Management of Ecosystem Services with a Focus on

Biodiversity: Fin an cing and Paying Services at Spatial

Level in Landscapes

Ernst-August Nuppenau

Justus-Liebig-University

Giessen, Germany

Content1 Introduction2 Background and Approach3 Nature Provision and Land Management 4 Objective Functions based on Land Allocation

for Specie Occurrence 5 Modelling “Supply” and “Demand” for a

Generic Social Optimum of ESS Provision 6 Social Welfare Optimization7 Readjusting for Land and Labour Constraints8 Summary

Background and Approach• “Payments for Ecosystem Services” (PES) are consi

der ed a tool for incentives in nature provision.• Also they should tell management what are priorities?• It has to be appreciated that charging fees for desired

BD (positively va lued) has to be linked to offering pay ments to farmers (providing BD).

• A question is: How can we preferably link fees for PES to payments for land or BD?

• We are of the opinion that land is better than directly linking payments to BD.

• For this we need to delineate a spatial outline for sup-porting governance in find ing “best” instrument design.

• It is the objective of the paper to show how questions on spatial land use, ESS outline and govern ance can be addressed by modelling farmers’ interactions.

Land Use and Spatial Diversity Fig. 1a: Traditional land use structure

Fig. 1b: Modern land use structure

tools for landscape modeling

s I e + XII x (1)• s: bio-diversity• x: local conditions change in farm size change in field size e = change in yields buffer strips additional labor

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a: stylized for mathematical presentation b: calculation of area where: ai = transversal stretch of a field (farm size) and number of farms “n” (for instance A/a=n for equal size) bij = longitudinal stretch of a field (cropping pattern)

Fig. 2: Stylized structure of landscape

three farms given in a “polder“

• where: increase: “” and decrease “” :• Iu

j = as profit

• crj = compensation per area of restricted ecology favoring agriculture, (profit )

• bj = size change of the field i on farm j, area cropped, (profit )

• Lj = Labor j on farm j, for nature (profit)

• C(.) = cost function on quantity of qij at field lij with the yield h=qij/lij, (cost=>profit)

• bij = field margins, individual cost reducing effect by biological activity (cost=>profit)

• s = side effect of a community based ecological structure, i.e. ecological effect from • main structure: measured as effect of specie occurrence; positive and negative• l j = labor, if labor is requested for nature in particular habitat

improvement(cost=>profit)• rj = input costs, farm specific (cost=>profit)

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- Finally, for aggregated ESS provision we need to horizontally add individual farm con tri buti ons. - Yet, as land and field parcels are not substitutes, a complex “supply” results:

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where: c = compensation f = charge for ES service

4 Modelling “Supply” and “Demand” for a Generic Social Optimum of ESS Getting 4.1 Supply

Modified Objective Function:

4.2 Demand•Vice versa, “s” should be a demand driven WTP, if prices prevail, derived from (9). •Techni cally, demand can be retrieved from shadow prices as a re sult of gi ven preva lence of joint provided “s”, i.e. if we take first derivatives for the constraint: appreciated BD:s. •Yet from pu blic finance it is understood that marginal benefit functions vertically add for public goods (Mitchell and Carson, 1989; only private goods add horizontally.•Note for re du ced form (15) WTP is similar to contingent valuation of species). λsi is given as sum of farm levels:

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- We would get a virtual equilibrium for social welfare, if: s´λs – c´b = 0

5 Eventual Equilibrium

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Social welfare Provision:-Tech nically function (17) is optimized in (18).-ESS provision “s” de pen ds on public ma na ge ment of “b” and “L”.-One can state individually optimal compensations and fees. -I.e. (17) is used in benefit-cost-analysis by taking first derivatives of b and L.-Social welfare is: W= Σ 1j (farms) Σ 1i (field) I 1ji (as vectors):

Optimization for b gives:

Optimization for L gives: (18a)

(18b)

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from optimal b*(allocation of strips) and L* (labor for nature)we get s

s is a preferred biodiversity from s get λ

is an option to set fees.λ

However payments and fees must match

> Next, since some services may also stretch beyond a community, WTP and WTA and managements can calculate cost and benefits as trading between communities can be foreseen. > For regional pricing purposes in PES schemes, district evaluations can be introduced which come with provision and using of otherwise public goods. This is similar to the introduction of BD as a user associations > The result is a set of price on “s” provided or requested between communities.> Also prices can be equ a ted with land rents and wage rates. > The aim for management would be to get a proxy for binding individual contributions to equilibria of cost-benefits.> Prices emerge for b and L if “s” is “traded” among communities. > A community can agree on larger or smaller provi sion by showing benefits from ESS.  

Pricing with Communities

• This paper outlines a spatial approach for ESS provision. • It reckons the supply capacity for ESS through habitat and land use waivers by farmers.• At the same time, farmers benefit from ESS being a public good.• It is shown how simulations can offer valuations of ESS. • Moreover management can put marginal benefits accrued by individual farms in the position to serve as source of finance: charge a fee.• However, the difficulty is that ESS provision, in the case of a landscape oriented service such as biodiversity BD, is joint and this re quests public management.• Nevertheless PES schemes can be simulated to get a pricing.

Summary