Proceedings of the
3rd Annual Beaufort Marine
Socio-Economic
Workshop
Organised by SEMRU (Socio-Economic Marine Research Unit), National
University of Ireland, Galway in association with the Marine Institute,
Ireland
Proceedings of the 3rd Annual
Beaufort Marine Socio-Economic
Workshop
Friday 11th November, Aras Moyola, National
University of Ireland, Galway, Co. Galway
Organised by SEMRU (Socio-Economic Marine Research Unit), J.E.
Cairnes School of Business and Economics, National University of
Ireland, Galway.
Compiled by:
Stephen Hynes (SEMRU, National University of Ireland, Galway)
Copies can be downloaded from:
http://www.nuigalway.ie/semru/documents/beaufortworkshop2.pdf
Table of Contents
Introduction .............................................................................................................. 2
Stephen Hynes, SEMRU, NUI Galway
Valuing Impacts to Recreation from the 2007 San Francisco Bay Oil Spill................... 3
Eric English, Stratus Consulting Inc.
The Role of Marketing in Driving the Sustainable Consumption of Seafood ............... 5
Judith Kildow, Centre for the Blue Economy, Monterey Institute for International
Studies
The Potential for an Irish Maritime Transportation Cluster:An Input-Output Analysis7
Karyn Morrissey, School of Environmental Sciences, University of Liverpool
Valuing the Dead Sea and Peaceful Cooperation in the Middle East .......................... 9
Alberto Longo, Anil Markandya and Ramon Ortiz, School of Biological Sciences,
Gibson Institute for Land, Food and the Environment, Queen's University of Belfast
The Role of Marketing in Driving the Sustainable Consumption of Seafood ............... 9
Ann Walsh, Department of Marketing and SEMRU, NUI Galway
Site Choices in Recreational Demand: A Matter of Utility Maximization or Regret
Minimization? ......................................................................................................... 10
Marco Boeri, Alberto Longo, Edel Doherty, Stephen Hynes
Coral-Fisheries Interactions in Bioeconomic Modelling – Comparing Norway and
Iceland’s Redfish Fisheries ....................................................................................... 12
Naomi S. Foley & Claire W. Armstrong, Norwegian Fishery College, University of
Tromsø, Norway
Valuing Marine Environmental Characteristics Associated with Changes to the EU
Bathing Water Directive .......................................................................................... 14
Stephen Hynes, Caitriona Scully and Alphonsus Browne, SEMRU, NUI Galway
Port and Shipping Data as Economic Indicators ....................................................... 16
Fergal Curtin, Shipping Market Analyst, Irish Maritime Development Office, Marine
Institute
Valuing the Non-Market Benefits of the WFD Implementation in Ireland ................ 18
Daniel Norton, Stephen Hynes, Edel Doherty SEMRU, NUI Galway; Cathal Buckley,
Teagasc; Danny Campbell, Queens University
The Impact of Cost Uncertainty on the Feasibility of Wave Energy Conversion Devices
and the Implications for Policy ................................................................................ 20
Niall Farrell, SEMRU, NUI Galway
Another Collective Tragedy? The Case of the Killary Mussel Farming Co-operative . 22
Peter Cush, School of Political Science and Sociology and SEMRU, NUI Galway
Annex 1 ................................................................................................................... 24
Annex 2 ................................................................................................................... 25
1
Introduction
Stephen Hynes, SEMRU, NUI Galway
Introduction
On Wednesday the 17th November, SEMRU (the
Socio-Economic Marine Research Unit) in
association with the Marine Institute held the third
annual Beaufort Marine Socio-Economic Workshop.
This annual workshop is an opportunity for
researchers and policy makers in the area of
marine socio-economics, working in Ireland, to get
together to meet and discuss their on-going work.
The J.E. Cairns School of Business and Economics,
NUI Galway hosted this year’s event.
The workshop was divided up into 3 sessions. The
topic of the morning session was “The Irish Ocean
Economy: National and Sectoral Perspectives”. The
second session was dedicated to “Marine
Ecosystem Service Valuation” and the evening
session showcased the research being conducted
by the SEMRU PhD students. Speakers on the day
included Dr. Marco Boeri and Dr. Alberto Longo of
Queens University, Belfast who presented the
results of a random regret model for water based
recreationalists and a CVM model related to
ecosystem services in the Dead Sea, respectively.
Dr. Eric English of Stratus Consulting, Colorado
spoke on valuing oil spill impacts in marine
environments while Dr. Judith Kildow of the
National Ocean Economics Program in the US
presented a paper on the US Ocean and Coastal
Economies. There were 9 presenters on the day
from both Irish and international academic
institutions.
SEMRU and the Beaufort Socio-Economic Award
This annual workshop is held as part of the work
program of the Beaufort Socio-Economic Research
Award. SEMRU, based in NUI, Galway was set up
though the commitment in funding from the
Beaufort Award under the Marine Research SubProgramme of the National Development Plan
2007–2013. Personnel from a range of university
departments are actively involved in the Beaufort
Socio-Economic Research Award Work Program.
These include members of the Department of
Economics, the Department of Marketing, the
Department of Management and the Department
of Political Science & Sociology. Other research
institutes across Ireland and the UK also
collaborate on projects within SEMRU. These
include the Rural Economy Research Centre,
Teagasc, the Irish Marine Institute and the
Department of Economics, Stirling University,
Scotland. Three PhD students are currently funded
under the Award with a further 3 PhD students
within the Unit. There are currently 16 active
research projects within the unit.
The main research focus of the unit is on the
economic importance of coastal and off-shore
marine environments. This involves examining the
economic utility of the marine environment (e.g.
transportation, recreation) and ecological value
(e.g. fisheries, aquaculture) derived from the
productivity of associated ecosystems. The coastal
and contiguous marine environment surrounding
Ireland and the EU in general provides the
geographical focus for the research of the unit.
Consideration of the human dimension in the
management of marine ecosystems is also a
critical component of all research projects within
the unit.
Unit projects include the collection and monitoring
of socio-economic marine data for Ireland, the
estimation of participation rates and value of
marine-related recreation activities and an analysis
of market orientation, competitiveness and
innovation of firms in the Irish seafood sector. The
unit has once again been successful in the last 12
month in getting EU INTERREG IV, EU FP7 and EPA
funding to carry out further research in the areas
of marine economic valuation in the Atlantic area,
assessing the preferences of fishermen for
fisheries management options and estimating the
economic value of a “water related features” (see
www.nuigalway.ie/semru for further information
on these projects and other activities of the unit).
Given one of the main aims of the Beaufort Award
is to build marine socio-economic research
capacity in Ireland, this third workshop was once
again a means of finding out what others in the
marine economic research community are working
on. The exchange of knowledge in the area of
marine socio-economics is of benefit not only to
the different research institutes but also marine
policy makers. In what follows, the presentations
given on the day of the workshop are reviewed in
two page summaries.
2
Valuing Impacts to Recreation from the 2007 San Francisco Bay Oil Spill
Eric English, Stratus Consulting Inc.
Introduction
On November 7th, 2007, in a thick fog, the Cosco
Busan container ship struck a concrete piling of the
Oakland Bay Bridge and released 56,000 gallons of
oil into San Francisco Bay. Over the next few days,
tidal waters flushed the oil in and out of the harbor
and winds and currents carried the oil up and
down the coast. Over 100 miles of coastal beaches
and marshes were ultimately contaminated. In
addition to establishing liability for cleanup costs
and private financial losses, the United States Oil
Pollution Act of 1990 requires compensation for
nonmonetary public losses associated with oil-spill
impacts to fish, wildlife, and habitats, and to
human-use ecosystem services such as recreation.
Experts working for local, state and federal
agencies evaluated impacts to habitats and wildlife
and determined the appropriate resource
restoration actions to compensate for ecological
impacts. Experts also estimated the lost value of
recreational activity at beaches and other
shoreline areas following the spill. With over 15
million recreation visits in the San Francisco Bay
Area each year, and with a substantial decline in
recreation following the spill, recreation impacts
formed the largest component of damages in the
Cosco Busan case. The final quantification of $18.8
million in damages for recreational losses was the
largest such claim in United States history at the
time.
In this article we report on lessons learned about
measuring recreational losses and defending a
travel-cost valuation model in the context of
potential litigation. The study of recreation losses
for the Cosco Busan incident relied on counts of
visitors at recreation sites as well as a telephone
survey of Bay Area residents about their recreation
behavior and response to the spill. The
simultaneous use of onsite counts and a general
population survey revealed some potential
discrepancies between data collected using the
two different modes, but also indicated that
certain results, such as the estimated percentage
decline in recreation due to the spill, were robust
across data collection modes.
The telephone survey data were also used to
estimate a valuation model of Bay Area shoreline
recreation. The model incorporated several
features common in oil spill assessments over the
last 20 years, such as the use of alternative-specific
constants to represent site characteristics and the
calculation of per-trip values that varied according
to the extent and severity of spill impacts at
various stages of the spill. The vulnerability of
survey results to criticisms about low response
rates and apparent inconsistencies in survey
responses are also discussed.
Estimating lost trips
A telephone survey to assess losses from the Cosco
Busan oil spill was administered beginning in June,
2008. The telephone survey was administered to
Bay Area residents using a random sample of landline telephone numbers. The objective of the
shoreline recreation survey was to estimate the
number of recreation trips lost due to the Cosco
Busan oil spill and to develop a travel-cost model
for estimating the value of lost trips. In order to
estimate the number of lost trips, respondents
were asked whether they went less often to any
shoreline sites because of the spill and if so, they
were asked to name the sites.
Respondents were then asked how many fewer
trips, in total, they took to these sites because of
the spill. Finally, respondents were asked whether
they went to alternative sites instead, and if so,
which sites and how many times. Information
about the decline in a respondent’s trips to
affected sites was obtained for each of the months
following the spill, up to the point when a
respondent reported their recreation was no
longer affected.
Information from the telephone survey was used
to estimate the percentage decline in recreation
trips. To estimate total lost trips, the percentage
decline was multiplied by an estimate of total trips
to San Francisco Bay Area beaches derived from
onsite surveys. The onsite surveys were conducted
during November 2008, and predictions of the
number of visitors throughout the year were
developed based on an estimated relationship
between daily weather and beach visitation.
The advantage of the telephone survey included
the ability to account for spill effects throughout a
lengthy period of impact without the high cost of
onsite data collection sustained over many months.
The advantage of onsite data collection was the
3
elimination of most potential error associated with
nonresponse bias, since information is collected
largely by visual observation.
The value of a lost trip
The valuation model for the Cosco Busan
assessment was specified using alternative specific
constants in a repeated-logit specification (Morey
et al. 1993). Since recreation impacts from a spill
often last less than a year or two, it is usually
feasible to compare recreation activity during the
spill to recreation activity at the same sites either
before or after the spill.
Since spill impacts are evaluated by comparing the
same set of sites at different points in time, the
only site characteristic that the researcher must
value is the impact of the spill itself. Alternative
specific constants can be used to capture the
combined value of all other characteristics, which
can be assumed to remain the same under spill
and non-spill conditions.
Logit models are the most widely used valuation
model in the field of recreation demand (Smith
and Phaneuf 2005) and have been widely used in
applied work, including many oil spill assessments
(Hausman et al. 1995; English 2008). The valuation
model was estimated using information from the
telephone survey on the origin and destination of
recreation trips taken by Bay Area residents.
The model estimate of the value per trips ranged
from $22.65 in November 2007 to $8.90 in June
2008. The decline in value throughout the spillimpact period reflects the greater availability of
substitute sites as the number of areas impacted
by oil declined.
Defensibility of the survey and model
When valuing oil spill impacts on behalf of public
trustees, the data collected and analysis developed
must withstand the scrutiny of opposing parties
and potentially a judge or magistrate, if the case is
ultimately litigated. The adversarial context
highlights any vulnerabilities of a study,
particularly those vulnerabilities that are simple to
understand or appear to contradict a commonsense understanding of what people value and the
choices they make.
For the survey, the key vulnerabilities involved
contradictions, apparent or real, between
responses to different questions by a given
individual, or between responses by different
individuals. For the model, the key vulnerabilities
involved complex economic concepts that cannot
be explained simply and intuitively, and aspects of
the model for which a common practice is not
firmly established in the literature.
References
English, E. (2008), Recreation Nonparticipation as
Choice Behavior Rather than Statistical Outcome.
American Journal of Agricultural Economics
90(1):186-196.
Hausman, J.A., G.K. Leonard, and D. McFadden.
(1995), A Utility Consistent, Combined Discrete
Choice and Count Data Model: Assessing
Recreational Use Losses due to Natural Resource
Damage. Journal of Public Economics 56:1-30.
Morey, E.R., R.D. Rowe, and M. Watson, (1993), A
Repeated Nested-Logit Model of Atlantic Salmon
Fishing. American Journal of Agricultural
Economics 75(3):578-592.
Phaneuf, D., and K. Smith. (2005), “Recreation
Demand Models.” In K. Maeler and J. Vincent, eds.
Handbook
of
Environmental
Economics.
Amsterdam:
Elsevier,
pp.
671-751.
4
The Role of Marketing in Driving the Sustainable Consumption of
Seafood
Judith Kildow, Centre for the Blue Economy, Monterey Institute for International
Studies
Introduction
The field of marine policy and coastal
management began in the early 1970s in the US,
following the release of a Congressionally
mandated study, The Stratton Commission
Report (Stratton Commission, 1969). It was
partially fuelled by the growing environmental
movement and partially by growing attention to
the economic possibilities offered by US coasts
and oceans. Striking a balance between
developing the riches of the coast and oceans
and protecting them so they remained healthy
over time has dominated much of the policy
world of the oceans for the past 50 years.
economy of California’s ocean sector. There had
been a few brief national studies on the US
Ocean Economy (Pontecorvo, Giulio, et al, 1993)
and the coastal economy, yet these were one-off
efforts, establishing only a baseline year, and the
beginnings of a methodology for building a long
term database. However in 1999, Judith Kildow,
then at the Massachusetts Institute of
Technology, began what has become The
National Ocean Economics Program, currently
part of the Center for the Blue Economy at the
Monterey Institute for International Studies
(www.oceaneconomics.org)
The struggles between onshore and offshore
development and environmental protection
fuelled record-breaking legislative initiatives,
creation of state and local coastal programs, and
international and federal law of the sea actions.
One would have thought that the health of the
oceans and coasts would have dramatically
improved. However by the mid-nineties, the
field of marine policy and law, and studies by
resource economists focusing on the oceans
indicated that problems were increasing, not
decreasing along certain shorelines. Shorelines
were becoming more unstable, pollution was
still problematic, estuaries were being filled,
depriving society of the wealth of services they
had provided, however there was only anecdotal
evidence to prove that things were not
improving, not the hard evidence used by
natural scientists and engineers.
Figure 1. Aerial View of Monetary Bay
What engineers and scientists used to build
theory and which formed the foundations for
their fields was missing from the social sciences,
especially
marine
policy
and
coastal
management. There were no time series of
quantitative information that could indicate
trends in human activities along the shore and
their impacts on natural systems, only data
about changing natural systems. In 1997, the
state of California held one of its first California
and the World Oceans Conferences in San Diego
California. That conference and the report that
followed included something quite revolutionary
in the marine policy field: a report on the
The National Ocean Economics Program
With a research team including chief market
economist, Professor Charles Colgan, regional
economist from the University of Southern
Maine, chief non-market economists, Professor
Linwood Pendleton, and later Professor Jason
Scorse, the program and its methodology has
become a standard for other nations doing their
own ocean or marine accounts. The goal of that
program was to develop an accurate ocean and
coastal economy time series, taking an ocean
slice of the US national Income accounts. Those
accounts had a history going back to 1933 when
5
they were established to help the US
government track the US economy to avoid
another 1929 depression. These accounts, albeit
respected and the official economic activity
accounting system in the US, were never
intended to measure the ocean or coastal
economies, so the NOEP took on the task to
formulate a reliable set of measurements and
legitimate categories of economic activity that
fairly reflected the ocean and coastal economies
for which users could have confidence.
Over the past 12 years, the NOEP has become a
reliable on-line queriable time series of
information for users in more than 100 nations,
tracking economic, natural resource and
demographic activities along the US coast and in
the coastal ocean. The NOEP website also
provides 1) a non-market heavily annotated
bibliography of reliable studies that reflect
environmental and recreational values, that fall
outside of the market place and are often
neglected or undervalued, and 2) US
government ocean expenditures, revealing the
funds spent by agency over the years and as a
portion of the US GDP.
The structure of the NOEP
The structure of the NOEP includes: 1) Annual
Market Data consisting of the A) Ocean Economy,
which uses four indicators – employment,
earned wages, number of establishments, and
Gross Domestic Product to track activities in six
ocean
dependent
sectors:
Maritime
Transportation, Offshore Oil and Gas, Coastal
Tourism, Coastal Construction, Ship and Boat
Building, and Commercial Fishing. B) Coastal
Economy, which uses the same indicators and
tracks US Government-derived 12 Supersectors
(all aspects of the economy), by geography:
coastal zip codes, coastal counties, watershed
counties, inland counties. 2) Natural Resources
categories are A) Annual fisheries landings and
values, and B) offshore oil and gas production
and values. Ports and Cargo information are also
available annually, by import and export
categories of cargo, tonnage, and value and on a
separate page.
Value of Marine Economic Data
The values derived from these accounts have
been helpful in estimating losses from natural
disasters such as Hurricane Katrina and
unnatural ones like the Gulf Oil Spill. Knowing
the value that the oceans and coasts contribute
to the national economy has provided the US
Congress with information to help them with
programs and budgets for ocean agencies. And,
knowing that the US coastal states contribute
more than 83% of the US Gross Domestic
Product and almost as large a percentage of jobs
and wages, has given ocean and coastal
resources a new importance in debates about
conservation
and
preservation
versus
development along the nation’s coasts.
Demographic and economic trends along the
coastal and in the coastal ocean have helped
planners and government officials for many
years. We have learned that economic indicators
are probably more important that demographic
ones when seeking a better understanding of
what is happening in these valuable and heavily
populated areas. With the US and other
developed nations now keeping ocean accounts
and revealing their importance, increasing
numbers of nations are anxious to do the same
so they can protect their valuable resources
from the onslaught of climate change impacts
and natural disasters, and from unintended
consequences of decisions made without proper
information.
References
Luger, Michael, et al. (1993), The Economic
Value of the Coastal Zone.
Pontecorvo, Giulio, et al. (1980), Contribution of
the Ocean Sector to the United States Economy.
Science, May 30, 1980. 208(4447):1000‐1006.
Stratton Commission. (1969), Our Nation and
the Sea: A Plan for National Action.
www.oceaneconomics.org
6
The Potential for an Irish Maritime Transportation Cluster: An InputOutput Analysis
Karyn Morrissey, School of Environmental Sciences, University of Liverpool
Introduction
Central to world trade, the maritime
transportation sector has undergone dramatic
changes in the last two decades. On one hand,
fuelled by the globalisation of economic activity
the international shipping sector grew dramatically
from the mid-1990s. However, the onset of the
global recession in 2007 resulted in the sector
presenting
marked
declines
in
output.
Furthermore, structural changes in the market;
including the flagging-out of vessels from high tax
industrialised countries, the introduction of open
ship registers, the hiring of seamen from low-wage
countries, extended vessel lives, and relocation of
ship building capacity from high-cost European
countries to lower cost locations like South Korea
and China has meant that most European
maritime transportation sectors are in decline.
In Ireland, acknowledging the need for direct
measures to halt the decline in the shipping
industry and to prevent the migration of the
industry to alternative jurisdictions the Irish
government introduced a range of policies aimed
at stimulating the sector. Core to these policies is
the development of a maritime transportation
cluster. Broadly defined a cluster is a population of
geographically concentrated and mutually related
business
units,
associations
and
public
organizations centred on a distinctive economic
specialisation. In terms of developing industrial
clusters, it is important to note that their
formation is not `natural’, rather they are
constructed by both scientists and policy makers.
Thus, the first step in developing cluster policy
involves identifying the cluster core. A cluster core
should comprise of a sector that has a relative
abundance of firms within a spatial concentration,
has a relatively high share of national or regional
output, has a high level of exports and most
importantly have strong pre-existing linkages with
related economic sectors (De Langan, 2002).
Based on this set of criteria and using input-output
(IO) analysis, this paper seeks to empirically
examine the potential for cluster formation within
the maritime transportation sector, based on the
strength of its linkages with its support services
and with the wider economy.
Input-Output Analysis
An important dimension of industry and cluster
development is the nature or strength of buyer–
supplier link. Sectors do not exist in a vacuum;
rather they rely on other sectors for inputs
(backward linkages) into their production process,
while simultaneously selling their output to sectors
(forward linkages) to generate profit. Backward
linkage effects are strongly induced by industries
with high intermediate input coefficients, such as
manufacturing industries. Symmetrically, strong
forward linkages are generally induced by the
primary and material industries, whose outputs
are used by other industries as intermediate goods.
The intensity of inter-sectoral linkages between
related industry groups has been highlighted as a
key determinant of the technical and competitive
progress of an economy. As such, the identification
of sectors that display strong linkages is believed
to be a useful planning tool for stimulating overall
economic growth.
Developed by the economist Wassily Leontief, IO
models may be used to trace the entire backward
(or forward) linkages within a sector. However,
while the computation of linkages is
straightforward within an IO framework and these
tables are produced to document the national
accounts of most countries, data limitations
surrounding marine based sectors have meant that
there has been limited use of IO analysis within the
sector.
However, recent research on devising and utilising
a methodology to value the commercial value of
the Irish marine resource found that the maritime
transportation sector generated €697 million in
turnover and provided €197 million in gross value
added (GVA) to the Irish economy in 2007
(Morrissey et al., 2011). With regard to maritime
support industries, there were approximately 200
commercial companies which provided a range of
services to the shipping sector. These services
included banking, finance, insurance, port services
and management consultancy. In 2007, these
industries generated approximately €292 million in
turnover and €182 million in GVA and employed
1,100 individuals in FTEs. Geographically, the
sector is spread along most of the coastline;
however recent research has identified a specific
cluster of maritime industries around the greater
7
Dublin area (Brett and Roe, 2010). Using this data
this paper presents the strength of linkages within
the maritime transportation sector.
Results
Using the IO methodology, it was found that the
magnitude of the water transportation sectors
backward linkage is €1.09. This implies that for
every €1 produced within the water transportation
sector, €0.09 is backward linked to its direct and
indirect upstream suppliers. Four cents of this
€0.09 belongs to the water transportation sectors
direct suppliers and €0.05 belongs to its indirect
suppliers (e.g. the suppliers of its direct suppliers).
Continuing the analysis it was found that the
sector has high backwards linkages (excluding
itself) with auxiliary transportation services,
computer and related services, maritime specific
auxiliary transportation services (these services
include berthing, liner and port services and
facilities), financial intermediation services, post
and telecommunications, insurance and pension,
petroleum and other manufacturing products and
motor fuel and vehicle trade and repair. These
results indicate that for the maritime
transportation sector the most important input
suppliers are within the professional service sector.
In terms of forward linkages maritime
transportation has a forward linkage greater than
one (120). This implies that every €1 produced by
the maritime transportation sector is forward
linked to €0.20 to the production of the sectors
direct and indirect downstream demanders. In
detail, for €1 of the production of water
transportation services, €0.49 is sold directly for
final consumption, including €0.08 for local
consumption and €0.41 for exports. The rest €0.20,
are bought by the water transportation sectors
downstream demanders.
and beverages (2), motor fuel and vehicle trade
and repair (2) and services auxiliary to financial
intermediation (2). It was further found that the
sector was an important input supplier to four of
the five sectors with the highest turnover in 2007,
construction (€47.5 billion), wholesale trade (€20.8
billion), financial intermediation (€20.2 billion) and
food and beverages (€19.1 billion). It was also an
important input supplier to two of the five sectors
with the highest exports in 2007, (€13.9 billion)
and wholesale trade (€9.2 billion).
Discussion
The main objective for developing cluster policies
is to improve the business environment,
capabilities, and performance of local firms in
targeted industries. It is within this context, that
Irish public policy in the maritime transportation
sector is focused on the development of a
maritime transportation cluster. This cluster would
include shipping operations at its core and with
potential linkages to high value added,
technological based professional services in areas
such as banking, law, maritime commerce, ship
finance and insurance. Using input-output analysis,
it was found that the maritime transportation
sector had the second highest backward linkage
and highest forward linkage in the Irish economy
in 2007. The sector also demonstrated strong
linkages to the best performing sectors in the
overall Irish economy. Based on these results there
is a clear rationale for the development of an Irish
maritime transportation sector.
References
Brett, V., and Roe, M. (2010), The potential for the
clustering of the maritime transport sector in the
Greater Dublin Region, Maritime Policy &
Management, 37(1), 1-16.
Morrissey, K., O’Donoghue, C., Hynes, S. (2011),
Quantifying the value of multi-sectoral marine
commercial activity in Ireland. Marine Policy, 35(5),
721-727.
De Langen, P. W., (2002), Clustering and
performance: the case of maritime clustering in
The Netherlands, Maritime Policy & Management,
29(3), 209-221.
Examining the sectors with which the maritime
transportation sector has high forward linkages,
(excluding itself) it was found that the sector has
high forward linkages with wholesale trade (15),
post and telecommunications (10), construction
work (10), auxiliary transportation services (6),
hotel and restaurant services (3), fishing (2), food
8
Valuing the Dead Sea and Peaceful Cooperation in the Middle East
Alberto Longo, Anil Markandya and Ramon Ortiz, School of Biological Sciences,
Gibson Institute for Land, Food and the Environment, Queen's University of Belfast
This paper provides the results of a payment card Contingent Valuation study carried out surveying
9,047 respondents in 18 countries to assess the non-use value of protecting the Dead Sea from
further decline and to promote peaceful cooperation between Israelis and Palestinians. The paper
also explores issues of ordering when using several contingent valuation questions in the same
questionnaire.
The Role of Marketing in Driving the Sustainable Consumption of
Seafood
Ann Walsh, Department of Marketing and SEMRU, NUI Galway
Being market oriented centres on having an in-depth understanding of the customer, in order to meet
or exceed the needs of the target market better than the competition. The academic literature states
that market orientation is a critical determinant of business performance and competitive advantage.
With the global population set to rise to 9bn (from 7bn today) by 2050 and many of the earth’s
natural resources under threat, the issue of sustainable food production and consumption has moved
to the top of the political agenda internationally. Marketers in the US point to a segment of
consumers who practice LOHAS – an acronym for ‘lifestyles of health and sustainability.’ One
organisation that tracks this group estimates that they make up about 16% of the adults in the US, or
35 million people and rising. There is a lot of evidence to suggest that this segment of the market is
growing internationally.
Hence, consumers are driving food markets and are demanding food
production to be more sustainable. Hence this presentation focuses on sustainability from the
perspective of sustainable consumption, more specifically on “The Role of the Consumer in Driving
Sustainable Consumption of Seafood”.
9
Site Choices in Recreational Demand: A Matter of Utility Maximization or
Regret Minimization?
Marco Boeri1, Alberto Longo1, Edel Doherty2, Stephen Hynes2
1.School of Biological Sciences, Gibson Institute for Land, Food and the Environment, Queen's University of
Belfast
2. Socio-Economic Marine Research Unit, J.E. Cairns School of Business and Economics, National University
of Ireland, Galway
Introduction
The use value of recreational activities of bathing,
boating, fishing, hiking, camping, kayaking,
horseback riding, and cycling within natural
resource systems such as beaches, rivers, lakes,
parks, and mountains has
been widely
investigated using the travel cost model. After
Hotelling’s (1947) intuitive recreation demand
model, and initial works by Clawson (1959), Trice
and Wood (1958), and Burt and Brewer (1971),
Hanemann (1978) presents the first application of
the Random Utility Model (RUM) to analyse the
demand for recreation when users have to choose
among several alternative sites to visit. Since then,
the RUM has been widely used to model the
demand for recreation.
On a given choice occasion, the Random Utility
multinomial logit model (RU-MNL) models the sitechoice of one out of a number of alternative sites,
as a function of attributes of the site, and central
to its application, it assumes that a user visits the
site that gives him/her the highest utility (Haab
and McConnell, 2002). As it is widely
acknowledged, the RUM’s popularity is mainly due
to its strong economic foundations, its conceptual
elegance and its formal tractability. Many RUM
based models have closed-form formulations for
choice probabilities, and most can be easily coded
and estimated using standard discrete choicesoftware packages. However, some observers have
pointed out that at least some of the assumptions
behind the RUM might lack in behavioural realism.
For example, research from psychology and
consumer behaviour claims that decision-makers
process information through limited capabilities
and resources, trying to make the best possible
choices within operational constraints (Ford et al,
1989).
Various attempts have been made to relax the RUMNL assumptions, mostly by adapting the RUM,
rather than by proposing completely new
representations of the choice process. Resulting
models, however, are, without exceptions, less
parsimonious and less tractable than the RUM’s
workhorse, the Mixed Multinomial Logit model.
Furthermore, they generally require researchers to
develop specific-purpose code for estimation.
Methodology
As an alternative to the RU-MNL, this paper
employs the Random Regret Minimization
Multinomial Logit model (RR-MNL). This is a
relatively new choice paradigm that relaxes the
assumption of utility maximization – and of fully
compensatory decision makers – while remaining
econometrically as parsimonious and tractable as
its utilitarian counterpart, the RUM (Chorus, 2010).
The RR-MNL model is built on the idea that, when
choosing, individuals aim to minimize their regret
rather than to maximize their utility – regret being
defined as what one experiences when a nonchosen alternative performs better than a chosen
one, on one or more attributes.
The RR-MNL model implies semi-compensatory
behaviour: improving an alternative in terms of an
attribute on which it already performs well relative
to other alternatives generates only small
decreases in regret, whereas deteriorating to a
similar extent the performance on another equally
important attribute on which the alternative has a
poor performance relative to other alternatives
may generate substantial increases in regret. As a
result, the extent to which a strong performance
on one attribute can make up for a poor
performance on another depends on the relative
position of each alternative in the choice set. This
choice set composition-effect, which has been well
established empirically in the field of consumer
choice (e.g. Kivetz et al., 2004), is called the
compromise effect. This effect states that
alternatives with an ‘in-between’ performance on
all attributes, relative to the other alternatives in
the choice set, are generally favoured by choicemakers over alternatives with a poor performance
on some attributes and a strong performance on
others.
In this paper, we explore how the RR-MNL can be
used to determine site-choice for recreational
goods by modelling travel cost data elicited from a
sample of kayakers in the Republic of Ireland. We
further explore whether choices are driven by a
10
utility maximization or a regret minimization
paradigm by running a binary logit model to
explain the likelihood of the two decision choice
paradigms using site visits and respondents
characteristics as explanatory variables. To our
knowledge, this is the first application of the RRMNL to a revealed preference dataset eliciting
recreation site-choice. Furthermore, this is the first
attempt to assess, at the choice level (as opposed
to the overall model fit), which model, the RUMNL or the RR-MNL, better describes the decision
process underlying each choice. This novel
application of the model potentially reveals very
useful information for policy makers interested in
the management of recreational sites. This paper
is also one of the first attempts to compute the
Logsum based on RR-MNL and to compare it to the
Logsum based on RU-MNL to assess the impacts of
hypothetical changes to the quality of recreational
sites.
Results
We find that the RU-MNL model performs slightly
better than the RR-MNL, although the difference is
relatively small. We also find some illuminating
factors that contribute to the RR-MNL’s ability to
describe choice behaviour more accurately, in
some instances, than the RU-MNL. In the
recreational site choice context that was
considered in this study, we find that the more
familiar a site is (denoted by the frequency of visits
to a site for recreation), the more likely the choice
of this site is better explained by utility
maximization (RU-MNL) than regret minimization
(RR-MNL). For many of the less familiar sites
compared to the Liffey, the choice of these sites
are better described by the RR-MNL than by the
RU-MNL. This can have important management
implications because it suggests that site managers
may require different management strategies if
they aim to increase kayakers’ visits to specific
sites. Also, intermediate and advanced skilled
kayakers’ choices would appear to be better
described by regret minimization rather than
utility maximization, reflecting the fact that this
group of recreationists may give more
consideration to the difference in the level of
attributes between sites compared to their basic
skilled level counterparts.
The analysis of the Logsum formulae under
hypothetical changes in recreation quality
highlighted both similarities and differences in
impacts arising from the two modelling
approaches. Both models show that the increase
in crowding at a site negatively impacts kayakers’
experience. For all respondents under the RU-MNL
the introduction of a parking fee reduces kayakers’
utility. However, the RR-MNL is able to show that
the introduction of a parking fee has some positive
effects, as it decreases the regret for a quarter of
kayakers.
Conclusions
Similar to previous studies that have applied both
RU-MNL and RR-MNL, the two approaches retrieve
similar model fits and estimate the same number
of parameters. However, they do imply slightly
different conclusions arising from the research.
This begs the question about which modelling
approach the researcher should apply. We would
argue that, given the ease with which RR-MNL
models can also be estimated using the standard
econometric approaches, the analyst should
consider applying both modelling approaches to
their data. Moreover, the results from this study
suggest that, since some choices are better
described by utility maximization and some by
regret minimization, then it may be prudent to
apply the model that best reflects the particular
choice behaviour. This approach would capture
the behavioural influences on choices more
accurately than assuming in all instances that
individuals always make choices within a utility
maximization framework.
References
Burt, O. & Brewer, D., (1971), Estimation of Net
Social Benefits from Outdoor Recreation
Econometrica,39(5):813-827.
Clawson, M., (1959), “Methods of Measuring the
Demand for and Value of Outdoor Recreation,”
Reprint No. 10, Resources for the Future:
Washington, D.C.
Chorus, C., (2010), A New Model of Random
Regret Minimization, EJTIR 10(2):181-196.
Haab, T. &
McConnell K., (2002), Valuing
Environmental
&
Natural
Resources,
Northhampton MA: Edward Elgar.
Hanemann, M., (1978), A Methodological and
Empirical Study of the Recreation Benefits from
Water Quality Improvement, Ph.D. Dissertation,
Harvard University (Economics).
Hotelling, H., (1947), “Letter to the National Park
Service,” Reprinted in An Economic Study of the
Monetary Evaluation of Recreation in the National
Parks [1949]. U.S. Department of the Interior,
National Park Service and Recreational Planning
Division, Washington, DC.
Kivetz, R., Oded N., & Srinivasan, V., (2004),
Alternative Models for Capturing the Compromise
Effect, Journal of Marketing Research 41 (August).
Trice, A & Wood, S., (1958), Measurement of
Recreation Benefits, Land Economics , 34(3):195207.
11
Coral-Fisheries Interactions in Bioeconomic Modelling – Comparing Norway
and Iceland’s Redfish Fisheries
Naomi S. Foley & Claire W. Armstrong, Norwegian Fishery College, University of Tromsø,
Norway
Introduction
There is very little bioeconomic modelling
work that explicitly takes into account the
interaction between marine habitats and
fisheries. Within the bioeconomic setting,
habitat can influence commercial stocks or
fisheries upon these stocks in a number of
ways. Habitat may positively contribute to
stock growth through the provision of
breeding, spawning or refuge sites. It is
possible that the price of species harvested
over particular habitats may also be affected,
not directly by habitat, but indirectly through
market and consumer preferences.
Some
habitats may encourage the
concentration of commercial species thus
reducing harvest costs (see Foley et al
(forthcoming) for an overview of interactions.
Specifically habitat can be (1) facultative, (2)
essential, or have (3) a positive effect on the
catchability or finally (4) a potential price
increasing effect. The former two are grouped
under biophysical effects, while the latter two
under bioeconomic effects as illustrated in
figure 1.
FISHERY
BIOECONOMIC
EFFORT
HARVEST
HABITAT
MANAGEMENT
BIOPHYSICAL
FISH STOCKS
Figure 1: Habitat interactions presented.
Interactions drawn in the ovals represent
biological entities associated with habitat
while squares and triangles are human
behavioural entities. Source: Foley et al
(forthcoming)
Of particular interest to this study are the
functional values or indirect use values
associated with cold water coral (CWC)
ecosystems, such as habitat, nurseries or
areas of refuge. Within the MEA framework
such services are classified as supporting
services, i.e. the functions that are necessary
for the production of all other ecosystem
services.
Two cases of coral-fisheries interactions are
studied in a bioeconomic model setting:
Norwegian and Icelandic redfish fisheries. The
cases are studied using time series data of
catch and effort in the fisheries, while
estimating for possible outer limits of cold
water coral decline. The two countries have
applied different types of management, where
Norway’s management has been closer to
open access, while Iceland has had a property
rights system of individual transferable quotas
(ITQ) in the time period studied.
The Model
Focusing on the biophysical effects of habitatfish connections we apply the production
function approach to two case studies, one
Norwegian and the other Icelandic. The
application can potentially link CWC to
fisheries, identifying to what degree profits
from commercial species are affected by the
loss of CWC. Given the identification of such a
link, this can then be modelled in order to
ascertain the losses involved when this link is
not included in management or conservation
decisions.
An essential fish habitat model is presented,
as used in Barbier and Strand (1998) and Foley
et al (2010). Defining Xt as the stock of fish,
changes in growth can be expressed as:
Xt+1 −Xt = F(Xt ,Ht ) −h(Xt , Et ), FX >0, FH >0, F(X,0) =0
(1)
The net expansion of the stock occurs as a
result of biological growth in the current
period, F(Xt, Ht), net of any harvesting, h(Xt,, Et)
12
1
, which is a function of stock as well as effort.
The influence of the coral area, Ht, as a habitat,
on the growth of the fish stock is assumed to
be positive ( ∂F / ∂H t = FH > 0 ) and
the effect of coral loss is greater in Norway
than Iceland. In both countries, however,
there are indications of economic losses due
to cold water coral decline. The open access
nature of the Norwegian fishery seems to
exacerbate these losses. The assumed loss of
30-50% of cold water coral gives an estimated
annual loss in harvest of approximately 400014000 tonnes of redfish in Iceland, resulting in
losses of between 2,5-8,4 million Euros.
essential ( F ( X ,0) = 0 ).
The logistic growth function is adjusted to
allow for the influence of the CWC habitat,
denoted by H, as in Foley et al (2010):
F ( X , H ) = rK ( H ) X (1 −
X
).
K (H )
(2)
For Norway the same cold water coral decline
explains harvest losses of between 2 and 7
thousand tonnes of redfish, worth between 2
and 6 million Euros. Though the losses in
Norway are lower than in Iceland, they make
up a much larger percentage of actual
harvests and revenues, as the fishery in
Norway is on average less than a fourth of the
Icelandic fishery.
Figure 2 illustrates the effect of assuming both
the carrying capacity and the intrinsic growth
rate are functions of H; i.e., a fall in CWC will
cause a reduction in both.
F(X,H)
K(H)
K
Figure 2: Logistic growth function, impact of
reduced habitat.
Analysis: Comparison of Norway and Iceland
Two management conditions are considered
for our comparative static analysis. The major
difference between the two case studies is the
management applied to the fishery. During
the period of the Norwegian analysis the
fishery was de facto open access and thus we
assume total revenues are equal to total costs,
i.e. all profits are dissipated, and this is used
to elicit costs. The Icelandic fishery has been
managed by ITQs. Icelandic harvest of redfish
is about four times that of the Norwegian
harvests. We therefore conduct a comparative
static analysis for an MEY fishery for the
Icelandic case. The MEY comparative static
analysis indicates that harvest decreases at a
decreasing rate with losses of habitat area.
X
References
Barbier EB and Strand I. (1998), Valuing
mangrove-fishery linkages – a case study of
Campeche, Mexico. Environmental and
Resource Economics, 12: 151-166.
Foley NS, Kahui V, Armstrong CW and van
Rensburg T. (2010), Estimating linkages
between cold water corals and redfish on the
Norwegian coast. Marine Resource Economics,
25: 105-120.
Foley NS, Armstrong CW, Kahui V, Mikkelsen E
and Reithe, S. (Forthcoming). A review of
bioeconomic modeling of habitat-fisheries
interactions. International Journal of Ecology.
The results indicate higher technological
development in Iceland than Norway, while
1
The Schaefer harvest function is assumed;
h = h ( E , X ) = qEX , where q denotes the
constant catchability coefficient, X is the stock
biomass and E is fishing effort
13
Valuing Marine Environmental Characteristics Associated with Changes
to the EU Bathing Water Directive
Stephen Hynes, Caitriona Scully and Alphonsus Browne, SEMRU, NUI Galway
Introduction
This paper reports the results of a Choice
Experiment (CE) study of the economic value of
potential improvements to coastal water quality
that may result from implementation of changes
to the European Union’s Bathing Waters Directive
in 2015. The focus is on potential benefits to
recreational users of coastal waters, and how
these vary according to the extent of exposure to
risks. The Choice Experiment method has been
applied in a number of recent studies to coastal
water quality changes and allows the researcher to
estimate separate values for different aspects, or
attributes, of water quality improvements which
are relevant both from a water quality
management perspective, and from the viewpoint
of peoples’ preferences over water quality
improvements and the benefits of coastal zone
protection.
Methods such as choice experiments help build a
picture of the economic values of protecting and
enhancing ecosystem services, thus contributing to
the evidence base for better management of
marine resources, and for improved policy-making
and regulation. For example, environmental
valuation methods allow the quantification of the
benefits of policies such as the EU Bathing Waters
Directive, which can then be compared with the
costs of implementing a policy in order to judge
the overall social efficiency of new regulation and
the desirability and targeting of “derogations”
from uniform targets.
A new European Union Directive on bathing water
(Directive 2006/7/EC) came into force on 24 March
2006. It repeals the existing 1976 Quality of
Bathing Waters Directive with effect from 31
December 2014. The 2006 Directive establishes a
new classification system for bathing water quality
based on four water quality classifications: ‘poor’,
‘sufficient’, ‘good’ and ‘excellent’ and requires that
a status of ‘sufficient’ be achieved by 2015 for all
bathing waters. Environmental regulators must
place warning signs on beaches which fail to meet
this standard. Repeated failures to meet the
standard will result in beaches being dedesignated. The new Directive on bathing water
establishes microbiological standards for two new
parameters, namely intestinal enterococci and
Escherichia coli. Since 2011, these two
microbiological parameters have been monitored
and used to classify bathing waters. In Ireland, the
Environmental Protection Agency is charged with
monitoring and testing the compliance status of
Irish bathing waters with EU bathing water quality
standards. The quality of Ireland’s bathing waters
is high, with 97% of bathing areas (127 of 131
areas monitored) complying with the minimum EU
mandatory values and achieving ‘sufficient’ water
quality status. However, other European countries
face more of a challenge in complying with the
Directive; in England, for example, around 7% of
beaches currently do not comply with the new
‘sufficient’ standard.
Survey design
The focus of the CE was on the valuation of
changes in coastal water quality to those who use
beaches in Ireland for recreation, principally
“active” recreationalists such as surfers, swimmers
and sea kayakers. This group of respondents are
likely to be particularly affected by improvements
to water quality which result from revisions to the
Bathing Waters Directive, since many of the water
quality parameters which the directive focuses on
are linked to human health. As water quality
improved, the exposure of beach users to illness
from contact with water-borne pathogens such as
faecal coliforms will decline.
The identification of attributes for the CE design
was based upon the changes being made to the
Directive. A number of other aspects of a
recreational trip to a beach were identified at the
piloting stage which individuals considered to be
important, such as weather and surf conditions,
crowding on the beach and the use being made of
the beach by other users. However, these were
excluded from the CE design as they will not be
directly changed by implementation of the
Directive. Verification that the attributes included
in the analysis were appropriate and
understandable was carried out through a pilot
survey of a sample of 40 active beach
recreationalists.
14
The attributes chosen for the CE describe three
aspects of coastal water quality: benthic health,
human health risks, and beach debris.
The
interviews for the main survey were
conducted face-to-face at beaches on the west
coast of Ireland from June to August 2011. The
surveys were conducted both during the week and
at weekends. A total of 382 individuals were
interviewed, yielding 365 observations which
could be used in the final analysis.
Results
Using the collected data, a 2 class latent class
model was employed to explain the preferences of
marine recreationalists in Ireland for a number of
beach and water related attribute levels that can
be associated with the recent changes to the EU’s
Bathing Waters Directive. Results showed that
people are willing to pay for all of the
improvements modelled, since they were willing
to incur higher travel costs to access
“hypothetical” beaches with these higher quality
levels compared with the status quo choice of
recreational location. Whilst it is not possible at
present to aggregate these numbers to a national
benefits figure – due to a lack of reliable national
data on participation in coastal water-based
recreation – the economic benefits of
implementing the Directive would clearly be
substantial.
Should national participation data become
available, per-trip estimates such as this could be
combined with such data and count models of
participation change as a function of the higher
utility from improved beach quality to generate
national willingness to pay estimates. Non-use and
“informal” recreational use values for these
improvements would also need to be estimated
(for an example of the former for coastal water
quality in the context of the original Bathing
Waters directive). National benefit estimates could
then be compared to national cost figures for
making these improvements in water quality, for
example through modifications of sewage
treatment, storm water overflows and pathogen
run-off from farmland.
Whilst the Irish government does not have choice
over whether to implement the revised directive,
it could use such information at the regional level
in targeting water quality improvements at sites of
high use in terms of on-water recreational activity.
Benefit estimates at the level of specific beaches
could be used to help make decisions on which
beaches should be targeted for improvements,
and which should be no longer designated as
bathing waters. This would be the case if the
economic costs of improving a site to a “sufficient”
water quality level outweighed the benefits. A
national, aggregate benefits figure substantially
less than aggregate costs would imply more
attention needing to be paid to finding more costeffective ways of achieving target improvements in
water quality.
In the LC analysis, in-water recreationalists were
found to be more likely to be in class 1, which has
a lower positive coefficient value for health risk
reduction to zero than class 2. However, the
smaller coefficient for cost (in absolute terms) for
class 1 than class 2 results in a higher marginal
valuation of reductions in health risks relative to
latent class 2. This result is being driven by the
sensitivity of the participants in each class to the
price associated with a management option rather
than their preference for the actual health
attribute level.
The reduced sensitivity to health risk reductions,
as shown by the preference parameters in the LC
model echoes results found by Hynes et al. 2008
and Boeri et al. (2012) who suggest that water
quality and the implied health risk is not generally
an important aspect of a dedicated water sports
recreationalist’s choice of site, unless the level of
water pollution is extreme. These water users, and
especially those with the higher skill levels, are
more interested in the recreational experience
that the site can offer rather than the marginal
health risks involved from using the site. However,
there may be a complex relationship between
selection in type of activity, subjective assessment
of personal risk and valuation of risk reduction
underlying these potentially contradictory findings.
Results from the RPL and LC models also showed
considerable variation in preferences across the
different recreational user groups. This suggests
that beach and coastal recreation site managers
and policy makers in charge of such sites should
think carefully about the particular type of
recreationalist utilising any site and the attributes
and facilities that such users value, in developing
site-specific management plans. Finally, at a more
general level, Ronnback et al. (2007) have argued
that “the evaluation of ecosystem goods and
services from both economic and ecological
perspectives is a necessary ingredient in practical
policy”. Stated preference methods, such as that
used here, provide one important means of
arriving at such economic evaluations.
15
Port and Shipping Data as Economic Indicators
Fergal Curtin, Shipping Market Analyst, Irish Maritime Development Office, Marine
Institute
Introduction
The Irish Maritime Development Office (IMDO) is
Ireland’s national dedicated development,
promotional and marketing agency for the
shipping services sector. The office was
established in 1999 and is part of the Marine
Institute which is a state agency responsible for
researching the potential of Ireland’s vast marine
resources. The Irish Maritime Development Office
operates under the auspices of the Department Of
Transport, Tourism & Sport, and is charged with
the responsibility for undertaking the following
activities through its statutory remit:
•
•
•
To promote and assist the development
of Irish shipping, Irish shipping services
and seafarer training.
To liaise with, support and market the
shipping and shipping services sector.
To advise the Minister on the
development and co-ordination of policy
in the shipping and shipping services
sector so as to protect and create
employment
The Irish Maritime Development Office: Economic
Data Analysis:
The Irish Maritime Development Office (IMDO)
produces a number of key economic and shipping
reports for industry. The IMDO Weekly Market
Review is produced in-house and offers an in
depth analysis into international/national shipping
markets and provides a national & global
economic overview of the week’s events. The
annual Irish Maritime Transport Economist is
published in April of each year. This is a statistical
bulletin and a comprehensive source of maritime
traffic, trade and global shipping markets data. A
capacity report is produced bi-annually on Lifton/Lift-off, Roll-on/Roll-Off, and Passenger
capacity in the Irish market. This update provides
the IMDO with information on operators, routes,
frequency of service, and destinations served from
Ireland. On a quarterly basis a traffic report is
published which provides ports, operators,
government and the general transport industry
with up-to-date information on how the market is
performing individually and as a whole. The IMDO
also produce a number of “Shipping Tradelinks”
reports which provide a succinct analysis of
Ireland’s trade with over a dozen European
Countries.
Port and Shipping Data as Economic Indicators:
The IMDO gather monthly, quarterly and annual
shipping market statistics which is then analysed
and reported to industry stakeholders. The IMDO
quarterly reports are generally released just over a
month after each quarter’s end. This provides a
vital insight into how the economy is performing
as nearly 99% of goods are transported by sea.
Analysing any of the major Irish shipping sectors
before and during the current recession will
accurately inform any reader of the demise in the
volume of commodities moving through Irish Ports
during this period. The IMDO data is analysed in
core market segments. Bulk Traffic is subdivided
into Dry, Liquid, and Break Bulk. Lift-On/Lift Off
traffic is subdivided into laden and unladen traffic
by import/export traffic. Finally Roll-On/Roll-Off
traffic is analysed into accompanied and
unaccompanied traffic again by imported or
exported traffic. These traffic segments
individually and combined are able to provide a
vital overview of how the shipping sector and
general economy are performing at any given
moment.
Recent Trends in the Irish Shipping Sector:
2010/11
A notable feature in the IMDO traffic analysis in
2010 was the continued growth in exports as there
was a wider recovery in global economic demand.
The IMDO estimated that export volumes on the
principle routes to the UK, Asia and US were up
overall by 7 per cent in 2010. Roll-On/Roll-off
volumes through all Irish ports recovered by 5 per
cent, lift on/lift –off declined by 4 per cent while
dry bulk volumes were up 18 per cent, and liquid
bulk increased by 2 per cent. Part of the return to
growth in the Dry Bulk sector was attributed to
strong global demand for ore and mineral products
such as alumina, while domestic demand in the
agricultural sector led to a rise in the imports of
grains, feeds and fertilizers. Moving onto 2011,
there was very little growth in volumes terms.
Total bulk volumes increased by 1 per cent. After a
relatively strong start to 2011, the majority of
ports witnessed a slowdown in bulk volumes in the
16
second half of 2011 compared to 2010, with only
three ports showing bulk volume growth in the
fourth quarter of 2011. This volume decline can be
mainly attributed to the global slowdown in
demand.
The current prevailing economic uncertainties are
clearly reflected in the load-on/load-off (lo/lo)
traffic volumes in 2011 which saw container
volume demand pick up early in the year before
falling away in the latter half of 2011, as
underlying concerns about the pace of recovery in
the domestic and global economy continued. Rollon/roll-off (ro/ro) traffic through All-Island Ports
remained relatively static in 2011. Ro-Ro volumes
have now declined by 10 per cent when compared
to 2007 levels but remain higher than levels
witnessed in 2009. Continued austerity programs
in both Ireland and the United Kingdom affected
disposable income which led to further downward
pressure on freight volumes between the two
nations as business sentiment, especially in the
United Kingdom, remained weak. The United
Kingdom accounts for 93 per cent of Ro/ro
volumes through Irish ports while the remainder is
made up of Continental traffic. Passenger volumes
through the Island of Ireland in 2011 declined by 6
per cent to 4.6 million while tourist cars volumes
showed a 4 per cent reduction compared to 2010
figures.
Port Financials:
The focus of the IMDO’s analysis is to extrapolate
key data from the annual company financial
reports in order to provide a comparative review
of Irish ports. The annual reports are analysed in
three separate parts, overall performance of the
ports, individual port analysis and composite ratio
analysis in which ports are compared across a
number of separate accounting ratios to measure
core efficiencies. Analysis from the IMDO “Review
of Port Financial Accounts” was utilised by the
Government’s Review Group, chaired by Colm
McCarthy, in its report on “State Assets and
Liabilities”, published in April 2011.
All-Ireland Freight Forum (AIFF) Project:
The AIFF was established by the North/South
Ministerial Council in April 2009 and held its first
meeting in January 2010. The main priorities of the
AIFF are: being competitive in a sustainable
manner; safer, compliant, eco-efficient, road
freight transport; Rail freight and other
alternatives; International connectivity and
Network management. The IMDO has been
charged with the remit of establishing a strong and
accurate baseline of international connectivity
from the Island of Ireland and has to date
researched a number of area’s in both maritime
and aviation freight movements.
Conclusion:
Reports by the IMDO are being used at the very
highest level by government and industry bodies
(both shipping and economic). Shipping data
collected by the IMDO on a daily, weekly, quarterly
and yearly basis is a vital tool for understanding
how the shipping industry/economy is performing
and is an essential tool in examining how shipping
markets will perform in the future. Stakeholders in
the Irish shipping industry look for the most up-todate data analysis in order to make key decisions
on how they will operate their businesses in the
future and the IMDO is well positioned to provide
such analysis.
References:
Irish Maritime Development Office Website:
http://www.imdo.ie
The Irish Maritime Transport Economist: (IMDO)
http://www.imdo.ie/IMDO/newsroom/recentpublications/
IMDO Weekly Market Review:
http://www.imdo.ie/IMDO/newsroom/WeeklyMa
rketReview.htm
Shipping Trade links: (IMDO)
http://www.shippingtradelink.com/
17
Valuing the Non-Market Benefits of the WFD Implementation in Ireland
Daniel Norton, Stephen Hynes, Edel Doherty SEMRU, NUI Galway; Cathal Buckley,
Teagasc; Danny Campbell, Queens University
Introduction
The Water Framework Directive (WFD) aim is for
EU waters to be at least ‘good ecological status’
(GES) by 2015. These water bodies include inland
surface waters, transitional waters, coastal waters
and ground waters. GES is a broader measure of
water quality than the physio-chemical indicators
that previous EU legislation used as an indicator of
water quality. In using the framework approach
the EU is letting member states flexibility in how
they achieve at least GES in waters or maintain
waters which are currently at good or high status.
The WFD calls for a consideration of the economic
costs and benefits of improvements to ecological
status in catchment management plans, along with
the introduction of full social cost pricing for water
use. Where achieving GES is ‘disproportionately
expensive’ (often took to mean where costs
outweigh benefits) then extensions to meeting
GES may be allowed up to 2027 for such water
bodies.
Many of the benefits of achieving GES are nonmarket goods and services including water based
recreation, angling, landscape values and non use
or existence values. Therefore, non-market
valuation studies may need to be undertaken to
capture the value of benefits before deciding if
cost of achieving GES is ‘disproportionately
expensive’
Benefit Transfer
There are a variety of non-market valuation
techniques (travel cost models, hedonic pricing,
contingent valuation, choice modelling, etc)
(Hanley and Barbier, 2009) which can be used to
estimate the value of non-market benefits. One of
the disadvantages of these methods is that they
are expensive and time consuming. An alternative
of these primary methods is a method known as
benefit transfer (BT). BT is a process of valuing a
non-market good or service of a policy site by
using values estimated for similar non-market
services at another study site and applying these
values to the policy site (Brouwer, 2000).
There are three main types of BT. The unit value
transfer can take two forms, a simple unit transfer
is where one value is directly transferred from a
study site to the policy site and an adjusted unit
transfer where one or more study sites values are
adjusted for various factors (income, exchange
rates, inflation, etc) before being transferred to
policy site. The second type is to transfer a value
function from the study site to the policy site and
the last BT type is the undertaking of meta analysis.
The advantages of BT are that it is relatively cheap
and fast to undertake. However, as it is a
secondary valuation method it suffers from the
risk that the values transferred may under or over
estimate the actual value of the site. This
difference is known as the transfer error.
Using BT to rank value of Irish water bodies
Ireland is divided into 8 River Basin Districts (RBDs)
for the purposes of implementing the WFD. Within
the Republic of Ireland these are subdivided into
151 Water Management Units (WMUs). Relevant
populations for each of the WMUs were based on
populations within the EDs which the WMU
overlapped (Figure 1.) An adjusted unit BT was
then carried out based on average adjusted values
from five studies which measured the value of
change in water status. The value of Irish WMUs
achieving GES was estimated based on change
from the current status to GES and on the relevant
population within the WMU.
Figure 1. EDs overlain by WMUs within Ireland
As the values are driven by both poor initial water
quality and population, it is not a surprise that the
WMUs which achieved the highest value of
attaining GES were those close to the urban areas
of Dublin and Cork. The Tolka WMU was the
18
highest valued WMU with a value of complying
with the WFD of €2.8 million.
Note that some areas such as offshore islands and
the area around Inner Dublin, which had the
highest population, are not covered by WMUs. If
the latter was considered a WMU it could have
been the highest valued WMU (€2.3 - 4.8 million).
If this area is included as a WMU then the total
value of meeting the WFD could be €30.6 million.
Further difficulties with this approach are that
values of people outside the WMU are not
considered and that this measures only the value
in the change of water status and is not an
estimate of the ‘total economic value’ of the water
bodies within the WMU. Therefore WMUs
currently at GES have a value of zero.
Figure 2. Buffer zones for the Boyne Catchment
Using BT to estimate value of an Irish water body
The previous approach is useful to help rank
various WMUs and help to allocate resources but
when the value of achieving GES of one water
body may be more important to a policy maker a
slightly different approach is used.
Conclusions
While primary valuations offer the best approach
in valuing environmental goods and services such
as improved water quality, BT can be a quicker and
cheaper methodology of estimating values of
water bodies achieving GES. There is the risk of
producing under or over estimates and economic
analysis should not the sole determinant within
the area of water policy in Ireland but used as a
tool to help inform the decision maker.
A previous study on the Boyne catchment was
undertaken by Stithou (2011). Undertaking a BT
exercise on the same water body would allow a
transfer error to be calculated. Most of the policy
site was at poor or moderate status and five UK
studies values were transferred to estimate the
value of achieving GES. Along with being adjusted
for income differences between countries,
exchange rates and inflations, the transferred
values were adjusted to allow for distance decay.
The concept of distance decay was used to
estimate the difference someone closer to a study
site will be willing to pay compared to someone
who lived further away from the site. This
phenomena has been observed for both use and
non-use values.
Buffer zones were setup around the policy site
(Boyne River Catchment) (Figure 2) based on
buffer zones described in Hanley et al (2003) and
the average of the values transferred were
adjusted based on the use and non-use figures
reported for each buffer in the same paper. The
total value for the catchment was calculated by
summing the use and non-use estimates for the
buffer zones together.
The value estimated was €13.6 million which was
less than the €19.1 million estimated generated by
Stithou (2011). This was a transfer error of -29%.
This is within the range as those reported for BT
studies within the literature.
Acknowledgement
This work was funded under the EPA STRIVE
Programme
References
Brouwer, R. (2000), Environmental Value Transfer:
State of the Art and Future Prospects, Ecological
Economics, 32: 137-152
Hanley, N., Schlapfer, F. and Spurgeon, J. (2003),
Aggregating the benefits of environmental
improvements: distance-decay functions for use
and non-use values, Journal of Environmental
Management, 68: 297–304
Hanley, N. and Barbier, E. (2009), Pricing Nature,
Edward Elgar, Cheltenham, UK
Stithou, (2011), The economic value of
improvements in the ecology of Irish rivers due to
the Water Framework Directive, PhD Thesis,
Stirling Management School, Economics Division,
University of Stirling
19
The Impact of Cost Uncertainty on the Feasibility of Wave Energy
Conversion Devices and the Implications for Policy
Niall Farrell, SEMRU, NUI Galway
Introduction
Feasibility analyses of Wave Energy Conversion
devices are subject to uncertainty as a lack of
operating experience has led to sparse information
availability for cost parameters. As such,
approximate point estimates of uncertain
parameters have been chosen for investment
appraisals to date. Such an approach overlooks the
potential extent of cost variability, and thus
investment risk, resulting from cost uncertainty.
This is an important consideration for both the
appraisal of private investment and mechanisms of
public support.
This study uses statistical simulation to incorporate
the risk presented by these uncertain cost
parameters in profitability estimations. In doing so,
the potential range of uncertain input values is
used in favour of potentially subjective point
estimates. The Conditional Value at Risk (CVaR)
metric is used to derive probability estimates for
certain cost and return values under different
conditions. Taking Ireland as a case study, this
methodology is applied to assess the adequacy of
Renewable Energy Feed-in Tariff (REFIT) support
mechanisms for the Pelamis WEC device.
Motivation
A Renewable Energy Feed-in Tariff (REFIT) is the
policy support mechanism in place to provide an
adequate return on investment for novel
renewable technologies in Ireland. This operates
by providing a hedge against market price
fluctuations by offering a guaranteed price of
€0.22 for each kilowatt hour (kWh) of electricity
generated. The effectiveness of this mechanism
depends on whether this provides sufficient
support for private investment. Estimations of
project cost are required in order for this to be
determined. Current estimates have been
calculated using deterministic methodologies, with
Irish estimates varying from €0.05-€0.20/kWh. This
range indicates a considerable degree of
uncertainty as to the true cost value, making policy
evaluation particularly difficult.
Furthermore, employing point estimates in a
deterministic analysis overlooks the impact that
uncertainty, and thus exposure to risk, may have
on the investment decision. This is an important
consideration as although a deterministic analysis
may suggest an adequate return, deviation of
costs from assumed values may yield an
inadequate return. The sensitivity of profitability
to potential deviations is an important
consideration as a prudent investor may abstain if
there is sufficient possibility that financial return is
inadequate. For policy appraisal, the exposure to
risk under a given policy regime may be defined as
the probability of effectively achieving the desired
investment environment.
Methodology
A Monte Carlo Simulation Procedure is employed
to elicit the probability of project cost and
profitability from a known range of potential cost
values. Monte Carlo simulation is a numerical
process to estimate the probability density
distribution of an uncertain parameter, such as
project profitability. This is carried out by repeated
calculation of project profitability, where each
calculation uses a different value for each
uncertain input. This value is sampled randomly
from a known distribution. In doing so, the
probability density distribution of project
profitability may be calculated based on the
proportional frequency of obtaining each
profitability estimate. Specification of the full
range of potential uncertain values in favour of
subjective point estimates provides an objective
estimation of project profitability, whilst allowing
for the simultaneous consideration of all potential
sources of cost variability. The simulation process
employed in this study has 11 random variables.
Once the Monte Carlo simulation is complete,
there exists 10,000 iterations of project cost and
profitability. One must determine the point in this
distribution at which project profitability may be
evaluated such that risk is mitigated. Thus, the
Conditional Value at Risk (CVaR) metric is
employed. CVaR is an augmentation of the
standard value at risk (VaR) metric. The VaR states
that with probability β the expected value will not
be lower than a certain threshold α. The CVaR
focuses on the tails of the distribution and
averages the values which fall short of threshold α
depending on the probability level β. Therefore,
20
the CVaR is a more conservative risk measure
(Gass et al., 2011).
Preliminary Findings: Cost Estimates
This methodology gives greater interpretational
power to existing uncertain cost estimations by
providing a degree of likelihood for each.
Preliminary results find that the most likely value
for 100 unit steel-based devices in Ireland is
€0.16/kWh. One can see that this central estimate
is quite similar to that quoted by Dalton et al.
(2010), where a cost of €0.15/kWh is quoted under
similar conditions. This provides an element of
validation for the model specification. The added
value of this model is realised when one analyses
the probability of achieving this cost value, and
thus the exposure to risk. It is found that there is
74% chance of achieving an LCOE greater than or
equal to €0.15/kWh, indicating that policy
formulated on this central estimate is subject to a
great deal of risk. As such, the cost of electricity is
re-evaluated at the 90% CVaR threshold. At this
point of estimation, this analysis recommends that
prudent policy formulation consider a cost value of
€0.187/kWh.
Preliminary Findings: REFIT Appraisal
The second goal of this paper was to ascertain the
degree of certainty to which current REFIT policy
of €0.22/kWh provides an adequate investment
environment. It has been stated that an Internal
Rate of Return (IRR) of 10% is required for Wave
Energy to be an attractive investment (Dalton et al.,
2010). For initial steel-based devices, it is found
that only device installations of 80 units or greater
provide the potential for positive rates of return,
with a <1% probability of 100 unit steel-based
devices yielding a negative rate of return.
Preliminary IRR estimates lie within a range of 3%28% for 100 unit steel devices, with a most likely
value found to be above the 10% threshold of
viability. However, there is still a 50% chance that
this policy results in a rate of return less than 10%.
This is reflected in the requirements for a prudent
policy formulation where IRR falls below 10% at
the 90% CVaR threshold. As such, these findings
indicate that current policy is potentially effective
in achieving the required rate of return, if
investors are certain that costs and outputs will be
at their most likely value. However, this is not
necessarily the case when one considers potential
variability and deviation from this most likely value.
This analysis finds that uncertain input costs create
a risk that REFIT policy may yield an inadequate
return for Wave Energy Conversion Device
investment. Preliminary results recommend that a
risk premium of €0.027/kWh be considered when
evaluating the cost of initial 100 unit installations.
This cost variability translates into considerable
risk that investment will yield an inadequate rate
of return. As such, it is recommended that a REFIT
premium of 12-25% greater than that required for
most likely cost estimates is required to take into
account the risk presented by cost uncertainty.
These findings relate to preliminary parameter
estimates and 100 unit installations. This work will
extend to consider the exposure to risk according
to installation size, technological maturity and
alternative parameter assumptions.
References
Allan, G., Gilmartin, M., McGregor, P. & Swales, K.
(2011), Levelised costs of Wave and Tidal energy in
the UK: Cost competitiveness and the importance
of “banded” Renewables Obligation Certificates,
Energy Policy 39(1), 23 - 39.
Dalton, G., Alcorn, R. & Lewis, T. (2010), Case study
feasibility analysis of the Pelamis wave energy
convertor in Ireland, Portugal and North America,
Renewable Energy 35(2), 443 - 455.
Department of Communications Marine Natural
Resources (DCMNR) (2005), Ocean Energy in
Ireland, Dublin: Stationery Office.
Gass, V., Strauss, F., Schmidt, J. & Schmid, E. (2011),
Assessing the effect of wind power uncertainty on
profitability, Renewable and Sustainable Energy
Reviews 15(6), 2677 - 2683.
Montes, G. M., Martin, E. P., Bayo, J. A. & Garcia, J.
O. (2011), The applicability of computer simulation
using Monte Carlo techniques in windfarm
profitability analysis, Renewable and Sustainable
Energy Reviews 15(9), 4746 - 4755.
Previsic, M. (2004), System level design,
performance, and costs of California Pelamis wave
power
plant,
Technical
report,
EPRI,
http://oceanenergy.epri.com/attachments/wave/r
eports/006_San_Francisco_Pelamis_Conceptual_D
esign_12-11-04.pdf.
SQW (2010), Economic Study for Ocean Energy
Development in Ireland: A report to Sustainable
Energy Authority Ireland and Invest Northern
Ireland.
21
Another Collective Tragedy? The Case of the Killary Mussel Farming Cooperative
Peter Cush, School of Political Science and Sociology and SEMRU, NUI Galway
Introduction
The natural resource base of Ireland has become a
critical component in addressing the rural
development problem. With the demise of small
scale farming in many rural areas, natural
resources became a key focus in diversifying small
local economies. ‘Inclusiveness’ and ‘Participation’
of those on the margins have become the policy
focus and these have permeated into much of
debate within the rural development discourse.
This ideological framework has looked at ways of
maximising community participation in resource
development. Of course the major difficulty with
this approach is that many of these resources are
finite and with a greater number of people using
the resources they become subject to depletion.
Community
participation
and
resource
sustainability are then two interdependent
variables with an inherent tension characterising
their relationship. Critical to their survival is the
ability of the users to draft cooperative
agreements between them and limit their use of
the resource.
However cooperation is not a natural social
phenomenon and relies heavily upon the local
social context and the wider structural framework
surrounding the resource users. The purpose of
this paper is to examine cooperative action in one
of Ireland’s largest aquaculture sites, Killary
harbour, North West Connemara. The argument
made is that while resources users demonstrated
an ability to cooperate themselves, such collective
action was ultimately undermined by a wider
policy framework that placed less of premium on
cooperative resource management. Cooperative
action in this instance was swimming against the
tide of the dominant ideological perspective that
placed more of an emphasis on ‘big business’ than
on cooperation as a form of economic organisation.
From a policy perspective, this case study proves
interesting and much can be learned from this
about the policy focus surrounding natural
resource management in Ireland. It was made
clear that managing rural marine resources formed
the basis of this paper, namely the question of
whether there is sufficient evidence to justify
moving from centralised command and control
policies to local level community based
management. Providing a clear answer to this
question requires us to look at two things, firstly
whether it can work and secondly if it can work
why we should actively seek to promote it.
Local level resource management
Let us deal with the first issue of whether local
level resource management can work. There is a
mixed message emanating from the above case
study as the co-operative showed a long period of
success coupled with a rapid period of decline.
However rather than giving a definitive answer to
the question, this case study shows that there are
numerous dynamics that make local level
management a success or a failure in any given
time and place. The 20 years of a success in Killary
highlights that in the right set of circumstances,
local people can develop cooperative strategies
that allow for successful resource management.
The issues that appear important here mirror
much of what is outlined in the literature on
cooperative management where communication,
a history of positive social relations, trust amongst
the participants and devolution of power from
centralised control form a critical piece of the
puzzle. However one issue that is not addressed is
the issue of economic necessity.
Discussion
The case of the Killary mussel farming co-operative
suggests that local level management will only
succeed as long as the users can be convinced that
it is in their economic interest to cooperate with
locally established rules. Without such an
economic incentive, local institutions legitimacy
will be called into question and can ultimately fail.
However if local institutions can balance these
dynamics then there is sufficient evidence to
justify the optimism attributed to local level
management.
The significant issue here is that it is important not
to be reductive. We cannot categorically state that
local level management is a good and positive
thing and that it will always work in Ireland. It will
not and examples provided in this study and by
22
Curtin (1993, 1988) show that the ‘rural’ is a
contested space and cooperation over a use of a
finite resource cannot be assumed to
automatically ensue. However on the other hand,
in certain instances it can work and when a group
of resource users demonstrate an ability to
develop cooperative strategies then the state
should actively work with this group of people and
provide technical, financial and legislative support.
From a policy perspective, governments
departments should be flexible and not base their
decisions on predefined notions of what local
people cannot do. Instead they need to develop
the capacity to look at cases independently and
understand the complex set of interpersonal
relations present and whether they will imbue
positive cooperative strategies. Only then, can
policy surrounding the management of finite
natural resources define itself as evidence based.
There is no right or wrong answer and cooperative
resource management can work in a given time
and place, and when it does, its success will be
determined by the political will to provide the
necessary space to resource users.
The next question that emerges is whether or not
policy makers should actively seek to promote
local level management. If policy makers can do
the same job as the resource users why then
should we seek to devolve power to a cooperative
group? Is there anything achieved in this process?
Does it not lead to unnecessary risk taking where
we entrust power to a group who we perceive of
as cooperative but who are ultimately
individualistic, deviant and non complaint? Should
policy as a result not just take the safe option and
always rely on command and control policies? The
problem with this argument is that there is nothing
to suggest that the state acts in the best interests
of the resource or the people who are reliant upon
these resources for their survival.
The case study presented above illustrated the
folly in assuming that the state always works
towards the optimum social outcome in the
management of finite resources. The department
of agriculture, fisheries and food took it upon
themselves to severely over license Killary harbour
in 1999/2000, a reality that has adversely affected
the producers ability to make a living from the
trade. For whatever reason, they felt that
overstocking Killary harbour was a positive step in
allowing the state to “manage an efficient and
effective regulatory framework in respect of
Aquaculture licensing and Foreshore licensing of
Aquaculture and Sea Fishery related activities, and
to secure a fair financial return from the state’s
foreshore estate”.
Conclusions
The steps taken in Killary harbour by the
department cannot in anyway be presented as an
“efficient and effective regulatory framework”.
Consequently this paper shows that the state is
just as likely to fail as a regulatory authority as the
resource users themselves. There is no ‘right’ or
‘wrong’, ‘proper’ or ‘improper’ in natural resource
management and both the state and the resource
users can fail as management agents. Managing
finite resource is a difficult task to achieve for any
regulatory authority, be it state or community
based. If a locally established institution proves
itself capable of managing a resource, policy
makers should not seek to dismantle it as it is not
in congruence with their pre-defined notions of
what ‘sustainable management’ means and
represents. If an agency proves itself successful at
managing a resource over a period of time, then
that surely justifies its legitimacy, and they should
not be overridden by biases and snobbery
surrounding
‘unsophisticated’
local
level
management practices. It is suggested that
regulatory authorities be more open and positive
about the potential of local level management and
realise that resource users do have the potential to
design rules that are better tailored to their own
needs and that of the resource that they have
regular interactions with.
The difficulty with much of the natural resource
policy in Ireland is that it is informed mainly by
marine biologists who define what is ‘best’ in
terms of managing the resources. However we are
reminded that “resource management is people
management” (Berkes and Folke 1998: 2) and that
unless policy makers show a better understanding
of people and the institutions that they can create,
only then can it be said to be clear, well defined
and supported by evidence. A key question that
needs to be asked here is that when resource
users demonstrate an ability to manage the
resources on their own are they facilitated and
given enough space to realise such potential? The
case of the Killary mussel farming co-operative
suggests that they are not.
References
Berkes, F. & Folke, C., 1998, Linking Social and
Ecological Systems: Management practices and
social mechanisms for building resiliance,
Cambridge University Press.
23
Annex 1
Beaufort Marine Socio-Economic Workshop Agenda
The 3rd Annual Beaufort Marine Socio-Economic Workshop
Friday 11th November 2011
Venue: MY129, Aras Moyola, National University of Ireland, Galway
8.45 -9.25
Registration and Tea/Coffee
Session 1.
Welcome and Overview of Days Proceedings – Professor John McHale
Ocean Economy Analysis: National and Sectoral Perspectives
9.30 - 9.55
9.55 - 10.20
10.20 - 10.45
10.45 – 11.15
11.15 - 11.35
Session 2.
Chairperson: Geoffrey O’Sullivan, Marine Institute
Some Observations about the US Ocean and Coastal
Economies from the National Ocean Economics
Program
Habitat-Fisheries Linkages in Bioeconomic Models
The potential for an Irish Maritime Transportation
Cluster: An Input-Output Analysis
Port’s and Shipping Data as Economic Indicators
Tea/Coffee
- Judith Kildow, The National Ocean
Economics Program, USA.
- Naomi Foley, Norwegian Fisheries College,
University of Tromsø, Norway
- Karyn Morrissey, University of Liverpool
- Fergal Curtin, IMDO and SEMRU.
Marine Ecosystem Service Valuation
Chairperson: Edel Doherty, SEMRU, NUI Galway
11.35 – 12.00
12.00 - 12.25
12.25 - 12.50
2.00 - 3.00
12.50- 2.00
Session 3.
3.25 - 3.50
3.50 - 4.10
4.10 - 4.30
4.30 - 4.50
Valuing the Dead Sea and peaceful cooperation in
the Middle East
Valuing marine environmental characteristics
associated with changes to the EU Bathing Water
Directive
Site choices in recreational demand: a matter of
utility maximization or regret minimization?
Valuing oil spill impacts in marine environments: The
United States experience and the 2007 oil spill in San
Francisco (Lecture Hall 1, St. Anthonys)
Lunch
- Alberto Longo, Queens University, Belfast
- Stephen Hynes, SEMRU, NUI Galway
- Marco Boeri, Queens University, Belfast
- Eric English, Stratus Consulting, Colorado,
USA.
SEMRU PhD Presentations
Chairperson: Dr. Judith Kildow,
Estimating the value of achieving ‘good ecological
status’ under the EU Water Framework Directive
The Role of the Consumer in Driving Sustainable
Consumption of Seafood
Another collective tragedy? The case of the Killary
mussel farming co-operative
The Cost of Wave Energy Conversion Devices and
Implications for Policy
Close of Workshop
- Danny Norton, SEMRU, NUI Galway
- Ann Walsh, SEMRU, NUI Galway
- Peter Cush, SEMRU, NUI Galway
- Niall Farrell, SEMRU, NUI Galway
Annex 2
Workshop Participants
Name
Organisation
Email
Mick O'Toole
Marine Institute
motoole@marine.ie
Dave Reid
Marine Institute
david.reid@marine.ie
Geoffrey O'Sullivan
Marine Institute
gosullivan@marine.ie
Trevor Alcorn
Marine Institute
talcorn@marine.ie
Patrick Tyndall
Bord Iascaigh Mhara
tyndall@bim.ie
Sean Lyons
Economic and Social Research Institute
sean.lyons@esri.ie
Cathal Buckley
Teagasc
cathal.buckley@teagasc.ie
Naomi Foley
University Tromso
naomifoley@gmail.com
Lava Yadav
National University of Ireland, Galway
lava.yadav@gmail.com
Stephen O'Neill
National University of Ireland, Galway
stepheno_neill_1999@yahoo.com
Ilaria Nardello
Marine Institute/NUIG
inardello@marine.ie
Michael Cuddy
SEMRU, NUI, Galway
michealcuddy@eircom.net
Colm Lorlan
Marine Institute
colm.lorlan@marine.ie
Oliver Tully
Marine Institute
oliver.tully@marine.ie
Ben Breen
SEMRU, NUI, Galway
B.BREEN1@nuigalway.ie
Ed Hind
SEMRU, NUI, Galway
Ed.hind1@nuigalway.ie
Will Maclennan
Natural England
willmaclennon@hotmail.com
Kevin Kilcline
National University of Ireland, Galway
kevinkilcline@gmail.com
Patrick Gillespie
SEMRU, NUI, Galway/Teagasc
patrick.gillespie@teagasc.ie
Natasha Evers
National University of Ireland, Galway
natasha.evers@nuigalway.ie
Paul Ryan
National University of Ireland, Galway
paul.a.ryan@nuigalway.ie
Meadbh Seoighe
Udaras na Gaeltacht
meadbh.seoighe@udaras.ie
Ann Walsh
National University of Ireland, Galway
ann.walsh@nigalway.ie
Brendan Kennelly
National University of Ireland, Galway
brendan.kennelly@nuigalway.ie
Thomas van Rensberg
National University of Ireland, Galway
thomas.vanrensburg @nuigalway.ie
John McHale
National University of Ireland, Galway
john.mchale@nuigalway.ie
Amaya Vega
National University of Ireland, Galway
amaya.vega@nuigalway.ie
Brian O'Toole
National University of Ireland, Galway
N/A
Edel Doherty
National University of Ireland, Galway
edel.doherty@nuigalway.ie
Maeve Edwards
National University of Ireland, Galway
maeve.edwards@nuigalway.ie
Pat Duggan
Department of the Environment
pat.duggan@environ.ie
Peter Cush
National University of Ireland, Galway
p.cush2@nuigalway.ie
Judith Kildow
NOEP, USA
judy@oceaneconomic.org
Peter Howley
Teagasc
peter.howley@teagasc.ie
Cathal O'Donoghue
Teagasc
cathal.odonnogue@teagasc.ie
Eugene Nixon
Marine Institute
eugene.nixon@marine.ie
Doris Laepple
National University of Ireland, Galway
Doris.Laepple@teagasc.ie
Alberto Longo
Queens University, Belfast
a.longo@qub.ac.uk
Marco Boeri
Queens University, Belfast
mboeri01@qub.ac.uk
Fergal Curtin
IMDO and SEMRU, NUI Galway
fergal.curtin@marine.ie
Eric English
Stratus Consultants, Colorado
eenglish@stratusconsulting.com
Niall Farrell
SEMRU, NUI Galway
niallfarrell@gmail.com
Paul Dunne
Department of the Environment
Paul_dunne@environ.ie
Dave Reid
Marine Institute
david.reid@marine.ie
Rebecca Corless
SEMRU, NUI Galway
rebecca.corless@nuigalway.ie
Dermot Hurst
Marine Institute
dermot.hurst@marine.ie
Wesley Flannery
Geography, NUI Galway
wesleyflannery@gmail.com
Michael O'Brien
BMW Assembly
mobrien@bmwassembly.ie