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Measuring Natural Capital

The paper provides the rationale for ecosystem accounting and suggests ways to operationalise it. Through the example of selected states - Gujarat, Jammu and Kashmir, Kerala, Rajasthan and West Bengal - it demonstrates application of the concept. The physical extent of change has been estimated for the period of 1991-2001. The loss of the inland wetland for this period comes to around 0.46 million ha during this 10-year-period. By applying the benefit transfer method, the monetary value of the annual physical loss in the inland wetland for the combined five states has been estimated to be $1,022 million at 1999-2000 prices. The paper estimates the per capita loss for the states. The maximum loss on per capita basis is for J&K ($221 and 11% of the state domestic product) while that for the combined states is $11.57. The paper attempts to suggest that ecosystem accounting is a required step towards developing the indicator for sustainable development, also referred to as the "green economy".

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Measuring Natural Capital

Accounting of Inland Wetland Ecosystems from Selected States of India

Pushpam Kumar

The paper provides the rationale for ecosystem accounting and suggests ways to operationalise it. Through the example of selected states – Gujarat, Jammu and Kashmir, Kerala, Rajasthan and West Bengal

– it demonstrates application of the concept. The physical extent of change has been estimated for the period of 1991-2001. The loss of the inland wetland for this period comes to around 0.46 million ha during this 10-year-period. By applying the benefit transfer method, the monetary value of the annual physical loss in the inland wetland for the combined five states has been estimated to be $1,022 million at 1999-2000 prices. The paper estimates the per capita loss for the states. The maximum loss on per capita basis is for J&K ($221 and 11% of the state domestic product) while that for the combined states is $11.57. The paper attempts to suggest that ecosystem accounting is a required step towards developing the indicator for sustainable development, also referred to as the “green economy”.

The author has benefited from discussions with Ritesh Kumar, Wetland International South Asia, New Delhi and Luke Brander, IVM, Amsterdam. The views are not necessarily of the organisation where the author works.

Pushpam Kumar (pushpam.kumar@unep.org) is with the United Nations Environment Programme.

T
he link and causality between ecosystem services and human well-being is well established (MA 2005; TEEB 2010; Bateman et al 2011). Continuous and sustained harvesting of ecosystem services enrich and enhance human welfare, but the conventional measurement of human welfare somehow does not capture the changes in the flow of services from ecosystems. This failure is partly due to methodological difficulty and partly due to paucity of reliable data especially on ecosystem services at an appropriate spatial scale. Monetisation of changes in service poses a further problem and prevents the integration with the mainstream accounts of the nation. In this context, this paper explores the rationale for ecosystem accounting and discusses a possible structure of such accounts. By choosing the changing status of the inland wetland ecosystem and subsequent changes in ecosystem services in selected states, the paper attempts to demonstrate the operationalisation of ecosystem accounting. We use existing valuations of wetland ecosystem services from India and apply the benefit transfer method to obtain a representative economic value which subsequently enables us to approximate the value of change in the ecosystem services of inland wetlands in the country. Finally, the paper suggests the necessary adjustment we should be making in the existing account to obtain net state domestic products for those states in consideration.

Beyond GDP and Ecosystem Accounting

Traditional indicators such as the gross domestic product (GDP) and the human development index (HDI) have basic limitations as measures of social progress. Neither GDP/capita nor HDI reflect the state of the natural environment and both focus on the short term, with no indication of whether current wellbeing can be sustained. The green economy initiative which will be one of the key points of discussion in Rio+20 in June 2012, provides a unique opportunity to guide a new development paradigm that addresses not only economic effi ciency but also social equity. However, to measure the progress the green economy is providing, a new metric of measuring progress is needed at the individual and also at the macro level. As a large body of theory and empirical testing demonstrates, asset accounts can provide robust indicators of social welfare. While considerable progress has been made in measuring asset values for produced and natural capital (in the form of commercial natural resources), significant gaps remain,

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particularly with respect to ecosystem services and the effects of climate change. Research is needed to fill these gaps. Some of the issues needing attention on a priority basis are measurement and valuation of ecosystem services, and assessment of the potential effects of climate change on asset values, including ecosystem assets.

Policy Context

The flagship development reports of the international institutions (Human Development Report of United Nations Development Programme (UNDP), World Economic Outlook of the International Monetary Fund (IMF), and Global Economic Prospects of the World Bank) share a common weakness with GDP when it comes to measuring social progress: they are all focused on current, short-run measures. The other flagship, the World Bank’s World Development Report, is more research-oriented and less focused on the short term, but its thematic focus shifts from year to year.

There have been recent advances in addressing the weaknesses in contemporary measures of welfare that are used to judge the progress and regress of nations. The World Bank’s Where Is the Wealth of Nations? (2006) and the Changing Wealth of Nations (2011) were such initiatives, but they were only two monographs rather than part of a regular report series, and they had acknowledged limitations concerning the measurement of ecosystem assets. Turning to the question of ecosystem services, the Millennium Ecosystem Assessment made important progress on measuring the extent of damage to the ecosystems providing these services, but was weak on the valuation of these services and the linkages to human well-being. On climate change, there is a general tendency to focus on cost-benefit analysis of climate actions or on least-cost paths for climate stabilisation. Measuring the effects of climate change on ecosystem assets, and more generally on the other assets underpinning well-being, is relatively rare.

The recently launched Wealth Accounting and Valuation of Ecosystem Services (WAVES) in partnership with the World Bank and United Nations Environment Programme (UNEP) would carry out the exercise for six countries for the next fi ve years. The Global Environment Facility (GEF) has supported the Project for Ecosystem Services (Proecoserve) that is being implemented by UNEP in the Caribbean, South Afroca, Lesotho, Chile and Vietnam is also moving in the direction of generating data on ecosystem services to enable the ecosystem accounting operationalisable in those countries. The Government of India has constituted a similar group under the chairmanship of Partha Dasgupta to measure the impact of economic growth on biodiversity and ecosystem services. The output of the proposed work programme in this area should be a biennial report on wealth and change in wealth in over 100 countries, with a focus on developing countries. Such initiative and resultant report series will progressively increase the coverage of asset values over time, particularly with respect to ecosystem assets (and their associated services) as well as the impacts of climate change on these assets. Underpinning this progressive increase in coverage will be a research programme on these and wider topics in asset accounting.

Rationale for Ecosystem Accounting

The national income account (NIA) is a fundamental macroeconomic statistic which shows the level and performance of economic activities in an economy. The System of National Accounts (SNA) of the United Nations attempts to provide a benchmarked framework for the computation and presentation of national income across all the activities and economies to make the calculation comprehensive and comparable. A crucial component of the SNA is the estimation of GDP where, at the market price, the gross value of all the goods and services accruing to the society is estimated. GDP is supposed to show the change in the level of human welfare. However, in whichever way the GDP is calculated it does not capture many elements of human well-being. The shortcomings of GDP to reflect welfare are widely known and discussed in the literature (van den Bergh 2009). Designing the HDI where GDP is combined with status of health and education shows some advancement but even HDI is silent on the role of natural capital and ecosystems (Dasgupta 2010). In the process of GDP estimation, many contributions of the ecosystem in terms of ecosystem services such as bioremediation by wetlands, storm and flood protection by mangroves and prevention of soil erosion by forests get ignored. Ecosystem services which have a welfare-enhancing role do not enter into the macro-level calculations of nations. This is due to their poorly perceived value. Often ecosystems and their services fall outside the domain of the market and hence they remain unpriced and unaccounted for. Therefore, the calculated GDP provides an illusory sense of gains or losses. This ultimately means that GDP underestimates the level of welfare that society actually enjoys due to the unrecognised and unappreciated contributions of ecosystems. Economists are now in agreement that instead of measuring GDP or income which is a flow concept, the comprehensive measurement of stock of wealth including natural capital is the correct approach (Dasgupta and Maler 2000; Arrow et al 2004; Dasgupta 2010). This has a further bearing on the conservation strategy for ecosystems, where poor people depend upon the services from ecosystems more than the rest and if those services are unaccounted for, it would lead to erroneous policy for poverty alleviation. The rationale for accounting of ecosystem services also emerged from the findings of the Millennium Ecosystem Assessment (MA 2005), and other recent influential studies conducted by various agencies (TEEB 2009; Roy et al 2010) and the Commission on the Measurement of Economic Performance & Social Progress led by Joseph Stiglitz (Stiglitz, Sen and Fitoussi 2009).

Theoretical developments in environmental and welfare economics have emphasised the importance of recognising the value of natural capital (ecosystems and ecosystem services) in developing accounting methods aimed at policy developments for poverty alleviation (Pearce et al 1996; Dasgupta and Maler 2000; Arrow et al 2004; Maler et al 2008; Dasgupta 2010). Characteristically, in the development of GDP estimates, natural

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capital, which contributes in particular to regulating and supporting services, is ignored. Since such resources and ecosystem services typically underpin the livelihoods and welfare of the poor (TEEB 2009; Kumar and Martinez-Alier 2011), strategies to underpin poverty alleviation require methodologies that ensure full accounting of both man-made and natural capital and the goods and services derived from them. Frameworks for ecosystem accounting have evolved during last few years (Maler et al 2009) and typically incorporate three accounting processes – the stocks of ecosystems; the flow of services from ecosystems, and integration of the value of these stocks and services with conventional economic accounts. Accounting for the natural stock of capital along with other man-made and social capital is critical for assessing the level and direction of economic activities; and moreover accounting for the flow of services alone does not ensure the objective of sustainability (Dasgupta 2010). In developing mechanisms of ecosystem accounting in the context of inclusive wealth (Arrow et al 2004), it is necessary to explicitly incorporate linkages between the condition of the capital base of the ecosystem and the sustainability of supply of ecosystem services. Integral to this development is the need to invoke shadow pricing to account for changes in the flow of ecosystem services which relate to the degradation of ecosystems and the consequential impacts on the poor. Whilst the theory of shadow pricing is well established, to date there has been no attempt to introduce and provide estimates of shadow pricing of ecosystem services within the framework of ecosystem accounting.

The Structure of Ecosystem Accounts

Typically, the accounting of an ecosystem would require the physical units used to measure the stocks of ecosystems and flows of ecosystem services. For accounting of wetland ecosystems thus, the stock of a wetland ecosystem should be measured in terms of its area, or a resource such as the population of a species that might be described in terms of numbers or density. Similarly, the production, regulating of cultural services that the system generates can be represented in terms of, for example, tonnes of fish harvested per day, the amount of water cleaned through bioremediation, or the annual number of visits to an area for recreational activities. The framework of an ecosystem differentiates various components of natural capital from those that can be used to make a connection with the various activity sectors used to characterise the economy. The accounts can be broken down into three major components:

First, basic accounts: this describes the important stocks and flows that constitute natural capital and its uses. These accounts describe the quantity of the different ecosystems, measured in terms of area (for habitats) or/and the use of these assets by different economic activity sectors. Also included in this basic set of tables are accounts that document the biodiversity status of the ecosystem units and its changes over time. Second, a set of accounts describing the condition of the ecosystem capital base which document the health status of the ecosystem. Essentially it is a measure of the integrity of the ecosystem, which they argue can be implemented at any scale

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and finally, third, a set of accounts that document the output of ecosystem services, their uses and values. They are mirrored by economic sector accounts that reflect the corresponding natural resource use (in physical and in monetary units) and the pressure on ecosystems as well as protection and management expenditures actually paid by governments and companies.

The services used directly by people include both marketed and non-marketed services. It is assumed that the value of the former is reflected in their observed market price. Accounting can be done and sometimes with sufficient persuasive power in physical terms. The closing and opening stock of ecosystems can provide adequate signal for required actions. The change in closing stock over a period of time would also refl ect the change in material balance. However, a meaningful integration for better sectoral allocation of resource in the economy, monetary valuation of changes in the stock or flow of services in time series experience would help in efficient design of policy instruments in the economy (Figure 1).

Figure 1: Structure of Accounts in SEEA 2003

Natural resources Economic assets Non-economic

Ecosystems (SNA) assets

Opening stocks Opening stocks Opening state Closing stocks Closing stocks Closing state Described in SNA SNA transactions and other flows Changes in stocks Changes in stocks Economic activities, natural processes, etc. Changes in state ⇐⇒⇒
Changing Inland Wetland Ecosystems of India

Wetlands, which are important providers of various types of services to the humans, are defined as “areas of marsh, fen, Peatland or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six metres” (Article 1.1, Ramsor 1971) and they are on decline due to various drivers of change. Wetlands constitute 9% of the land area and more than half of them are degraded on the verge of being lost (Zedler and Kercher 2005). Pollution, draining of water for agriculture and climate change are the major drivers identified for the loss of wetlands. In Asia, around 27% inland and coastal marshes were lost by 1985 (MA 2005). In India, a variety of wetlands are found, from natural to man-made. Assessment of the extent of wetlands in India, as globally, is fraught with dealing with the difficult issues of definition and methodologies. Consistent data sets are available only for certain wetland types, for example, mangroves, which have been covered under the biennial forest surveys since 1987. However, changes in assessment methodologies limit interpolation of the data to assess the trend of wetlands in the country. The national database of inland wetlands is much more inconsistent. The first major attempt to map the wetlands of India using remote sensing techniques was made in 1998. Using the satellite data for 1992-93, wetlands were mapped on scales of 1:250000 and for certain regions at a scale of 1:50000. Owing to the limitations of the resolutions, wetlands below 56.25 ha on a 1:250000 scale and 2.25 ha on

Table 1: Physical Area Loss of Wetlands in Several States in India over 1991-2001

State Inland 1991 Inland 2001 Changes in Area
(Thousand Ha) (Thousand Ha) (Estimated) (Thousand Ha)
Gujarat 209.21 151.65 -57.56
Jammu * Kashmir 406.78 78.08 -328.70
Kerala 34.20 14.71 -19.49
Rajasthan 344.96 293.31 -51.66
West Bengal 8.10* 5.84** -2.26
Total -459.66

* It is the data of year 1960; ** It is the data of year 2003.

a 1:50000 scale were not mapped. The Salim Ali Centre for Ornithology and Natural History (SACONH) further built on the inventory for selected districts for 13 states, mapping wetlands having an area two ha and above using the satellite data for 1999-2001 period. The assessment included change analysis for 72 districts using the data from the two time periods. Until recently, there has not been comprehensive and scientifi c assessment of wetland areas in India. The SACONH and the Space Application Centre (SAC) of the Government of India have been entrusted with the task of assessing wetland areas in India. Both of them have focused on inland wetlands, but the mapping of coastal wetlands still remains a challenging task. The SAC has generated information on the extent of inland wetlands

Table 2: Overview of Valuation Studies Used

for 1990-91 covering 71 districts of India and the SACONH has generated information on the inland wetlands for 2000-01. As expected, the two data have different resolutions of imageries. Still, they are probably the only basis for obtaining a glimpse of the time series trend of the extent of the wetlands. The two data points have another important dimension to keep in mind. During 1990-91, the wetlands with size greater than 56.25 ha were considered for assessment while during 2000-01, all the wetlands in size larger than two ha were considered. Logically, one would expect that the total wetland area would go up but, surprisingly, the study reports a country-wide decline in the inland wetlands by 38% during this period (Vijayan 2004). This decline has also been validated by other sources like the Department of Agriculture and Cooperation, which reports that the waterlogged area has declined in 15 major states of India. There is an area of 58.2 million ha of wetlands, including paddy fields (Prasad et al, 2002). For the purpose of maintaining the consistency in data, coverage and scope, we have taken the following major states of India – Gujarat, Jammu and Kashmir (J&K), Kerala, Rajasthan, and West Bengal. By combining the studies by SCA and SAcoNH for 1990-91 and 2000-01, the decline in the wetland areas is alarming. Table 1 provides the details.

During 1991-2001, the inland wetlands in Gujarat, J&K, Kerala, Rajasthan and West Bengal declined by approximately 0.46 million ha. This loss is just from five states. For other states, the reliable estimates are either absent or are not comparable over the same period due to differences in scale of imageries and sample size of the surveyed areas. The loss in the inland

Name of Study Type of Wetland Location Valuation Method 1 Verma et al (2001) 1999-2000 values Lake Bhoj, MP Market prices Market prices Water chestnut cultivation CVM Recreation 2 Saudamini (2010) 1999 values Mangrove forest Orissa Market prices 3 Hirway and Goswami (2004) 2003 values Mangrove forest Gujarat Replacement cost CVM Recreation Carbon sequestration Erosion control 4 Badola and Hussain (2005) Mangrove forest Orissa Damage cost avoided 5 Pattnaik et al (2004) Coastal lake Orissa Market prices 6 Ranjani and Ramachandra (1999) Lake Karnataka CVM 7 Ramachandra and Rajinikanth (2000) Lake Karnataka CVM 8 Chaudhry (2006) 2005 values Park Chandigarh CVM 9 Paliwal et al (1999) Cultivable land NCR Delhi Hedonic pricing 10 Shah (2002) National Park Gir, Gujarat Market prices 11 Ninan (2001) Wildlife sanctuary Karnataka Opportunity cost 12 Prasad, Kumar and Floodplain Delhi Production function Babu 1999-2000 values Alternate cost Water recharge to households Replacement cost Nutrient retention Market prices Fish production Indirect substitution method Fodder Market value Thatching grass production CVM Recreation 13 Singh, Gopal and Kathuria (2002) Lake Uttaranchal Market prices TCM Recreation Damage cost avoided Erosion control 14 Chattopadhyay (2001) Wetland Kolkata, West Bengal Opportunity cost 15 Das et al (2000) Wetland West Bengal Market prices Fish production 16 Murty and Menkhaus (1998) National Park Rajasthan CVM 17 Chopra and Adhikari (2004) Keoladeo National Park Rajasthan TCM 18 Gupta (2006) Lake Maharashtra CVM June 2, 2012 vol xlviI no 22 80 Service Fish production Storm protection function Tourism Storm protection Benefits to crop productivity Agricultural use Agricultural use Recreation Agricultural use Fodder Biodiversity Water recharge to agriculture Fodder Agricultural use Aquaculture Biodiversity Recreation Recreation Economic & Political Weekly
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wetland comes to be 0.04 million ha per annum if the declines is assumed to follow a linear path. The drivers of change in the wetland area have been mostly anthropocentric. Agricultural expansion and urbanisation processes have been the two most dominant forces of change (Kumar 2009). While the account of physical loss is a fi rst step towards operationalising the accounting approach, it does not help much in its integration with the existing accounts of those states. Monetary estimates of the loss would greatly facilitate the integration.

Methodology

In order to estimate the economic value of the wetland loss, we have used the benefit transfer method in this study. The benefit transfer method (BTM) or value transfer method can also be used for transfer of cost figures from a similar study known as “study site” to the site in question known as the “policy site”. Typically, the decision-maker requires an economic value of the loss and gain arising from the changes in ecosystem services and transfer of value from a similar study can help in that at far lower cost and time. Technically, transfer of value method is done by estimating the value of an ecosystem or ecosystem services by borrowing the existing valuation for a similar system. They can be in form of a unit value transfer and assumes that the marginal value to an average individual at the study site from an ecosystem service is the same as that which will be enjoyed by the average individual at the policy site (Navrud and Ready 2007). On the other transfer of unit value, transfer uses the value of ecosystem services per unit of spatial scale. Other form of transfer of value could be in the form of adjusted unit value transfer and value function (Brander et al 2009). In this study, we have used a meta analytic function transfer. Under meta analytic analysis, primary valuation studies are collated and analysed in a group and the results from each study are treated as a single observation in the new analysis of a pooled data set. This will enable us to evaluate the infl uence of the characteristics of ecosystem services, the features of the valuation method and other related assumptions. The resulting regression equations explaining variations in unit values can be used together with socio-economic contextual data on the independent variables in the model. The meta analytic transfer can be used for various purposes like costs benefi t analysis, environmental costing, natural resource damage assessment and ecosystem accounting. There have been successful examples of application of benefit transfer methods by Eshet et al (2007) and Johnston and Rosenberger (2009).

Benefit Transfer Method

The preliminary meta regression attempts to ascertain the impact upon wetland value (in constant terms) of wetland characteristics, valuation characteristics (both vectors of qualitative variables) and demographic characteristics of the region in which the wetland exists.

Data: The data of valuation studies is collected from the peerreviewed published and grey literature. There are 19 articles, including 17 study sites distributed in 13 states that have

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been used. Table 2 (p 80) gives an overview of them. We derive the current data set that includes types of wetlands, 14 categories of ecosystem services and five types of valuation methods. Tables 3 and 4 reflect some patterns in the data.

Some adjustments have been made to make the data comparable. First, the wetland values are in unit values, if the study does not already give value per ha. Second the wetland values pertain to different years so they have been adjusted to year 2007 prices uS dollars. The current values are deflated or infl ated (as the case may be) using 1999-2000 as the base year. Deflators are obtained by dividing the current year’s GDP by that of the base year (sourced from the Central Statistical Office), and then applied to the appropriate year’s value. Next, rupee values are converted into dollars (1$ = Rs 40, which is an approximate value that has been sustained during 2007-08, according to the Reserve Bank of India).

State-wise socio-economic data includes GDP per capita and population density. State GSDP is obtained from the RBI Handbook of Statistics (2007) and is similarly brought to the base year and in $ terms.

Meta Regression Model: The general regression model is: Y1 = α+ b X +bXE +bcX _u1 (1)

ssw c

where Yi is the dependent variable in $ value per ha in 1999-2000 terms; XS, XE and XC are three types of explanatory

Table 3: Cross Table of Ecosystem Service and Type of Wetland

Type of Wetland

Ecosystem Service Cultivable Floodplain Lake Mangrove National Wildlife Total Land Forest Park Sanctuary

Agricultural use 1 1 3 0 0 0 5

Aquaculture 0 1 0 0 0 0 1

Biodiversity 0 0 0 0 1 1 2

Carbon sequestration 0 0 0 1 0 0 1

Erosion control 0 0 1 1 0 0 2

Fish production 0 2 1 0 0 0 3

Fodder 0 1 1 0 1 0 3

Nutrient retention 0 1 0 0 0 0 1

Recreation 0 1 3 0 2 0 6

Storm protection 0 0 0 2 0 0 2

Thatching grass production 0 1 0 0 0 0 1

Water chestnut cultivation 0 0 1 0 0 0 1

Water recharge 0 2 0 0 0 0 2

Total 1 10 10 4 4 1 30

Table 4: Principal Services and Goods Provided by Wetlands and Valuation Methods Commonly Used to Estimate Their Value

Category Wetland Service Valuation Method

Provisioning Agricultural use CVM(2), market prices (1), replacement cost (2*) Aquaculture Market prices (1) Fish production Market prices (3) Fodder Market prices (2 ), opportunity cost (1) Thatching grass production Market prices (1) Water chestnut cultivation Market prices (1)

Regulating Biodiversity CVM (1), opportunity cost (1)

Carbon sequestration CVM(1)

Erosion control Market prices (1), opportunity cost (1)

Nutrient retention Replacement cost (1)

Storm protection Market prices (1), replacement cost (1)

Water recharge Production function (1), replacement cost (1)

Cultural Recreation CVM(4), production function (2)

CVM = Contingent valuation method.

* Figures in brackets indicate the number of observations for each wetland service according to the most commonly used valuation methods.

Table 5: Explanatory Variables Used in the Meta-Regression Model

Group Variable Variable Type Levels/Measurement Unit N

Study (X) Valuation method Nominal Replacement cost 5

S

CVM 9 Opportunity cost 3 Production function 3 Market prices 10

Wetland (X) Wetland size Ratio In hectares 30

c

Wetland type Nominal Lake 10 Cultivable land 1 National park 4 Mangrove forest 4 Floodplain 10 Wildlife sanctuary 1

Service provided Agricultural use 5 Aquaculture 1 Biodiversity 2 Carbon sequestration 1 Erosion control 2 Fish production 3 Fodder 3 Nutrient retention 1 Recreation 6 Storm protection 2 Thatching grass production 1 Water chestnut cultivation 1 Water recharge 2

Socio-GDP per capita Ratio 1999-2000 US$ person-1 year-1 in economic state in which wetland is found 30

Context (X) Population density Population per sq km in state in

C

which wetland is found 30

N = number of observations for each variable or variable level.

variables used in the meta-regression model, XS: study characteristics, mainly referring to the valuation methods, XE: wetland ecosystem characteristics, including wetland size, wetland types and wetland ecosystem services, XC: the socio-economic vector, including GDP per capita (1999-2000 $ person-1 year -1) and population density (Population per sq km) in the state where the wetland is found. The explanatory variable vectors are given in Table 5.

Dummy variables are essentially a device to classify data into mutually exclusive categories (Gujarati 2003). Dummy variables applied in this model are to reveal the difference due to different valuation methods that have been adopted and to distinguish the values produced by different wetland types and different wetland ecosystem services.

Results: The model fit was considerately improved and the hetero skedasticity got mitigated when the natural logarithm of the dependent variable was used. Since the coefficient of wetland size is not statistically significant every time, we omit it for a better overall simulation. Table 6 presents the results, which were obtained using ordinary least squares (OLS). In this logarithmic model, the coefficients of the dummy variables measure the difference of the log values among different categories of each characteristic group, while the coefficients of the explanatory variables expressed as logarithms represent the elasticity, that is, the percentage change in the dependent variable, given a (small) percentage change in the explanatory variable. The values of R2 (=.932) and adjusted R2 (=0.671) are high, which means that the regression curve came very close to the points. F test is 3.566, p=0.060, indicating a less than 10% level of significance to reject the null hypothesis that all the parameters equal to zero simultaneously. This could be due to the extremely small sample size rather than fit of the model.

The constant term is the mean value achieved for the base category. The base category in this model is the valuation method, market prices, wetland type, and ecosystem service. This constant term is statistically significant at a less than 10% level.

Of the study characteristics, the valuation method, replacement cost method, is statistically significant and the coeffi cient is large but negative. This means that the values achieved with a replacement cost method are significantly less than the ones achieved with the base valuation method, market prices, keeping other variables as unchanged. Opportunity cost method has a high and positive coefficient, which means that the use of this method tends to produce a high value, although not statistically significant. The absolute values of the coeffi cients for CVM and the production function valuation method are relatively small, which means the values estimated with these two methods might be within the results with replacement cost and opportunity cost methods respectively. However, these two coeffi cients are not statistically signifi cant either.

Several variables capturing wetland characteristics turn out to be statistically significant. For the wetland types, the

Table 6: Results Obtained with the Meta-Regression Model of Wetland Values

Variable Coefficient p-value

(Constant) -49.762* .081
Study variables Replacement cost CVM Opportunity cost Production function -7.277* 1.024 8.133 -3.943 .060.745.126.314
Wetland variables Lake Cultivable land National park Mangrove forest Floodplain Agricultural use Aquaculture Biodiversity Carbon sequestration Erosion control Fish production Fodder Storm protection Nutrient retention Recreation Thatching grass production Water chestnut cultivation 12.884 41.021** 15.654 36.623** 30.347** -9.680* -15.765** -5.418** -28.239 -27.410** -10.161** -18.344* -25.831** -7.157** -9.445* -13.680** -9.510** .176.017.124.034.030.078.045.033.431.026.021.059.019.028.094 .042 .023
Context variables GSDPusd (in) Population per sq km (in) 17.658** -9.502*** .011 .007
  • (a) Dependent Variable: LogY1999-2000.
  • (b) OLS results. R2 = .932; Adj.R2 = 0.671. F = 3.566; p-value = 0.060. Significance is indicated with ***, ** and * for 1, 5 and 10% statistical significance levels respectively.
  • Table 7: Change of Wetland Values according to the Estimated Unit Value and the Physical Change of Wetland Area

    State Predicted Change in Change in Value State Domestic Lost Value Value Area (Million $/Year) Product at as a % of ($/Ha) (000 Ha) Current Price SDP

    ($ Million)

    Gujarat 8,470.03 -57.56 487.51 23,135.20 2.1

    Jammu and Kashmir 1,112.73 -328.70 365.75 3,383.25 11.0

    Kerala 6,016.50 -19.49 117.30 15,160.75 00.8

    Rajasthan 941.66 -51.66 48.64 18,342.50 00.3

    West Bengal 1,328.28 -2.26 3.00 31,202.00 00.0

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    coefficients are all positive, which means the values of all these wetland types are higher than that of the wildlife sanctuary, the base type in the regression model. Among them, the cultivable, mangrove forest and floodplain are much higher and simultaneously statistically significant at a 5% level. The estimates of the lake and national park are not statistically signifi cant.

    All the regression coeffi cients of the ecosystem services are negative, and are statistically significant except the carbon sequestration. That means that the values of these services are significantly lower than that of water recharge, the base service. The lowest values come from the services of carbon sequestration, erosion control and storm protection.

    Since the natural logarithm of the dependent variable is used in the regression model, the coefficients of these dummy

    The loss calculated as percentage of the respective SDPs come out the maximum for J&K as 11% while for Gujarat it is 2.1% at 2000-01 price (Economic Survey 2011). For other states, they are not very significant but this loss is just annual loss on account of inland wetland. There would be other losses in many other ecosystem services which remain unaccounted for. Any adjustment dealing with man-made capital must acknowledge and internalise these depreciations as well.

    Table 8 gives the accumulated lost values due to the loss of wetland areas in these states in the future. Assuming that the annual wetland area loss will be unchanged in those states, we calculate the accumulated values of the lost wetlands within the next 10, 20 and 50 years respectively, considering the social discount at a 3%, 4% or 5% rate.

    Table 8: Accumulated Values of the Loss of Wetlands in India in 10, 20 and Infinite Years Respectively

    State Z Annual Loss 10 Yrs 20 Yrs Infinite
    (Loss during 1991-2000-10) 3% 4% 5% 3% 4% 5% 3% 4% 5%
    Gujarat 38.77 330.7 314.4 299.4 576.8 526.9 483.1 1,292.3 969.2 775.4
    Jammu and Kashmir 215.88 1,832.9 1,742.8 1,659.2 3,196.8 2,920.2 2,677.8 7,162.5 5,371.9 4,297.5
    Kerala 1.38 11.8 11.2 10.7 20.6 18.8 17.2 46.1 34.6 27.6
    Rajasthan 10.35 88.3 84.0 79.9 154.0 140.7 129.0 345.0 258.8 207.0
    West Bengal 0.034 0.3 0.3 0.3 0.5 0.5 0.4 1.1 0.8 0.7
    Total 265.41 2,264.0 2,152.7 2,049.4 3,948.6 3,607.0 3,307.6 8,847.0 6,635.3 5,308.2
  • (1) The future values are in million $ current price, except the Z Annual Loss, which is in million $ 1999-2000 price;
  • (2) For time period, n = 10 and 20 respectively, the accumulated loss calculation formula is: “
  • A = Z* 1/(1+ r)i, and for infinite, the formula is: A =z/r, where Z Annual Loss (Loss during 2001-1991/10) in $ 1999-2000 price, and r = rate of discount, 3%, 4%, 5%, respectively.

    i=1

    variables, reflecting a difference of the log values, indicates a larger difference of the wetland values themselves among different categories. In Table 6, we can see that the magnitude of the coefficients of wetland types is approximately 25, that of wetland services is about 15 and for the valuation methods, the absolute value average is about five. That means that the infl uences on the wetland values can be approximately ordered and the order is wetland type greater than wetland service and wetland service greater than valuation method.

    The two contextual variables, GDP per capita and population density of the surrounding area of the wetland site are statistically significant and however, one is positive and the other negative, indicating respectively a positive and a negative elastic effects, as a natural logarithm is adopted for these two variables.

    Value Transfer: Further regression analysis has been done to explore the relationship between the wetland values and the social-economic data alone. A regression relationship is obtained that is not only statistically significant in the constant term and the coefficients of the explanatory variables, but overall it is statistically significant. This regression model has been applied to estimate the values of the wetlands in all the states in India and to further calculate the total loss for several states in India.

    Table 7 gives the calculated results of the wetland monetary loss in five states, the product of the annual values per unit and the lost wetland area. The annual values per unit are predicted by the above regression model, with the state population density data of 2001 and income per capita data of the duration of 1999-2000. The lost areas are derived from the results of the survey of some districts in the states, with their average change ratios of the surveyed districts used as the ratio for the whole state.

    The figures of loss represent the annualised value of inland wetland capital.

    According to the demographic statistic data, we further calculate the loss per capita of these states and the results are presented in Table 9. Table 9: Loss Wetland Wealth Per Capita over 1991-2001

    State Loss over 1991-2001 Population in Loss Per Capita over
    (in Million USD 2001 (Million) 1991-2001
    1999-2000 price) (in USD 1999-2000 Price)
    Gujarat 387.68 50.67 7.65
    Jammu and Kashmir 2,148.75 10.14 211.83
    Kerala 13.82 31.84 0.43
    Rajasthan 103.51 56.51 1.83
    West Bengal 0.34 80.18 0.004
    Total 2,654.11 229.34 11.57

    Maximum loss is in J&K while the lowest is in West Bengal. In J&K, the loss per capita is up to $211.83 over 1991-2001 and the lower population and higher wetland loss in J&K are responsible for this significance. The loss in J&K shows the drastic decline of the wetlands while we have been able to consider only one inland wetland in West Bengal (East Kolkata Wetland) due the paucity of data for other inland wetland in the state.

    Conclusions

    In this paper, the operationalisation of the concept of ecosystem accounting has been demonstrated by applying a benefi t transfer method to calculate the annual monetary value of the physical loss in wetlands in five selected states – Gujarat, J&K, Kerala, Rajasthan and West Bengal during 1991-2001. The maximum loss is in J&K, with its loss per capita up to $211.83 while that for the combined states is $11.57. Also, the use of the benefit transfer method for valuation is dependent upon the

    Economic & Political Weekly

    EPW
    June 2, 2012 vol xlviI no 22

    quality of primary studies. Therefore only methodologically The report by Stiglitz et al demonstrated that the current sound studies should be included in a meta-analysis. measures of income and wealth do not capture many contribu-

    Here, the wetland loss in the wetland natural wealth is just in tions from environment and ecosystems. But what is alarming one component (inland wetland) of the wetland ecosystems. that the contributions which matter the most for the poorest of The accounting of other ecosystems like forests, coastal and the poor are under-reported the most. For example, the cultivated lands would make up a sizeable loss in the natural conventional measure provides the contribution of fi sheries wealth. Application of the benefit transfer method is quick and and forestry sectors for Brazil, Indonesia and India as 6.1, 14.1 cost effective but this method would be good to the extent the and 16.5% respectively but if the ecosystem services are primary studies are good. In India, some of the listed studies accounted it becomes 17.4, 14.5 and 19.6% respectively. The are far from satisfactory as application of the database and share of the poor in those unaccounted contributions from methodologies are not coming from peer-reviewed sources. So, ecosystems are 90, 75 and 47% for Brazil, Indonesia and India the monetary valuation of a loss should be treated as a “fl oor respectively (TEEB 2010). A corrected and revised indicator like value”. Ideally, calculation of accounting prices for each service the net national product (NNP) would help policymakers in dein each state would be ideal. Besides these limitations, the signing better sectoral policies and efficient resource allocation study clearly shows the monetary value of the wetland loss in in the economy which will eventually help the poverty alleviathe selected states. Such accounting would signal not only the tion goal. Adjusted NNP against the natural capital would also direction in which the economy is moving but it would fl ag the help in achieving the targets of the “Green Eco nomy” (improimminent danger of losing ecosystems and ecosystem services ved well-being and social equity while signifi cantly reducing which are the productive base of the economy. the environmental risks and ecological secu rities, UNEP 2011).

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