Chapter 7-

Summary, Conclusions and Suggestions.

171

The jungles of India have existed from time immemorial. They house a rich variety

of flora and fauna, mammals and insects .In India nearly 40% of the energy needs

of the country and about 30% of fodder needs of the country’s total livestock

population are met by forests. A wide range of non timber forest products (NTFPs)

are collected by local forest residents partly for their own consumption and partly

for sale in the market .Forests also provide employment to a large number of

people forest related activities. Watershed benefits, eco-tourism, carbon

sequestration and other ecosystem services are some other benefits derived from

forests which are normally less quantified and their price not allocated through

market. The legal forest area (as per the legal definition) of the Chhattisgarh state

is 59772 sq. km. This accounts for 44.2 % of the geographical area of the state.

The forests of Chhattisgarh can also be seen as the various Wild life Sanctuaries

and National parks .There are 11 Wild life Sanctuaries and 3 National parks here.

From the point of view of wildlife tourism Chhattisgarh has ample prospects. The

wildlife sanctuaries of Chhattisgarh namely Barnawapara Wildlife Sanctuary,

Tamor Pingla Wildlife Sanctuary, Semarsot Wildlife Sanctuary, Pameda

Wildlife Sanctuary, Sitanadi Wildlife Sanctuary, Achanakmar Wildlife

Sanctuary, Badalkhol Wildlife Sanctuary, Gomarda Wildlife Sanctuary,

Udannti Wildlife Sanctuary, Bhoramdev Wildlife Sanctuary, Bhairamgarh

Wildlife Sanctuary and the 3 National Parks namely Indravati National Park,

Kanger Valley National Park and Guru Ghasidas National park, house some

of the rare and endangered species of the world.

The total area of these wild life sanctuaries and National parks in Chhattisgarh is

8,210.425 sq. km which is about 16.80% of the total forest area and 7.30 % of the

total geographical area of Chhattisgarh.

Economic valuation may be termed as giving a number to the utility from current

production ,which may either be consumed or saved. Recent developments in

environmental economics have extended the concept of value to encompass

172

Option, Bequest and Non- Use Values as well. Use Value can be defined as the

value of benefits attributed to the present consumption of the resource, emerging

out of exchange or outside exchange through the self consumption of the resources,

which the individuals have access .Use value can be seen as Direct Use Value e.g

Timber, fodder etc, Indirect Use Value can be seen as recreation,carbon

sequestration , regulation of climate, maintainance of hydrological cycle and

Option Value which refers to the willingness of the people to keep the option of

postponing the decision regarding the use of resources in the present. Non Use

Value refers to the value which are independent of the people’s present use of the

resources .This can further be classified as Existence Value and Bequest Value.

Existence value refers to the value an individual is willing to pay for the

environmental amenity, even though he/she has no direct benefits from it e.g

preserving biodiversity. Bequest value refers to the individual’s willingness to pay

for the preservation of the resource for the future generations. Putting economic

values to non-marketed benefits derived from natural resources has the potential to

change radically the way we look at forests.

Objectives:-

The specific objectives of the study are :-

(a) To analyze the direct use value derived from national parks and sanctuaries.

(b)To investigate the functional relationship between travel cost and park or

sanctuary visitation;

(c) To determine the factors that affect the Willingness to pay (WTP) of villagers

for the benefits derived from the park and sanctuary;

(d) To find out whether improvement in recreational benefits of the park/sanctuary

would lead to a higher demand for park or sanctuary visitation; and

(e) To suggest policy recommendations as to how overall benefits of the parks and

sanctuaries can be improved.

173

Hypothesis :-

The hypothesis framed for the study are:-

1 Travel cost of a visitor is inversely related to the visitation rate of a national

park or sanctuary in a particular year.

2 Willingness to pay of villagers is directly affected by perception of forest

degradation.

3 Willingness to pay of villagers is directly affected by Per capita income.

4 Willingness to pay is directly affected by educational level.

Limitations

1 An important limitation of the study is that a few of the wildlife sanctuaries

and National parks are under the deep threat of Naxalism and hence are

considered risky from the point of Primary data collection. Hence, these

parks and sanctuaries and had to be deliberately excluded from the study due

to safety reasons. 4 Wildlife sanctuaries and 1 National park have been taken

as sample (35.7% sample size) for the purpose of this study.

2 Valuation Studies are usually incomplete as it is not possible to assign

monetary values to attributes like cultural and religious significance.

Sometimes the needed sufficient data in not available. Moreover valuation

does not gurantee protection and conservation.

3 Money and time constraints pose practical problems in conduction of

valuation study. Valuation of some indirect use values related to ecological

factors like carbon sequestration, climate regulation and control require a

large fund which is normally beyond the budget of an average researcher.

4 Valuation studies can only be used to assess the value of a environmental

good at a particular point of time. The values cannot be extrapolated or used

to draw inferences about another group or time or for any generalization.

174

Review of literature

Review of literature pertaining to the present study gave useful insight into the

nature of the problem and guided me to direct my study in the right

direction.According to Abala, D.O., (1987) who conducted the study to estimate

the willingness to pay for recreational services in case study of Nairobi national

park , gave suggestions about the ways to get better estimates of output quantities,

marginal quantities and prices.. Moran, D., (1994) measured the consumer surplus

of Kenyan protected area by using Contingent valuation technique and suggested

policy measures to protect Biodiversity and conservation of forests.Kaosa-ard et al.

(1995) used TCM to measure the use value of Khao Yai National Park and the

CVM method to measure its non use value. The findings indicated that the value of

Khao Yai National Park was certainly positive and of a reasonable magnitude.

When compared to the marginal cost, it indicated that park improvements would

yield a net gain to society.Isangkura (1998) used the contingent ranking method to

measure the value of environmental benefits of three recreational areas in northern

Thailand. The study found that it was easier for the respondents to indicate their

preferences and responses in the contingent-ranking format than in the open-ended

WTP format. The indirect utility functions were used to calculate the welfare gains

derived from visiting these recreational areas. These welfare gains were then used

to determine the entrance fees. The findings of the study showed that an increase in

the entrance fees would raise park revenues which could be used for recreational

management and would help ensure the continuity of recreational services

provided by national parks in Thailand.Madhu Verma (2000) while doing

economic evaluation of forests of Himachal Pradesh emphasized the need for

sustainable management of forests which is possible only if the policy makers and

planners understand the real worth of the forest stock.Arin and Sills (2001) studied

developing tourism in the national parks of the Republic of Georgia. The study

175

used CVM to determine potential revenue capture by the park, with a split sample

evaluating the impact of ‘annual pass’ vs. ‘daily entrance fee’ payment vehicle on

WTP and on expected numbers of and length of visits. According to this study the

model of WTP for an annual pass had a greater number of significant coefficients

on variables to influencing WTP, including size of household, car ownership,

leisure budget, and number of past visits to natural areas. The study found that

older respondents’ and women’s WTP were less as were households who listed

picnicking as one of their outdoor activities. Himayatullah (2003 ) valuated

environmental resources in a case study of Margalla Hills National Park in

Northen Pakistan and found that they do not only perform ecological functions

but also provide recreational facilities to those who visit these sanctuaries,

however, sanctuaries, tend to be threatened by forest fire, soil erosion, and human

settlement inside the sanctuaries, pollution created by villagers or visitors to the

sanctuaries as well as encroachment by local villagers.

Research methodology

Our study was based on both Primary and Secondary data . Primary data was

collected from Tourists visiting the sanctuaries/parks (250 sample size) through the

method of random sampling .T.C.M was used to calculate the indirect use value.

Primary data was also collected from Local people residing in the periphery of the

sanctuaries/parks within a radius of 5 Kms (250 sample size) through the method

of random sampling and C.V.M was used to calculate the Non Use value.

Valuation of Direct use value derived from National parks and Wildlife sanctuaries

of Chhattisgarh State was done on basis of Secondary data collected from various

records,reports, handouts etc published by different machineries of Govt. of

Chhattisgarh like development chhattisgarh 2008( an official publication of

Planning commission , govt.of chhattisgarh ),Yearly administrative reports of

forest survey of India - 2007, 2009 , 2010 and 2011-12.To estimate the Direct use

176

value techniques like Market value (Price) , Percentage ,Graphical and

Diagramatic Representation etc were used.

For estimation of Indirect Use Value pertaining to recreation Travel Cost

Method (T.C.M) technique was used. In this study we used this method to find

out the causative relationship between park visitation rate and many independent

variables like roundtrip total cost (TC) ,Household income (Rs per month (Y) ,Age

of visitor (A), Educational level (E),Family size (FS),Travel cost to substitute site

(ST) and dummy variables like Sex (D1),Type of dweller (D2 ), and perception

about the sites whether the recreational facilities are good or bad (D3).We tried to

see whether there existed the established relationship between travel cost and

visitation rate. We also tried to find out the other significant relationships between

variables. Linear Regression model was adopted to establish the relationships. For

T.C.M the data was collected with the help of an on-site sample survey. Random

sampling method was used to collect data from a sample of 250 visitors,visiting

different sanctuaries and parks during the period of 1 year from January 11 to

Dec 11.The responses were then analyzed using SPSS 16.0.

For estimation of Non use value (preservation of bio diversity) Surrogate

Valuation method was used. We have used Contingent Valuation Method

(C.V.M ). 5 surrounding villages of each park and sanctuary were selected on

random basis. From each village 10 household families were selected randomly

.Data was thus collected from a total of 250 respondents. Information regarding

preservation of environmental values was got as responses through the structured

questionnaire/schdule. For estimating preservation value through C.V.M

technique, data pertaining to Willingness to pay (WTP), Family

size(FS),Age(A),Perception of forest degradation (PFD), Educational level of the

family (EDU), Distance travelled to collect fuel (DTFU), Per capita

Income(PCI),Family income (FI) and Ratio of female to male (RFM) was

177

collected. The Linear Regression model was used for contingent valuation method

to establish the causative relationship between variables. The responses were then

analyzed using SPSS 16.0.Beta coefficients of T.C.M and C.V.M were tested by

test of significance( t test ) at 0.01 and 0.05 probability level (Student’s t test ) and

R2 Value and ‘ F ’ Ratio were calculated.

Valuation of Direct Use Value

Produce obtained from forests can broadly be divided into 2 categories:-1) Major

forest produce and 2) Minor forest produce

Major forest produce:- Timber production and fuel stacks are kept under this

category.

Minor forest produce :- Bamboo, tendupatta, imli, mahua, gum, harra, lakh,

chironjee, khair,babool, anjan,saal seeds,saaja, chaal, hawda, charoata seeds,

vantulsi mango seed, chind grass ,nagarmotha ,amlafruit ,chaarguthli, kusum

seeds,,baheda,dhawai flowers, bail etc come under this.

The total wood production in Chhattisgarh in the year 2010 amounted to 143075

cubic mts in terms of timber production, while that of Dengri was 9794 cubic mts

and that of Fuel stacks was 68499 cubic mts. The total production of Bamboo

(Industrial) was 18719 notional tons while that of Bamboo (Commercial) was

12626.107 notional tons.

The Total sale value of Tendu leaves in the year 2011 was collectively worth Rs

35531.03 lakhs in 32 district unions. Sal seed crop was quite weak in 2011 and

amounted to a total .39 lakh quintal. In the collection year 2011-12, Harra was

auctioned in advance for Rs 433.94 lakhs while Gum collected from the 5 units of

the state was advance auctioned at Rs 260.54 lakh Rs. Gum from the second

category (Dhawda, Khair and Babool) was advance auctioned at Rs14.79 lakhs.

Chhattisgarh is also the Largest producer of Lac in India. Total production of Lac

in india in 2010-2011 was 2000 million Ton , market price being 60.00 crore at the

178

rate of Rs 300 per Kg kg which is 22% of country’s production. Due to the

extremely rich biodiversity of the state with a large amount of medicinal, aromatic

and dye plants, the state of Chhattisgarh has been declared as the herbal capital of

the country. .More than 600 varieties of medicinal and herbal plants are found in

the state. Chhattisgarh Medicinal plant Board was established on 28.7.2004 for the

preservation ,protection and extraction of medicinal plants without harming them.

Further it aimed at processing of the plants and coordination between different

organisations related to marketing of the products.

Valuation of Indirect Use Value

T.C.M was used to estimate the indirect use value derived from recreation

.Analysis of the data revealed that 67.2 % were males while 32.8 % were

females,70.8 % came from urban background while 29.2 % were from rural

background .Young and aged tourists numbered less while middle aged people

tourists were more in number. Most of them preferred to come by private transport

.A mixed response was got when the tourists were asked about the facilities

provided in the sanctuaries/parks. More than half of the tourists i.e 168 said that

the facilities were good while 132 were not satisfied with the facilities and stressed

the need of improvement. If money was needed for providing better facilities at the

sanctuaries/parks then, what mode it ought to be arranged by - tourists were

divided in opinion about increasing the entrance fees and increase in the

government budget.108 said that they were ready to pay the increased amount of

fees, while 99 advocated in favour of increasing the government budget, 25 said

that donation could generate funds needed for the purpose while 18 said other

sources could be tapped.

Simple least square multiple regression approach was used to analyze the impact of

different variables on visitation rate and the Correlation matrix shows that

visitation rate is moderately and negatively correlated with travel cost (-.355 )

179

.Zero order correlation matrix also shows a high correlation between family

income and travel cost (0.617). A moderate correlation is found between travel

cost of substitute site and family income (0.300) and age and family income

(0.3555). Education is negatively correlated with sex (-.079) and locality (-.023).

Travel cost is negatively and moderately correlated with urban population.

Travel cost of a visitor is negatively contributed to visitation rate. The value

of the β coefficient is -0.006 and‘t’ value is - 3.012 .It shows that the impact of

travel cost is negative and significant at 1% level of probability. Our first

hypothesis thus gets accepted in respect of valuation of travel cost .There is a

positive and significant impact of age (β = 0.017) at 5 % level of significance.

Contribution of family size (β = 0.059) is found significant at 5 % level of

significance. It is evident from our TCM regression analysis that our first

hypothesis regarding relationship between travel cost and visitation rate is proved

significantly.

The value of is R2 is . 651.It means that 65 percent variation in visitation rate

occurred due to explained variables. R2 value is found significant at 99%

confidence level or 1% level of probability, hence the model is best fit.

The value of ‘F’ ratio is found significant at 99% confidence level, which further

justifies the relationship between our explained variables and visitation rate.

The Non Use Value derived from the National parks/Wildlife sanctuaries from the

chosen samples was estimated using the Contingent valuation method (C.V.M).

Responses from 50 respondents from each sample were obtained by means of a

questionnaire and recorded. The respondents comprised of local people residing in

the periphery of the parks/sanctuaries within a radius of 5 km. The responses were

then coded and then analyzed with the help of SPSS Package 16.0.

In context of Tamor Pingla Sanctuary very high degree of positive correlation

(.902) between Willingness to pay(WTP) and Per capita income (PCI ) was found

which means that as the PCI of local residents of Tamor Pingla sanctuary

180

increases ,WTP increases and vice versa . Similarly a very high degree of positive

correlation exists between Family income (FI) and WTP (.853), which denotes that

as the Family income increases, the Willingness to pay also increases and the

opposite is also true. A moderate correlation exists between education and WTP

(.340) showing the fact that Willingness to pay increases with the educational level

.High degree of positive correlation (.516) is found between WTP and Distance

traveled to collect fuel (DTFU) which means that the local residents of Tamor

Pingla who travel a greater distance for fuel are willing to pay more for the

preservation of forests. A moderate degree of correlation (.486) exists between PCI

and Education denoting the fact that those who are more educated tend to have a

higher PCI. A very high degree of positive correlation (.830) exists between Ratio

of female to male (RFM) and Family size(FS) underlying the fact that as the size of

a family increases the ratio of females to male also increases and vice versa. A

moderate degree of positive correlation exists between Family income ( FI ) and

DTFU (.488) indicating the fact that residents with lower family income tend to

travel a greater distance to collect wood for fuel.

Regression results show that Per capita income has positive impact on

Willingness to pay (WTP) of local residents of Tamor Pingla .The β coefficient is

.032 and the t value is 2.6621 which is significant at 5% level. Profession has

positive impact on WTP- β value being 52.625 and t value being 2.413 which is

significant at 95% confidence level. Family size has adverse impact on WTP, β

value being -41.028 and t value being -2.035which is significant at 5 % level.

Family income has a positive impact on WTP- β value is .050 and t value is 4.343

which is significant at 1% level of significance. Further Distance traveled for fuel

has a negative impact on WTP, β value being -47.121 and t value being -2.160

which is significant at 5 % level. The R2 value is .948 which is significant at 1%

level which means that the explained variables account for 94% change in the

181

dependent variable i.e Willingness to pay. The F ratio is 38.491 which is

significant at 1% level which means that the equation is best fit.

In case of Barnawapara Sanctuary it was seen that high degree of positive

correlation (.563) between Willingness to pay (WTP) and Family income (FI)

which means that as the FI increases Willingness to pay increases and vice versa .

Similarly a very high degree of positive correlation (.774) exists between FI and

Per capita income (PCI), which denotes that as the Family income increases, the

PCI also increases and the opposite is also true. High degree of negative

correlation (-.608) is found between Ratio of female to male and Family size which

denotes that the increase in family size has a negative impact on the RFM.

Regression results show that Profession has positive impact on WTP (Willingness

to pay), β value being 114.567 and t value being 2.136 which is significant at 95%

confidence level. Age has adverse impact on WTP, β value being – 20.389 and t

value being -2.551 which is significant at 5 % level. Family size has a positive

impact on WTP- β value is 114.373 and t value is 2.024 which is significant at 5%

level of significance. Further Perception of forest degradation has a positive

impact on WTP, β value being 67.841 and‘t’ value being 2.2194 which is

significant at 5 % level. R2 value is .502 which is significant at 1% level which

means that we can say with 99% confidence level that the contribution of the

explained variables is .502 to induce a unit change in the dependent variable. The F

ratio is 4.360 which is again significant at 1 % level, hence the equation is best fit.

Talking about Kanger Valley National Park, a high degree of positive correlation

(.711) between Willingness to pay (WTP) and Per capita income (PCI ) was seen

which means that as the PCI increases WTP increases and vice versa . Similarly a

high degree of positive correlation (.508) exists between WTP and Education

(EDU), which denotes that as the education increases, Willingness to pay also

increases and the opposite is also true. High degree of positive correlation (.657) is

found between WTP and FI (Family income ) which denotes that the increase in

182

family income leads to an increase in WTP.A moderate positive correlation exists

between education and PCI (.505) underlying the fact that as education increases

PCI also increases. A moderate positive correlation (.508) exists between

education and Willingness to pay indicating the fact that as education increases

WTP also increases. A high positive correlation (.702) exists between PCI and

Family income showing that as the family income increases PCI also increases. A

moderate positive correlation (.497) exists between education and family income

which indicates that education increases, family income also increases .There is a

negative moderate correlation (-.305) between Ratio of female to male (RFM) and

Family size (FS) which implies that the increase in family size has adverse impact

on ratio of female to male.

Regression Coefficients show that Perception of forest degradation ( PFD ) has

positive impact on Willingness to pay - β value being 70.371 and ‘t’ value being

3.101 which is significant at 99% confidence level. DTFU (Distance traveled to

collect fuel) has adverse impact on WTP, β value being -56.850 and t value being -

2.120 which is significant at 5 % level. PCI (Per capita income) has a positive

impact on WTP- β value is .055 and t value is 2.066 which is significant at 5%

level of significance. Further Education has a positive impact on Willingness to

pay, β value being 88.263 and t value being 2.095 which is significant at 5 % level

.Family income too has a positive impact on WTP β value being .006, t value being

3.001 which is significant at 1% level. R2 value is .629 which is significant at 1%

level which means that the explained variables account for 62 % change in the

dependent variable. The F ratio carries a value of 8.467 which is significant at 1%

which shows that the regression equation is best fit.

In case of Achanakmar Sanctuary a high degree of positive correlation (.709)

between WTP (Willingness to pay )and Per capita income-PCI was seen which

means that as the PCI increases WTP increases and vice versa . Similarly a high

degree of positive correlation (.742) exists between Willingness to pay and Family

183

income-FI, which denotes that as the family income increases, WTP also increases

and the opposite is also true. High degree of positive correlation (.726) is found

between PCI and FI which denotes that the increase in family income leads to a

increase in PCI. A moderate negative correlation (-.347) exists between FI and

profession.

Regression results show that Per capita income has positive impact on WTP-

Willingness to pay, β value being .063 and ‘t’ value being 2.101 which is

significant at 95% confidence level. Education has a positive impact on WTP, β

value being 40.105 and‘t’ value being 2.955 which is significant at 5 % level.

Profession has a positive impact on Willingness to pay - β value is 43.761 and t

value is 2.258 which is significant at 5% level of significance. Further Family

income has a positive impact on WTP, β value being .021 and t value being 2.046

which is significant at 5 % level. Distance traveled to collect fuel (DTFU) has a

negative impact on WTP, β value being -76.037, t value being -2.507 which is

significant at 5% level. β value of Perception of forest degradation(PFD) is

299.148 and ‘t’ value is 2.971 which is significant at 5 % level. The R2 value is

.680 which is significant at 1 % which means that the explained variables used in

the analysis account for 68% change in the dependent variable WTP (Willingness

to pay). The F ratio value is 10.636 which is significant at 1% level of

significance, hence the model is best fit.

For Gomarda Sanctuary we find a high degree of positive correlation (.795)

between Willingness to pay(WTP) and Per capita income which means that as the

Per capita income increases Willingness to pay increases and vice versa . Similarly

a high degree of positive correlation (.746) exists between WTP and FI (Family

income), which denotes that as the family income increases, Willingness to pay

also increases and the opposite is also true. High degree of positive correlation

(.908) is found between Per capita income (PCI) and Family income which denotes

that the increase in FI leads to an increase in PCI. A moderate correlation (.532)

184

exists between Willingness to pay and education (EDU) which means that with an

increase in education Willingness to pay also increases. A high positive correlation

(.610) exists between Per capita income and Education. A high degree of positive

correlation (.565) also exists between FI and EDU which implies that a higher

family income is associated with a higher education. A high positive correlation

(.583) exists between WTP and Perception of forest degradation (PFD) which

means that a higher perception of forest degradation leads to high Willingness to

pay and vice versa. Similarly a high positive correlation (.587) exists between Per

capita income and PFD indicating the fact that people with a higher Per capita

income are more susceptible to perception of forest degradation .Again a high

positive correlation (.540) is found between EDU and PFD, which means that

education is positively correlated with perception of forest degradation.

The regression results show that Per capita income has positive impact on WTP

(Willingness to pay), β value being .212 and t value being 2.692 which is

significant at 95% confidence level. Distance traveled for fuel has a negative

impact on Willingness to pay β value being - 84.787, t value being -2.094 which is

significant at 5% level. Perception of forest degradation has a positive impact on

Willingness to pay, β value being 65.057 and t value being 2.102 which is

significant at 5 % level. The value of R2 is .683 which is significant at 1% level

which means that the explained variables used in the study account for 68 % of the

change in the dependent variable Willingness to pay. The F ratio is 11.031 which is

significant at 1% level, hence the model is best fit.

After the analysis of 50 responses for each of the five selected sanctuary/ National

park being done separately, we collectively tried to analyze them and see the

causative relationship between them. For this purpose, the responses of 250

respondents were analyzed collectively and following findings were seen:-

A high degree of positive correlation (.625) between Willingness to pay(WTP) and

Per capita income(PCI) which means that as the Per capita income increases

185

Willingness to pay increases and vice versa . Similarly a high degree of positive

correlation (.587) exists between WTP and Family income (FI), which denotes that

as the family income increases, Willingness to pay also increases and the opposite

is also true. High degree of positive correlation (.749) is found between Per capita

income and FI which denotes that the increase in family income leads to an

increase in Per capita income. A moderate correlation (.329) exists between Family

income and Perception of forest degradation (PFD) and education .A moderate

positive correlation (.314) exists between WTP and PFD. A moderate degree of

positive correlation (.380) also exists between Per capita income and Perception of

forest degradation which implies that a higher per capita income is associated with

a greater perception of forest degradation. A moderate positive correlation (.444)

exists between Family income and Family size. Similarly a moderate positive

correlation (.324) exists between Family income and Education.

The regression results show that Per capita income has positive impact on WTP-

Willingness to pay, β value being .101 and t value being 5.076 which is

significant at 99% confidence level. Distance traveled to collect fuel has a

negative impact on Willingness to pay β value being - 47.642, t value being -

2.626 which is significant at 5% level. FI has a positive impact on WTP, β value

being .007 and t value is 2.072 which is significant at 5 % level. Perception of

forest degradation has positive impact on WTP, β value being .076 and t value is

2.887 which is significant at 5% level. Profession has a positive impact on

Willingness to pay, β value being 22.754 and t value being 2.852 which is

significant at 1 % level.

The value of R2 is .643 which is significant at 1% level which means that the

explained variables used in the study account for 68 % of the change in the

dependent variable Willingness to pay. The F ratio is 21.268 which is significant at

1% level, hence the model is best fit.

186

Hence we may conclude by saying that National Parks sanctuaries of a particular

area provide a variety of benefits such as Direct use values, recreation

opportunities, watershed protection, wilderness, and wildlife habitat and the

recreational experience of wilderness areas can be recognized as probably the

highest valued service provided by natural forests. The debate over management

goals for National Parks often centers on how to strike a balance between leaving

areas in their natural or near-natural state, and developing and exploiting them. The

primary concern of National Park management authorities would not be

maximization of the economic value of a National Park as a whole. Promoting

tourism is not the sole primary role of National Parks. Nor is the preservation of

species biodiversity or the provision of a rich natural resource, which permits

scientists, educators and the community at large to meet their various needs. There

are ecological functions attached with the parks and some inherent existence and

Bequest values attributed to it also. Preservation value may stem from altruistic

motives such as sympathy, responsibility and a concern about the state of the world

that some people may feel towards non-human objects, but the value is still

anthropocentric and does not reveal the value independent of human wants. Hence

a valuation of the parks and sanctuaries should encompasses all these values .We

have tried to do for National parks / sanctuaries of Chhattisgarh by valuation of

Direct use value, indirect use value in terms of recreation through TCM and

estimation of Existence values through CVM. The major findings of our study can

be seen as under.

Conclusions

1 There exists an inverse causative relationship between Travel cost and

Visitation rate to a National park or sanctuary. β value is -0.006 and ‘t’

value is – 3.012 which is significant at 1 % level. Thus it can be inferred

that High travel costs lead to less visits and low travel costs imply more

187

visits. Hence our first hypothesis that Travel costs have an adverse

impact on visitation rate gets accepted.

2 Mean visitation rate was 1.94 while mean travel cost was Rs 2820.82 per

person.

3 This study points out to the fact that a improvement in the condition of

roads, sight seeing facilities, information and traffic signs and setting up

of more tourist information centres were some of the improvements

solicited by the tourists visiting the selected sanctuaries/parks.

4 The average amount a local personnel was willing to pay or WTP was

Rs 246.51.Average per capita income was Rs 2025.08.

5 There is an positive impact of Perception of forest degradation (PFD) on

WTP ( Willingness to pay ) -this finding is supported by the results of

analysis of Barnawapara sanctuary( significant at 5 % level ), Kanger

valley national park ( significant at 1 % level ), Achanakmar sanctuary

( significant at 5 % level ), Gomarda sanctuary( significant at 5 % level ).

Thus our second hypothesis that WTP is directly affected by PFD-

stands proved in context of these sanctuaries but is rejected in

context of Tamor Pingla Sanctuary.

6 WTP is directly affected by Per capita income (PI) – our third

hypothesis is supported by analysis of responses of local people in

Tamor Pingla sanctuary, (significant at 5 % level ) Kanger Valley

National Park ( significant at 5 % level ) , Achanakmar Sanctuary

( significant at 5 % level ),Gomarda Sanctuary ( significant at 5 %

level ) but stands rejected in Barnawapara Sanctuary.

7 WTP is directly affected by educational level- Our fourth hypothesis is

proved by results obtained from analysis of responses from Kanger

Valley National Park ( significant at 5 % level ) and Achanakmar

188

sanctuary( significant at 5 % level ) but stands rejected in Tamor

Pingla Sanctuary,Barnawapara Sanctuary and Gomarda Sanctuary.

8 DTFU ( Distance traveled for fuel ) has a negative impact on WTP- this

is proved by results of analysis in Tamor Pingla Sanctuary( significant at

5 % level ),Kanger Valley National Park ( significant at 5 % level ),

Achanakmar Sanctuary( significant at 5 % level ) and Gomarda

Sanctuary( significant at 5 % level ).

9 Family income has a positive impact on WTP – as shown by analysis of

data in Tamor pingla Sanctuary (significant at 1 % level),Kanger Valley

National Park ( significant at 5 % level ) and Achanakmar Sanctuary(

significant at 5 % level ).

10 Family size has an adverse impact on WTP as seen in Tamor Pingla

Sanctuary (significant at 5 % level) while a Positive impact is seen in

Barnawapara Sanctuary( significant at 5 % level ).

11 Profession has a positive impact on WTP as shown by the analysis of

data in Tamor Pingla sanctuary ( significant at 5 % level ),Barnawapara

Sanctuary (significant at 5 % level ) and Achanakmar Sanctuary

( significant at 5 % level ).

12 Aggregate analysis of CVM of the 5 samples - Tamor Pingla Sanctuary ,

Brnawapara Sanctuary, Kanger Valley National Park, Achanakmar

Sanctuary and Gomarda Sanctuary taken together shows that PFD have a

positive impact on WTP ( significant at 5 % level )-so our second

hypothesis is proved .Our third hypothesis that PI has a positive impact

on WTP ( significant at 5 % level )also stands proved . In addition

Distance traveled for fuel has a negative impact on WTP( significant at 5

% level ) while Family income( significant at 5 % level ) and Profession

( significant at 1 % level ) have a positive impact on WTP.

189

Our fourth hypothesis that WTP is affected by Educational level is not

proved by our results and stands rejected.

.

Our study being a methodological work, more leaning to valuation of forest

resources of Chhattisgarh, the major suggestions and policy implications are for

present statistical system of data collection and accounting. However significant

pointers also emerge with respect to the directions that the government and the

forest management should take empirical investigation into, in the of forests of

Chhattisgarh state .Valuation of forest resources is only a part of the total natural

resources and hence such investigations would lead to a better assessment of the

total resources of the state.

A few suggestions can be given on the basis of the study conducted.

SUGGESTIONS

1 A better accounting system should be used for the forests .The present

system of accounting has many lacunas and hence a better and more

sophisticated system of accounting should be used based on the modern

technology. Satellite mapping and GIS system can be used for the

collection of data, which will not only ensure accuracy, but also speed

and connectivity between different data points.There should be a separate

cell in the government to maintain an account of forest resources since

forests are an important natural resource and asset of the state .This cell

should maintain updated information and account of facts pertaining to

forests like exact number of different plant and animal species, annual

collection of timber and NTFP’s, forest density, change in area under

forests , growing stock of resources and output available on basis of this

physical capital account set.

190

2 Chhattisgarh is endowed with great forest wealth but these resources are

generally undervalued. Hence, conduction of T.C.M and C.V.M surveys

at different local levels can lead to better estimation of these total values.

Moreover, these surveys can lead to the strengthening of the Tourism

sector by leading to increased number of tourists on one hand and

increase in the number of employment opportunities to the local people,

thereby reducing their dependency on government.

3 Minor forest produce (MFP) specially the medicinal plants have a great

potential of employment and income generation in Chhattisgarh for the

tribals and local people residing in the nearby areas. Instead of exporting

them in the raw form, value addition practices at the local level should be

encouraged .This would not only lead to employment generation but also

work as source of income to the local people.

4 The study revealed that majority of the local people have to travel a long

distance - 2.12 Kms on an average, to collect wood as fuel for household

purposes.It is also necessary that augmentation of fuel wood production

is done to meet the domestic energy requirements of the people on

sustainable management basis, and development of alternative sources of

energy like solar energy, Bio gas are done so that the increase in pressure

on forests for fuel is relieved.

5 Joint forest management practices should be increased as no govt.

programme can be successful without people’s participation who are the

major stakeholders for sustainable development. Adequate participation

and involvement of the people at all levels of decision making – Gram

panchayats ,Village forest committee (VFC), Forest protection committee

(FPC) etc should be encouraged.

191

6 Generally the Protected areas (PAs) are undervalued or their values not

assessed correctly cause many benefits derived from them are not sold or

assessed through traditional markets .It should be understood that the

PAs should be managed for multiple benefits and markets rather than

only protection .Only then they can generate sufficient funds for their

continued existence and compete with alternative land uses.

7 Forests are a source of livelihood for many local people and source of

recreation for tourists. Care should be taken that there should not be any

conflict between the interests of these two stakeholders. Special grazing

and fuel collection zones, restrict the harmful activities of the locals,

which can have adverse effect on tourism, similarly special zones should

be demarcated for the tourists so that they cannot adversely affect the

social life of the local people.

8 Most of the respondents in this study stressed on the need of better waste

disposal facilities in the parks and sanctuaries .The administration should

provide separate disposal facilities for bio degradable wastes and non

degradable wastes so that the environment of the sanctuary / park is not

tampered with .National parks like Bandhavgarh and Pachmari Biosphere

reserve in M.P are declared as polythene free areas. Similar practices can

be adopted here.

9 Improvement in the condition of roads, sight seeing facilities, setting up

of information and traffic signs and more tourist information centres will

definitely lead to increase in the number of tourists and increase in

visitation rate .So the Government should keep these points in mind

while allocating funds for the sanctuaries and national parks.

192

10 The benefits derived from PAs are often undervalued, inspite of the fact

that they have ample scope for revenue generation through tourism. It is

recommended that the level of fees that should be raised in light of the

large consumer surplus drawn by the tourists from the PAs and taking

into consideration that the visitors willingness to pay is far more than the

entry fees charged. Talking on the same lines since a very nominal

amount or no fee is charged for entry into the National parks/ sanctuaries

of Chhattisgarh, levying a fee or raising the fee to 50-100 Rs can generate

revenues for the government which can be used for the purpose of

development of the parks/ sanctuaries.

11 Proper valuation can lead to a correct assessment of the value of a PA

and thereby confer the correct place of PA in economic decision making .

This further can justify the cause of integrating Pas into the mainstream

of financial planning and a point of serious thought for the policy makers.

For this purpose, Non government institutions, specialists identified in

this context like environmentalists, Economists, Botanists , Zoologists ,

geologists, etc should be made a part of forest management and

development plans to develop a multifaceted, inter disciplinary and cross

sectional approach.

12 C.S.R (Corporate social responsibility) constitutes an indispensable part

of the activities of majority of the big business houses and this fund can

be actively used for the development of the better facilities for the

tourists in the parks/ sanctuaries .Local people can be entrusted with the

management of these facilities.