Ding H., Nunes P., (2013) Modeling the links between biodiversity, ecosystem services and human welleing in the context of climate change - Results form an econometric analysis of

8/10/2019 Ding H., Nunes P., (2013) Modeling the links between biodiversity, ecosystem services and human welleing in the

1/14

Methodological and Ideological Options

Modeling the links between biodiversity, ecosystem services and humanwellbeing in the context of climate change: Results from an econometricanalysis of the European forest ecosystems

Helen Ding a,, Paulo A.L.D. Nunes b,c

a Biodiversity Governance Research Unit (BIOGOV), Center for Philosophy of Law (CPDR), Universit catholique de Louvain, Belgiumb WAVESWealth Accounting and Valuation of Ecosystem Services, Agriculture and Environmental Services Department, The World Bank, Washington, DC 20433, USAc Department of Agricultural and Resource Economics TESAF, University of Padova, Campus di Agripolis, Viale dell'Universit, 16, 35020 Legnaro, Pd, Italy

a b s t r a c ta r t i c l e i n f o

Article history:

Received 9 November 2012

Received in revised form 14 September 2013

Accepted 9 November 2013

Available online 1 December 2013

Keywords:

Natural capital indexComposite biodiversity indicator

European forest ecosystem services

Simultaneous equation modeling

3SLSNature-based policy for climate change

mitigation

This paper constitutes a rst attemptto model the relationship between climate change, biodiversity,and ecosys-temservices, with a specic emphasis on European forests. Firstly, we construct a compositebiodiversityindicator

that integrates quantitative and qualitative changes of biodiversity projected to 2050 for the EU-17 under futureIPCC scenarios. Secondly, this indicator is integrated into two simultaneous equation models to capture the

marginal impacts of changes in biodiversity on the value of ecosystem goods and services (EGS) due to climatechange.

Our estimation results conrm the role of biodiversity as a nature-based policy solution for climate changemitigation, shedding light on the policy actions that generate co-benets by enhancing ecosystems' capacity tomitigate climate change impacts, while conserving biodiversity and sustaining the ows of EGS for human

livelihoods. Especially, nature-based mitigation policies are more cost-effective and better at coping with the

ethic and inequality issues associated with distributional impacts of the policy actions, compared to the puretechnical solutions to improving energy efciency and reducing emissions. However, the strength of biodiversityas a nature-based policy option for climate change mitigation depends on both the nature of the EGS and the

geographical area under consideration. 2013 Elsevier B.V. All rights reserved.

1. Introduction

Current model projections have consistently indicated that biodiver-sity would continue to decline over the 21st century, under differentsocioeconomic scenarios with trajectories of key indirect drivers ofecological changes, such as human population growth and greenhouse

gas emissions (Leadley et al., 2010; Pereira et al., 2010). This in turnwill impose threats to the benets of future humanity and result in achange in our production and consumption patterns in the long run(Martens et al., 2003), as biodiversity underpins a variety of ecosystem

goods and services (EGS) thatare vital to human well-being. Biodiversi-ty by denition encompasses the variety of life on earth from genes tospecies, through to the broad scale of ecosystems across time andspace (Loreau et al., 2001). It is important in terms of determining the

health of ecosystem, ensuring the stability and productivity of ecosys-tem, as well as contributing directly or indirectly to human wellbeing

(MEA, 2005). In this regard, the term biodiversity is used broadly as

an assumed foundation for ecosystem processes, rather than simplythe changing number of species on a species list (Loreau et al., 2001).The relationship between biodiversity and ecosystem functioning orprimary productivity has been of long-standing interest to ecologists

(Cameron, 2002; Kinzig et al., 2001; Loreau et al., 2001, 2002). Overthe past years, the subject has been researched in various ways viaexperimental eld research, the formulation of mechanistic theories

and quantitativeeldobservation, most of whichhaveled to a commonconclusion that a large variety of species has a positive inuence on theproductivity and stability of ecosystems, as greater biodiversity cancope with various circumstances in a given habitat and thus lead to

the more efcient use of available natural resources (Loreau et al.,2001; Martens et al., 2003). Nonetheless, quantifying the link betweenbiodiversity andecosystemgoods andservices remains a major scientic

challenge to date (Pereira et al., 2010), because there does not exist ageneral ecological relationship between ecosystem function and diversi-ty owing to species-specic effects and important tropic links (Paine,2002; Willims et al., 2002). Certainly, biodiversity loss will negatively

affect ecosystem functioning by changing the composition and distribu-tion of species (Bloger, 2001; Giller and O'Donovan, 2002; Loreau et al.,

Ecological Economics 97 (2014) 6073

Corresponding author at: BIO IS-Deloitte, Deloitte's Sustainability Services in France,

Deloitte Touche Tohmatsu Limited, 185 avenue Charles De Gaulle, 92524 Neuilly, Ile-de-

France, France. Tel.: +33 652422258.

E-mail address:[email protected](H. Ding).

0921-8009/$ see front matter 2013 Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.ecolecon.2013.11.004

Contents lists available at ScienceDirect

Ecological Economics

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e c o l e c o n
http://dx.doi.org/10.1016/j.ecolecon.2013.11.004http://dx.doi.org/10.1016/j.ecolecon.2013.11.004http://dx.doi.org/10.1016/j.ecolecon.2013.11.004mailto:[email protected]://dx.doi.org/10.1016/j.ecolecon.2013.11.004http://www.sciencedirect.com/science/journal/09218009http://www.sciencedirect.com/science/journal/09218009http://dx.doi.org/10.1016/j.ecolecon.2013.11.004mailto:[email protected]://dx.doi.org/10.1016/j.ecolecon.2013.11.004http://crossmark.crossref.org/dialog/?doi=10.1016/j.ecolecon.2013.11.004&domain=pdf


2/14


3/14

2.2. Socio-economic, Climatic and Ecological Data Projected Under Future

IPCC Scenarios

Under different IPCC storylines, projections have been developed todescribe possible outcomes of different political, economic, demographic,technological and social assumptions for the future development. Theseinclude the projected trends of GDP, population, incremental tempera-

ture, ecosystem productivity, and distribution of species and so on,subject to the changes in a set of key assumptions on which the IPCCstorylines are based. In this study, we explore the use of climatic,socio-economic and ecological projections to investigate the pressure

on biodiversity and to quantify the consequent quality and quantitychanges of terrestrial biodiversity following the four future develop-ment paths dened by IPCC. Since we have empirical evidence showingthat the impacts of changing climate conditions are highly spatially

heterogeneous, as organisms, populations and ecological communitiesdo not respond to approximated average of global warming (Waltheret al., 2002), we optto work at thecountry level.This however, requiresa strong investment in data, both in terms of data from earth observa-

tion systems (e.g. current land use patterns) as well as from simulationmodel architectures (e.g. projection of species diversity for 2050). Inthis context,we optto work with all theEuropean countries that report

(projected) values for biological species under the four IPCC storylinesunder consideration. To account for regional climate differences/commonalities, where similar climatic patterns and taxa might beidentied, we propose to map the EU-17 in terms of three geo-

climatic clusters, including the Mediterranean Europe (Greece, Italy,Portugal, Spain), the Central North Europe (Austria, Belgium, France,Germany, Ireland, Luxembourg, Netherlands, Switzerland, United

Kingdom), and the Scandinavian Europe (Denmark, Finland, Norway,

Sweden). The data used are independently published by a number of

IPCC data distribution centers across the world for 2050, downscaledat country level. The demographic and economic trends represented

by the future per capita GDP, population density are projected and dis-tributed by the Center for International Earth Science InformationNetwork (CIESIN) (2002)at Columbia University. The annual mean

temperature are projected by the Tyndall Centre in the UK (www.tyndall.ac.uk), who combines the use of Global Circulation Models/SRES (including CGCM2, CSIRO2, HadCM3 and PCM) to estimate thepossible increase of temperature in degrees Celsius for each country

under different IPCC scenarios. The biophysical changes of biodiversitycomprise the quantitative changes measured in terms of changes inthe area of forest habitat,and thequalitative change indicated by changesin the number of terrestrial species (including plant, tree, bird and

herptile). The future trends of these changes under IPCC scenarios are

projected in the frame of the Advanced Terrestrial Ecosystem Analysisand Modeling (ATEAM) project (Schrter et al., 2004). In particular,species richness under current and future conditions are projected

taking into account total 383 bird species, 108 reptile and amphibianspecies, 1350 plant species and 125 tree species appeared in the EU.To keep the consistency across a large range of data sources, we deriveall data from projections that representa combinationwith the HadCM32

model, which directly relates socioeconomic changes to climatic changesthrough greenhouse gas concentration, and relates land use changes

through climatic and socioeconomic drivers such as the demand forfood (Schrter et al., 2004).

Table 1

IPCC scenarios of future global biodiversity patterns.

Source: adapted fromMartens et al. (2003)and ATEAM model assumption.

Storyline Key assumptions Summary of major effects of the scenario Impacts on biodiversity

A1 (offers an

unfavorable

perspective for

biodiversity)

Slight population increase till 2050, then decrease;

very rapid economic growth; high level of income;

a global mean increase in temperature of at least

4.4 C (std 0.9) toward 2080; forest area is stable

due to increasing timber demand and recreational

land use pressure; and signicant conversion ofagricultural land from food to bioenergy

production.

Many pristine natural areas are converted intoman-made areas; costs of preserving natural areas

areveryhighdue toincreasein land prices;reduced

ecosystem quality due to increased population

densities, increased tourism, etc.; and higher

concentrations of GHG due to a substantialincrease in energy use and land conversion

Patterns of bird and herptile species richness willnot changedramatically; and plant and tree species

richness will decrease in the southern part of

Europe but increase in central and Scandinavian

Europe.

A2 (offers a

heterogeneous

world)

Continually growing humanpopulation(15 billion

by 2100); slow economic growth; economic

development is primarily oriented and uneven;

regional self-reliance in terms of resources; weakglobal environmental concern; Total consumption

of natural resources is considerable; a global mean

increase in temperature of at least 3.5 C (std 0.7)toward 2080; slightly decrease of forest area; and

signicant conversion of agricultural land from

food to bioenergy production and human

settlement.

Sharply increasing demand for foods, water, energy

and land will result in a signicant loss of natural

ecosystems and species; regional competition for

good-quality natural resources will negatively

affect the economic conditions in these countries

andreduceattentionfor thepreservation of natural

resources; and an increasing number of people will

compete for a declining number of naturalresourcesat thecostof quantityand quality ofthose

remaining resources.

Patterns of bird and herptile species richness will

not changedramatically; and plant and tree species

richness will decrease in the southern part of

Europe but increase in central and Scandinavian

Europe.

B1 (offers a more

favorable

perspective for

biodiversity)

A sharp reduction in arable farming and cattle

breeding acreage due to a strong increase in

productivity; the estimated temperature increase

is about 2.7 C (std 0.6) toward 2080; pressurefrom population growth is considerably lower;

forest area increases; and signicant conversion

of agricultural land from food to bioenergy

production and human settlement.

A lot is done to improve ecological capital and

therefore reduce threatening factors and prospects

for biodiversity; cropland production is

concentrated in optimal locations; and grassland isprotected by policy.

Natural ecosystems are less affected both in

quantity and quality

B2 (very locally

concentrated

social, economic

and ecologicalproblems)

The pressure on natural system is greatly reduced

due to high average education levels and high

degree of organization within communities;

stable population; relatively slow economicdevelopment; regionally and locally oriented

environmental policies are successful; a global

mean increase in temperature of at least 2.0 C

(std 0.7) toward 2080; and land-use changes

from food to bioenergy production or forestry.

The general picture of biodiversity in the future

largely depends on the introduction of socio-

economic policies that support local and regional

initiatives to achieve structural solutions.

Hard to estimate global biodiversity trend due to

the high heterogeneity

2 HadCM3, Hadley Centre Couplet Model Version 3 is a coupled atmosphere ocean

GCM developed at the Hadley Centre and described by Gordon et al. (2000).

62 H. Ding, P.A.L.D. Nunes / Ecological Economics 97 (2014) 6073
http://www.tyndall.ac.uk/http://www.tyndall.ac.uk/http://-/?-http://-/?-http://www.tyndall.ac.uk/http://www.tyndall.ac.uk/


4/14

Our knowledge about to what extent biodiversity can respond toclimate change is limited and the quantication of associated economic

gains or losses to human welfare cannot be straightforward but throughvaluing biophysical changes of ecosystem services under future climateconditions.In this study, values of EGS provided by the European forestsare taken fromDing et al. (2010), who provided detailed projections of

ecosystem values following four future IPCC storylines vis--vis to thebaseline year of 2000. The valuation exercises were conducted sepa-rately for three types of forest ecosystem servicesdened in MillenniumEcosystem Assessment, i.e. provisioning, regulating and cultural ser-

vices (MEA, 2005). More specically, forest provisioning servicescontained the benets derived from the production of timber and otherwood forest products, regulating services provided non-monetarybenets from CO2sequestration in the forest, and cultural servicesprovided humans with direct incomes from the related tourism

industries and non-monetary benets from the enjoyment of existingforests. All values were rst projected to 2050 and then adjusted to2005 US$. All original data used in the present study are presented intheAnnex see alsoDing et al. (2010)andSchrter et al. (2004)for

more details.

3. Biodiversity Metrics

3.1. The Rationale for the Use of a Composite Indicator

As previously mentioned, there is a growing interest among scien-

tists to develop a single, simple or composite biodiversity measurethat is able to encompass essential biological information, incorporatesocioeconomic impacts, and guide policy interventions towards more

effective biodiversity management. A simple format may allow it to bemore inuential in the public and business decision-making and moreeffective in communications, justlike the use of Gross Domestic Produc-tion (GDP) in economic analysis and the Dow Jones indicator in stock

market (Balmford et al., 2005; Halpern et al., 2012; Mace and Baillie,2007). To this extent, the existing biodiversity data will be useful fordeveloping quantitative scenarios of the future trajectories of biodiver-

sity (Pereira et al., 2010). Moreover, from a methodological perspective,there is a general need of creating a workable calculusof biodiversity

that allows not just global summation, but also estimation of the morelocalized marginal gains and losses from global changes induced by

socioeconomic development and land use changes in different places(Faith, 2005). This is particularly the case when assessing climate-change-induced biodiversity changes, because individual indicatorsalone, such as species richness or abundance of a certain species, do

not provide sufcient information to enable a better understanding ofthe impacts of increased temperature or precipitation rate on theecosystem functioning and overall performance. Therefore, this sectionwill focus on the development of a methodological approach to

construct such a composite biodiversity indicator, which will serve asa biodiversity variable in the econometric model later on.

By far, a number of composite indicators have been developed in theliterature. For example,the NaturalCapital Index (NCI) is constructed as

a weighted sum of theproductof the extentof each ecosystem (relative

to a baseline) with the condition of the ecosystem, where the conditionis measured as the population size of a group of indicator species rela-

tive to a baseline (ten Brink, 2000). A similar indicator is the BiodiversityIntactness Index recently developed byScholes and Biggs (2005), whoalso takes into account different ecosystems being weighted by theirspecies richness and population size being estimated for each land-use

class in each ecosystem. Apparently, the latter requires more detailedinformation of species under each type of land-use. Our biodiversitydata are limited, as we have only the country data on species richnessprojected under future climate scenarios. Thus we propose to adopt

the NCI approach to construct an NCI-like composite biodiversity indi-cator for analyzing climate change impacts on the biodiversity andecosystem services in Europe. This way, the future state of ecosystemin both quality and quantity terms under different climate change sce-

narios may be assessed with respect to a selected baseline.NCI framework considers biodiversity as a natural resource contain-

ing all species with their abundance, distribution, and naturaluctua-tions. Human direct and indirect interference may affect ecosystem

size (through land conversion) and exert pressures on ecosystemquality (such as over-exploitation and fragmentations). As a result,both decreased ecosystem quantity and quality will lead to the loss ofbiodiversity. In this context, the development of NCI framework aims

at providing a quantitative and meaningful picture of the state of andtrends in biodiversity to support policymakers in a similar way as

GDP, employment and Price Index do in economics. Moreover, thestructure of NCI also allows the analysis of socio-economic scenarios

on their effect on biodiversity. In technical terms, NCI is the productof changes in the size of ecosystems (ecosystem quantity) and thechanges inabundance of a core setof species (ecosystem quality) with-in the remaining ecosystem, where both quality and quantity areexpressed relative to an optimal or intact baseline (ten Brink, 2000).

Equation of the NCI:

NCI = ecosystem quality (% of species abundance)

ecosystem quantity (% area of the country)

(Source:ten Brink, 2000)

Originally, the NCI chooses the use of less modied pre-industrial

baselineso that major anthropogenic impacts on the changes of biodi-versity quality (e.g. loss of species abundance) and quantity (e.g. loss ofnatural habitat) can be observed and compared. The NCI score rangesfrom 0 to 100% representing an entire deteriorated (0%) and intact eco-

system (100%), respectively. It summarizes the extent to which a land-scape has preserved its original (baseline) natural capital and enablesthe analysis of biodiversity effects in different socio-economic scenarios.Obviously, one of the advantages of the NCI is that it allows us to aggre-

gate many biodiversity parameters to a few or perhaps a single, more orless representative biodiversity index for the entire ecosystem see

Fig. 1.

Fig. 1.Trends of ecosystem quality and quantity using NCI.

Source:Ten Brink (2000)pp. 2.

63H. Ding, P.A.L.D. Nunes / Ecological Economics 97 (2014) 6073


5/14

3.2. Constructing a Composite Forest Biodiversity Indicator

In the present study, an NCI-like composite biodiversityindicator,i.e. Composite Forest Biodiversity Indicator (CFBI) is constructedfollowing a three-step approach, to measure the overall change of forest

biodiversity in Europe between 2000 and 2050 under different IPCCscenarios. The rst step is to compute the average changes offorestecosystem quality, which contains the changes of total numbers of fourcore species, i.e. tree, plant, bird and herptile, for each of the EU-17

countries, under different climate change scenarios. Note that for eachcountry, the change of individual species under each scenario isexpressed as the ratio between species richness of the species in 2050and that of the baseline. Furthermore, we aggregated individual

percentage changes of species richness for tree, plant, bird and herptile

to get a country average score, which depicts the changes of country'sforest ecosystem quality under each IPCC scenario with respect to thebaseline. The second step is to calculate the forest ecosystem quantity,

which is expressed as the percentage of a country's forest coverage toits total land area under different IPCC scenarios. The third step is toproduce theCFBI, which is the product of percentage changes offorestecosystem quality(calculated instep 1) and the percentage changes of

forest ecosystem quantity (calculated in step 2). Therefore, we mayexpect that the computedCFBI scorecan also reect the direct land-

use change impacts on biodiversity. In particular, the expansion offorest area in many parts of Europe can have a positive impact on the

CFBI score.Fig. 2presents a ow chart to visualize how our NCI-likecomposite biodiversity indicator is constructed.

It is important to note that our data cover only the post-industrialera, during which many stringent environmental policies have been

successfully implemented among the 17 most developed Europeaneconomies, in terms of pollution reduction, sustainable resource man-agement and greening economy. As a result, many countries are found

to have a stable increase in either forest area or increased species rich-ness or both, and thus the original NCI score range cannot apply.Instead, we set up two intervals to indicate the future state of the forestbiodiversity:

(1) 0 bCFBI b100% indicates a degradation of forest biodiversity inquantity and/or quality terms in 2050, with respect to the base-line year 2000. This implies that the state of biodiversity in a

country is deteriorated because of the decreased forest coverageas a result of land conversion to agricultural production orhuman settlement, and/or because of the decreased species rich-ness in the country.

(2) CFBI N100% indicates an improved state of forest biodiversity in2050 with respect to the baseline year 2000. However, such

Country

average

biodiversity

quality

IPCC scenarios in

2050

Forest

biological

diversity

Species richness in

2000

Plant

Tree

Bird

Herptile

A1

A2

B1

B2

Baseline (2000) Target (2050)

SBIA1

SBIA2

SBIB2

SBIB1

Country

ecosystem

quantity

ForestA1

ForestA2

ForestB2

ForestB1

x

x

x

x

Constructed NCI-

like indicator

CFBIA1

CFBIA2

CFBIB2

CFBIB1

Fig. 2. Constructinga NCI-like indicator to estimate thetrendof biodiversityin futureIPCCscenarios(note: SBIrefers tothe aggregated average score ofspecies richness of plant,tree,bird

and herptile species).

Fig. 3.Computed CFBI score for the EU-17 under four IPCC storylines.

http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80http://localhost/var/www/apps/conversion/tmp/scratch_4/image%20of%20Fig.%E0%B2%80


6/14

improvement may not be necessarily caused by the increasedspecies richness as a result of some effective conservation mea-sures. It may be due to the increased ecosystem coverage/areaas an outcome of national conservation policies. Moreover, it is

also important to note that aCFBIscore greater than 100% doesnot necessarily mean that the individual species are not underthreats, rather it indicates an overall improvement of the biodi-

versity status.

Thus, to better interpret the CFBIscore, we need to look closely at thenational/regional forest management policies and their effectiveness.

3.3. Mapping Composite Forest Biodiversity Indicator Across Different IPCC

Scenarios

The calculatedCFBIscores for the EU-17 under four different IPCC

scenarios are presented inFig. 3. Overall, the CFBIscore decreaseswhen moving towards the economic oriented development paths (asrepresented by the A1 and A2 scenarios), as increased pressures fromthe fast growing economy and population, rising global temperature

and unbalanced land-use conversion will most likely worsen futurestate of forest biodiversity across Europe. Among all others, the warmer

region, i.e. Mediterranean Europe suffers the greatest loss of biodiversityquantity and quality in both scenarios compared to the other colder

regions.On the contrary, the CFBIscore increases following the environmen-

tal oriented development paths (as represented by B1 andB2 scenarios)in most of the European countries, except Portugal, Spain, Finland,

Norwayand Sweden. This implies that the adoption of sustainable forestmanagement practices in Europe is successful in general. However,given the relatively high level of forest status in the Scandinavian coun-tries in the baseline year, we will not foresee any signicant increase in

forest ecosystem quantity over the next 50 years, independentfrom thefuture standpoints. Finally, although the Mediterranean forests appearto be the most vulnerable to global change (Lindner et al., 2010; Salaet al., 2000), forinstance,in Portugal and Spainthree outof four selected

species are projected to decrease by 2050 (seeTable A6inAnnex), wecan still observe a general improvement of the future state of forestbiodiversity in Italy and Greece owing mainly to a mixed effect of

increasing in certain species richness and slowing down economicgrowth and land conversion in the region.

4. The Econometric Model

4.1. European-aggregated Model Specication

This section focuses on econometric model specication so as tocapture the marginal impacts of changes in biodiversity on the value

of EGS due to climate change. The CFBI will be a constituent of ourmodel. In particular, we would like to test:

(1) Whether climate change, here expressed as an increase in tempera-

ture, will alter the pattern of biodiversity distribution and species

richness presented in a geo-climatic zone, which is measured

by the CFBI. In particular, we want to test (i) whether increases in

temperature will have effects over the CFBI and across geo-climatic

clusters.(2) Whether the climate-change-induced CFBI changes will further

affect the ecosystem's capacity of providing goods and services

and their respective values. Similarly, we want to test whether

climate-change-induced CFBI changes will have effects over the

economic value of EGS. In particular, we want to assess (ii)

whether this effect will change across the geo-climatic regions,

and (iii) whether it will differ depending on the type of EGS

under consideration.

In this context, we propose to model the relationship betweenbiodiversity, ecosystem and human welfare in a simultaneous equationsystem. More specically, we choose to run a three-stage-least-squares

(3SLS) regression instead of a two-stage-least-squares (2SLS) regres-sion. Theoretically, 3SLS performs in a similar manner as 2SLS in termsof regressing endogenous regressors against all predetermined vari-ables of the system to get theoretical values (Verbeek, 2000), but the

main difference is that 2SLS focuses on individual equations, whichcauses inefciency. 3SLS is preferred in this study because it considersnot only the simultaneous correlations between various equations'error terms, but also the inter-temporal (and not simultaneous) corre-

lations between error terms, which has an obvious advantage inassessing climate change impacts at different points in time. In the pres-entstudy, the simultaneous structural system contains the followingthree equations:

ln EVi 10i 11iln fa 12i ln t 13iCFBI 1i 1

ln fa 20 21ln GDP 22ln t 23ln popdens 2 2

CFBI 30 31t 32t2

33nts34nbs35nps36nhs

37ln popdens 38ln GDP 33

where

EV the estimated economic value of theith type of ecosystemservice by 2050 under future IPCC scenarios (in million $)

i 1, 2 and 3 types of ecosystem services under consideration,

i.e. provisioning services (PS), cultural services (CS), andregulating services (RS)

fa projected forest area (million ha) in 2050 under future IPCCscenarios

t increased Celsius degrees of local temperature by 2050 underfuture IPCC scenarios

CFBI Composite Forest Biodiversity Indicator is measured as thechanges (%) of average biodiversity status between 2000

and 2050

GDP projected gross domesticproduction (billion $) in 2050 underIPCC scenarios

Pop_dens projected population density (heads/ha) in 2050 under IPCC

scenarios

nts number of tree speciesprojectedin 2050 under IPCC scenarios

nbs number of bird speciesprojectedin 2050 under IPCC scenarios

nps number of plant species projected in 2050 under IPCC

scenarios

nhs number of herptile species projected in 2050 under IPCC

scenarios.

Table 2

Descriptive statistics (summary).

Variables Obs Mean Std.

dev.

Min Max

Forest area (fa) 68 7.02 7.36 0.07 25.88

Population density (pop_dens) 68 1.24 1.02 0.08 3.33

GDP 68 1110.28 1310.00 22.38 5569.02

Number of tree species (nts) 68 38.42 13.51 10.96 70.96

Number of bird species (nbs) 68 130.26 13.58 106.47 154.31

Number of plant species (nps) 68 259.64 36.52 199.61 361.78Number of herptile species (nhs) 68 20.00 11.04 1.72 39.39

The composite forest biodiversity

indicator (CFBI)

68 1.08 0.34 0.47 2

Temperature (t) 68 3.69 1.22 1.5 6.9Economic value of provisioning

services (EVPS)

68 4776.07 5214.79 100.95 17,600

Economic value of cultural services

(EVCS)

68 454.07 568.80 3.13 2615.14

Economic value of regulating services

(EVRS)

68 2041.77 2023.33 71.39 7465.75

http://-/?-http://-/?-


7/14

We assume that EV,CFBIand fa are endogenous variables in the

system and 1, 2 and 3 are the stochastic disturbance terms thatcapture all unobservable factors, which may inuence the dependentvariables. In the rst two equations, all variables, except CFBIare intheir log-transformations indicating that the estimated beta coefcients

measure theelasticity of dependentvariables with respect to thechangesin a setof explanatoryvariables. As forthe warming impact onbiodiver-sity, it is estimated using Eq. (3) by regressingCFBIon temperaturevariables (t and t2), along with other biological and socio-economic

variables that may explain the trends of biodiversity changes in thefuture scenarios. In particular, the temperature variablet will capturethe marginal impact of climate change on biodiversity with increment of

1 C in the temperature and the squared t is introduced to capture the

rate of this change. Finally, the impact of GDP growth in the EU-17 onbiodiversity and forest ecosystems is mixed. On the one hand, it maypositively affect biodiversity if the future GDP growth goes hand-in-hand withenhanced publicawareness of environmentaland biodiversi-

ty protection, as demonstrated by the Environmental Kuznets Curve(EKC) that in the early stages of economic growth degradation andpollution increase, but beyond a turning point, the trend reverses, sothat at high-income levels economic growth leads to environmental

improvement (De Bruyn, 2000). On the other hand, this impact maybe negative, if the future GDP growth is associated with overexploita-tion of natural capital, which can directly result in the reduction of theecosystem quantity indicator (i.e. forest land cover) and/or the degra-

dation of the ecosystem quality indicator (i.e. species richness of corespecies). Ex ante, the nal impact is therefore ambiguous and will be

depend on the result of the empirical exercise.

InTable 2, we summarize the descriptive statistics of all the vari-

ables. For each variable, we have four observations under four IPCCstorylines for total 17 countries under consideration, which gives riseto total 68 observations.

Next, we proceed withrunninga 3SLSregression in a global condition,where all data are pooled together without considering the regionalheterogeneity of climate change impacts. This allows us to estimatesimultaneously (1) the determinants of economic value of ecosystem

services; (2) the determinants of land-use changes (i.e. the changes offorest cover); and (3) the determinants of changes in biodiversity.

Eq. (1) attempts to explainthe economic value of ecosystem servicesas a function of forest area,average annual temperature and the state of

forest biodiversity. We simultaneously test the hypotheses that the

enlarged forest area and improved state of biodiversity will positivelyaffect the ecosystem values, whereas the rising temperature may havea negative impact. Since the ecosystem values vary greatly depending

on the types of ecosystem services under consideration, we shall treatseparately the three types of values to reduce bias.

Eq.(2) attempts to explain that the change of forest coverage isdetermined by three variables: the wealth of the nation (expressed as

GDP), the population density and the average annual temperature, butin opposite directions. We expect that GDP growth in the EU-17 isbeyond the EKC turning point assumption and will positively affectforest cover, as in these wealthy states increasing demand for forest

EGS, including timber product consumption and recreational use offorests (especially natural forests), which in turn will direct the forestmanagement practices towards more sustainable-sound use and man-

agement of forest resources. On the contrary, the mounting population

Table 3

Aggregated model estimation results.

Provisioning services Cultural services Regulating services

Eq. R-sq chi2 P-value Eq. R-sq chi2 P-value Eq. R-sq chi2 P-value

(1) 0.401 54.25 0.000 (1) 0.931 548.13 0.000 (1) 0.839 198.57 0.000

(2) 0.534 78.04 0.000 (2) 0.537 78.80 0.000 (2) 0.536 79.36 0.000

(3) 0.615 141.77 0.000 (3) 0.624 138.74 0.000 (3) 0.636 135.69 0.000

Eq.(1) Eq.(1) Eq.(1)

Dep. var.: lnEVi Dep. var.: lnEVi Dep. var.: lnEVi

Var. Coef. z P N |z| Var. Coef. z P N |z| Var. Coef. z P N |z|

lnfa 0.671 5.68 0.000 lnfa 1.060 22.19 0.000 lnfa 0.740 12.27 0.000

lnt 1.032 2.04 0.041 lnt 0.664 3.24 0.001 lnt 0.670 2.59 0.010

cfbi 2.299 3.94 0.000 cfbi 0.895 3.73 0.000 cfbi 1.202 3.97 0.000

Eq.(2) Eq.(2) Eq.(2)

Dep. var.: lnfa Dep. var.: lnfa Dep. var.: lnfa


lnGDP 0.850 8.00 0.000 lnGDP 0.837 7.85 0.000 lnGDP 0.836 7.84 0.000

lnt 0.854 2.16 0.030 lnt 0.819 2.07 0.038 lnt 0.813 2.06 0.040

lnpop_dens 0.453 3.65 0.000 lnpop_dens 0.539 4.27 0.000 lnpop_dens 0.555 4.40 0.000

Eq.(3) Eq.(3) Eq.(3)

Dep. var.:CFBI Dep. var.:CFBI Dep. var.:CFBI


t 0.492 4.48 0.000 t 0.519 4.51 0.000 t 0.494 4.28 0.000

t2 0.054 4.01 0.000 t2 0.058 4.12 0.000 t2 0.055 3.90 0.000

nts 0.016 5.03 0.000 nts 0.017 5.33 0.000 nts 0.020 6.20 0.000

nbs 0.004 1.72 0.085 nbs 0.001 0.45 0.653 nbs 0.000 0.02 0.986

nps 0.001 0.91 0.363 nps 0.001 0.60 0.548 nps 0.001 1.07 0.286

nhs 0.001 0.72 0.474 nhs 0.000 0.04 0.972 nhs 0.005 2.11 0.035

lngdp 0.022 1.01 0.311 lngdp 0.024 1.12 0.264 lngdp 0.032 1.46 0.145lnpop_dens 0.046 1.85 0.064 lnpop_dens 0.022 0.86 0.391 lnpop_dens 0.014 0.55 0.585

Nr. of observations: 68.

Endogenous variables: lnEVi, lnfaandCFBI.

Exogenous variables: lnt, lngdp, lnpop_dens,t,t2,nts,nbs,npsandnhs.



8/14

in all future IPCC scenarios is assumed to increase the pressure onnatural forest land, and lead to more severe competition between natu-ral forests occupation and other land uses, such as agricultural lands orhuman settlement. Finally, we assume that temperature may playa role

in affecting the forest natural regeneration process, but the direction ofits impact on forest coverage is ambiguous.

Eq.(3)tests statistically whether the RHS variables, such as rising

temperature, changes of species richness in terms of a setof keyspecies,

and changes of socio-economic and demographic conditions underdifferent climate change scenarios can inuence the dependent variable

CFBIin different future states. Especially, we are interested in testing

whether warmer conditions will negatively affect forest biodiversity,which is integral part of the EGS and the value it provides. Moreover,high population density is expected to impose high pressure on biodi-versitythrough the intensive land conversion from forest habitats to ag-

ricultural lands or human settlements. Furthermore, we would like toempirically test how GDP growth can directly affect biodiversity.

4.2. European-regional Model Specication

In addition to the global effecttested above, this section will focuson testing the presence of heterogeneous effectsof regional climate-change-inducedCFBIimpact on EGS. To do this, the model specication

is modied in two steps to count for the three geo-climatic zones,i.e. Mediterranean (M),Central-North (C) and Scandinavian (S) Europe,respectively. First,we introduce a cross-effect betweenCFBIand regionaltemperature variables to generate a matrix of CFBI_Tregion, whichcontains three region specic CFBI variables, namely cfbi_ts, cfbi_tmandcfbi_tcfor the S, M, and C regions, respectively. This gives rise toEq.(4), in whichCFBIis replaced byCFBI_Tregionto capture the indirectimpacts of climate change on the value of ecosystem services throughassociated biodiversity effect in the regional forests (i.e. the climate-

change-induced biodiversity effect, CCIBE). Second, in Eq. (6), theregional warming effects on biodiversity are captured by introducinga matrix of regional temperature variable, tregion, which consists ofthree temperature variables ts, tm and tcto represent changingtemper-atures in the S, M, and C regions, respectively. As a result, we can obtain

a modied structural simultaneous system below:

ln EVi 10i 11i ln fa 12i ln t 13iCFBITregion 1i 4

ln fa 20 21ln GDP 22ln t 23ln popdens 2 5

CFBI 30 31tregion 32t2

33nts 34nbs35nps36nhs37ln popdens 38ln GDP 3:

6

The regional effects can be estimated by repeating the 3SLS regres-sion in this modied structural system.At this stage,we arein conditionsto proceed with the empirical estimation of the two simultaneous equa-tion systems, andto estimate the respective beta coefcients.The results

are presented and discussed in the following section.

5. Empirical Results

5.1. European-aggregated Model Estimates

Table 3below reports the 3SLS results of the European-aggregatedmodel Eqs.(1)(3). The goodness of the linear approximation in the

structural simultaneous system is assessed based on the coefcients ofdetermination (R2). For most of the simultaneous equations, acrossthe three different types of forest ecosystem services including provi-sioning, cultural andregulating services, the R2 statistic is larger than 0.5

(with P N0.0000) and thus conrm the overall goodness oft of theperformed model specications. Moreover, most of the estimated beta

coefcients carry the expected sign.

Firstly, in Eq. (1), it shows that the value of forest ecosystem servicesis statistically signicantly related to the forest size. In other words, anadditional hectare of forest will be associated with an increase in theeconomic value across all the forest ecosystem services under consider-

ation. Bearing in mindthe estimation results, expressedin terms of elas-ticity, vary from 0.67 for the provisioning services, to 0.74 for theregulating services, and to 1.06, for the cultural services. This means

that an increase in forest area is always associated with an increase in

the economic value of the forest EGS. In particular, 10% increase of theforest size is associated with an increase of 10.6%, 6.7%, and 7.4%increase in the economic value of cultural services, provisioning ser-

vices, and regulating services, respectively. From a strict cost benetanalysis perspective, the cultural value of forest is ranked the mostvaluable forest EGS ceteris paribus, and shall be given careful consider-ation while making the forest land use planning.

Moreover, the estimated coefcients of CFBI reveal to be statisticallysignicant for all ecosystem services, implying that biodiversity hasmarginal impact on mapping the economic value of forest EGS. Thedirection of the impact is, however, mixed. On the one hand, the mar-

ginal impact ofCFBIis found positive for both regulating and provision-ing services, which is estimated between 1.202 and 2.299, respectively.On the other hand, the marginal impact ofCFBIis found negative forculture services, which is estimated to be 0.895. This means that, on

average, a marginal increase of one-unit ofCFBI is associated with229.9% ( e31

100percentage) change in the economic value of

forest provisioning services and 120.2% change in the economic valueof regulating services, ceteris paribus, but with 89.5% decrease in theeconomic value of cultural services. Since this is an aggregated global

effect, we suspect this is due to the fact that pooling data across geo-climatic regions may lead to the neglected spatial heterogeneity of theimpacts. We will further investigate on this point using a modied

regional model specication.Finally, as expected, changes in the temperature are statistically

signicant, at the margin, in explaining the economic value of thethree forest ecosystem services under consideration. According to the

elasticity estimates, we can see that an increase of 10% of the averagetemperature may result in 10.32% increase of the value of forest provi-

sioning services and 6.7% increase of the value of forest regulating ser-vices. These results reconrmthe recentnding in scientic research

thatthe changing climate can increase both forest productivity andcarbon stock in the boreal forests in the Scandinavian Europe for atleast the short run(Garcia-Gonzalo et al., 2007). However, our estima-

tion results show that temperature impacts are negatively associatedwith the economic value of forest cultural services. According to theparameter estimate, a 10% increase in the temperature causes, on aver-age, a decrease of 6.64% in the economic value of cultural services pro-

vided by the European forests.Secondly, Eq.(2)shows that all selected explanatory variables are

statistically signicantly related to changing forest coverage. The esti-mated coefcients of each variable are found similar across all ecosys-

tem services, suggesting the robustness of our results. In particular,

our estimation results show that GDP growth is positively correlatedwith the extension of forest areas. As argued previously, this result isconsistent with the phenomena explained by Environmental Kuznets

Curve. This indicates that economic growth in the EU-17 will furtherstimulate the social desire for improved environment and extendedforest coverage in Europe. Moreover, the rising temperature is foundpositively correlated with the forest area, which might be explained

by the fact that warmer climate will result in increased timber produc-tivity due to prolonged growing season for boreal forests in Scandina-vian Europe (Garcia-Gonzalo et al., 2007), which in turn may stimulatethe decision of extending forested area for timber production. Next,

the population density is found to have a negative impact on the forestarea, independent from the type of ecosystem services under consider-ation. According to the estimation results,an increase of 10% in the pop-

ulation density causes, on average, a decrease of 4.53% to 5.55% in the



9/14


10/14

is captured by the beta coefcient estimated for the temperature vari-able. Second, we have the indirect regional climate-change-induced-biodiversity effect (CCIBE), which is modeled as a cross-effect between

the regional temperature and the biodiversity indicator,CFBI. All in all,estimation results show that the estimated directions of direct climatechange impacts on the economic value of provisioning and culturalservices remain the same as in the European-aggregated model, where-

as the regional CCIBE show a great heterogeneity across geo-climaticregions and forest EGS.

First, for provisioning services, the model estimates show that thedirect climate change impact is now 0.193, still positive but smaller

than in the European-aggregated model, ceteris paribus. Furthermore,the CCIBE is found statistically signicant only in the MediterraneanEurope, with an estimated marginal impact of 0.493. This meansthat an increment of 1 C in the Mediterranean temperature can cause

at the margin, a reduction of 57% in the CFBI score (estimated inEq.(6)), which together with the direct impact of rising temperature(estimated in Eq.(1)), will be responsible for at least 49% loss in everyone-dollar value derived from the provisioning services. In other

words, the cross-effect of rising temperature and biodiversity loss inthe Mediterranean forests goes hand-in-hand, causing a net negativeimpact on the total economic value generated from this EGS.

Second, for the cultural services, the model estimates show that the

direct climate change impact is 0.290, which is still negative, but at asmaller magnitude, when comparedto the European-aggregated model

specication, ceteris paribus. Furthermore, the CCIBE is estimated to be0.059, negative and statistically signicant for the Scandinavian

Europe. This means that CCIBE will accelerate the loss of economicvalue of cultural services provided by the Scandinavian forests, andthis effect is estimated to be nearly 6% of every additional one-dollar

reduction of the value of cultural services provided. On the contrary,CCIBE will increase, at the margin, the economic value of culturalservices provided by the Mediterranean forests, whose effect amountsto nearly 28% of every additional one-dollar increase of the value of

cultural services provided.Finally, for the regulating services, the model estimates show that

the direct climatechange impact is not statisticallysignicant. However,the CCIBE is revealed to be positive and statistically signicant for both

forests located in Mediterranean and Central Europe. According to

estimation results, these impacts are estimated to be 0.251 and 0.259,respectively. This means that for the forest in the Mediterranean andCentral Europe, very one-unit biodiversity change caused by the incre-

ment of the regional temperature will correspond to, at the margin,25% of every additional one-dollar increase of the value of these forestregulating services.

5.3. Comparative Analysis

By comparing the results obtained from European-regional specicclimate model with the European-aggregated model, we are able to

highlight the following two key ndings:

(1) Climate-change-induced biodiversity effect, which is cap-

tured by the cross-effect between regional temperature and

the composite biodiversity indicator, has clear heterogenic char-acteristics. In particular, it is characterized by the reduction ofthe estimated magnitude of average effect for the EU-17, and

thus can hamper the direct temperature effect at regional level.Aswe can see from Table 5, the average direct temperature effectis reduced from 1.032 (aggregated model) to 0.193 (regionalmodel) and from 0.664 (aggregated model) to 0.290

(regional model) respectively for the economic value of theEuropean forest's provisioning and cultural services. In addition,as far as the regulating service is concerned,on average, the esti-mated direct temperature impact on the economic value of these

forest ecosystem services is reduced from 0.670 (aggregatedmodel) to an impact that is not statistically different from zero(regional model).

(2) The impacts of CCIBE on the economic value of EGS are mixed.

In some cases, this impact amplies the (reduction of the posi-tive) direct temperature impact. This is the case for the cultural

services provided by the Scandinavian forests, where the CCIBEimposes a negative impact on the economic value of the culturalservices and thus play as an additionalKeynesian multiplicative

effectof the aggregated climate change impact on the EGS inthis geo-climatic region of Europe (addingup to the also negativedirect impact of temperature). A similar situation is observed forthe provisioning services provided by the Mediterranean forest,

where the CCIBE also negatively affect the economic value of

these services. Furthermore, the estimated magnitude of thisregional marginal effect suggests that the aggregated climate

change impact for this EGS in this geo-climatic region ofEurope is most likely to be negative (reversing the marginaland positive direct impact of temperature).

However, in other cases, we observe the opposite cross-effectsof biodiversity and temperature change on the value of ecosystemservices. This is the case for the cultural services provided by the Medi-

terranean forests.Here, the CCIBE can leadto a marginal positive impacton the economic value of the services and therefore will play as

buffering impactto the negative direct impact of the temperature.Furthermore, the estimated magnitude of this regional marginal

impact suggests an offset effect of the marginal negative direct

impact of temperature. In other words, the aggregated climatechange impact for this EGS in this geo-climatic region of Europe ismost likely to be null. A similar situation is observed for the regulating

services provided by the Central and Mediterranean forests, where theCCIBE is responsible for a positive aggregate impact on the economicvalue of this service.

6. Policy Recommendations: the Role of Biodiversity as a

Nature-based Policy Option for Climate Change Mitigation

These estimation results conrm therole of biodiversity as a nature-based policy option for mitigating climate change impacts. Policies assuch can generate co-benets by enhancing ecosystems' capacity tomitigate climate change impacts, while conserving biodiversity and

sustaining the ows of EGS for human livelihoods. In particular, well-

Table 5

Climate change marginal impacts on the economic value of European forest's ecosystem services.

Provisioning Cultural Regulating

Aggregated model Regional mo del Aggre gated model Regional model Aggregate d model Regional model

Europea 1.032 0.193 0.664 0.290 0.670 n.s.s.

Scandinavianb n.s.s. 0.059 n.s.s.

Centralb n.s.s. n.s.s. 0.251

Mediterraneanb 0.493 0.279 0.259

a

Expressed in terms of elasticity.b Expressed in terms of perceptual change in the economic value of the forest ecosystem services under consideration.



11/14

dened natural resource management and biodiversity conservationpolicies may provide sustained ows of EGS for local livelihoods andcost-effective options for natural adaptation and mitigation of climatechange impacts. The policy implication of our model results is that,

when the marginal benets derived from biodiversity conservationefforts are large enough to compensate the marginal loss of ecosystemproductivity and values as a result of direct negative impacts of global

warming (i.e. the CCIBE), policy-making should be directed to favor

the actions/measures that improve the overall biodiversity condi-tions, for example by either increasing the species richness or enlarg-ing the total area of natural habitats. Thus the ecosystem functioning

can be strengthened and serve as a natural mitigation means tosequester CO2emissions, while providing shelter and other ecosys-tem services to safeguard human livelihoods. Estimation resultsshow, however, that the strength of biodiversity as a nature-based

policy solution to climate change mitigation will be depending onboth the nature of the EGS as well as the geo-climatic region underconsideration. Moreover, estimation results inform us, that the po-tential of the biodiversity as a nature-based policy option for climate

change mitigation will be of particular interest to themanagementofMediterranean forests.

It is important to highlight the impacts of data limitation on themodel results. As mentioned earlier, the use of time-span of 50 years

between 2000 and 2050 to describe the evolution of number of speciesunder climate change scenarios has resulted in an observed increase ofspecies richness as well as forest areas in many EU-17 countries. Thisgives rise to a very large range of CFBI scores (between 0% and 200%),

which increases the difculty to perceive the negative impacts of globalwarming on biodiversity. However, the robustness of the econometricmodel results can be improved by selecting the pre-industrial base-lineas the reference year may allow us to observe more sensitive

reaction of biodiversity (in terms of estimated CFBI scores) to thechanges of temperature over time. In particular, this effect may bemore signicant when conducting sensitivity analysis of the hetero-geneity of regional climate change impacts and help us to better

understand the cross-effects between biodiversity and temperatureas well as the pattern in which they affect the value of ecosystem

services.

7. Conclusions and Further Research

This paper attempted to model the relationships between climatechange, biodiversity and the value of ecosystem services with a specicemphasis on the climate change included biodiversity effects inEuropean forests. To our knowledge, this represented one of the rst

attempts in the literature to formally model and empirically test thestrength of biodiversity as a nature-based policy option for climatechange mitigation.

The research begun with the construction of a Composite Forest

Biodiversity Indicator (CFBI) that integrated quantitative and qualita-tive changes of biodiversity projected under different future climate

scenarios. The model results suggested that CFBI could serve its multipledesign purposes better than other individual biodiversity indicators for

two reasons. First, it was capable of aggregating the core biological in-formation to measure and predict the trends of biodiversity changesin response to both global warming and socio-economic changes overa period of time under different future climate change scenarios

and then link the biodiversity state to its capacity of providing EGS.Second, the computation of the CFBI was standard and simple,which led to a simple format of biodiversity indicator with the scalesof measurement that could be both easily understood by, and

effectively communicated with a variety of stakeholders, includingnature scientists, social scientists, economists, policymakers andthe broader audience.

We also made the best use of existing data released by a large num-

ber of IPCC data distribution centers, regarding the projected trends of

population and economic growth, future species richness and increasein local temperature under different future climate scenarios. Economicvalues of ecosystem services were derived from a most recent assess-

ment study on the climate change impacts on forest ecosystems inEurope (Ding et al., 2010). In this data setting, we used 3SLS regressionto simultaneously estimate (1) the determinants of economic value of

ecosystem services; (2) the determinants of land-use changes (i.e. thechanges of forest land cover); and (3) the determinants of changes

in biodiversity. The investigation was conducted

rst in a baselinemodel at an aggregated European level, where a global effect of

climate change was assessed, followed by a regional model, wherethe marginal magnitudes of CCIBEs were assessed at three specicgeo-climatic regions.

Despite the data limitation, our preliminary results from a 3SLS

regression were promising. First, European-aggregated model specica-tion results conrmedthat rising temperature negatively affectedbiodi-versity conditions at an accelerating rate across geo-climatic regions in

Europe by 2050. Second, we also found a strong relationship betweentemperature and the value of EGS, but the direction of this relationshipdepended on the type of EGS under consideration. For example, thisrelationship was estimated to bepositive for provisioning and regulating

services, but negatively related to cultural services. Third, the regional

model specication results suggested that the negative impacts ofclimate change on biodiversity (i.e. CCIBE) could go against the positive

direct climate change impact on forest growth and generate a net neg-ative impact on total value of EGS, such as for the provisioning servicesin the Mediterranean Europe. However, the model estimates also

showed that these impacts were region specic and shall be assessedaccordingly. In some cases, this marginal impact could amplify the(reduction of the positive) direct temperature impact, e.g. for thecultural services provided by the Scandinavian forests; whereas in

some other cases, we could observe an opposite cross-effect of biodiver-sity and temperature change on thevalue of ecosystemservices, such asfor the cultural services provided by the Mediterranean forests. In thelatter case, the climate-change-induced biodiversity effect was respon-

sible for a marginal positive impact on the economic value of the cul-tural services, through its buffering impacton the negative direct

impact of the temperature. To conclude, independent from the signand magnitude of the effects, these estimation results conrmed

the role of biodiversity as a nature-based policy solution for climatechange, shedding light on the policy actions that generate co-benetsby enhancing ecosystems' capacity to mitigate climate change impacts,

while conserving biodiversity and sustaining the ows of EGS forhuman livelihoods. Especially, nature-based mitigation policies couldbe morecost-effective andbetter at coping with the ethic and inequalityissues associated with distributional impacts of the policy actions, com-

pared to the pure technical solutions to improving energy efciency andreducing emissions.

Finally, notwithstanding the success in modeling the relationshipbetween biodiversity, ecosystem services and human wellbeing in the

context of climate change, we acknowledge that several aspects deserve

future investigation for the future development of the model. For in-stance, the degree of heterogeneity within the three geo-climaticzones is not tackled in the present model. A further extension of this

model may focus on investigating the marginal difference of climatechange impacts on forest EGS across countries within the same geo-climatic zone, and the results may be useful for guiding the design ofspecic national forest management strategies. In addition, a sensitivity

analysis of the model's outputs to the changes of the policy-mix under-pinningthe scenarios shall be conducted, so as to allow policymakers tocompare the marginal impacts of environmental policies implementedin different socio-economic contexts. Finally, future research may also

be extended to other countries outside Europe, preferably to areasin the globe, where present a wider biological diversity with respectto their geo-climatic conditions as well as the economic signicance

of forest EGS to their economies. We are convinced that these



12/14

roadmaps for potential future research will present promising,follow-up economic analysis, with which we can benchmark the resultspresented here.

Acknowledgment

This research received nancial support from the European Invest-ment Bank University Research Sponsorship Programme (EIBURS).

Particular thanks go to Rafat Alam, Jonah Busch and Peter Carter andparticipants of the 12th annual BIOECON conference held in Venice,Italy, fortheir comments on a previous version of the paper. The authorsare also grateful to Laura Onofri for the discussions on the econometricwork.

Annex

Table A1

Trends of European forest area projected under IPCC scenarios (estimates in 1000 ha).

Country 2005a 2050 A1FIb 2050 A2b,c 2050 B1b 2050 B2b

Greece 3752 2292 2360 3762 3598Italy 9979 8346 8253 11,677 11,893

Portugal 3783 2170 2174 3254 3283

Spain 17,915 12,052 11,969 17,389 17,633

Austria 3862 5298 5177 5199 5471

Belgium 667 526 545 698 842France 15,554 15,094 16,056 20,080 21,926

Germany 11,076 10,049 10,075 12,696 14,033

Ireland 669 442 379 638 656

Luxembourg 87 80 78 103 94

Netherlands 365 151 421 333 413

Switzerland 1221 1985 1913 2113 2121

UK 2845 1986 2145 2780 3476

Denmark 500 414 677 434 839

Finland 22,500 18,224 17,999 16,517 17,079

Iceland 46 30 29 28 28

Norway 9387 6478 6277 5141 5761

Sweden 27,528 22,704 22,198 25,884 22,704Source: a data from FAO; b projections by ATEAM andCLIBIO onthe basis of theIntegrated

Model to Assess the Global Environment (IMAGE), developed by Netherlands Environ-

mental Assessment Agency;c interpreted by the European Commission as the baseline

scenario, i.e. the scenario characterized by policy inaction.

NB: For the detailed data explanation, please refer toDing et al. (2010).

Table A2

Projected total value of wood forest products in EU-17 by 2050.

Country (Mill ion US$ 2005)

2005 2050 A1FI 2050 A2 2050 B1 2050 B2

Greece 141 101 104 166 158Italy 3225 1465 1447 1884 2082

Portugal 1859 1760 1844 2279 2301

Spain 3337 2212 2197 2870 3233Austria 5990 7510 7236 5186 6897

Belgium 4807 4832 3343 3513 4306

France 7204 4909 5281 5684 6211Germany 16,636 12,741 12,712 12,620 14,906

Ireland 506 299 250 304 384

Luxembourg 216 107 104 137 125

Netherlands 3693 2568 9289 5134 6375

Switzerland 2003 2120 2039 2095 1847

United Kingdom 2665 2997 2925 2543 3361

Denmark 465 439 1067 410 714

Finland 12,067 15,913 15,333 12,985 14,183

Norway 1863 2021 1625 1476 1708

Sweden 13,200 17,606 16,984 17,310 16,052

Note: value estimates are derived fromDing et al. (2010).

Table A3

Projected total value of forest carbon stocks in EU-17 by 2050.

Country (Million US$ 2005)

2005 2050 A1FI 2050 A2 2050 B1 2050 B2

Greece 9052 2695 2775 4424 4230

Italy 4768 2617 2628 3236 3075

Portugal 614 273 264 364 337

Spain 2911 1796 1784 2269 2218

Austria 3372 3690 3748 3985 3900Belgium 364 185 203 222 212

France 7020 6408 6750 7466 7097

Germany 6703 3972 4144 4969 4752

Ireland 198 140 136 169 174

Luxembourg 197 89 87 115 104

Netherlands 184 71 166 114 124

Switzerland 1035 1349 1357 1502 1428

United Kingdom 1232 668 796 913 924

Denmark 186 111 208 135 160

Finland 5487 2459 2429 2831 2539

Norway 1740 693 670 731 724

Sweden 7816 3879 4043 5746 4370

Note: value estimates are derived fromDing et al. (2010).

Table A4Projected total cultural value of forest in EU-17 by 2050.

Country (Million US$ 2005)

2005 2050 A1FI 2050 A2 2050 B1 2050 B2

Greece 390 239 247 566 490

Italy 1039 869 863 1756 1619

Portugal 394 226 227 489 447Spain 1864 1254 1251 2615 2401

Austria 402 222 206 308 218

Belgium 69 22 22 41 34France 1619 632 640 1191 872

Germany 1153 421 402 753 558

Ireland 70 19 15 38 26

Luxembourg 9 3 3 6 4

Netherlands 38 6 17 20 16

Switzerland 127 83 76 125 84

United Kingdom 296 84 86 194 156Denmark 52 17 27 30 38

Finland 2342 462 459 1039 833

Norway 977 164 160 323 281

Sweden 2865 576 566 1629 1107

Source: value estimates are derived fromDing et al. (2010).

Table A5

Trends of GDP and population in IPCC scenarios (2050).

Country Population density

(head/ha)aGDP per capita (000'US$)

A1FI A2 B1 B2 A1FI A2 B1 B2

Greece 0.41 0.41 0 .41 0 .37 27.38 2 1.36 2 1.87 1 9.30Italy 1.06 1.06 1.06 0.92 73.52 57.36 58.73 53.65

Portugal 0.63 0.63 0.63 0.56 23.17 18.08 18.51 16.70

Spain 0.57 0.57 0.57 0.51 47.14 36.78 3 7.66 3 3.13Austria 0.87 0.87 0.87 0.89 72.06 56.21 57.56 44.74

Belgium 3.33 3.33 3.33 3.05 59.96 46.78 47.90 41.50

France 1.10 1.10 1 .10 0 .93 57.47 4 4.83 4 5.91 4 2.67Germany 2.12 2.12 2.12 1.85 70.11 54.70 56.01 50.67

Ireland 0.53 0.53 0.53 0.35 26.44 20.63 21.12 25.44

Luxembourg 2.99 2.99 2.99 1.78 45.75 35.69 36.55 48.56

Nethe rlands 3.169 3.19 3.19 2.73 54.83 42.77 43.80 40.53

Switzerland 0.93 0.93 0.93 1.02 117.04 91.30 93.49 67.46

Unite d Kingdom 1.66 1.66 1.66 1.49 49.00 38.23 39.14 34.44

Denmark 0.14 0.14 0.14 0.13 77.26 60.27 61.71 52.19

Finland 0.12 0.12 0.12 0.12 85.15 66.43 68.02 54.17

Norway 0.10 0.10 0.10 0.09 69.32 54.08 55.37 50.38

Sweden 2.11 2.11 2.11 2.32 88.51 69.05 70.70 50.90

Source:CIESIN (2002).



13/14

Source:Schrter et al. (2004).

References

Balmford, A., Bennun, L., ten Brink, B., Cooper, D., Ct, I.M., Crane, P., Dobson, A., Dudley,N., Dutton, I., Green, R.E., Gregory, R.D., Harrison, J., Kennedy, E.T., Kremen, C., Leader-Williams, N., Lovejoy, T.E., Mace, G., May, R., Mayaux, P., Morling, P., Phillips, J.,Redford, K., Ricketts, T.H., Rodriguez, J.P., Sanjayan, M., Schei, P.J., van Jaarsveld, A.S.,Walther, B.A., 2005.The convention on biological diversity's 2010 target. Science

307, 212213.Bloger, T., 2001.The functional value of species biodiversity: a review. Biol. Environ. Proc.

R. Ir. Acad. 101 B (3), 199224.Cameron, T., 2002.2002: the year of the diversity-ecosystem functiondebate. Trends

Ecol. Evol. 17 (11), 495496.Chiabai,A., Travisi, C.,Markandya, A.,Ding,H., Nunes,P.A.L.D.,2011. Economic assessment

of forest ecosystem services losses: cost of policyinaction. J. Environ. Resour. Econ. 50(3), 405445.

CIESIN, 2002.Environmental sustainability index. The Center for International EarthScience Information Network. 296 (February).

Costanza, R., Fisher, B.,Mulder, K.,Liu, S.,Christopher,T., 2007. Biodiversityand ecosystemservices: a multi-scale empirical study of the relationship between species richnessand net primary production. Ecol. Econ. 61, 478491.

De Bruyn, S.M., 2000.Economic growth and the environment: an empirical analysis.Economy & Environment, 18. Kluwer Academic Publishers, Dordrecht, Boston andLondon.

Ding, H., Silvestri, S., Chiabai, A., Nunes, P.A.L.D., 2010. A hybrid approach to the valuationof climate change effects on ecosystem services: evidence from the European forests.FEEM Working Paper. No. 2010.050.

Faith, D.P., 2005.Global biodiversity assessment: integrating global and local values andhuman dimensions. Glob. Environ. Chang. 15, 58.

Garcia-Gonzalo, J., Peltola, H., Briceo-Elizondo, E., Kellomki, S., 2007.Changed thinningregimes mayincreasecarbonstockunder climatechange: a casestudyfroma Finnishboreal forest. Clim. Change 81, 431454.

Giller, P.S., O'Donovan, G., 2002.Biodiversity and ecosystem function: do species matter?Biol. Environ. Proc. R. Ir. Acad. 102B (3), 129139.

Gordon, C., Cooper, C., Senior, C.A., Banks, H., Gregory, J.M., Johns, T.C., Mitchell, J.F.B.,Wood, R.A., 2000.The simulation of SST, sea ice extents and ocean heat transportsin a version of the Hadley Centre coupled model without ux adjustments. ClimateDynam. 16, 147168.

Halpern, B.S., Longo, C., Hardy, D., McLeod, K.L., Samhouri, J.F., Katona, S.K., Kleisner, K.,Lester, S.E., O'Leary, J., Ranelletti, M., Rosenberg, A.A., Scarborough, C., Selig, E.R.,Best, B.D., Brumbaugh, D.R., Chapin, F.S., Crowder, L.B., Daly, K.L., Doney, S.C., Elfes,C., Fogarty, M.J., Gaines, S.D., Jacobsen, K.I., Karrer, L.B., Leslie, H.M., Neeley, E., Pauly,D., Polasky, S., Ris, B., Martin, K.S., Stone, G.S., Sumaila, U.R., Zeller, D., 2012.Anindex to assess the health and benets of the global ocean. Nature 488, 615620.

IPCC, 2000. Special Report on Emissions Scenarios.Cambridge University Press,

Cambridge.

Table A6

Trends of tree, bird, plant, and herptile for the EU17 projected between 2000 and 2050.

Country Biodiversity

indicators

Baseline

2000

A1FI

2050

A2

2050

B1

2050

B2

2050

Greece No. of tree species 24 22 30 26 31

No. of bird species 98.28 108.71 111.09 110.62 110.44

No. of plant

species

262.40 236.66 247.95 214.89 228.45

No. of herptile

species

38.85 38.24 38.52 39.39 39.22

Italy No. of tree species 33 34 43 41 45


No. of plant

species

330.55 273.96 303.47 269.50 287.84

No. of herptile

species

30.80 29.51 30.31 31.01 30.68

Portugal No. of tree species 18 11 14 13 13

No. of bird species 89.10 106.47 110.33 111.13 110.11No. of plant

species

237.24 242.43 231.88 215.35 204.92

No. of herptile

species

38.76 37.84 37.51 37.77 37.78

Spain No. of tree species 23 16 19 20 21


No. of plant

species

284.12 243.05 246.59 231.15 230.89

No. of herptilespecies 35.00 34.81 34.18 35.70 34.95

Austria No. of tree species 46 52 69 70 71


No. of plant

species

257.74 305.52 361.78 338.09 348.90

No. of herptile

species

20.32 19.71 19.68 19.86 19.62

Belgium No. of tree species 30 31 46 52 52


No. of plant

species

281.29 230.98 256.88 261.99 252.35

No. of herptile

species

22.04 23.08 23.62 23.81 23.37

France No. of tree species 32 20 31 34 38

No. of bird species 126.36 130.55 129.93 130.26 129.97No. of plant

species

307.62 227.17 265.41 268.48 262.96

No. of herptilespecies

24.28 24.33 25.02 25.11 25.03

Germany No. of tree species 32 36 45 50 49


No. of plant

species

309.47 263.87 281.88 297.25 279.91

No. of herptile

species

22.86 22.86 22.50 22.57 22.64

Ireland No. of tree species 21 28 33 37 37


No. of plantspecies

236.79 227.62 243.49 253.40 252.58

No. of herptile

species

6.65 7.28 7.63 7.70 7.53

Luxembourg No. of tree species 29 21 37 45 46


No. of plant

species

262.75 203.00 230.00 255.00 243.00

No. of herptile

species

20.83 21.25 21.42 22.67 21.83

Netherlands No. of tree species 28 44 48 51 51No. of bird species 145.43 145.86 145.51 146.65 145.04

No. of plant

species

281.45 261.31 283.51 279.56 274.16

No. of herptile

species

19.39 20.70 20.85 20.17 20.15

Switzerland No. of tree species 47 41 60 63 63


No. of plant

species

344.60 225.68 281.27 282.64 273.33

No. of herptile

species

19.01 18.30 18.22 18.24 18.04

Denmark No. of tree species 31 41 45 49 48


Table A6(continued)

Country Biodiversity

indicators

Baseline

2000

A1FI

2050

A2

2050

B1

2050

B2

2050

No. of plant

species

308.02 298.86 318.13 359.58 337.47

No. of herptile

species

17.35 16.85 16.77 16.82 16.63

United

Kingdom

No. of tree species 22 30 34 37 37


No. of plantspecies 231.94 245.09 260.11 254.19 251.73

No. of herptile

species

9.69 9.64 10.03 10.44 9.95

Finland No. of tree species 21 31 33 36 36


No. of plant

species

129.59 240.21 211.84 204.93 199.61

No. of herptile

species

5.58 5.42 5.41 5.71 5.43

Norway No. of tree species 25 36 40 41 42


No. of plant

species

171.62 291.60 256.03 243.24 249.37

No. of herptile

species

2.00 1.94 1.82 1.85 1.72

Sweden No. of tree species 24 33 36 39 38


No. of plantspecies

165.85 250.59 235.68 226.14 230.55

No. of herptile

species

6.14 5.34 5.20 5.66 5.37

http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0005http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0005http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0005http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0005http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0160http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0160http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0160http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0160http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0010http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0010http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0010http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0010http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0010http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0010http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0010http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0010http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0015http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0015http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0015http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0015http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0015http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0165http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0165http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0025http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0025http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0025http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0025http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0025http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0030http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0030http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0030http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0170http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0170http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0170http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0035http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0035http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0035http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0035http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0040http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0040http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0040http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0040http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0040http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0045http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0045http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0045http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0045http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0050http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0050http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0050http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0050http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0050http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0050http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0050http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0055http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0055http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0055http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0055http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0055http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0055http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0060http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0060http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0060http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0060http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0055http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0055http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0050http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0050http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0050http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0045http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0045http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0040http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0040http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0040http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0035http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0035http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0170http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0170http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0170http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0030http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0030http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0030http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0025http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0025http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0025http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0165http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0165http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0015http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0015http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0015http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0010http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0010http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0160http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0160http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0005http://refhub.elsevier.com/S0921-8009(13)00333-9/rf0005


14/14

Kinzig, A.,Pacala,S.P.,Tilman,D. (Eds.),2001.TheFunctional Consequences of Biodiversity.Princeton University Press, Princeton, New Jersey, USA.

Kontoleon, A., Pascual, U., Swanson, T., 2007. Introduction. In: Kontoleon, A., Pascual, U.,Swanson, T. (Eds.), Biodiversity Economics: Principles, Methods and Applications.Cambridge University Press, Cambridge, p. 692.

Leadley, P., Pereira, H.M., Alkemade, R., Fernandez-Manjarrs, J.F., Proena, V.,Scharlemann, J.P.W., Walpole, M.J., 2010.Biodiversity scenarios: projections of 21stcentury change in biod

Documents

Ding H., Nunes P., (2013) Modeling the links between biodiversity, ecosystem services and human welleing in the context of climate change - Results form an econometric analysis of