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1 Saudamini Das, Assoc Prof. Institute of Economic Growth, Delhi, and SANDEE Fellow Economics of Climate Change Adaptation Workshop, USAID, UNDP, ADAPT.

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Presentation on theme: "1 Saudamini Das, Assoc Prof. Institute of Economic Growth, Delhi, and SANDEE Fellow Economics of Climate Change Adaptation Workshop, USAID, UNDP, ADAPT."— Presentation transcript:

1 1 Saudamini Das, Assoc Prof. Institute of Economic Growth, Delhi, and SANDEE Fellow Economics of Climate Change Adaptation Workshop, USAID, UNDP, ADAPT Asia- Pacific, 24-26 Oct 2012, Bangkok Conserving Mangroves for Storm Protection Services The views expressed are those of the presenter and should not be attributed to either UNDP or USAID. Furthermore, it is strongly recommended that both the PowerPoint slides and the videos of the presentation of content included herein are viewed in conjunction in order that statements appearing in the PowerPoint slides are not interpreted out of context.

2 Approach to Storm Hazard There are limitations & uncertainty Accurate prediction of intensity, landfall with sufficient time gap Limited compliance of community to State warning: (wait & watch, faith on GOD, less faith on Govt) All investments limited to protection of movable properties Uncertainty from Climate change Bay of Bengal, South China Sea and North Pacific Ocean are core area of Cyclogenesis (IPCC 1997) Emphasis on scientific & engineering approach to storm risk management by policy Better prediction Early warning Evacuation Storm shelters, storm resistant houses, dikes

3 3 Natural buffers are important - need to examine their role Do mangroves provide storm protection ? - historical context, - concerns, knowledge gap - careful examination of this service Is mangrove preservation an economically viable adaptation strategy to climate change? How do they fare vis-à-vis the other approaches like early warning, storm shelter, dikes, etc?

4 Mangroves break, stop and channelise the storm surge

5 Debated since the Bhola Cyclone (Nov 1970) in East Pakistan (Bio.Conserv, 1971) Much focus due to prominent natural disasters like 2004 tsunami, Katrina, Sidr, Nargis, Aila etc. Accepted as a prominent ecosystem service (Barbier et al, Science 2008; Day et al, Science 2007; Gedan et al, Climate Change 2010). Most of the empirical works are questioned. Storm protection by Mangroves - Background

6 Quantifies Mangrove Protection during 1999 Super Cyclone Examines mangrove protection for movable as well as immovable properties (house damages) Large sample and maximum possible control to separate Mangrove impact on Cyclone Damage Use Scientific, GIS and Socio-Economic data Robust finding that mangroves reduced cyclone damages and support for mangrove conservation to get storm protection Present Study

7 Kendrapada Jagatsinghpur Bhadrakh Puri Super Cyclone Path Bay of Bengal Mangrove Forest cover before 1950 (30,766 hectares) 7

8 Kendrapada Jagatsinghpur Puri Bhadrakh Super Cyclone path Bay of Bengal Mangrove Forest cover in 1999 (17,900 hectares) 8

9 9 A B

10 Human death in Kendrapada

11 Description of Deaths AreaNo of Villages Mean death Mini mum Maxi mum Entire District11800.39021 Villages with no or little mangrove protection (M ≤ 0.5) 7220.54021 Villages with high mangrove protection (M > 0.5) 4580.14010 Villages inside mangrove habitat ( established by cutting mangroves) 961.11013 Villages outside mangrove habitat 10840.32021 11

12 How do I identify Mangrove Impact during Cyclone? Estimate Cyclone Damage Function using multiple control variables along with mangroves Use variables to control for: - Physical features of mangrove habitat - Cyclone Impact - Topography - Hydrology - Infrastructure - Socio-Economic Well-being - Governmental Institution Calculate avoided damages from marginal effect 12

13 Confounding effect of mangrove with effect of distance from coast ● ● Village A Village B Mangrove Sea

14 ---- with effect of Mangrove Habitat A●A● C●C● Exclude village A, include village C Test that protection at B is due to vegetation, not habitat B●B● Sea Mangrove habitat Mangrove

15 Marginal effect - death ( villages within 10km from coast) VariablesMarginal EffectVariablesMarginal Effect Mahakalpada Tahasil 0.22 ***Droad0.0006 Patamundai Tahasil -0.06Roadumy0.04 Surge0.02Pop990.00005 *** Dcoast0.02**Literate-0.16 Mangrove-0.111***Schedulcaste-0.10 Mhabitat-0.022 ***Cultivator0.05 Topodumy0.294 ***Aglabor0.03 Casuarinadumy-0.045Hhworker0.958 Dmajriver0.025 ***Margworker0.36*** Dminriver-0.004Otworker-0.26

16 Deaths averted by Mangroves, Actual death due to super cyclone392 Predicted deaths if there were no mangroves603 Predicted deaths if current mangroves were at 1950 level 31 Averted deaths under assumption 1 (603 – 392) = 211 211 (54%) Averted deaths under assumption 2 (392 – 31) = 361 361 (92%) 16

17 Other damages averted by Mangroves (%) Damage type 1999 mangrove 1950 mangrove Human death 5492 FC17100 PC-17-100 Cattle1567 Buffalo2752 17

18 Area / Mangrove unit Value of 1km width Value of 1 hectare VillageRs3,928/Rs217/ Entire study area Rs33,39,166/ (USD 68, 586) Rs1,82,080/ (USD 4335) Storm Protection Value of Mangroves during super cyclone 18

19 Type of damageValue/km of 1999 mangrove/village Value/km of 1950 Mangrove/village Human death2743.951478.28 Fully collapsed houses1368.553235.85 Partially collapsed houses -245.59-580.85 Fully collapsed + Partially collapsed 1123.362655 Buffaloes8.774.83 Cattle49.9445.07 Weighted Average Storm Protection Value of km width of Mangroves per Village (in INR)

20 Will we optimize benefit by going back to 1950 level? NO X axis: km width of mangrove \ village Y axis: averted death \ km width of mangrove \ village Turning point: at approximately 1.5 km \ village

21 21 Mangroves vs. others Damag e Actu al Death without mangrove s Death without storm shelters Death without early warning Death without dikes Human death 1973314041602257 74% of all the Swept Away houses and 80% of all the cattle loss in Kendrapada occurred from villages next to dikes. Source: Das & Vincent ongoing (not to be quoted) Source: Das (sandee@10)

22 Meteorology: Orissa is the most cyclone prone state in east coast of India. Frequency of high intensity cyclones increasing over years. PeriodFrequency of VSCS & SC Annual Probability 1900-1920 00.00 1920-194020.10 1940-196010.05 1960-198030.15 1980-200030.15 Should we preserve mangroves to adapt to climate change? Yes, on both meteorological and economic grounds.

23 Economic cost & benefit (1999 prices) Opportunity cost of conserving mangroves Market value of land in coastal Kendrapada: Rs172, 970 \ ha Annual return from land (8%): Rs13, 837\ha\yr Annual return from land(12%): Rs20, 756\ha\yr Benefit from conserving mangroves Storm protection value (only for 3 damages):Rs182, 080\ha Annual Probability of VSCS and SC: 0.15\yr Annual Storm Protection Value (for 3 damages): Rs27, 312\ha\yr

24 Does reduction in only death risk justify mangrove preservation? Benefit from death risk reduction No of lives saved: 0.01 \ ha VSL for Orissa from Indian wage-risk study: Rs10,918,132\ Annual probability of VSCS &SC: 0.15\yr Annual benefit from reduction of death risk : Rs17, 469\ha\yr Opportunity cost of Mangrove Preservation: Rs13, 837 – Rs20, 756\ha\yr Benefit ≥ Cost VSL for India: Rs13.7-14.2 to Rs55.5-60.6million at 2000 - 01prices ≈ Rs17.8-18.4 to Rs72 -78.12m at 2002-08 Per capita income.

25 Conclusions Mangroves reduced human death, livestock loss and house damages during the T-7 Super cyclone of October 1999. Human death toll would have been nearly doubled in absence of mangroves. Annualized storm protection benefit of mangrove for reducing three damages was found higher than annual return from land justifying mangrove conservation as a viable adaptation strategy to climate change. 25

26 26 Policy Implications Use of engineering approach to manage storm disaster may not be the only option. Along with engineering and scientific approaches, mangrove protection and regeneration should be undertaken to manage storm risk more efficiently Along with movable properties like lives, mangroves also protect immovable assets like houses.


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