environmental valuation techniques - how much is hyde park worth?

8/11/2019 Environmental Valuation techniques - how much is Hyde Park worth?

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How much is Hyde Park worth?

A short review of environmental valuation models

and frameworks, and a travel cost case study.

Martin Knutli

4920 words

Imperial College London

September 4, 2014

Abstract

This paper seeks to outline some of the major frameworks, conceptsand models in environmental/ecological economics and their specific uses.Examples are hedonic pricing, travel cost method, market based approaches,the contingent valuation method and choice experiments. Furthermore, anatural capital valuation of Hyde Park will be undertaken, using a travelcost framework. The hope is to demystify these processes, and influenceand incentivise ma jor players to incorporate these frameworks in theirbudgeting and accounting work.

1 Introduction

The majority of scientists worldwide advise individuals, corporations and gov-ernments to act rapidly to prevent further emission of greenhouse gases. Theworld is already seeing the beginning signs of what could turn into a paradigmshift in the way of living for the human civilisation. Ice caps melting, sea levelsand global temperatures rising, acidification of the seas, extensive logging ofrainforests and some cities so polluted that stepping outside is more cancerousthan passive smoking (IARC 2013), are only a few examples. Slowly, variousinstitutions and organisations have come to terms with the pressing need tomove away from fossil fuels and instead rely on renewable energy. However,

climate change is not caused solely by the release of greenhouse gases. Forestsand oceans absorb enormous amounts of these gases, making it fair to say thatthese natural assets provide a tangible service to society. Consequently, thepreservation and restoration of these assets is important.

Economics, as the study of how to allocate limited resources, relies on val-uation to provide society with information about the relative level of resource

scarcity. The value of ecosystem services and biodiversity is a reflection of what

This paper only portrays the views and work of the author, and is not necessarily repre-

sentative of the views of Imperial College London. The author takes full responsibility of the

content of this paper.

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we, as a society, are willing to trade off to conserve these natural resources.

Economic valuation of ecosystem services and biodiversity can make explicit tosociety in general and policy making in particular, that biodiversity and ecosys-

tem services are scarce and that their depreciation or degradation has associated

costs to society. If these costs are not imputed, then policy would be misguided

and society would be worse off due to misallocation of resources (TEEB 2010).

Valuation of private goods and services have well-developed methods and mod-els. The problem with valuing ecosystems and biodiversity is how much of it isregarded as a public or common good, leading to over-consumption (tragedy ofthe commons), and more importantly, how it is regarded as inherently intangi-ble. There are few and/or imperfect markets for biodiversity goods, resulting inuncertainty about the demand and supply of natural resources, both now andin the future. However, biodiversity and ecosystems can be viewed as natu-

ral capital, and the services these ecosystems provide can be regarded as theinterest on that capital. In the same sense an investor chooses a portfolio ofcapital assets to minimise risk, policy makers should choose a level of biodiver-sity and ecosystems that generate a flow of services which maintain the presentand future level of environmental quality. This level should be maintained byimposing various incentive schemes (e.g. eco-taxes, payment for ecosystem ser-vices1, green public procurement2, CAT-bonds3 or species swaps4) onto privateand public actors. Alternatively, as this paper advocates, simplifying and de-mystifying the process of including natural capital into accounting frameworkscould also prove beneficial.

2 Theories, Frameworks and Concepts2.1 Preference-based approach and Biophysical approach

There exists a vast number of valuation models, some specifically developed toaddress environmental issues, and others which have been customised to producerelevant results. In order to understand the hierarchy of conceptual approaches,disciplinary frameworks and specific models/methods, Figure 1 has been madeto represent all major approaches.

This figure represents the two overarching value theories preference-basedvaluation and biophysical valuation each broken down into several subcat-egories, which subsequently are broken down into specific valuation methods.When attempting to assign a monetary value to natural capital, the preference-

based approach is most convenient. Biophysical valuation aims to designatea non-monetary value to the natural capital in question, meaning that e.g. apreservation projects value can be measured in terms of number of species pro-tected, square metres of trees saved from logging or litres of water kept cleanfrom runoff. While these measurements can provide valuable data over time,normally referred to as indicators (Reyers et al. 2010), these measurements cap-ture the intrinsic value of nature. Assigning a monetary value is thus neither

1See Redford and Adams, 20092See European Environmental Agency, 20143See article in Le Monde Diplomatique for in depth discussion on this4See Mandel, Donlan and Armstrong, 2009

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Figure 1: Approaches to valuation (drafted from TEEB 2010).

the goal nor particularly useful. This paper will therefore in the following fo-cus on preference-based approaches, which rely on models of human behaviourand rest on the assumption that values arise from the subjective preferencesof individuals (TEEB 2010), and estimates the instrumental value of naturalcapital.

2.2 Use Values and Non-Use Value

There is a range of value definitions in environmental and ecological economics,some of which are especially relevant in the context of this paper. In particular,use and non-use values are of interest, and can be found in Figure 1. Use valuesare divided into three main categories; direct use value, indirect use value andoption value. Direct use value stems from the direct use of a natural resource,e.g. consumption of water or food (NOAA 2014). Since most resources thatprovide direct use value normally are distributed through markets, the marketvalue of the good is usually the relevant measurement. Indirect use valuesreflect the services received from natural resources, e.g. clean air because ofphotosynthesis or flood prevention from mangroves (ibid.). Option values arethe potential benefits obtained from using the resource in the future (ibid.),

e.g. discovering medicinal uses of a rainforest plant or knowing that you canuse a park tomorrow and into the future. Option values can be further splitinto existence value (utility derived from knowing something exists), altruisticvalue (benefits from knowing someone else benefits) and bequest value (utilitygained from future improvements in the well-being of ones descendants) (Hein,2010). Non-use value is derived from attributes inherent to the ecosystem itself(Kolstad, 2000). It can be anthropocentric, meaning it has a natural beauty, orecocentric, meaning that plants and animals have a right to exist (Hargrove,1989, as cited in Hein, 2010, pp. 36).

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2.3 Total Economic Value

In Figure 1, output value is the sum of use values and non-use values. Thismeasure is more commonly referred to as Total Economic Value (TEV). TEVtries to capture the current benefits of ecosystems (which is the output value) aswell as the discounted future benefits of an ecosystem service. An ecosystemscapacity to maintain a sustained flow of benefits is referred to as insurancevalue, and can include ecosystem functions such as water provisioning, purifi-cation and regulation (TEEB, 2010).

Total Economic Value = Output Value + Insurance Value

What TEV tells us is that the market price (i.e. the direct use value) of e.g.timber is not a true reflection of the goods value. A living tree has additionalproperties, such as air cleansing, soil erosion control or habitat services (Ku-

mar, 2010 as cited in TEEB, 2010, pp. ) properties that provide indirectbenefits to humans that are not accounted for in the market price, which onlyreflects the supply and demand. It should be emphasized that total in totaleconomic value is summed across categories of values (i.e., use and non-usevalues) measured under marginal changes in the socio-ecological system, andnot over ecosystem or biodiversity (resource) units in a constant state (ibid.).

Within the TEV framework, values are derived, if available, from infor-mation of individual behaviour provided by market transactions relating directly

to the ecosystem service. In the absence of such information, price information

must be derived from parallel market transactions that are associated indirectly

with the good to be valued. If both direct and indirect price information on

ecosystem services are absent, hypothetical markets may be created in order toelicit values. (ibid.)

These hypothetical markets are what makes the foundation for many valuationtools in environmental economics. The three main categories direct marketvaluation, revealed preference and stated preference are listed in the figure be-low, along with valuation methods within each category. In the following, eachvaluation tool will be quickly discussed, before choosing the tool to be used inorder to perform a natural capital valuation of Hyde Park. In table 1, differenttechniques have been summarised.

Valuation category MethodDirect Market Valuation Approach Market Price-based Approach

Cost Based-approachProduction function-approach

Revealed Preference Approach Hedonic pricing methodTravel Cost method

Stated Preference Approach Contingent Valuation MethodChoice Modelling

Table 1: Valuation Techniques (TEEB, 2010)

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2.3.1 Market price-based approach

The goal of a market price-based approach is to value a provisioning service,e.g. food, fibre, fuel, biochemical, fresh water supply etc. Looking at the marketprice of a commodity which a) exists due to the provisioning service in questionand b) sold in a market, it is possible to derive preferences and marginal cost ofproduction. Multiplying the price of the commodity with the marginal productof the ecosystem service, gives an indication of the value of the provisioningservice.

2.3.2 Cost based-approach

Using a cost based-approach, the aim is to estimate the cost of artificially recre-ating the benefits provided by an ecosystem. The approach is split into threesubcategories: mitigation/restoration cost method (the cost of mitigation orrestoration of ecosystem services that have been lost); avoided cost method(costs that one would have faced in the absence of ecosystem services5) and;replacement cost method (cost of replacing ecosystem services by artificial tech-nologies) (ibid.).

2.3.3 Production Function-based approach

With the production function-based approach, you look at an existing servicedelivered in some market, then measure how much of that service delivery ispossible due to the contribution of an existing ecosystem service.

2.3.4 Travel Cost Method

The travel cost method estimates the value of e.g. recreational sites through col-lection and regression of visitor data. As a revealed-preference method, the cost(and sometimes time) of travel to the site is used as a proxy for price/willingnessto pay (Carr and Mendehlson, 2003). By calculating and extrapolating the costof travel and cost of time for visitors, a regression can be run to estimate ademand function of the site. Once a demand function has been identified, cal-culating the consumer surplus gives an indication of the value of the park. Thetravel cost method will be explained in more detail, and carried out in part 3.

2.3.5 Hedonic Pricing Method

The hedonic pricing model has been used to identify the effect green areas have

on the property values of surrounding estates and through this put a priceon green areas. GLA Economics (2010) have conducted an in-depth study inLondon in cooperation with Transport for London and London DevelopmentAgency, looking at the effect green areas had on housing prices.

The hedonic pricing model is a linear regression model relying on observeddata, or more particularly, data types. A regression seeks to explain a depen-dent variable, e.g. housing prices (Hprice), through a series of independent

5The 2013 typhoon in the Phillipines, Hayan, is often used as an example. Here, the

damages caused by the typhoon could have been mitigated if the mangroves that used to

cover the shores were still intact. Instead, these mangroves had been cut to make room for

shrimp farms. In this case, it would have been a retrospective study to calculate the avoided

cost.

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variables, e.g. square meters of property, proximity to transport and city cen-

tre, property age, number of rooms, socio-economic attributes, facilities andproximity to green areas (referred to as X1,X2, . . . ,X n). As a general rule, themore variables you include to explain a dependent variable, the higher explana-tory power (denoted R squared) the model gets6. One of these independentvariables is the proximity to green areas. From the expression below, the repre-sents the effect Xhas on Hprice , i.e. ifX increases by 1%, is how much thatchange affects Hprice .

Hprice = 0+ 1X1+ 2X2+ . . . + nXn+

In the GLA Economics study, four different regression models were run, withdifferent numbers of independent variables (from one variable to 17 variables).As more independent variables were added to the model, the explanatory power

of the model also increased. Interestingly, as more variables were added, theproximity to green areas effect on housing prices became less and less impor-tant. With 17 independent variables and an (adjusted) R squared of .767, thevalue of green area proximity was estimated to be .03, which was much lowerthan in the previous models. It was stated that some of this reduction was dueto the inclusion of a variable taking into account private gardens, which loweredthe demand for other park services (GLA Economics, 2010). The best approxi-mation from this research is that each hectare of formal green space within 1kmof housing increases house prices by approximately 0.08% to house prices withthis model, in addition to the presence of a regional park within 600 metresadding 1.9-2.9% (ibid.). These results are backed up by other studies, from e.g.San Fransisco, where one study (Edwards, 2007) found that parks on averageadd a green premium of $125.838 to properties within 500 feet of the park.

2.3.6 Contingent Valuation Method

A contingent valuation method (CVM) seeks to directly elicit individuals pref-erences about public goods, and through this, the willingness to pay (WTP)for the good through surveys, questionnaires or interviews (or alternatively, thewillingness to accept compensation for cases where e.g. an environmental nui-sance brings individuals to a lower level of utility). By creating hypotheticalmarkets and scenarios, respondents are presented a good and the scenario underwhich this good exists, and are then asked how much they would be willing topay for this good. The CVM focuses on valuing a specific change in an envi-ronment, and therefore also produces a single value for environmental quality

(NOAA, 2014). The weakness of the approach is that it is vulnerable to biases(e.g. strategic bias) and yeah-saying from the respondents.

2.3.7 Choice Modelling Experiment

The choice experiment method (CE) is an alternative valuation method to theCVM. It has gained traction over the last years, due to its ability to elicit pref-erences on a variety of attributes related to a non-market/non-tradable good.Unlike the CVM, which generates a single value, the CE provides independent

6See University of Strathclyde source for more information

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values for many individual attributes of an environmental program. This allows

the researcher to examine the preferences at a more refined level (ibid.).When choosing attributes and levels, it is important that the most relevantattributes are chosen. It is advised that during this stage, the researcher shouldrun focus groups and interviews to identify the best attributes (Mangham, Han-son and McPake, 2008).

Attribute LevelsSilence No noise Voices Car noise and voicesAir quality Smell of grass No smell Smell of garbageDensity of green area Full park Some trees No treesVisitors Alone People, not full People, completely fullDistance from home Walk distance Bike distance Tube/car distance

Table 2: Attributes and Levels of a choice experiment

The next step would be to select levels for each attribute, as illustrated inTable 2. Ensuring that the levels are realistic and meaningful will increase theprecision of parameter estimates (Hall et al., 2004). Following this, generatingso-called choice cards would be the final step before proceeding to carry outsurveys and interviews. A choice card is generated by creating a hypotheticalscenario, made up of a certain combination of levels. When all levels have beenselected, they would normally be assigned a number (referred to as coding). Do-ing this for each level and attribute would create a matrix, called the informationmatrix. It has been recommended that using the so-called D-optimal designgives the most precise information. D-optimality occurs when the determinantof the information matrix is maximised (Boon, 2007). When D-optimality isreached, the matrix is uncoded in order to get choice cards with combinationsof levels that allows extraction of the most precise information possible. Af-ter data has been collected, the next step is analysis. According to Mangham,Hanson and McPake (2008) the analysis typically includes a regression with adichotomous or polychotomous categorical dependent variable. Simply stated,sources of utility can be defined as a linear expression where each attribute isweighted by a unique parameter to account for that attributes marginal utility,with a regression equation looking something like this, where it is the differencesin attribute levels between the choices that are analysed:

Y =0+ 1(X1i X1j) + 2(X2i X2j) + . . . + K(XKi XKj ) + (i j)

The parameters estimated here represent the marginal utility associated with achange in the attribute level. Furthermore, the error terms are not consideredindependent, meaning panel data techniques are required (ibid.). The end re-sult is individual preferences (willingness-to-pay) for the various attributes, andtheir corresponding levels. This also allows the researcher to rank the differentattributes in terms of importance for the average consumer.

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3 Study

3.1 Survey

In this simple experiment, we try to find a monetary value of Hyde Parkthrough the use of the travel cost method, where travel cost acts as a proxyfor price/willingness to pay. The benefits of this method is that valuation isbased on peoples actual behaviour, rather than stated behaviour. Through col-lection of visitor data, and regression of this data, a demand function is createdfor the site through the estimated regression equation, and by examining howvisitation rates varies when the price varies. If the park entrance fee is zero,hypothetical prices are used, under the assumption that people view travel costsand entry fees similarly. In the travel cost study, the following questions wereasked

Which zone the respondent lives in

What mode of transport was used to get there (tube/car/bike/walk)

How many times they visited the park in the past year

If the person owns an Oyster Card

The persons income or other socioeconomic characteristics to estimatethe value of their time

In the study, a total of 112 random visitors in Hyde Park were asked the samequestions. Of those, 9 chose to not give out their yearly income, and wereremoved from the sample, leaving a sample of 103 respondents. People were

only asked questions if they confirmed spending time in the park - not justwalking through. The questions were asked at different times of the day, overfour days. Some respondents were tourists while others were London residents.For tourists, only the travel cost they incurred by going from their hotel to thepark was included - not the cost of travelling from their home country.

In the following, these five steps are followed:

1. Estimate travel cost of using tube, car and bike

2. Estimate cost of time spent travelling

3. Estimate a regression equation that relates visits to travel costs and othervariables

4. Use results from regression to create demand function

5. Estimate area under demand curve

3.2 Travel Costs

We calculate the travel cost as

TC = ccd + wh

where cc = marginal cost of driving per mile, d= distance, w= wage rate, h= time spent travelling to the park. Using numbers from the Association for

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Automobiles (2014), an average marginal cost of driving of 0,2338 per mile

was calculated. The cost of driving from each zone was calculated using theAutomobile Associations milage calculator, resulting in an average measure foreach zone. The tube cost is counted as off-peak prices, with or without Oystercards while bike cost is 2 for 24 hours.

The cost of time is an estimate based on Google Map estimates on traveltime from the various zones using different modes of transport, multiplied bythe hourly wage of the respondent7 in order to find the alternative cost oftravelling. A weakness in the study is that respondents were not asked howmuch time they spent at the park, meaning cost of time spent while at the parkcannot be measured. The cost of actual travel time can still be estimated.

3.3 Regression and Results

The responses described income (to calculate alternative cost), zone (to calculatetravel distance and time), transport mode (to find correct travel cost) and howmany times they had been in the park. A regression was run to see if the numberof times visited can be explained by the travel cost, using the equation below.We expect the estimate of to be negative, which means that if travel costsincrease, the number of visits decrease.

3.3.1 Model 1

Visitsi = + 1Travel Costi+ i

Variable Coefficient Std. Error t-stat

Intercept 1.5385 0.8186 1.88Travel Cost 0.0463 0.006 7.7R2 0.364Observations 103

Table 3: Results of regression model 1

Using the results listed in table 3, the estimated regression equation, usinga standard lin-lin approach, becomes

Visits = 1.5485 + 0.0463TravelCost

Normally, one would use this regression equation to calculate a demand function.

That is done by setting a hypothetical entry price, then measure the numberof visits as the entry price increases. This would yield a demand curve whichultimately would be used to calculate the consumer surplus by estimating thearea under the graph through the following formula

CS=

TCchokeTC0

V(TC,X) dTC

(Parsons, 2004) where TCchoke is the choke price - the travel cost that wouldimply zero visits. Surprisingly, the estimate for is in our case non-negative.

7Annual wage divided by 220 working days per year, divided by 8 working hours per day

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Since travel cost acts as a proxy for price, one would expect the demand (number

of visits) to fall as the travel cost increased. In this model, we find the oppositetendency. The constant term is not significant, suggesting low visits whenprice is low/zero, then increased visits when prices increase.

3.3.2 Model 2

In the second model, we include the variable Income to account for differencesin income among the respondents.

Visitsi = + 1Incomei+ 2TravelCosti+ i

Running this model yields the following results:

Variable Coefficient Std. Error t-statIntercept 7.082 1.31 5.40Income -0.000132 2.58 105 -5.10Travel Cost 0.0497 0.0054 9.17Adj.R2 0.49Observations 103

Table 4: Results of regression model 2

Visits = 7.082 0.000132Income + 0.0497TravelCost

In this run, the R squared of the model increases by 12.6 percentage points, andall three variables are now significant. While the model in itself is stronger, itstill supports the results found in the previous run.

3.4 Discussion

There are many reasons why the results of the models could be unreliable. Usinga linear regression model may not fit the data properly, reflected in a standarderror of regression of 4.49 and 4.02 respectively. Running a log-log-model, lin-log-model and log-lin-model does not improve any factors. There could be othervariables that indirectly affect the results, but have been left out (because thatdata was not collected in the survey), resulting in omitted variable bias, mak-ing the estimated coefficients unreliable. In the models, this means that whencreating a demand curve, the demand would be increasing with price, making

it difficult to give any reliable estimate of park access value.

The observed effect in both models resembles that of a Giffen Good - a situationwhere the income effect dominates the substitution effect, suggesting that HydePark is an inferior good. This means that when peoples income increases, theyvisit the park less. Arguably, that seems reasonable, in the sense that going tothe park is less important when you work high-paying jobs. Some may worklong hours (and thereby have a high annual income - simply because of longhours and not due to high hourly wages), and simply have no time to visit thepark. This would notsupport the notion of Hyde Park being an inferior good,but rather that the person values work more than free time. However, if the

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respondent works fewer hours, but with very high pay, an inferior Hyde Park

may be closer to the truth. This can not be confirmed or refuted, due to noavailable data on specific hours worked among the respondents.

4 Conclusions

In this paper, several methodologies and concepts within the world of environ-mental economics have been reviewed. Direct market valuation techniques, suchas market-price approach, cost-approach and production function-approach, arebest used when valuing a good generating a direct value for consumers and whenmarket prices are readily available. When such prices are not available, and thegood in question rather generates indirect values, such as clean air or recreationopportunities, revealed preference approaches, like hedonic pricing or travel cost,

are better used. Both of these approaches fall within the category of direct use.If the goal is to value a non-market good which generates value even if youdo not directly use it, a contingent valuation method, or a choice modellingexperiment, would be the smartest approach, using peoples stated preferencesas basis for the experiment. The methodologies mentioned in this paper doesnot cover all the varieties and options within each framework. Therefore, if youwant to carry out an experiment using any of the frameworks provided in thispaper, the author advocates to collaborate with statisticians or environmentalresearchers to ensure rigoruos analyses.

In the study, a travel cost method was attempted. The regression results provedunreliable, which could be traced down to a variety of factors. Because of thelow reliability of the estimated coefficients, a demand function was not created,and a monetary value therefore not reached. The results from both modelssupport an idea of Hyde Park being a Giffen good, often translated to meaningan inferior good. Due to time constraints and word limits, the various factorspotentially affecting the models were not investigated in more depth.

Further studies, where more care is taken in the survey design, scope, vari-ables included and statistical rigour, should be carried out before reaching anyfinal conclusions on asset value.

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