measuring economic values & integrating them in ... · extension services exposure to extension...

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Measuring Economic Values & Integrating them

in Interdisciplinary Sustainable Management of

Natural Resources & other Public Goods:

Prof. Phoebe Koundouri et al.

Athens University of Economics Business, School of Economics

ICRE8: International Center for Research on the Environment & the Economy

London School of Economics, Grantham Institute

ATHENA Research and Innovation Center

Is there a dominant driving force

shaping Economic Values?

Crucial Questions to be answered:

Does relevant information exist?

Who owns & understands relevant information?

What is the level of information uncertainty?

How does uncertainty interact with risk preferences?

How is information diffused over time/over space?

It is important to explicitly incorporate the level

& dynamics of information in the theoretical and

empirical attempts to measure values.

Information is Interdisciplinary!

Interdisciplinary Nature of Information

Pops up in all Stages of Analysis:

Theoretical Model

Empirical/Econometric Model

Data Collection (market or non-market)

Model Application & Estimation

Analysis of Results

Institutional Analysis

Policy Recommendations

ICRE8: International Centre for Research on the Environment & the Economy, www.icre8.eu

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Non-for-profit Research Centre, established 2014

Interdisciplinary Research on:

Environment Economy

Energy Eco-innovations

+ electronic versions (hence E8)

ICRE8 Mission: The pursuit of excellence in conducting and presenting research and a commitment to explore relevant environmental, natural resources and energy issues, for a variety of circumstances and stakeholders and across different temporal and spatial scales.

EC DG-Research

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INLAND WATER

FP4: Cyprus

FP5: ARID CLUSTER

FP6: EROLIMPACS

FP6: AQUASTRESS

FP7: GENESIS

FP7: GLOBAQUA

COASTAL WATER

FP6: IASSON

FP6: SESSAME

FP7: THESUS

MARINE OFFSHORE

FP7: MERMAID

FP7: TROPOS

FP7: H2OCEAN

WFD MSFD

Floods

Directive

Recent World-wide Water Projects

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EXAMPLES INTER ORG

World Bank: Arsenic Contamination, Bangladesh, India

World Bank: Water Pricing, China, Bangladesh

WWC: Green Water Growth: Egypt, China, Nepal, Australia

OECD: Economic Instruments to Protect Freshwater Resources in Lake BaikalBasin.

IIED: Integrated WM Methodologies

EXAPLES NATIONAL GOVT

UK, EA: Economic Value of

Groundwater

Finalnd, MoE: Agricultural use

of Water and CAP

Greece, MoE: ASOPOS RB

Cyprus, MoE: WFD Atrs 5 & 11

Abu Dhabi, MoE: Economic

Valuation of Groundwater

Namibia, MoE: Groundwater

Pricing

OTHER PUBLIC GOODS

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WHO: Assessing Fairness of Greek Health System Financing

UK Treasury & DEFRA: Discounting for LR CBA

DIFID: South Africa: Incentives for Leaded Gasoline

The impact of REACH on the environment and human health

EU Chemicals Directive: Economic Evaluation

CYPRUS: Amiantos Mines

GREECE: CBA mining-metallurgical installation Hellas Gold

Bank of Greece: Report on Climate Change

The Hashemite Kingdom of Jordan: Waste Management

OpenAIRE: Open Access Scientific Information

Etc.

The Value of a River Basin

Example 1

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Asopos River Basin

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Area 724 km2 , flows into Evoikos Gulf

Habitat of more than 140 bird species

Coastal zone destination for recreational activities

200,000 citizens (including second houses)

Largest industrial area in Greece

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USE & OPTION

Agriculture

Production Function

Residential

Optimal Control

Differential Game Simulation

Industrial

Value Transfer

Health

Lab experiment

Recreation

Choice Experiment

NON-USE

Existence Value Choice

Experiment

Warm Glow

Lab experiment

Future Value Choice

Experiment

TEV

GIS based DSS (with CC scenarios)

Total Economic Value of RB

Policy Recommendations

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The Value of Water in Irrigated Agriculture

Information Transmission in Technology Diffusion: Social Learning, Extension Services, Spatial Effects

AJAE, 2013

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CONTRIBUTION:

First Model that combines:

Dynamic Adoption and Diffusion under Uncertainty

Different Learning Processes: social networks, extension, learning by doing

Peers Identification & respective information transmission channels

Risk Preferences characterization & estimation

Socio-economic, Environmental and Spatial Characteristics

Theoretical and Empirical Models are Generic

Policy Recommendations:

incentivizing welfare increasing technology adoption & diffusion

water value, pricing and allocation

METHOD:

Theoretical Model: dynamic maximization behavior model

Empirical Model: econometric duration analysis model merged with:

- factor analysis for identification of info transmission paths and peers

- flexible method of moments for risk attitudes estimation

Applied model: micro-dataset olive farms

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Discounted profit

before adoption

Discounted profit after

adoption (technology

lifetime) cost

Discounted profit

beyond technology

lifetime

Farmer’s Trade-off:

Benefit: Delaying investment by one year allows the farmer to purchase the modern irrigation technology at a reduced cost.

Cost: Delaying adoption by one year results in producing with the conventional less efficient technology and bearing a higher risk of water shortage (thus a loss in expected profit).

Heterogeneity of Adoption Decision Deriving

from heterogeneity in E(π), which derives from:

Farm-specific expected Cost for Technology & Water Efficiency Index

WHY? 3 channels of Knowledge Accumulation :

extension services before and after adoption

social learning before and after adoption

learning by doing after adoption

Farm Specific Knowledge Accumulation depends on:

socioeconomic characteristics (age, education, experience)

farm location

identification & behavior of influential peers

Farm-specific characteristics:

input & output prices

spatial location

environmental conditions (defining min water crop requirements)

risk preferences

Methodology:

- Theoretical model of adoption by risk-averse agents under production risk

- Approximate it with flexible empirical model: higher-order moments of profit.

- Derive risk preference from estimation results.

- Use risk preferences to explain adoption through a discrete choice model.

Results:

- Risk preferences affect the prob. of adoption: evidence that farmers invest in new

technologies as a means to hedge against input related production risk.

- The option value (value of waiting to gather better information) of adoption,

approximated by education, access to information & extension visits, affects the

prob. of adoption.

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Measuring Risk Attitudes

Integrating work from AJAE (2006)

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Taking a Taylor approximation of EU, the FOC of the max problem:

1XXk

1/2!2XXk

FX/2XFX/1X

1/3!3XXk

FX/3XFX/1X

. . .1/m!mXXk

FX/mXFX/1X

k 1, . . . ,K inputs

The following model is estimated for each k:

1XXk

1k 2k2XXk

3k3XXk

. . .mkmXXk

uk

where : 2k a2k 1/2!,3k a3k 1/3!, . . . ,mk amk 1/m!

: ajk FX/ jX/FX/1X

1XXk

: marginal contribution of input k to expected profit

2XXk

: marginal contribution of input k to varaince

3XXk

: marginal contribution of input k to skewness

amk : weight attributed by farmer to the mth moment of profit

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Peers: farmers exerting a direct or indirect influence

on the whole population of farmers in their respective areas.

Homophilic

Farmers exchange information and learn

from individuals with whom they have

close social ties and with whom they

share common professional or/and

personal characteristics (education, age,

religious beliefs, farming activities etc.)

Heterophilic

Farmers may also follow or trust the

opinion of those that they perceive as

being successful in their farming

operation, even though they

occasionally share quite different

characteristics.

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Measuring the extent of information transmission via different

channels & identifying its role is difficult:

1. Set of peers difficult to define : we need to go beyond the simplistic

definition of peers as (physical) neighbors.

2. Distinguishing learning from other phenomena (interdependent

preferences & technologies; related unobserved shocks) that may give rise

to similar observed outcomes is problematic.

Factor Analysis:

Identification Peers and Information Transmission Paths

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Peers are not JUST physical neighbors!

The observed (correlated) variables are modeled as linear

combinations of the unobserved factors, plus error terms.

The information gained on interdependencies between observed

variables is then used to reduce the set of variables in a dataset.

Estimated Factors will be included in empirical hazard function.

SOCIAL NETWORK CHANNEL I:

Total no. of adopters in farmer's reference group

Stock: stock of adopters on the year the farmer adopted

HStock: stock of homophilic adopters (farmers in same age group (6 year range) and with similar

education levels (2 year range)).

RStock: stock of homophilic adopters as identified by the farmer (computed as stock of homophilic

adopters among those identified by farmer as belonging to his reference group).

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SOCIAL NETWORK CHANNEL II:

Distance of farmer to adopters in her reference group

• Dista : average distance to adopters

• HDista: average distance to homophilic adopters

• RDista : average distance to homophilic adopters as identified by the farmer

EXTENSION SERVICES CHANNEL I:

Overall exposure of farmer to extension Services

• Ext : no. on-farm extension visits until the year of adoption

• Hext: no. on-farm extension visits to homophilic farmers

• RExt : no. on-farm extension visits to homophilic adopters as identified by the farmer

EXTENSION SERVICES CHANNEL II:

Distance of farmer to Extension Agencies

• Distx : distance of the respondent to the nearest extension agency

• HDistx : average distance of homophilic farmers to the nearest extension agency

• RDistx : average distance of homophilic adopters as identified by farmer, to the nearest EA

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The variable of interest: length of time between the year of drip

irrigation technology introduction (1994) and the year of

adoption. Mean adoption time is 4.68 years in our sample.

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T : duration, ve RV with ft continuous prob. density function

Ft 0

tfsds PT t : cumulative distribution function

St 1 Ft 1 0

tfsds

t

fsds : survival function

Prob. of survival (of old technology) beyond t

1 St : prob. farmer adopts by t

1 St : EInnovation Diffusion share of adopting farmers

ht : hazard function (rate), rate at which individuals will adopt thetechnology in period t, conditional on not having adopted before t:

ht lim0

Ft FtSt

ftSt

empirical counterpart of adoption equation from theoretical model.

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Assume T follows a Weibull distribution the hazard function is:

α : scale parameter

α > 1: hazard rate increases monotonically with time

α < 1: hazard rate decreases monotonically with time

α = 1: hazard raTe is constant

vector zit : variables that determine farmers' optimal choice

Some vary only across farmers (e.g. soil quality and altitude) other vary across farms

and time (e.g. cost of acquiring the new technology)

β : corresponding unknown parameters

ht,zit,, t1it

it expzit

Empirical Hazard Function

DATA COLELCTION:• Sample of 265 randomly selected olive-growing farms.• Questionnaire-based interviews undertaken by the extension personnel from the Regional Agricultural Directorate.• Farmers were asked to recall:

- time of adoption (drip or sprinklers)- variables related to their farming operation on the same year: production patterns, input use, gross revenues, water use and cost, structural and demographic characteristics.

• 265 farms in the sample: 172(64.9%) adopted drip irrigation technology• Environmental Data

ESTIMATION:• Estimate higher moments of profit (FMM). • Estimate factor scores (PCA & varimax rotation). • Merge profit moments & factor scores in hazard function and estimate a duration model (right censored ML).• Consistent standard errors via stationary bootstrapping (Politis & Romano 1994)

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Discussion of Results I : Epidemic Effects

Scale parameter of the Weibull hazard function is statistically

significant and well above unity in both models.

Endogenous learning as a process of self-propagation of information

about the new technology that grows with the spread of that

technology:

reductions in uncertainty resulting from extensive use of the new

technology: learning-by-doing effects

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EXTENSION SERVICES

Exposure to extension services induces faster adoption (-0.306 years).

The bigger the distance from extension outlets the shorter the time before adoption (- 0.0531). Extension agents primarily targeting farmers in remote areas, as these farmers are less likely to visit extension outlets.

SOCIAL LEARNING

Larger stock of adopters in the

farmer's reference group induces

faster adoption (-0.293 years).

Greater distance between adopters

increases time before adoption

(0.172 years).

Empirical Results II : Extension & Social Learning

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The impact of social learning is comparable to the impact of information

provision by extension personnel.

(mean marginal effects on adoption times are -0.293 and -0.306 for the stock

of adopters and exposure to extension services, respectively).

Interaction term between the two channels of information transmission is

statistically significant and negative: complementarity.

- rules of thumb (manuals and blueprints) utilized by extension personnel

- strong social networks already engaged in learning-by-doing.

The complementarity between the two communication channels in

enhancing technology diffusion points to the need of redesigning the

extension provision strategy towards internalizing the structure and

effects of farmers' social networks.

Empirical Result III:

Complementarity of Information Channels

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The marginal effect of farmer's age on adoption time is -0.010 years

Time before adoption of drip irrigation technologies decreases with age up

to 60 years (experience effect) and then follows an increasing trend

(planning horizon effect).

Time until adoption increases with education whenever education level is

less than 9 years (elementary schooling).

For more than 9 years of education, higher educational levels lead to faster

adoption rates: only highly educated farmers are more likely to benefit from

modern technologies.

Empirical Result IV: Human Capital VariablesSignificant Impact of AGE & EDUCATION

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Empirical Result V: Risk Attitudes

Important Determinants of Adoption Behavior

Expected profit and profit variance are highly significant. I.e. higher

expected profit & higher variance of profit induce faster adoption.

Risk averse and adversely affected by a high variability in returns.

The adoption of the modern irrigation technology allows these farmers to

reduce production risk in periods of water shortage (confirms Koundouri et

al. 2006, 2008).

3rd & 4th moments of profit insignificant: farmers are not taking downside

yield uncertainty into account when deciding whether to adopt new irrigation

technology.

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Unit value of irrigation water: 0.076 euro

An increase of one euro cent in the water price has a very significant effect on both the hazard and the mean adoption time, speeding up the diffusion of new irrigation technology (0.145 and -0.95, respectively): Efficient water pricing important

Higher crop price delays adoption rates (marginal effect is 0.343 years) as farmers have reduced incentives to change irrigation practices as means of increasing farms expected returns.

Adverse weather conditions induce faster irrigation technology adoption (magnitude of the effect is small).

Farms with high tree density are adopting faster than farms engaged in more extensive spatial cultivation.

Empirical Result VI:

Environmental Variables, Input & Output Prices,

Important Determinants of Adoption Behavior

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USE & OPTION

Agriculture

Production Function

Residential

Optimal Control

Differential Game Simulation

Industrial

Value Transfer

Health

Lab experiment

Recreation

Choice Experiment

NON-USE

Existence Value Choice

Experiment

Warm Glow

Lab experiment

Future Value Choice

Experiment

TEV

GIS based DSS (with CC scenarios)

Total Economic Value of RB

Policy Recommendations

Managing the effects of multiple stressors on

biodiversity and functioning of aquatic

ecosystems

http://www.globaqua-project.eu

Funder: European Commission, FP7 Collaborative project

Number of Partners: 21 European 2 Non.- European

(Morocco- Canada)

Duration: 5 years

Budget: 9.990.594 €

i. Provisioning services: products obtained by ecosystems.

ii. Regulating services: benefits arising from regulation of ecosystem

processes such a carbon sequestration, flood protection, erosion

control and waste management.

iii. Cultural services: use and non-use benefits

iv. Supporting services: services that are necessary for the

production of all other ecosystem services.

Millennium Ecosystem Assessment

Ecosystem Services

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Biophysical strucure and process Functions Services Benefits

Rainfall-vegetation interaction Water provisioning Water provisioning

Water for drinking purposes

Water for irrigation purpose

Hydropower production

Biotic and abiotic processes

Removal or

breakdown of Oxenic

nutrients and

compounds

Waste treatment

Better surface water and

groundwater quality

Enjoyment of recreational

areas

Vegetation and geomorphology Sediment retention Erosion protection

Higher surface water quality

avoided soil losses

Extension of water

management infrastructures

lifetime

soil carbon storage

Enjoyment of recreational

areas

Habitat availability

Refugium for species

and maintance of

genetic diversity

Habitat for species

Existence/ conversation of

genetic and species

diversity

From biophysical characteristics to economic value

Benefits

Water for drinking purposes

Water for irrigation purpose

Hydropower production

Better surface water and

groundwater quality

Enjoyment of recreational

areas

Higher surface water quality

avoided soil losses

Extension of water

management infrastructures

lifetime

Soilcarbon storage

Enjoyment of recreational

areas

Existence/ conversation of

genetic and species diversity

Valuing Ecosystem Services

Us e

&

Non use

Hedonic pricing

Travel cost method

Avertingbehavior

Market prices

Choice Modeling & CVM

Meta-analysis

Benefit transfer

…

MethodsValues

The Value of Tomorrow’s Oceans

EXAMPLE 2

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The Ocean of Tomorrow

European Strategy for Marine and Maritime Research

Call FP7-OCEAN-2011: Topic 1: Multi-use offshore platforms

Budget: 20 million EUR, 3 projects

… to develop novel innovative designs for multi-use offshore

platforms … assess the technical, economical and environmental

feasibility of constructing, installing, operating, servicing, maintaining

and decommissioning, together with the related transport aspects.

… shall target ocean renewable energy, and in particular offshore

wind, aquaculture and the related transport maritime services.

Policy: Marine Strategy Framework Directive

Initiatives: Blue Growth, Energy 2020

OCEAN.2011-1: Multi-use offshore platforms3 funded projects

Development of a wind- wave power open-sea platform equipped forhydrogen generation with support for multiple users of energy

http://www.h2ocean-project.eu/

Innovative multi-purpose offshore platforms: planning, design and operation

http://www.mermaidproject.eu/

Modular multi-use deep water offshore platform harnessing andservicing Mediterranean, subtropical and tropical marine resources

http://www.troposplatform.eu/

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TEV Example: Marine Resources

MISEA: Methodology for Integrated Socio-Economic Assessment based on Marine ES

EU Marine Strategy Framework Directive (MSFD) aims to achievegood environmental status of the EU's marine waters.

MISEA provides decision-makers with a decision tool to assesswhether a MUOP project increases overall social welfare andhence should be undertaken.

MISEA crucial decision tool for implementation of MSFD.

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Assessment Tool

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A. Technical Feasibility Assessment (TFA)

B. Environmental Impact Assessment (EIA)

C. Financial and Economic Assessment (FEA)

D. Social Cost Benefit Analysis (SCBA)

Engineers, Lawyers, Institution Analysts

Ecologists and Marine Scientists

Economists

http://www.madgik.di.uoa.gr/mermaid/?q=datasets

Results: http://www.madgik.di.uoa.gr/mermaid/?q=datasets

For effects with no relevant transferable

values: Choice Experiment

Lancaster (1966): any good can be described in terms of its

attributes and their levels.

After identification, experimental design theory is used to generate

different profiles of the public good in terms of its attributes and their

levels.

These profiles are then assembled in choice sets which are

presented to the respondents, who are asked to state their

preferences.

Monetary cost and benefit attribute and the random utility framework

allow the estimation of welfare indicators (WTP/WTAc) based on the

levels of attributes.

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For Effects with no transferable Values:Choice Experiment: Liuqiu Island, Taiwan

To elicit stakeholder preferences for alternative MUOPsdesigns:

Design 1. Aquaculture facilities (fish + algae)

Design 2. Aquaculture facilities +

Renewable energy + Leisure facilities

Two levels of mitigation impacts:

Optimal

Acceptable

Design 1. Aquaculture Facilities

Design 2. Aquaculture Facilities + Renewable

Energy: OTEC plant + Leisure Facilities

Choice Card Example

The Value of Open Access to

Scientific Information

EXAMPLE 3

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Sustainability Project

OpenAIRE is the main representative of Open Access in scientific information in European Union.

Funded by European Commission,

DG for Communications Networks, Content and Technology)

www.openaire.eu

Why Open Access?

Open Access evolution in Europe

http://vimeo.com/openaire/2014

OpenAIRE is the European online

network that makes research

output openly accessible to all.

It manages diverse research outputs

across all disciplines, promotes and

shares the results, effortlessly.

It is all about sharing, reusing and

linking research information.

Measuring the Economic of Open-Access

to Scientific Information

Choice Experiment: Full-Preference Rank

Data are obtained with a twice repeated best-worst approach on a

choice set with 5 alternatives:

{A1, A2, A3, A4, SQ}

Responses: {y1b, y1w, y2b, y2w}

subscripts: 1st best, 1st worst, 2nd best & 2nd worst.

Preference ordering: {y1b > y2b > y2w > y1w > yr }

r : residual alternative.

Rank-ordered logistic regression to calculate the willingness to pay

(WTP) of respondents for each attribute.

https://www.surveymonkey.com/s/PK3ZH6C

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The General PublicValues Scientific Information and Recognizes itsContribution in NRManagement

GENESIS Project

JEEP, 2012

Humanity has the ability to make development sustainable: toensure that it meets the needs of the present withoutcompromising the ability of future generations to meet theirown needs. WCED, 1987.

There is something awkward about discounting benefitsthat arise a century hence. For even at a modest discount

rate, no investment will look worthwhile. The Economist, 1991.

The Value of Distant Benefits:The socially efficient discount rate

The Value of Distant Benefits & Uncertainty[series of papers with C. Gollier; EP, 2008]

In an Uncertain Environment:- Persistent shocks on the growth rate of consumption- Persistent shocks on short-term interest rates- Persistent shocks on growth expectations, translate into persistent shocks on interest rates

Determine the shape of the term structure of the socially efficient discount rate & imply DDR.

Estimate Theory Consistent DDR trajectory• Using Historical Data•Without a Structural Model•Using univariate time series regime switching models:

- describe stochastic dynamics of the real IR - future properties of the IR are determined by its own past behaviour

Information accumulation may transmit patterns of preferences towards risk & influence time preferences & attitudes towards the environment.As environment becomes more important and current generationscare more about the future: DDR for PV of LR effects!

Published and Forthcoming Books

Forthcoming: Koundouri, P. (editor) Innovative Multi-Purpose Offshore Platforms: Economic Engineering and Ecological Perspectives. Springer Publishing, 2015. Koundouri, P. (editor) The Economics of Marine Energy Production: A framework of analysis and its application in European oceans. Springer Publishing, 2015.Markandya, A., and Koundouri, P. (editors) Fisheries Policy and Management. World Scientific Publishing, 2016. Koundouri, P. (editor) Handbook on Ecosystem Services and Water Resources Management. Cambridge Uni Press.