6 8 19

3.35 3.35 3.38 3.38

4.12 4.12 4.16 4.16

4.42 4.42 4.46 4.46

Table 3b MAI values under the SRES scenarios for the short and long term respectively. MAI values increased by 23%, 32% under A1B and A2 respectively and decreased by 9% under B1. Zone\SRES

No CC (mm)

A1B (mm)

A2 (mm)

B1 (mm)

3 6 8 19

4.07 4.07 2.01 2.01

5 5 2.41 2.41

5.4 5.4 2.65 2.65

3.7 3.7 1.83 1.83

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Table 4a The increase/decrease between the IPCC scenarios for carbon stock and cumulative land in the short term 2020 for SR species. 2020: SR

No CC

Zone

Price

Cumul. reforested land ('000 ha)

3

$0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100

26.40 27.43 29.48 35.43 44.46 44.17 45.89 49.32 59.28 74.39 34.95 36.33 39.11 47.13 59.31 16.22 16.86 18.15 21.87 27.53

6

8

19

A1B

A2

Carbon stock (Mt C)

Cumul. reforested land ('000 ha)

Carbon stock (Mt C)

Cumul. reforested land ('000 ha)

Carbon stock (Mt C)

7.15 7.17 7.20 7.29 7.42 11.28 11.31 11.36 11.51 11.74 12.80 12.82 12.86 12.99 13.17 5.77 5.78 5.79 5.85 5.94

26.40 27.90 30.90 39.02 51.64 44.17 46.68 51.70 65.29 86.41 34.95 36.93 40.98 51.85 68.75 16.22 17.15 19.02 24.06 31.91

7.21 7.24 7.29 7.43 7.66 11.39 11.43 11.51 11.76 12.12 12.89 12.92 12.99 13.19 13.48 5.81 5.82 5.85 5.94 6.08

26.40 28.03 31.28 40.05 53.81 44.17 46.90 52.34 67.02 90.04 34.95 37.12 41.41 53.18 71.64 16.22 17.23 19.25 24.68 33.25

7.24 7.27 7.32 7.49 7.74 11.43 11.48 11.57 11.85 12.27 12.92 12.96 13.03 13.26 13.60 5.82 5.84 5.87 5.98 6.14

4.1. Short term (2020) The biggest increase of additional land gained by the year 2020 are for the SR plantations for a carbon price of $100 for all four of the agro-ecological zones at 14% for A1B and 17% for A2 compared to the “no CC” case. This is because within this timescale, even with amounts devalued, the credits still have substantial value to make it a worthwhile venture. The effect on land gain is as much as 11% and 9% respectively under the SR plantation for all the zones under A2 and A1B with carbon at $50. The lower carbon price paths reflecting present day values, the increase under all conditions including the “no CC” is only 1–2% (Tables 4a, 4b). The carbon stocks increase by a maximum of just over 4% under A2 and at a price of $100 for zone 6 followed by zone 19, both regions include the dense forested parts of Karnataka. Dependent on the size

of these projects and even at the lower percentage increase, this can still be very profitable especially for projects using SR species. Under LR plantations, zone 3 comes out ahead in terms of the percentage of land gained for $100 under both A1B and A2 10% and 14% respectively. These are followed by zone 6 at a $100 and Zone 3 at $50. The largest carbon stock increases are also in zones 3 and 6 and once again under the $100 price scenario. Nearly all the extra significant vegetation productivity is in these two zones under all the price scenarios. Zones 8 and 9 under all price and climate scenarios, barely manage a 2% increase. Under the B1 scenario there are no significant differences in land or carbon gained from the “no CC” case. Zone 3 at $100 in correlation to the other two scenarios represents the biggest drop by 4% in land and 2% in vegetation productivity under the B1 scenario.

4.2. Medium term (2050) Land that becomes available compared to the “no CC” scenario using SR plantations is at a similar rate to 2020 but slows down by approximately 1% from the previous time period. Once again the $100 scenario represents the biggest gains for all the zones followed by $50. The carbon stock using SR species causes the percentage increase to more than double in all the cases for all climate and carbon price scenarios. The carbon amounts are cumulative; this follows that the differences would also double in almost twice the time (Tables 5a, 5b). Under the LR scenario, the maximum land available has decreased by 3% compared to 2020 but is again under the $100 scenario in zone 6. This is due to the longer rotation periods of the projects and thus having shorter harvesting seasons as is the case for SR plantations. The carbon stock has increased immensely for zone 3 and 6 for carbon price $100 under A2 by 18% from the “no CC” scenario and 13% under A1B. At $50 carbon price there are increases of 10% and 14%, approximately double the figure of 2020. The zones that reflect the biggest gains show the biggest losses under B1 scenario.

4.3. Long term (2100) Under the long term mitigation period most scenarios cause land to hit maximum capacity except in zones 8 and 19 under the $50 and $100 scenario. This is due to the money devaluing to such an extent as to make mitigation worthless, proving that the carbon price

Table 4b The increase/decrease between the IPCC scenarios for carbon stock and cumulative land in the short term 2020 for LR species. 2020: LR

No CC

Zone

Price

Cumul. reforested land ('000 ha)

3

$0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100

12.90 13.95 16.03 20.30 28.10 21.59 23.31 26.76 33.80 47.02 17.08 18.09 20.11 23.96 30.84 7.93 8.40 9.35 11.16 14.41

6

8

19

A1B

A2

B1

Carbon stock (Mt C)

Cumul. reforested land ('000 ha)

Carbon stock (Mt C)

Cumul. reforested land ('000 ha)

Carbon stock (Mt C)

Cumul. reforested land ('000 ha)

Carbon stock (Mt C)

3.41 3.44 3.48 3.60 3.80 5.40 5.44 5.51 5.70 6.04 5.91 5.92 5.95 6.01 6.12 2.66 2.67 2.68 2.71 2.76

12.90 14.07 16.40 21.25 31.12 21.59 23.51 27.37 35.38 51.08 17.08 18.17 20.34 24.58 32.14 7.93 8.44 9.46 11.45 15.02

3.47 3.50 3.56 3.71 4.00 5.48 5.53 5.63 5.88 6.37 6.20 5.96 5.99 6.07 6.18 2.68 2.68 2.70 2.74 2.80

12.90 14.12 16.56 21.66 32.48 21.59 23.60 27.63 36.06 52.87 17.08 18.18 20.44 24.82 32.67 7.93 8.45 9.50 11.57 15.27

3.49 3.52 3.59 3.76 4.09 5.52 5.58 5.69 5.97 6.52 5.95 5.97 6.01 6.09 6.24 2.68 2.69 2.71 2.75 2.82

12.90 13.90 15.89 19.91 27.10 21.59 23.23 26.52 33.17 45.47 17.08 18.06 20.02 23.72 30.36 7.93 8.39 9.31 11.05 14.18

3.39 3.41 3.46 3.56 3.72 5.36 5.40 5.46 5.63 5.93 5.89 5.91 5.93 5.99 6.08 2.65 2.66 2.67 2.70 2.74

K. Khatun et al. / Forest Policy and Economics 12 (2010) 277–286 Table 5a The increase/decrease between the IPCC scenarios for carbon stock and cumulative land in the short term 2050 for SR species. 2050: SR

No CC

Zone

Price

Cumul. reforested land ('000 ha)

3

$0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100

75.90 82.46 95.92 35.43 122.17 127.00 138.03 159.93 165.71 204.41 100.47 109.40 127.14 131.67 162.87 46.63 50.78 59.48 61.11 75.59

6

8

19

A1B

A2

Carbon stock (Mt C)

Cumul. reforested land ('000 ha)

Carbon stock (Mt C)

Cumul. reforested land ('000 ha)

Carbon stock (Mt C)

8.13 8.23 8.45 7.29 8.97 12.92 13.09 13.45 13.62 14.33 14.11 14.25 14.54 14.68 15.25 6.37 6.44 6.58 6.64 6.90

75.90 85.50 104.79 39.02 140.45 127.00 143.07 175.17 181.00 235.00 100.47 113.19 139.04 143.69 186.90 46.63 52.60 64.67 66.69 86.75

8.35 8.53 8.90 7.43 9.73 13.29 13.59 14.20 14.44 15.59 14.41 14.63 15.14 15.33 16.26 6.51 6.62 6.85 6.94 7.37

75.90 86.31 107.16 40.05 145.77 127.00 144.41 179.19 185.38 243.89 100.47 114.37 141.76 147.08 193.86 46.63 53.08 66.05 68.26 89.97

8.44 8.65 9.06 7.49 10.01 13.44 13.78 14.47 14.75 16.06 14.52 14.80 15.33 15.58 16.64 6.56 6.69 6.95 7.05 7.54

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4.4. Rate of change for land availability Table 7 shows that overall, the higher carbon price yield shortages of land faster. Under the SRES scenarios, the land availability does not change by any meaningful amounts. The land available in some zones runs out well before 2100. In zone 6 for both LR and SR, the land hits a maximum capacity as early as 2059 with SR and 2060 using LR under the $100 price scenario. The price of carbon is the factor that has a significant effect on land availability in zone 3. Under the mitigation value $15 + 5% there is an effect of the land hitting a maximum capacity as many as 16 years (SR) and 25 years (LR) earlier in comparison to the $50 path respectively. This is not unexpected, as keeping a hypothetical price that is constant over a century will devalue substantively over such a long period. SR is more significant as there can be as much as one rotation period between the climate scenarios. Zones 3 and 6 yield similar results and zone 19 produced comparable outputs to zone 8. There are no major effects observed in zones 8 and 19 for the SR and LR plantations between the three climate scenarios; however, the different carbon prices result with maximum capacity being reached 16–18 years earlier for LR and SR with carbon priced at $15 +5% compared to the $5 + 5%. The maximum land capacity is not reached under the $50 and $100 conditions. 4.5. Analysis of the impacts on land and carbon stocks under the SRES scenarios for the three time periods

considered needs to be considered for both the short and long term to monitor the rate at which land gets used up (Tables 6a, 6b). By the year 2100 the difference between the scenarios for the cumulative carbon stock from LR and SR start to decrease. The different carbon price paths reflect similar increases or decrease in carbon stock between the “no CC” scenario and the A1B between 5% and 7% and 7% and 9% for A2. In the year 2100, there are more price scenarios that cause a higher percentage difference between the “no CC” and the B1 due to the land becoming scarcer and the prices catching with each other. The zero change signify that by year 2100 all land in that specific zone is used up and therefore the model calculates it as a “0” value. Carbon stocks continue to increase but at a much slower rate than under the other two time frames.

Results from this study show that as the carbon price rises, it impacts the rate at which the land becomes available, the value of that land also rises. Under the $100 and $50 carbon price case, there are significant additional areas gained under the A1B and A2 scenarios and carbon mitigation is gained in the short and medium terms compared to the GCOMAP results prior to adding the inputs from LPJ. Other price paths for carbon are more effective in the medium–long term as the dollar value continues to increase compared to the constant carbon prices that devalue with time. The differences for land availability between the climate scenarios reduce with time as there is less land overall and thus hitting the maximum land capacity heading into the medium to long term. This does not pose a problem for short term mitigation efforts as there will be a number of rotation

Table 5b The increase/decrease between the IPCC scenarios for carbon stock and cumulative land in the short term 2050 for LR species. 2050: LR

No CC

Zone

Price

Cumul. reforested land ('000 ha)

3

$0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100

37.09 43.04 54.99 51.51 65.81 62.06 71.89 91.64 85.88 110.11 49.09 54.88 66.45 62.52 75.96 22.79 25.51 30.95 29.10 35.42

6

8

19

A1B Carbon stock

A2

B1

Carbon stock (Mt C)

Cumul. reforested land ('000 ha)

Carbon stock (Mt C)

Cumul. reforested land ('000 ha)

Carbon stock (Mt C)

(Mt C)

Cumul. reforested land ('000 ha)

5.34 5.59 6.11 6.36 7.40 8.61 9.04 9.90 10.31 12.06 7.49 7.65 7.96 8.09 8.69 3.40 3.47 3.62 3.68 3.96

37.09 43.73 57.23 53.36 70.64 62.06 73.04 95.33 88.96 116.62 49.09 55.32 67.76 63.73 78.38 22.79 25.71 31.56 29.67 36.56

5.71 6.05 6.73 7.07 8.47 9.25 9.81 10.93 11.48 13.86 9.22 7.93 8.31 8.48 9.10 3.51 3.60 3.78 3.86 4.21

37.09 44.02 58.21 54.16 72.74 62.06 73.53 96.96 90.28 119.43 49.09 55.40 68.28 64.20 79.35 22.79 25.79 31.80 29.89 37.02

5.88 6.25 7.01 7.39 8.97 9.52 10.14 11.39 12.01 14.69 7.84 8.04 8.45 8.63 9.44 3.56 3.65 3.85 3.93 4.31

37.09 42.76 54.12 50.76 64.14 62.06 71.43 90.21 84.65 107.53 49.09 54.71 65.95 62.06 75.02 22.79 25.43 30.71 28.89 34.99

5.19 5.42 5.88 6.09 6.97 8.36 8.74 9.50 9.86 11.40 7.40 7.54 7.83 7.94 8.49 3.35 3.42 3.55 3.61 3.87

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Table 6a The increase/decrease between the IPCC scenarios for carbon stock and cumulative land in the short term 2100 for SR species. 2100: SR

No CC

Zone

Price

Cumul. reforested land ('000 ha)

3

$0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100

158.41 174.66 174.66 174.66 174.66 265.04 274.57 274.57 274.57 274.57 209.68 317.36 317.36 264.06 317.36 97.32 142.72 142.72 122.56 142.72

6

8

19

A1B

A2

Carbon stock (Mt C)

Cumul. reforested land ('000 ha)

Carbon stock (Mt C)

Cumul. reforested land ('000 ha)

Carbon stock (Mt C)

9.76 10.21 10.20 10.21 10.21 15.64 16.05 16.05 16.05 16.05 16.28 18.25 18.60 17.33 18.37 7.38 8.26 8.36 7.86 8.32

158.41 174.66 174.66 174.66 174.66 265.04 274.57 274.57 274.57 274.57 209.68 317.36 317.36 284.70 317.36 97.32 142.72 142.72 132.14 142.72

10.25 10.78 10.78 10.78 10.78 16.47 16.94 16.94 16.94 16.94 16.94 19.48 19.64 18.63 19.64 7.68 8.80 8.84 8.47 8.83

158.41 174.66 174.66 174.66 174.66 265.04 274.57 274.57 274.57 274.57 209.68 317.36 317.36 290.51 317.36 97.32 142.72 142.72 134.83 142.72

10.44 11.00 11.00 11.00 11.00 16.79 17.29 17.29 17.30 17.29 17.19 19.96 20.00 19.11 20.04 7.80 8.99 9.02 8.69 9.01

climate scenarios; it is the value of carbon that determines the speed at which land is used up. However, over the long term, results would suggest that whilst zone 6 is better climatically for plantation projects under most of the scenarios, it is worth considering; combining zone 6 with planting in land from zone 8 as the rate at which the land is used up is slower than in zones 3 or 6. This would allow for more flexibility to provide employment in the early periods whilst countering the temptation to utilise all the “best” land for plantations using the fastest growing crops opposed to the most appropriate forests, thus selecting species suitable for the various locations and differing conditions will be paramount. The impact of climate change on the land and vegetation will also need to be observed over longer time periods and activities chosen in accordance to those related changes. 5. Discussion on the implications of the findings from the case study

periods within that time and land use over longer periods can be decided on local environmental and social circumstances. Based on results from the GCOMAP model, the carbon price that emerges as the most beneficial in terms of the number of credits gained for the short and medium term is $100 for SR project in zones 3 and 6. Zones 8 and 6 are bigger in area and have the most available land for A/R projects. The early years are more likely to have the most impact in providing employment and other immediate environmental gains by sequestering carbon from the atmosphere as well as restoring the biodiversity of the region (Smith and Scherr, 2002), particularly for SR crops as these have more harvesting cycles. With all four of the zones, it appears that there are no significant differences between the LR and SR in the rate at which land runs out under the different

Sustainable development for India requires that there is a poverty reduction potential from CDM projects, which in turn depends on the economic value of the carbon sequestered. To date, this value has been hard to pin down (Vickers and Mackenzie, 2007). Ravindranath et al. (2007a) use the GCOMAP and combine the AEZ classification system with the Global Trade Analysis Project (GTAP) to integrate the two types of land classification systems based on two carbon prices $50 and $100 respectively. They aim at estimating India's forestry mitigation potential based at a regional level. Their findings indicated that substantial additional area was bought under the $100 scenario compared to the $50 scenario, thus illustrating the importance of an economical incentive, particularly in the short term. The findings from this study concur with their investigation. This analysis adds to this by illustrating that other price paths can also achieve the same objective. Valuing credits at a lower price such as the $5 + 5% and £15 + 5% reflect the current status more realistically. It is also possible to imagine a modest annual increase to compensate for less available land for A/R projects. We have also shown that under different climate conditions, even relatively short time periods can have a significant impact on the credits accrued and the land available for A/R projects. Whilst the trends of impacts could be considered as robust, the magnitudes in this study should be viewed with caution, due to the uncertainty in climate projections and confidence in climate change models being quite low. LPJ considers changes in biomes over a time

Table 6b The increase/decrease between the IPCC scenarios for carbon stock and cumulative land in the short term 2100 for LR species. 2100: LR

Baseline

Zone

Price

Cumul. reforested land ('000 ha)

3

$0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100 $0 $5 + 5% $15 + 5% $50 $100

77.40 85.34 85.34 85.34 85.34 129.51 134.17 134.17 134.17 134.17 102.46 155.07 155.07 119.30 136.15 47.55 69.74 69.74 55.47 63.40

6

8

19

A1B

A2

B1

Carbon stock (Mt C)

Cumul. reforested land ('000 ha)

Carbon stock (Mt C)

Cumul. reforested land ('000 ha)

Carbon stock (Mt C)

Cumul. reforested land ('000 ha)

Carbon stock (Mt C)

9.13 10.74 11.13 10.58 11.14 14.97 17.10 17.43 17.02 17.53 10.69 12.63 14.33 11.66 12.62 4.88 5.77 6.49 5.33 5.79

77.40 85.34 85.34 85.34 85.34 129.51 134.17 134.17 134.17 134.17 102.46 155.07 155.07 120.81 139.17 47.55 69.74 69.74 56.18 64.81

10.14 12.09 12.39 11.96 12.46 16.64 19.23 19.34 19.19 19.50 13.72 13.68 15.57 12.53 13.52 5.19 6.25 7.05 5.74 6.30

77.40 85.34 85.34 85.34 85.34 129.51 134.17 134.17 134.17 134.17 102.46 155.07 155.07 121.40 140.35 47.55 69.74 69.74 56.46 65.37

10.57 12.67 12.91 12.56 13.01 17.36 20.15 20.17 20.13 20.36 11.60 14.08 16.06 12.88 14.16 5.30 6.44 7.27 5.90 6.50

77.40 85.34 85.34 85.34 85.34 129.51 134.17 134.17 134.17 134.17 102.46 155.07 155.07 118.72 134.98 47.55 69.74 69.74 55.20 62.85

8.74 10.20 10.62 10.04 10.55 14.30 16.26 16.65 16.15 16.71 10.44 12.23 13.85 11.32 12.20 4.76 5.58 6.27 5.18 5.59

K. Khatun et al. / Forest Policy and Economics 12 (2010) 277–286 Table 7 The corresponding values showing the mitigation years in which land hits maximum capacity for the 4 agro-ecological zones corresponding to Karnataka. Where there is no value land does not run out. zone

Price Scenario

$5 + 5%

$15 + 5%

$50

$100

3SR

A1B A2 A1B A2 B1 A1B A2 A1B A2 B1 A1B A2 A1B A2 B1 A1B A2 A1B A2 B1

2082 2081 2078 2077 2080 2079 2078 2075 2075 2077 2094 2092 2095 2094 2096 2092 2091 2093 2093 2095

2067 2066 2062 2062 2065 2065 2064 2061 2060 2063 2076 2075 2077 2076 2079 2075 2074 2076 2075 2077

2081 2079 2086 2085 2090 2076 2075 2080 2079 2084 – – – – – – – – –

2063 2061 2064 2062 2072 2059 2058 2060 2059 2066 2088 2084 – – – 2085 2082 – –

3LR

6SR 6LR

8SR 8LR

19SR 19LR

period, what is not apparent is the distinction of what fraction of the contribution is due to precipitation and what part is due to the changing temperatures as the most prominent driving factor in biomass increase under A1B and A2 scenarios. It is difficult to clarify if variables really represent true differences in regional biodiversity to any specific degrees. The implications of rising temperature and variation in precipitation in some areas may mean that certain parts of the climate will be drier and warmer, particularly when considering times scales reaching 2100. Over the years the ideas on species to plant may shift due to weather and climate, Ravindranath et al. (2007b) have shown this to be the case for India. The species representing SR and LR crops within GCOMAP in this study will likely experience such shifts, but current rules state that all selection of vegetation has to be produced for the duration of the project. The model does not give information on which pools are likely to decrease because of project activities; only pools measured and monitored can be claimed for carbon credits. Within this study, the model uses perfect foresight for the differing scenarios. Another limitation is that inconsistency arises when policymaker have an incentive to deviate from an original plan made. The investors as well as the horizon of policies are much shorter than mitigation studies have the scope to predict. The GCOMAP model is a purely econometric model but has been used to recommend mitigation options to the Worldbank by Sathaye et al. (2008) and to the Environment Agency in America as well as for policymakers in India. This study highlights an example of a science and policy mismatch. The perceived value of forests based on their different uses influence decisions made on forest resources and land use and a forest's monetary value is highly contingent on which user perspective is applied. The response of governments, forestry official, private entities, and rural communities are likely to be influenced by the price path of carbon prices over time. Mitigation appraisal studies at the national and global-level must estimate any technical mitigation potential, taking into account that all land that has potential for mitigation cannot necessarily be used for this purpose. Tools such as GCOMAP can be misleading regarding the scale of the programs that can be implemented and do not reflect the underlying social issues directly linked with the land availability and use. Studies using the model are useful for trends but need to acknowledge that the reality may be that all such land are not available due to a number of barriers such as tenurial status, misclassification of wasteland as well as issues of access to the resources of state land (Khatun, 2009). Therefore the actuality is, there would be fewer areas available than the figures obtained from data may

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originally suggest (Jodha, 2000). There is a need to identify indicators to ensure that as the value of land increases, forestry projects do not result in an adverse effect on the environment, the local biodiversity and on the communities that utilize them. 6. Conclusion The CDM holds considerable promise to bring cash and other benefits to poor households in developing countries through involvement in forest plantation schemes (Vickers and Mackenzie, 2007). Whilst using wasteland can be a unique opportunity to establish vegetation on lands that are degraded making it applicable for eligibility for the CDM project norms, afforesting wasteland compared to other forms of forestry activities will incur higher initial transaction costs. Therefore the CDM and its aim towards sustainability needs to incorporate decisions and planning that allow for the “best” allocation of land. All benefits accrued have to be identified based on sustainable development indicators such as employment, resource allocation and direct income from the CDM projects. Plantations in particular offer opportunities in sustained employment due the continuous rotation of harvesting periods well after initial implementation stage but have other associated issues such as impacts on biodiversity and crop diversity (Smith and Scherr, 2002). Hence, the challenge here is to create economic systems which are environmentally viable, that are also culturally and socially feasible. A/R activities have the potential to move towards one of the major objectives in India of alleviating poverty and hence contribute towards the sustainable development goals for the country. They can provide income where there was none and with standing vegetation, the benefits to the environment and local livelihoods can continue well into the future, past the crediting period. A cautionary and a more integrated approach to assessment and implementation lie at the heart of the success and integrity of the CDM. Acknowledgements The writing up of this research was funded by the University of Bristol. We would like to gratefully acknowledge Professor N.H. Ravindranath, and I.K Murthy at the Indian Institute of Science, Bangalore. The Authors would also like to thank the reviewers for their comments on this document. References Forest Survey of India, 2005. The State of Forest Report Ministry of Environment & Forest, Dehradun. http://www.fsi.nic.in/sfr_2005.html. Haxeltine, A., Prentice, I.C., 1996. BIOME3: an equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types. Global Biogeochemical Cycles 10 (4), 693–709. Haxeltine, A., Prentice, I., 1997. A general model for the light use efficiency of primary production. Functional Ecology 10, 551–561. IPCC, 1996. In: Bruce, J., Lee, H., Haites, E. (Eds.), Climate Change 1995: Economic and Social Dimensions of Climate Change. Cambridge University Press, Cambridge, UK. IPCC, 2007. Climate change 2007: synthesis report. Fourth Assessment Report of the Intergovernmental Panel on Climate Change. http://ipcc-wg1.ucar.edu/wg1/docs. Jodha, N.S., 2000. Waste Lands Management in India: Myths, Motives and Mechanisms, Vol. 35, 6. Economic and Political Weekly, pp. 466–473. Feb. 5–11, 2000. Khatun, K., 2009. An Investigation into the Effectiveness of Using Forestry Projects for Sustainable Development in India Under the Clean Development Mechanism. Bristol University. Kindermann, G., Obersteiner, M.B., 2008. Global cost estimates of reducing carbon emissions through avoided deforestation. Proceedings of the National Academy of Sciences 105 (30), 10302–10307. http://www.pnas.org/content/105/30/10302.full. Kolshus, H.H., Vevatne, J., Torvanger, A., Aunan, K., 2001. Can the Clean Development Mechanism attain both cost-effectiveness and sustainable development objectives? CICERO Working Paper 2001, p. 8. Makundi, W., Sathaye, J., 2003. GHG Mitigation Potential and Cost for Tropical Forestry — a Relative Role for Agroforestry. Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720. Ministry of Environment and Forests (MoEF) (2004). India's Initial National Communication to the United Nations Framework Convention on Climate Change. UNFCCC. http://unfccc.int/national_reports/non-annex_i_natcom/items/2979.php. Nakicenovic, Swart, R., 2000. Special Report on Emissions Scenarios Cambridge. Cambridge University Press.

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