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ENVIS CENTRE ON ENVIRONMENTAL ECONOMICS
ENVISAGE Volume 7 - No: 1, March 2010
MSE Mentoring Excellence
ENVIS CENTRE Madras School of Economics Gandhi Mandapam Road, Chennai-600 025 Phone: 044-22300304 Fax: 044-22354847 E-mail:
[email protected] Web: www.mse.ac.in/envis
Ministry of Environment and Forests
Editorial
Editorial Team Prof. K.S. Kavi Kumar Member Secretary Centre of Excellence Dr. Zareena Begum I Assistant Professor Dr. Sukanya Das Lecturer
Natural disasters are the manifestation of natural hazards like flood, tornado, hurricane, volcanic eruption, earthquake, or landslide that affect the environment, and lead to financial, environmental and human losses. The resulting loss depends on the capacity of the population to undertake effective adaptation measures, and their resilience. This understanding is summarized in the formulation: “disasters occur when hazards meet vulnerability.” A natural hazard may hence not result in a natural disaster in areas without vulnerability, e.g. strong earthquakes in uninhabited areas. With the tropical climate and unstable
Technical Assistance
landforms, coupled with high population density, poverty, illiteracy and lack of adequate infrastructure, India is one of the most
V. Vivek
vulnerable developing countries to suffer very often from various
Jr. Environmental Economist
natural disasters, namely drought, flood, cyclone, earth quake,
A. Revathy Web Programmer
landslide, forest fire, hail storm, locust, volcanic eruption, etc. which strike causing a devastating impact on human life, economy and the environment. Though it is almost impossible to fully recoup the damage caused by the disasters, it is possible to (i) minimize the potential risks by developing early warning strategies
Contents
(ii) prepare and implement developmental plans to provide
v Economic Implications of Flood Impacts in India
resilience to such disasters (iii) mobilize resources including
v Trends in Flood Damage in India
communication and tele-medicinal services, and (iv) to help in rehabilitation and post disaster reconstruction. Against this backdrop, the present edition of ENVISAGE focuses on few aspects
v Forthcoming Conferences on
of natural disasters in India. In the lead article, Mr.Chandrasekar,
Natural Disaster Management
Research Scholar at Madras Institute of Development Studies, Chennai explores socio-economic implications of floods in the
v Web Sources on Natural Disaster Management
eastern state of Orissa. Further a brief write up by Prof. K.S. Kavikumar elucidates the trend in damages caused by floods in India.
ECONOMIC IMPLICATIONS OF FLOOD IMPACTS IN ORISSA, INDIA 1.1. Introduction:
Orissa is highly prone to floods, this study therefore makes an attempt to analyze both actual and normalized flood damages (e.g. population affected and houses damaged)1 since the 1970’s, and research question asked by this study is whether socio-economic factors are important to derive the total damage cost.
The state of Orissa is geographically situated on the eastern coast of India, particularly at the head of the Bay of Bengal that consists of coastal stretch of around 480 km. In addition, a large number of perennial rivers (e.g. Mahanadi, Brahmani, Baitarani, Rushikulya, Budhabalanga and Subarnarekha etc.), and its tributaries pass through Orissa. Having a long coastal stretch and large numbers of rivers, Orissa is highly prone to floods that occur due to both frequent cyclonic storms and high erratic rainfall which have negative economic impacts (World Bank, 2008). Since the 1970’s, it has experienced floods for 25 years.
1.2. Data and Methodology: This study has collected flood damage statistics data from different sources, e.g. Human Development Report 2004, white papers on the super cyclone of 1999, and various ‘Annual Reports on Natural Calamities’ published by Special Relief Commissioner, Revenue and Disaster Management Department, Govt. of Orissa, Bhubaneswar (2001-2008). Further, the demographic data for the year 1971, 1981, 1991 and 2001 has been collected from ‘State Environment Report 2007’2 . After calculating the annual growth rate from the decadal variation, this study has extrapolated the demographic statistics for each year. As we don’t have data for the year 2011 at present, it has extrapolated the demographic statistics of 200108 assuming annual growth rate of 1991-2001. In addition, the census household data has been collected from ‘Census of India’ reports. Like demographic statistics, this study has also extrapolated household data for each year till 2008.
The economic impacts of extreme events in general depend on their frequency and intensity (e.g. wind speed in case of cyclones, and rainfall in the case of floods and droughts), and location specific economic activity and population (Nordhaus, 2010). Combining floods, cyclones and droughts, the Orissa Human Development Report (2004) has estimated that the value of property loss was around Rs.105crore during the 1970’s, and the damage cost has increased to nearly seven times in the 1980’s and more than 10 times in the 1990’s (GoO, 2004). Based on this estimation, the existing studies (e.g. GoO, 2004) conclude that the damage costs were increasing due to the increase in frequency and intensity of such extreme events. However, these studies under-estimate socio-economic dimensions such as population, location- specific economic activity, etc. Increasing real income, inflation, population and households in the susceptible regions, more people and properties are exposed to such events. Unless considering such factors in the ongoing disaster mitigation policy, the damage will be increased in the decade yet to come.
In order to normalize both population and household statistics, this study has followed the methodology as, Normalized Population Affected/ human causalities with reference to the year 2008 (see Pielke and Landsea, 1998; and Pielke et al., 2008),
P2008 = PY ´ PM LLL
(1)
Normalized Houses Affected with reference to the year 2008 (see Pielke et al., 2008)
In the context of the empirical results, Pielke and Landsea (1998), Collins and Lowe (2001), Raghavan and Rajesh (2003), Pielke et al. (2008) and Barredo (2009) have established that these socio-economic factors play an important role in deriving the total damage with adopting ‘normalization methodology’. In contrast to Emanuel’s assessment on hurricanes in the US that has shown that there was an increasing trend of destruction, Pielke’s own long term assessment, has shown that there was no upward trend once the data is normalized to remove the effects of the societal change (Pielke, 2005). As
HH 2008 = HH Y ´ HH M LLL
(2 )
The population and the household are adjusted with calculating multiplier for the both variables (Pielke et al., 2008).
PM =
P2008 LLL PY
HH M =
3
HH 2008 LLL HH Y
(3) (4 )
Table 2: Decade wise Occurrence Floods (1950-2009)
Where,
Decade 1950-60 1961-70 1971-80 1981-90 1990-2000 2000-09 Total (1950-2009)
PY Õ Population for the reported damage year; P M Õ Population Adjustment or multiplier; HHY Õ Household for the reported damage year; and HHM Õ Household adjustment or multiplier.
1.3. Flood Events in Orissa: Frequency and Trends As mentioned above, Orissa is highly prone to floods. In table 1, we see that Orissa has faced flood for 35 years during 1950-2009. During the 1971-80, Orissa has experienced floods for the 9 years, which is the highest since the 1950’s (see table 2). Followed this, it has faced floods for 8 years during the current decade (2000-09). However, the floods have occurred 23 times during the current decade, and importantly five times in a particular year 2005, which is the highest in the current decade (see table 3). Further, Orissa has experienced floods 4 times in 2007, 3 times in 2003 and 2006 (see table 3).
No. of years 2 5 9 5 6 8 35
Table 3: Frequency of Floods (2000-2009) Year 2001 2003 2004 2005 2006 2007 2008 2009 Total
Table 1: Occurrence Flood Events (1950-2009)
Frequency 2 3 2 5 3 4 2 2 23
1995, 1997, 1999, 2001,
In addition, table 4 shows the plan wise flood affected area (in million hect.) in Orissa and India respectively. Starting from the first to the eleventh plan period, the mean area affected by floods is 1.93 million hect, which is 6.08 percent of India (i.e. 31.75 million hect) and 12.39 percent of the geographical area of Orissa. The table 5, on the other hand, outlines the average flood damage in Orissa and India during the 1953-2002. It shows that the average area affected by floods in Orissa is 0.447 million hect, which is 5.96 percent of India. Further, the average population affected is 2.449 million (i.e. 7.41 percent of India) in Orissa. Besides, the total value of damages including crops, houses and public utilities are Rs.59.988crore (i.e. 4.37 percent of India) in Orissa.
2003, 2004, 2005, 2006,
1.4. Actual and Normalized Flood Impacts in Orissa
2007, 2008
In this section, the study has compared both actual and normalized impacts of the floods in Orissa to empirically test the importance of the socio-economic factors in total damages. Table 6 illustrates both the actual and normalized impacts (i.e. population affected, human lives lost and houses damaged) of floods in Orissa since the 1970’s, and figure 1 and 2 show the actual and normalized population affected (in lakh) respectively. Further, table 7 explains the frequency of the population affected by the floods.
Flood Events (1950-2009)
No. of Years
1955, 1956, 1961, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1980, 1981, 1982, 1984, 1985,
35
1990, 1991, 1992, 1994,
and
2009
1 It can be estimated for the economic damage cost, however the data on economic damage cost was not available in the context of Orissa. 2 http://www.cesorissa.org/soe/SoER.html,downloaded on 22nd February 2010
4
5
19.22
3.43
4.24
India
% of India
% of Geographical Area of Orissa
1.54
6.84
19.01 9.89
8.79
33.66 22.53
2.96
4.12
11.3
9.26 26.46
9.43
19.01 43.69
1.76
0.71
6.49
2.79 4.56
3.3
36.17 21.51
1.01
16.51
5.58
46.03
2.57
14.77
4.59
50.09
2.29
0.73
2.11
12.04
11.99 4.72
2.28 13.56
8.17
15.64 32.27 25.85
1.87
3.34
8.99
4.17
7.499
5.96
India
% of India
7.41
33.064
2.449
7.7
3.66
0.282
1.97
602.071
11.867
Area in Value Million hect. (in Rs Crore)
Damage to Crops
5.94
1193694
70869
Number
1.81
190.439
3.456
Value (in Rs Crore)
Damage to Houses
4.36
88522
3856
Cattle Lost
2.23
1569
35
11.69
568.201
66.42
4.37
1351.035
58.988
Total Damage of Damage to Human Crops, Houses, and public Utilities Lives Lost Public Utilities (Rs in (in Rs Crore) Crore)
5: Average Flood Damage in Orissa and India (1953-2002)
6.08 21.45 12.39
7.36
Source: http://www.indiastat.com/meteorologicaldata/22/naturalcalamities/179/stats.aspx, downloaded on 3rd June, 2010
0.447
Orissa
Area Population State/ Affected Affected (in Country in Million Millions)
Table
1.93
33.52 45.35 31.75
1.4
Note: *p Flood prone area reported by State to the XI plan working group Source: http://www.indiastat.com/meteorologicaldata/22/naturalcalamities/179/stats.aspx, downloaded on 3rd June, 2010
0.66
Orissa
State/ Country
First Secon Third Annual Fourth Fifth Annual Sixth Sevent Annual Eighth Ninth Tenth Elevent Plan d Plan Plan Plan Plan Plan Plan Plan h Plan Plan Plan Plan Plan h Plan Mean (1953- (1956- (1961- (1966- (1969- (1974- (1978- (1980- (1985- (1990- (1992- (1997- (2002- (200756) 61) 66) 69) 74) 78) 80) 85) 90) 92) 97) 02) 07) 12)*
Table 4: Plan wise Flood Affected Area (in Million hect.)
In 1982, the actual population affected was 54 lakh, which is lower in comparison to the recent years, which are 67.39 lakh, 79.06 lakh and 60.18 lakh in 2006, 2007 and 2008 respectively. While this study has normalized the actual value, it, in contrast, finds that 82.23 lakh population were affected in the year 1982 –
it is higher in comparison to the recent years, which are 69.45 lakh, 80.26 lakh and 60.18 lakh in 2006, 2007 and 2008 respectively (see table 6). Further, in table 7, it finds that there are bigger events in the case of normalization rather than actual.
Table 6: Actual and Normalized Impacts of Flood Events in Orissa (1972-2008) Actual Normalized Normalized Actual Actual Normalized Houses Populatio Population Houses Human Human Affected Damaged Damaged Causalities n Affected Causalities (in ‘000) (in lakh) (in ‘000) (in lakh)
Year
No. of Districts Affected
No. of Villages Affected
1972
5
3514
17.38
31.81
18.75
52.93
8
15
1975
8
7527
31.41
54.38
144.15
385.20
74
128
1976
6
4358
2.54
4.32
2.45
6.42
8
14
1977
10
4680
21.61
36.07
18.18
46.84
41
68
1978
12
3727
26.01
42.63
19.97
50.52
21
34
1980
10
3620
26.39
41.69
163.53
398.96
73
115
1981
5
1017
NA
NA
1.72
4.11
15
23
1982
8
NA
54
82.23
510.05
1199.35
127
193
1984
8
6960
35.11
51.55
19.39
43.93
27
40
1985
23
28135
114.17
164.58
122.83
273.11
2458
3543
1990
1
NA
3.62
4.76
9.38
19.00
1
1
1991
18
22221
107.84
139.3
96.77
192.38
52
67
1992
22
20543
76
96.7
160.57
311.18
43
55
1994
18
11,244
70.46
87
NA
NA
73
90
1995
23
55,741
185.6
225.73
188.23
337.97
76
92
1997
17
6452
27.35
32.28
43.72
74.61
17
20
1999
22
25388
125.69
143.93
1959.35
3177.62
10029
11485
2001
24
18790
84.27
93.64
187.58
289.11
0
0
2003
23
14090
76.24
82.2
185.48
210.65
93
100
2004
5
564
3.1
3.29
2.10
2.32
10
11
2005
15
5549
20.56
21.51
18.10
19.54
19
20
2006
27
22381
67.39
69.45
130.46
137.27
105
108
2007
12
10569
79.06
80.26
104.71
107.41
91
92
2008
21
9794
60.18
60.18
258.16
258.16
110
110
Source: Actual Damage Data – GoO (1999); GoO (2004: 173); and various ‘Annual Report on Natural Calamities’ published by Special Relief Commissioner, Revenue and Disaster Management Department, Govt. of Orissa (2001-2008); and Normalized Damage Data – Author’s own calculation from the actual damage data
6
Figure 1: Actual Population Affected (in Lakh)
Figure 2: Normalized Population Affected (in lakh) Normalized Population Affected (in lakh) 250
200 150
No. of People Affected
No. of People Affected
Actual Population Affected (in lakh)
Y=2.411×+24.68 2 R =0.135
100 50
2007
2005
2003
1999
1995
1992
1990
1984
1981
1978
1976
1972
0
200 Y=1.775×+46.54 R2=0.050
150 100 50 0
19
Actual Population Affected (in lakh)
7 2 9 7 6 9 7 8 9 8 1 9 8 4 9 90 9 92 9 9 5 9 99 0 0 3 0 05 0 07 1 1 1 1 1 2 1 1 1 2 2
Normalized Population Affected (in lakh)
Linear (Actual Population Affected (in lakh)
Linear (Normalized Population Affected (in lakh)
Table 7: Frequency of Population Affected in Flood People Affected
Frequency Actual Normalized
> 200 lakh
0
1
100 - 200 Lakh
4
3
50 -100 Lakh
8
10
10 - 50 Lakh
8
6
< 10 Lakh
4
4
Total
24
24
Source: Author’s own calculation
1.5. Conclusion: After normalizing the actual impact data, this study
we are becoming wealthier and more populated in
finds that there were bigger damages during the
comparison to the earlier period, it, in sum, concludes
1980’s and 1990’s when compared to the current
that flood damages are increasing as more valuable
decade. It can be inferred that the incurrence of
property and people are exposed to the floods. If
higher damages in the current decade are due to
these factors aren’t considered in the ongoing
socio-economic factors and hence it is the cautionary
disaster mitigation policy, the damage will be
warning to the disaster mitigation policy makers. As
increased in the decades to come.
7
References:
Pielke, Roger A. Jr. and Christopher W. Landsea, (1998), “Normalized Hurricane
Barredo, J. I., (2009),“Normalized Flood Losses in
Damages in the United States: 1925-
Europe: 1970-2006” Natural Hazards
95” Weather Forecast, 13 (3): 621-
and Earth System Sciences, 9: 97-
631.
104. Pielke, Roger A. Jr., (2005), “Are there trends in Collins, D. J. and S. P. Lowe, (2001), “A Macro
Hurricane Destruction?” Nature, 438:
Validation Dataset for US Hurricane
E11, doi:10.1038/nature04426.
Models”, Casualty Actuarial Society Forum, Casualty Actuarial Society,
Pielke, Roger A. Jr., Joel Gratz, Christopher W.
Arlington,(http://www.casact.org/pu
Landsea, Douglas Collins, Mark
bs/forum/01wforum/01wf217.pdf,
A.Saunders and Rade Musulin,
downloaded on 22nd March 2010).
(2008), “Normalized Hurricane Damage in the United States: 1900-
GoO, (1999), “White paper on Relief and
2005” Natural Hazards Review, 9 (1):
Rehabilitation Work aftermath of the October
1999
Super
29-42.
Cyclone”
Prepared by Revenue Department,
Raghavan, S. and S. Rajesh, (2003), “Trends in
Govt. of Orissa, Bhubaneswar.
Tropical Cyclone Impact: A Study in Andhra Pradesh, India” American
GoO, (2001-08), “Various Annual Reports on Natural
Meteorological Society, May, pp: 635-
Calamities” Published by Special Relief
644.
Commissioner, Revenue and
Disaster Management Department,
World Bank, (2008), “Climate Change Impacts in
Govt. of Orissa, Bhubaneswar.
Drought and Flood Affected Areas: Case Studies in India” Sustainable
GoO, (2004), “Human Development Report 2004
Development Department, South
Orissa” Published by Planning and
Asia Region, New Delhi
Coordination Department, Govt. of Orissa, Bhubaneswar.
Chandrasekhar Research Scholar
Nordhaus, William D., (2010), “The Economics of
Madras Institute of Development Studies
Hurricanes and Implications of Global
[email protected]
Warming”
Climate
Change
Economics, 1 (1): 1-20.
8
Trends in Flood Damages in India India is affected by frequent floods leading to agricultural, property and infrastructural losses besides causing significant human misery. Over the years the concerns about the increasing trend in such losses is rising and there is a tendency to attribute the increase in losses due to climate extremes to the global climate change. While this could be true, it is also important to note that several other factors contribute to the increase in looses due to climate extremes such as floods. For instance, firstly there could be simple more human settlements in the flood prone areas over years leading to both monetary losses and human misery. Secondly, over the years there could be an increase in the wealth of the population and as a result more value could be at stake to be lost due to floods. Finally, the losses should also be adjusted for the price changes. In sum, the actual reported losses due to floods should be normalized with respect to population, wealth and price changes to get a true picture of the trend in flood losses.
This note attempts to provide an overview of the actual and normalized flood losses in India over the years 1953 to 2007. The losses in monetary terms as well as physical terms are reported for a better clarity on the trend in flood related impacts. Figure 1 shows the actual and normalized flood losses expressed in monetary terms. The actual losses are the reported losses of crops, property and infrastructure, while the normalized losses correspond to the same three categories but adjusted for population, wealth and inflation factors explained above, with 1990 as the base year (i.e., in 1990 both the actual and normalized losses are same). As could be seen from the figure, though the actual losses show a marked increasing trend, the normalized losses do not reflect any such trend. In fact the losses are more pronounced in 1970s and 1980s compared to the later years. Figure 2 shows the physical losses due to floods in the form of human lives lost and cattle killed. Again there is no perceptible trend in these losses corroborating the observation made from the trend of normalized monetary losses. Finally, figure 3 presents the trend in the product of human lives lost and the normalized monetary damages – which in some sense combines the information contained in the physical and monetary losses. As could be observed from the figure, there is no clear trend in this parameter also, suggesting that the available information does not provide any clear indication of increasing trend in the losses imposed by the climate extremes such as floods. However, this by no means should be interpreted as nullification of the potential role played by the global climate change on the intensity and frequency of climate extremes.
On the other hand, loss of human and animal lives due to floods is affected to a large extent by: (a) the population factor - increase the exposure of population to the flood prone areas positively affects the human and animal loss; and (b) the information factor – increase in the awareness about the floods and flood related damages negatively affects the human and animal loss. The observed trend in human and animal losses should be interpreted keeping these factors in mind.
Actual Damages (in Rs. Cr.)
10000 9000 8000 7000 6000 5000 4000 3000 2000 1000
1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
1953 1955 1957
0
(a) Actual Damages due to Floods in India: 1953-20
9
Normalized Damages (in Rs. Cr.) 9000 8000 7000 6000 5000 4000 3000 2000 1000
1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
1953 1955 1957
0
(b) Normalized Damages due to Floods in India: 1953-2007 Figure 1. Flood Damages in India – 1953 to 2007 Animal Loss (in 000s) 700
10000
600 Animals (000s)
12000
8000 6000 4000
500 400 300 200
2000
100
0
0 Year
Year
Figure 2. Physical losses due to Floods in India – 1953-2007
Product of Lives Lost and Normalized Damages 80000000 70000000 60000000 Damages
Humanbeings
Human Loss
50000000 40000000 30000000 20000000 10000000 0 Year
Figure 3. Product of Lives Lost and Normalized Damages due to Floods in India – 1953-2007 Dr.K.S.Kavikumar Professor Madras School of Economics
[email protected]
10
Forthcoming Conferences on Natural Disaster Management 1.
Comprehensive disaster risk management framework02 Aug - 10 Sep 2010 Online (http://www.unisdr.org/english/events/v.php?i d=14527)
4.
International Forum on Natural Disasters and Building and Construction Safety, The United Nations Economic Commission for Europe (UNECE)16-17 November 2010 Baku, Azerbaijan (http://www.unece.org)
2.
Fourth United Nations International UN-SPIDER Bonn Workshop on Disaster Management and Space Technology12 – 14 October 2010 Langer Eugen UN Campus, Germany (http://www.un-spider.org/)
5.
IWDENS 2011 - The Fourth International Workshop on Disaster and Emergency Information Network Systems (IWDENS 2011) Mar 22, 2011 - Mar 25, 2011 Singapore (http://aina2011.i2r.a-star.edu.sg/cfp.html)
3.
3rd International Conference on Geo-information Technology for Natural Disaster Management & Rehabilitation19-20 October 2010 Chiang Mai, Thailand (http://e-geoinfo.net/git4ndm2010/)
6.
2nd International Conference on Disaster Management and Human Health: Reducing Risk, Improving Outcomes11 - 13 May 2011 Orlando, USA (http://www.wessex.ac.uk/11conferences/disastermanagement-2011.html)
7.
IAEM 59th Annual Conference & EMEX 2011 11th – 17th Dec, 2011 Las Vegas (http://www.iaem.com/)
Web Sources on Natural Disaster Management 1. National Institute of Disaster Management (NIDM)-(http://www.nidm.net/)
12. Centre for Disaster Management & Humanitarian Assistance, New Orleans(http://www.cdmha.org/)
2. National Center for Disaster Management, New Delhi-(http://www.ncdm-india.org/)
13. The World Association for Disaster and Emergency Medicine, USA(http://wadem.medicine.wisc.edu/)
3. Disaster Mitigation Institute, Ahmedabad, Gujarat- (http://www.southasiadisasters.net/)
14. Emergency Management, Charles Sturt University, Australia(http://www.csu.edu.au/study/artscourses/emergency-management/)
4. Joint Assistance Centre, Gurgaon, Haryana(http://www.jacindia.org/) 5. Amity Institute of Disaster Management(http://www.amity.edu/aidm/)
15. The Canadian Centre for Emergency Preparedness (CCEP), Canada(http://www.ccep.ca/)
6. World Institute of Disaster Risk Management(http://www.drmonline.net/) 7. All India Disaster Mitigation Institute(http://www.aidmi.org/index.asp)
16. The Consortium of Universities for Research in Earthquake Engineering (CUREE), (http://www.curee.org/)
8. World Vision- (http://www.worldvision.org.uk/) 9. Pacific Disaster Center(http://www.pdc.org/iweb/pdchome.html)
17. Disaster Research Institute, Canada(http://www.umanitoba.ca/institutes/disaster_ research/research.html)
10. World Institute for Disaster Risk Management at Virginia Tech- (http://www.drm.ictas.vt.edu/)
18. Asian Disaster Reduction Center(ADRC)(http://www.adrc.asia/)
11. Centre For Excellence in Disaster Management and Humanitarian Assistance, Hawaii(http://coe-dmha.org/)
11
For further information please contact
The Coordinator, ENVIS Centre, Madras School of Economics,
[email protected] We would appreciate if you send your comments and suggestions to
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