Carbon management in Indian forests: a policy analysis to ...wgbis.ces.iisc.ernet.in/biodiversity/pubs/ces_pubs/pubs...of wasteland [5]. As opposed to the use of agricultural land,

future science group 109ISSN 1758-300410.4155/CMT.10.19 © 2010 Future Science Ltd

Forests provide ecological, economic and social ser-vices to natural systems and humankind, including refuges for biodiversity, provision of food, medicines and other forest products, regulation of the hydrologic cycle and protection of soil resources. Furthermore, they help to mitigate the effects of climate change, thus making afforestation, especially on wastelands, a win–win proposition. India has a National Forestry Action Program (NFAP), which is a comprehensive strategic long-term plan for the next 20 years. One of the principal objectives of the NFAP is to bring a third of the geographic area under forest/tree cover [101]. Furthermore, under the national action plan on cli-mate change, India has announced the greening India mission, which endeavors to bring an additional 6 Mha under forest cover by the year 2020 [102]. This is over and above the current afforestation rate of approxi-mately 1.1 Mha per annum [102]. One of the positive benefits of such a large-scale afforestation program

is its possible contribution to climate change mitiga-tion through carbon sequestration. A number of such mitigation estimates are available for the Indian for-estry sector [1–4]. Estimates for national mitigation potential for 2005–2035 by the forestry sector ranged from 210 MtC to 853 (770–3127.67 Mt CO

2). These

studies primarily analyzed the carbon sensitivity to the mitigation potential of Indian forests. None of the studies analyzed the policy sensitivity to the mitiga-tion potential of Indian forests, considering the 33% forest area target.

This study estimates the carbon mitigation and offset potential of a rapid afforestation policy, while a discussion of the impact of carbon pricing on the carbon-offset potential is outside the scope of this article. We analyze this policy under two scenarios: a rapid afforestation rate (i.e., achieving this goal by 2020) and moderate afforestation rate (i.e., achieving this goal by 2030).

Carbon Management (2010) 1(1), 109–117

Carbon management in Indian forests: a policy ana lysis to assess mitigation potential

Rajiv K Chaturvedi1, Ranjith Gopalakrishnan2, R Sukumar1 & NH Ravindranath†2

The forest sector provides mitigation opportunities to address climate change. Afforestation and reforestation have been identified as important mitigation options. Thus, there is a need for estimating the mitigation potential at local, national and global levels. India has an ambitious plan to bring approximately 33% of its geographical area under forest cover. In this article, we analyze the mitigation potential of this policy over the period of 2010–2030, considering two scenarios: a rapid afforestation rate (scenario-2020, achieving the 33% goal by 2020) and a moderate afforestation rate (scenario-2030, achieving the 33% goal by 2030). Assessment using the generalized comprehensive mitigation-assessment process model shows that afforestation could mitigate 5.2 GtCO2 under scenario-2020 over the 2010–2030 period, compared with 3.96 GtCO2 under scenario-2030, over the same period. Furthermore, we show that the rapid afforestation scenario has the potential to offset approximately 9% of India’s average national emissions projected by five models, for the same period. The afforestation under the two scenarios provides an incremental mitigation potential of 1.8–3.2 GtCO2 over the baseline scenario, for the period of 2010–2030, with the potential to offset 3.0–5.4% of the average national emissions projected for the same period.

Policy Focus

1Center for Ecological Sciences, Indian Institute of Science, Bangalore, 560012, India2Center for Sustainable Technologies, Indian Institute of Science, Bangalore, 560012, India†Author for correspondence: Tel.: +91 80 2334 1838; Fax: +91 80 2360 1428; E-mail: [email protected]

For reprint orders, please contact [email protected]

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Carbon Management (2010) 1(1) future science group110

Policy Focus Chaturvedi, Gopalakrishnan, Sukumar & Ravindranath

Methodology for mitigation potential assessmentIn this article, we estimate the miti-gation potential of the afforestation policy of bringing approximately 33% of the total geographic area of India under forests. According to latest statewise estimates from the NRSA, India has 55.26 Mha of wasteland [5]. As opposed to the use of agricultural land, the use of wasteland does not threaten the food security and livelihood of agri-cultural communities. In addition, the opportunity cost of wasteland is less than, for example, an agricul-tural land. Hence, only wastelands are considered for afforestation activities in this study. The steps followed in this study have been enumerated below:

� Step 1: select a suitable mitigation-assessment model;

� Step 2: divide the geographic area into homogeneous zones, for mitigation assessment;

� Step 3: obtain latest area estimates for suitable wasteland categories, for each such zone;

� Step 4: identify land categories suitable for the miti-gation activities appropriate for their biophysical and social characteristics – short rotation (SR) plantations (harvested within approximately 10 years of planting) and long rotation (LR) plantations (harvested in approximately 40 years of planting);

� Step 5: obtain the existing area under forest cover in each of the zones under each plantation/forest subcategories;

� Step 6: estimate the gap between India’s forest cover target of 33% and the existing forest cover at the national level;

� Step 7: allocate the suitable land for annual afforesta-tion in each of the zones under plantation sub-categories under different afforestation policy scenarios;

� Step 8: obtain biomass and soil carbon stocks and growth rates for each of the zones and plantation sub-land categories from published literature and field ecological studies;

� Step 9: estimate the carbon stock estimates for dif-ferent periods for the baseline as well as in different afforestation policy scenarios, and the net additional carbon stock changes;

� Step 10: compare and aggregate the carbon stock estimates of the different zones.

� Selection of mitigation-assessment modelA range of models are available for the assessment of miti-gation potential in land use change and on the forestry sector. We reviewed the salient features of seven widely used mitigation-assessment models [6–11,103]. Based on the key model features, a decision matrix was created. Nine indicators, as detailed in Table 1, were identified as key factors in assessing the mitigation-assessment models.

Mitigation-assessment models were evaluated based on the weightage of these indicators, with a score between 0 and 5. Based on this scoring, a decision matrix (Table 2) was created.

Table 2 suggests that the Generalized Comprehensive Mitigation Assessment Process (GCOMAP) received the highest score for the specified decision criteria. However, this does not suggest that other models are not useful; the current ana lysis only suggests that GCOMAP is best suited to answer the questions outlined in this paper.

Table 1. Framework of the decision criteria for mitigation models.

Indicators Options (weightage) Score range

Pools considered AGB, BGB, DW, SoC, litter; out of these five how many pools are modeled (1–5) 1–5Key outputs expected Incremental carbon stock in mitigation over baseline (Y/N) 0 and 5Availability of reliable data Out of five key input variables, the availability of reliable data (1–5) 1–5Model structure Model structure (e.g., simple econometric models get a score of 1, partial

equilibrium models get 2, equilibrium models get 3, while dynamic models get 5)

1–5

Carbon price sensitivity to the land area change Y/N 5/0 A&R option availability Y/N 5/0Whether the model can be used at multiple levels

Y/N 5/0

Ease of operation/model familiarity Y/N 1/0A&R: Afforestation and reforestation; AGB: Above-ground biomass; BGB: Below-ground biomass; DW: Deadwood; N: No; SOC: Soil organic carbon; Y: Yes.

Key terms

Climate change: A change of climate that is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and that is in addition to natural climate variability over comparable time periods.

Afforestation: Direct human-induced conversion of land that has not been forested for a period of at least 50 years, to forested land.

Mitigation: Implementation of policies to reduce greenhouse gas emissions and enhance carbon sinks.

India’s Forest cover target: India’s ambitious plan to bring approximately 33% of its geographical area under forest cover.

Reforestation: Direct human-induced reconversion of nonforested land (that was previously forested but had been converted to nonforested land) back to forested land.

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Carbon management in Indian forests Policy Focus

future science group www.future-science.com 111

� GCOMAP modelThe Generalized Comprehensive Mitigation Assessment Process is a set of models developed by Lawrence Berkeley National Laboratory (LBNL) aimed at estimating the quantity of carbon sequestration achieved for a given year or over a period of years, as well as the financial implications and cost effectiveness of forestry mitiga-tion projects [8]. These models use linear growth rates for biomass and soil carbon increments. It establishes a baseline scenario of land used for potential mitigation activities for the specified time frame, with no addi-tional afforestation. The model usually imposes a car-bon price at different rates, and simulates the response of forest and wasteland managers to these price incen-tives, and estimates additional land brought under the mitigation activity above the baseline level. However, in this study no carbon price has been assumed in either of the scenarios. In the current simulation, the model provides the estimates of the additional carbon seques-tration benefits in the alternate policy scenarios in the response to increased afforestation rates, while meeting the annual demand for timber and nontimber products.

� Stratification of geographic area into homogeneous zonesFor ana lysis of mitigation potential in the Indian forestry sector, two zoning schemes have been primarily used: the agro-ecological zones of India (AEZs) [12], and the Global Trade Project (GTAP) [104]. GCOMAP requires uniform growth rates in biomass and soil pools for each models region. The AEZ categorization is based on the length of growing period (LGP) concept, derived from climate, soil and topography data, with a water balance model and knowledge of crop requirements. LGP refers to the period during the year when both soil moisture and temperature are conducive to plant growth. The AEZ approach has been adopted globally for assessing the growth potential of crops, and can be extended to the growth of forest or plantation biomass. In the light of the uniform growth rate requirements of GCOMAP,

Table 2. Decision matrix for the choice of the mitigation-assessment model for land use, land-use change and forestry sector.

Models/objective functions

Incremental carbon

stock

Carbon pools

considered

Carbon price sensitivity of carbon stock

Carbon prices sensitivity of area changes

A&R option

Multi scaling

Availability of input

variables

Model structure

Ease of operation

Total score

FASOM GHG 0 3 5 0 0 5 3 1 0 17MiniCAM 5 1 0 0 5 5 3 4 0 23Sim Amazonia1

5 2 0 0 0 0 - 3 0 10

EFI-GTM 0 2 5 5 5 5 3 3 0 28CO 2 fix 5 5 0 0 5 5 5 5 1 31GCOMAP 5 5 5 5 5 5 5 3 1 39IMAGE 0 2 0 0 5 0 2 5 0 14A&R: Afforestation and reforestation; GCOMAP: Generalized Comprehensive Mitigation Assessment Process; GHG: Greenhouse gas.

Figure 1. Location and distribution of the agro-ecological zones in India. AEZ 1: Western Himalayas; 2: Western plain Kutch; 3: Deccan plateau, hot and arid; 4: Northern plains and central highlands; 5: Central highlands; 6: Deccan plateau, hot semi-arid; 7: Deccan plateau (Telengana), Eastern Ghats; 8: Eastern Ghats and Tamilnadu uplands; 9: Northern plains, hot sub-humid; 10: Central highlands (Malwa and Bundelkhand); 11: Eastern plateau (Chattisgarh); 12: Eastern plateau (Chotta Nagpur); 13: Eastern plain, hot sub-humid; 14: Western Himalayas; 15: Assam and Bengal plain; 16: Eastern Himalayas; 17: Northeastern hills; 18: Eastern coastal plains; 19: Western Ghats and coastal plains; 20: Island regions. AEZ: Agro-ecological zone.

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the AEZ approach was chosen since it is based on agro-climatic constraints, which heavily influence the plant and forest growth determinants. Thus, near-uniform growth rate can be assumed for each AEZ zone.

� AEZ-based stratification for regional level assessmentIndia has adopted the principles of AEZ and developed its own methods to define geographic zones. India’s geographic area is divided into 20 AEZs based on, for example, moisture and temperature regimes, soil type and land form (Figure 1).

Agro-Ecological Zone 20 was excluded from our ana lysis as it refers to the Andaman and Nicobar island groups, which cover less than 0.2% of the national land area, and for which insufficient data was available.

Data requirements & sourcesData requirements of GCOMAP may be divided into the following two categories: static data requirements and dynamic data requirements.

Static data refers to the datasets that do not change with time and with AEZ (static parameters values are listed in supplementary table: www.future-science.

com/doi/suppl/10.4155/cmt.10.19/suppl_file/suppl_table_1.xls). Meanwhile, dynamic data refers to those datasets that change across AEZ and time (Table 3). Mean annual increment estimates for biomass and soil carbon accu-mulation rates were obtained from literature for all the AEZs, while for AEZs 8 and 19, field-based estimates were used. Estimates of available wasteland along AEZs were prepared from district-wise wasteland data from the National Remote Sensing Agency [5], while forest cover data was obtained from Forest Survey of India [13].

� Preparation of input dataLand availability for afforestation & reforestation in IndiaIn this study, only wastelands are considered to be avail-able for afforestation and reforestation (A&R) activities. Wastelands in India include degraded grasslands, forests and several other land types, and are classified into 28 categories by the National Remote Sensing Agency [5]. However, in the present study, we have excluded the following two categories: land affected by strong salin-ity/alkalinity and barren rocky/stone waste/sheet rock area. The distribution of wasteland area according to the administrative districts is shown in Figure 2.

Baseline area under forest coverThe baseline (current) area under forest cover was obtained from [13]. This data was spatially explicit and, hence, was used to get the AEZ-wise baseline forest cover area. The distribution of forestland area a ccording to the administrative districts is shown in Figure 3.

Past & current A&R ratesIndia has been implementing a large afforestation and reforestation program since 1980; and between 1.00 and 1.25 Mha are brought under tree cover anually. The total area afforested in India since 1980 is over 30 Mha. Figure 4 shows the cumulative area afforested. The mean annual rate of afforestation has been approxi-mately 1.16 Mha/year and has remained stable over a long period of time since 1980.

Table 3. Dynamic input variables required by Generalized Comprehensive Mitigation Assessment Process.

Dynamic parameters Source

Mean annual increment Literature and field workStanding vegetation (total/ha) Literature and field workTotal available land [5]

Annual land planted Estimated Current planted land [13]

Initial soil carbon stock (total carbon/ha) Literature, field study

Soil carbon accumulation (total carbon/ha/year) Literature, field study

Figure 2. Distribution of wasteland in India (in Km2), according to administrative districts.

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So, a mean annual A&R rate of 1.16 Mha/year is used as the baseline scenario for this assessment. Allocation of total A&R area to different zones is based on the proportion of wasteland in each AEZ (technical potential).

Land categories & activities selected for mitigation assessmentThe current assessment is limited to only wastelands, degraded or marginal lands. Wasteland categories are degraded lands that are often unsuitable for crop produc-tion. Mitigation activities considered for such land cat-egories are short- and long-rotation (SR and LR, respec-tively) plantation forestry. The features of SR and LR are given in Table 4. As per the observed trend in Indian afforestation programs, two-thirds of the wasteland area was allocated to the SR option, while a third of wasteland was allocated to LR options in each of the AEZs [14].

Carbon uptake rates in biomass & soil

National level and AEZ-wise carbon uptake rates in biomass and soil carbon are estimated based on a broad literature compilation (Table 5). Although multiple one-time soil carbon stock estimates are available from dif-ferent locations in India, long-term observations of soil carbon from the same plots are not available, which results in acute paucity of data for soil carbon-uptake rate for Indian AEZs (Table 5).

Scenarios for mitigation potential assessmentWe have considered the following policy scenarios for this ana lysis:

� Baseline scenarioThis assumes that afforestation continues with the current baseline rate of 1.1 Mha/year until the total forested area reaches 108 Mha by 2047. Carbon stock under baseline changes over time in response to new land afforested.

� Scenario-2020This scenario analyses the mitigation potential over the 2010–2030 period. Here, approximately 33% of the geographic area is brought under forest cover by 2020. This scenario assumes rapid afforestation, so that the total forest cover reaches 108 Mha by 2020. The affores-tation rate is 4.14 Mha/year, which is almost four-times higher than the baseline scenario.

� Scenario-2030This scenario also analyses the mitigation potential over the 2010–2030 period. Here, approximately 33% geo-graphic area is brought under forest cover by 2030. This scenario assumes slower afforestation rate compared with scenario-2020.

Total forest cover change under different scenarios is shown in Figure 5. The figure shows that under the base-line scenario, 1.1 Mha of land is planted annually until the forest cover reaches 108 Mha by 2047. Meanwhile, scenario-2020 assumes an afforestation rate of 4.14 Mha per annum from 2010 to 2020; thereafter, the forested area becomes stabilized. Scenario-2030 assumes a lower

Figure 3. Distribution of forest land in India (in Km2), according to administrative districts.

Figure 4. Cumulative area afforested in India for the period 1951–2005.

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afforestation rate of 2.07 Mha per annum until 2030. All of the scenarios assume that the area under forests (including afforested plantations) will not be subject to harvest leading to land use change.

Mitigation potential under different policy scenariosThe GCOMAP model was run for 19 zones and the mitigation potential was estimated. In Table 6, we outline the mitigation potential as per AEZ zones. It can be seen that the aggregate mitigation potential in scenario-2020 is estimated to be approximately 3.2 GtCO

2 over the

baseline, while in scenario-2030, the overall increase in mitigation potential is approximately 1.8 GtCO

2. This

shows that rapid afforestation under scenario-2020 will lead to an incremental 1.4 GtCO

2 mitigation potential

over scenario-2030. An aggressive afforestation policy brings an incremental area under plantation cover at an early date compared with the moderate scenario, which starts producing carbon benefits earlier. Thus, by 2030, the aggressive scenario ends up producing more carbon benefits than the moderate scenario.

Furthermore, it can be observed that AEZ 4 has the highest mitigation potential of all the scenarios, since this AEZ has a high availability of the wasteland and good growing conditions. AEZ 4 coincides with the cen-tral Indian states of Madhya Pradesh and Maharashtra. This is followed by the AEZs 14, 17 and 6.

Figure 6 shows the cumulative mitigation potential for different sce-narios. It suggests that the amount of carbon sequestered by afforesta-tion is 5.3 GtCO

2 in the period of

2010–2030 in the most aggressive afforestation scenario. A mitigation potential of 3.96 and 2.12 GtCO

2

is estimated for scenario-2030 and the baseline scenario, respectively. Thus, rapid afforestation is able to create a sink of approximately 2.4-times more carbon compared with the baseline scenario and approxi-mately 1.8-times higher than sce-nario-2030. The ana lysis further suggests that the incremental miti-gation potential for scenario-2020 as

Table 4. Mitigation activities and their features.

Activity Dominant species Rotation period (years)

Short-rotation plantation

Eucalyptus spp.Casuarina spp.Acacia spp.Gmelina arborea

10

Long-rotation plantation

Tectona grandisShorea robsutaDalbergia sissooPinus sppShorea robustaTectona grandisTerminalia spp.

40

Table 5. Agro-ecological zone mean annual increment in biomass and soil carbon.

AEZ Mean annual increment (tB/ha/year)

Soil carbon uptake rate (tC/ha/year)

LR SR LR SR

1 1.02 0.83 0.499 0.32 0.53 0.90 0.499 0.33 4.07 3.35 0.499 0.34 6.63 5.41 0.499 0.35 4.93 3.8 0.499 0.36 4.07 3.38 0.499 0.37 4.07 3.18 0.499 0.38 2.01 7.32 0.499 0.39 4.07 5.41 0.499 0.310 3.4 4.01 0.499 0.311 4.1 3.67 0.499 0.312 5.7 3.67 0.499 0.313 4.3 5.41 0.499 0.314 5.1 3.63 0.499 0.315 4.51 8.74 0.499 0.316 4.51 8.74 0.499 0.317 4.51 8.74 0.499 0.318 5.8 4.66 0.499 0.319 4.9 3.66 0.499 0.3Carbon uptake rates in biomass and soil for different zones are estimated based on a literature survey of 70 published studies and field work carried out in AEZ 8 and 19. AEZ: Agro-ecological zone; LR: Long rotation; SR: Short rotation; tB: Tons of biomass; tC: Tons of carbon. Data taken from [15].

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Figure 5. Change in total forest area with respect to time, in various scenarios considered.

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well as for scenario-2030 will peak in 2030. Even after the incremental mitigation potential peaks and after the afforestation goal of a third of the geographic area is reached, these forests will continue to hold and expand the carbon sink, which could potentially be used to buy time to implement other greenhouse gas-reduction strate-gies. AEZ 4 has the highest mitigation potential under both the policy scenarios, followed by AEZ 14 and 17. AEZ 4 coincides with the north India plains and central Indian highlands, while AEZ 14 coincides with Western Himalayan regions and AEZ 17 coincides with Eastern and Northeastern India.

Forestry potential to offset India’s national emissionAccording to a report by the Ministry of Environment and Forests, Government of India, India is likely to emit approximately 50–68 GtCO

2eq over the period of

2010–2030 (total emission of 58.7 GtCO2eq, when aver-

aged over the five studies) [105]. Meanwhile, this paper suggests that Indian forests can sequester 5.3 GtCO

2 in

scenario-2020 and 3.96 GtCO2 in scenario-2030. Thus,

9.0 and 6.7% of the total emissions can be offset under scenario-2020 and scenario-2030, respectively. The tra-jectory of annual emissions versus forestry mitigation can be seen in Figure 7.

Conclusions of this study & its limitationsIn this study, an attempt is made to estimate the mitiga-tion potential of achieving a target of 33% of geographic area under forest cover by 2020 and 2030. The study has considered two afforestation scenarios: rapid (33% by 2020) and moderate (33% by 2030) afforestation rates. Furthermore, a baseline scenario consisting of affores-tation continuing at the current rate of 1.1 Mha/year

is also analyzed. We find that rapid afforestation (sce-nario-2020) can sequester approximately 2.4-times more carbon than the baseline, while a more moderate afforestation rate (scenario-2030) sequesters 1.8-times more carbon during the period 2010–2030. We further estimate that such afforestation alone can mitigate 9.0

Table 6. Mitigation potential of different agro-ecological zones under different scenarios.

AEZ Baseline scenario

Scenario-2020 (million tons of CO2)

Scenario-2030 (million tons of CO2)

Total Total Incremental Total Incremental

1 22.22 55.37 33.15 41.71 19.492 132.24 327.63 195.39 248.82 116.583 30.87 53.78 22.91 40.05 9.184 271.08 684.42 413.34 508.47 237.395 116.76 294.77 178.01 219.02 102.266 138.04 347.63 209.59 258.78 120.747 91.27 230.04 138.77 171.09 79.828 99.44 244.67 145.23 186.27 86.839 41.84 104.62 62.79 78.51 36.6810 132.58 332.35 199.77 248.98 116.4011 42.79 108.16 65.38 80.64 37.8512 137.18 347.98 210.80 257.56 120.3813 58.38 146.38 87.99 109.70 51.3214 236.93 599.52 362.59 444.72 207.7915 64.73 160.76 96.03 121.37 56.6416 136.87 340.02 203.15 256.71 119.8417 220.74 548.65 327.91 414.26 193.5118 75.13 190.14 115.00 141.30 66.1719 72.44 182.96 110.52 135.88 63.44Total 2121.53 5299.85 3178.32 3963.83 1842.29Incremental mitigation potential denotes the additional potential realized in a particular scenario over the baseline scenario. AEZ: Agro-ecological zone.

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and 6.7% of the projected national emissions, under scenario-2020 and scenario-2030, respectively, for the period of 2010–2030. These figures seem to suggest that afforestation be given high priority, when it comes to policy formulation. In the long term, it should be noted that these forests will continue to hold and expand the carbon sink, after the afforestation goal (of a third of the geographic area) is reached. This sink could be valu-able in the future and could be used to buy time to implement other greenhouse gas-reduction strategies.

However, the results of this study should be inter-preted in the light of the model and data limitations. In India, mitigation potential estimates are generally limited by the lack of data on land area available for afforestation and AEZ-wise and species-wise growth rate estimates for biomass and soil carbon. In addition, the study under the two policy scenarios assumes all the suitable wastelands to be available for afforestation activities that, however, may not be actually available in the real world owing to competing land use require-ments and differing opportunity costs for different land use activities. The latest district-wise wasteland

data is available for the year 2005; existing forest area has also been used for the same year in this study and due to the lag period in reporting of wasteland and for-est statistics, little change has been assumed between the period 2006 to 2009. Out of the 20 AEZs, we have excluded Andaman and Nicobar island groups from this ana lysis owing to a lack of data.

AcknowledgementsWe thank the Royal Norwegian Embassy and National Communications Projects for supporting research on climate change at the Indian Institute of Science, Bangalore, India.

Financial & competing interests disclosureRaman Sukumar acknowledges the BP Pal National Environment Fellowship from the Government of India. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

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