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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.