Article

Determining a Suitable Local Green Biorefinery Model for

Adoption by Irish Livestock Farmers Using a Mixed-Method
Co-Design Employing Economic and Geographical Information
Systems Analysis
Alice Hand 1, *, Emily Marsh 1 , Carmen Giron Dominguez 1 , Abhay Menon 1 , Theresa Rubhara 1 ,
Helena McMahon 1,2 , Breda O’Dwyer 3 , Paul Holloway 4,5 and James Gaffey 1,2, *

1 Circular Bioeconomy Research Group (CIRCBIO), Munster Technological University, Dromtacker, Tralee,
V92 CX88 Co. Kerry, Ireland; emilymarymarsh@outlook.com (E.M.); abhaysmenon@gmail.com (A.M.);
theresa.rubhara@mtu.ie (T.R.); helena.mcmahon@mtu.ie (H.M.)
2 BiOrbic, University College Dublin, Belfield, D04 V1W8 Dublin 4, Ireland
3 Centre for Economic and Entrepreneurial Development (CEED), Munster Technological University,
Dromtacker, Tralee, V92 CX88 Co. Kerry, Ireland; breda.odwyer@mtu.ie
4 Department of Geography, University College Cork, College Rd., T12 CY82 Cork City, Ireland;
paul.holloway@ucc.ie
5 Environmental Research Institute, University College Cork, Lee Rd., Sunday’s Well,
T23 XE10 Cork City, Ireland
* Correspondence: alicehand2013@hotmail.co.uk (A.H.); james.gaffey@mtu.ie (J.G.)

Abstract: To support the ambitious bioeconomy vision outlined in Ireland’s Bioeconomy

Action Plan, there is an urgent need to bring together the necessary stakeholders required to
implement this vision. Farmers and other primary producers who oversee the production
of sustainable biomass constitute one of the most important categories of stakeholders in
the bio-based value chain. To ensure scalable, long-lasting bioeconomy collaboration, it is
essential that farmers are involved in developing this bioeconomy vision. The current study
Academic Editor: Fabio Gresta
provides a mixed-methods approach to co-design a green biorefinery vision with Irish farm-
Received: 19 November 2024
ers and other key value-chain actors. The selected value chain targeting a medium-scale
Revised: 10 January 2025
grass silage biorefinery focused on the production of eco-insulation materials, with protein
Accepted: 24 January 2025
Published: 7 February 2025
and biogas co-products for local markets. This was then assessed economically using an
economic model, which provided a payback period of five years. To identify suitable sites
Citation: Hand, A.; Marsh, E.; Giron
Dominguez, C.; Menon, A.; Rubhara,
for deployment of the green biorefinery in rural areas, geographical information systems
T.; McMahon, H.; O’Dwyer, B.; (GIS) analysis was undertaken, considering various environmental, socio-economic and
Holloway, P.; Gaffey, J. Determining a infrastructural variables, which identified 26 potential sites for deployment of the green
Suitable Local Green Biorefinery biorefinery model in Ireland. This study found that early engagement with and inclusion
Model for Adoption by Irish Livestock
of the farmers in a co-designed process of innovation and alternative revenue streams for
Farmers Using a Mixed-Method
them is essential. While a preferred cooperative-based business model for a grass silage
Co-Design Employing Economic and
Geographical Information Systems
biorefinery was identified in consultation with the multiple stakeholders, further research
Analysis. Grasses 2025, 4, 7. on its long-term commercial sustainability is proposed as future research.
https://doi.org/10.3390/
grasses4010007 Keywords: biorefinery; bioeconomy; grass; co-design; collaboration; geographical information
Copyright: © 2025 by the authors.
systems; sustainability; co-op business model
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons 1. Introduction
Attribution (CC BY) license
As the global population is expected to rise to a projected 10 billion by 2050, the
(https://creativecommons.org/
licenses/by/4.0/).
importance of the agriculture sector has never been more paramount. However, agriculture

Grasses 2025, 4, 7 https://doi.org/10.3390/grasses4010007

Grasses 2025, 4, 7 2 of 24

faces major sustainability challenges, with the food system accounting for 31% of total
global greenhouse gases (GHGs), and livestock farming alone accounting for 14.5% [1]. In
Europe, this challenge is also stark, with about 10% of all Europe’s GHGs being generated
by agriculture [2]. Among EU member states, Ireland faces a particularly difficult challenge
of reducing its agricultural sectoral emissions, with the sector accounting for over 35% of
national GHGs in 2020 [3]. By 2030, it is predicted that agriculture will form around
39.7% of Ireland’s GHG emission output [4]. This rise in GHG emissions is due to the
intensification of farming practices, particularly the increase in livestock numbers in the
dairy industry [5]. The Irish government along with many governments across Europe have
been busy introducing binding regulations aimed at reducing these sectoral emissions [6,7].
In addition to these sustainability challenges, many farmers also face economic pres-
sures, as several agricultural sectors operate within relatively tight margins and are very
susceptible to price increases for inputs such as feed, fertiliser, and energy. The already
challenging environment has been made even more challenging due to the current global
situation. For example, between the second quarters of 2021 and 2022, following the Rus-
sian invasion of Ukraine, the average price of goods and services used within agriculture
in the EU jumped by 36% [8]. These economic issues particularly impact beef and tillage
farmers in Ireland, who have seen decreases to farm incomes due to changes in market
demand and increasing production costs [9,10]. For example, beef farmers in Ireland saw a
fluctuation in family farm incomes in 2019 due to a reduction in Irish cattle prices along
with higher production costs [11]. Demand for beef also decreased by 0.9% in the European
market in 2019, and British demand fell by 2.1% [12]. Tillage farmers saw a decrease in
farm incomes in 2020 due to low production of crop yields and changing European market
prices [13]. In Ireland, almost all farms (99.7%) are family-run [14], with approximately
74% of their income being provided by government funding [15,16]. The combination of
market demand and price challenges, with increasing production costs having led to lower
farm incomes, has resulted in more farmers searching for off-farm employment [9,16,17].
In 2021, only 20% of family-run farms in Ireland relied on agriculture as the sole source of
income [18], while the remaining 80% had to rely on an additional off-farm income. This
was mostly seen in cattle, sheep and tillage farming families [19].
The concept of circular bioeconomy has been promoted over recent years as a mech-
anism for building a more sustainable economy through the improved use of primary
production resources and side-streams, from the agricultural, forestry and marine sectors,
to develop sustainable bulk and high-value food, materials and energy required for every-
day life [20]. It has also been strongly promoted as a mechanism for primary producers to
remain competitive and diversify their incomes in a sustainable manner, producing new
sustainable products to meet market demands [20]. Since primary producers oversee the
production of biological raw materials which underpin the development of the bioeconomy,
their involvement within a developing bioeconomy is inevitable. For example, primary
producers may supply the biological raw materials directly to a company which can, in turn,
manufacture new materials for the market. On the other hand, there are opportunities for
primary producers to directly create new materials by adopting new processes and target-
ing new markets. Several longstanding examples of primary producer cooperative-based
bio-based industries exist across Europe. The Cooperative Biorefinery Model of Grand Est,
based at Pomacle Bazancourt in France, is one such example, in which farmer cooperatives
have interlinked with research and development and commercial partners, to add to the
traditional food production chain [21]. As a result, the biorefinery complex is now produc-
ing first- and second-generation biofuels (bioethanol), bio-based chemicals (succinic acid),
cosmetics and a variety of sustainable value-added materials, in an ecosystem which shares
resources (energy, water and material) and knowledge in a collaborative manner [21]. Food
Grasses 2025, 4, 7 3 of 24

is still a central product, but the circular bioeconomy approach now allows for greater value
and sustainability to be achieved. This ecosystem is one of Europe’s largest biorefinery
complexes. Recently, small-scale bio-based technologies have been gaining interest, due to
their ability to be deployed with lower capital expenditure, with relatively low complexity
and closeness to farms, potentially allowing farmers to play a role of biomass value creator
and not only biomass supply within these new value chains [22,23]. These technologies
may be farm-based or may take a decentralised approach combining multiple different
farms inputting into a larger model [22,24]. However, challenges still remain in achieving
widescale bioeconomy implementation in rural regions. These include challenges linked to
knowledge, finance and collaboration and business models among others [25–27].
In the current context of market challenges, price inputs and a high share of sectoral
emissions coming from the agriculture sector, there has been a growing interest in the
deployment of a circular bioeconomy in Ireland’s agriculture sector. Ireland’s Bioecon-
omy Action Plan highlights the government’s ambition to become a global bioeconomy
leader, and actions across a range of key pillars to help achieve this [28]. With grassland
accounting for just under 60% of total land use in Ireland, green biorefineries have been
noted as a particular technology of interest [29]. Recent Irish research and demonstra-
tion of green biorefinery has highlighted the potential for green biorefineries to produce
multiple bio-based products, helping to address some farm input challenges while offer-
ing sustainable economic diversification opportunities [30]. This work has shown that
a pulp fibre co-product produced from the green biorefinery can replace silage in dairy
cattle diets without negatively impacting milk productivity, and offering environmental
benefits [31], while the extracted protein co-product has been successfully integrated as a
soybean substitute within pig diets offering a significantly lower carbon footprint [32,33].
Additional co-products such as fructans, bioenergy and fertiliser can also be produced as
co-products of the same model. Ireland is not the only country investigating the potential
of green biorefineries, with research and demonstration activities underway across most of
northern and western Europe, including Denmark, Finland, Sweden, Netherlands, Belgium,
Germany and Austria [34–40]. These models vary both in terms of green biomass used
(grasses, legumes and crop residues) and products produced, including food, feed, nu-
traceuticals, bio-based materials (eco-insulation, bio-plastics and bio-composites, chemical
building blocks, building materials, paper and packaging), bioenergy and biofuels and
many more [30,41].
While green biorefineries can be a potential diversification pathway for farmers to
participate in Ireland’s emerging bioeconomy, for this to happen, it is important that the
farmers be included within the decision-making process [27]. Since farmers are the stake-
holders overseeing the main biorefinery raw material, and as bio-processing facilities often
require large capital investment and need to be operational for a long-term period to ensure
profitability, securing farmer buy-in to develop this vision is an important requisite for
the establishment of this new industry. This engagement is also an important step in
supporting a just transition for primary producers and rural communities. The aim of
this paper is to demonstrate methodology which facilitates the process of engagement
and co-design with farmers and other relevant stakeholders to identify a suitable local
green biorefinery model for adoption by Irish farmers. In addition to co-design, this mixed
method also builds out an economic analysis of the selected value chain and a geographical
information systems (GIS) analysis to identify suitable biorefinery site locations under
specific assumptions. Collectively, this approach can help to build consensus and collab-
oration among stakeholders, while also informing the future potential of this model at a
local level. While earlier studies have investigated and analysed the techno-economic and
environmental aspects of implementing green biorefinery technologies [34,42], and have
 Grasses 2025, 4, x FOR PEER REVIEW 4 of 24

Grasses 2025, 4, 7 4 of 24
local level. While earlier studies have investigated and analysed the techno-economic and
environmental aspects of implementing green biorefinery technologies [34,42], and have
used aatechnical
used technicalanalysis
analysisofofgreen
greenbiorefineries
biorefineriesto
todevelop
developaablueprint
blueprintfor
forgreen
greenbiorefinery
biorefinery
development[43],
development [43],this
thisstudy
studycombines
combineseconomic
economicand andtechnical
technicalanalysis
analysiswith
withaamechanism
mechanism
for co-design which includes the voice of primary producers and other key
for co-design which includes the voice of primary producers and other key stakeholdersstakeholders
withinthe
within thedecision-making
decision-makingprocess.
process. No
No previous
previous study
study has
hasbeen
beenfound
foundininthe
theliterature
literature
which aims to incorporate this mixed-method approach aimed at determining
which aims to incorporate this mixed-method approach aimed at determining a suitable a suitable
localgreen
local greenbiorefinery
biorefinerymodel
modelfor
foradoption
adoptionbybyIrish
Irishlivestock
livestockfarmers.
farmers.

2.2. Materials
Materialsand
andMethods
Methods
Thisstudy
This studywas
wasdivided
dividedinto
intothree
threeinterlinked
interlinked phases:
phases: thethe co-design
co-design phase
phase as as phase
phase 1,
1, the economic analysis as phase 2 and the GIS analysis as phase 3, as shown
the economic analysis as phase 2 and the GIS analysis as phase 3, as shown in Figure 1. in Figure 1.
Theco-design
The co-designphase
phasewas
wasused
usedtotofirst
firstidentify
identifykey
keystakeholders
stakeholdersand anddesign
designaalocal
localgreen
green
biorefinery model with input from these stakeholders. Findings from this phase
biorefinery model with input from these stakeholders. Findings from this phase were then were then
usedto
used toinform
informthe
theeconomic
economicanalysis
analysisof ofthe
theselected
selectedbiorefinery
biorefinerymodel.
model.Finally,
Finally,the
theresults
results
ofboth
of boththe
theco-design
co-designand
andeconomic
economicphase
phasewerewereused
usedtotoinform
informthetheGIS
GISphase
phasetotoidentify
identify
themost
the mostsuitable
suitablelocations
locationsat
ataalocal
localscale
scaleforforlocal
localgreen
greenbiorefinery
biorefinerydeployment
deploymentunder
under
specificconditions.
specific conditions.

Figure1.1.The
Figure Thethree
threephases
phasesof
ofdata
datacollection,
collection,which
whichform
formthe
theoverall
overallframework
frameworkofofthe
theresearch.
research.The
The
resultsfrom
results fromthe
theco-design
co-designphase
phaseinform
informthetheeconomic
economicand
andGIS
GISphase,
phase,while
whilethe
theresults
resultsfrom
fromthe
the
economic
economicphase
phasewere
wereused
usedtotoinform
informthe
theGIS
GISphase.
phase.
Grasses 2025, 4, 7 5 of 24

2.1. Phase 1: Co-Designing a Green Biorefinery

2.1.1. Identifying Biorefinery Models and Key Stakeholders
Different green biorefinery systems across Europe were first identified and a compara-
tive review undertaken to ascertain which may be suitable for adoption within Ireland’s
grassland sector. The review provided insights to criteria that can be used to describe
and compare various green biorefinery models, along with the understanding of current
state-of-the-art technologies available [30,41,44–46]. From this review, a variety of green
biorefinery pathways were shortlisted to be considered using the following criteria: (i) feed-
stock type, (ii) size and capacity of the plant, (iii) type of processes/technologies involved,
(iv) any economic information given, (v) technological readiness level (TRL) of the model,
(vi) end products, (vii) environmental goals of the model and finally (viii) any limitations
found within the literature.
Key stakeholders deemed most relevant to the green biorefinery model were then iden-
tified using design thinking workshops with experts supported with the Lego® SERIOUS
PLAY® method [47]. This was employed to build a 3D model of a green biorefinery value
chain and identify the key stakeholders involved in this process. These workshops were
informed by application of the design thinking approach and were carried out over a 2-day
period with six experts in different backgrounds, including environmental, socio-economic,
business and biorefinery research. The purpose of the workshops was to gain multiple
perspectives regarding the biorefinery model and to use this diverse expertise to consider
the voice of the farmer, consumers and industry actors. Several practical exercises guided
by the design thinking methodology were used to identify the key stakeholders involved in
a green biorefinery implementation. These exercises included insight mapping, stakeholder
value mapping and persona development, action plan design and development of a visual
model using the Lego® SERIOUS PLAY® method to represent the biorefinery model and re-
quired stakeholders. This model was further revised to identify the key contributors for the
empirical research process. It was found that the input of the primary stakeholders would
be best captured through a targeted focus group and that of the secondary stakeholders
through semi-structured interviews.

2.1.2. Stakeholder Data Collection

The choice of the above two qualitative methods of data collection would provide the
opportunity for one-to-one engagement with the key stakeholders [48]. Semi-structured
interviews were carried out with a sample of experts who provided a cross-sectoral perspec-
tive representing the dairy and beef sectors, agricultural cooperatives, finance sector, policy
makers and industry market partners (including eco-insulation market partners and animal
protein feed market partners). A selection of both general and focused questions (to their
areas of expertise) was asked of the stakeholders to gain expert knowledge and opinions
regarding the benefits and challenges of implementing a green biorefinery in Ireland. Due
to COVID-19 regulations at the time, interviews were carried out online for 30 min each
between June and August 2021. Each interview was recorded and later transcribed to a
written format for analysis. Using thematic analysis, the findings from these interviews
were then used to inform and structure the approach and topics of the focus group which
was carried out with the farmer stakeholders.
The topics for the focus group included: (i) their preferred model, (ii) type of busi-
ness model, (iii) scale and supply chain, (iv) location, (v) end products market size,
(vi) knowledge gaps for farmers surrounding the circular bioeconomy and green biorefiner-
ies, (vii) future proofing methods available to farmers, (viii) models for selection (shortlisted
to 6 unique green biorefinery models for consideration) and (ix) an overview of each model
for selection. The focus group was carried out in an online format with a selected group
Grasses 2025, 4, 7 6 of 24

of farmers on the 23rd of August 2021. A PowerPoint presentation covering each topic
was presented to a selection of farmers representing dairy, beef and tillage agriculture
at the beginning of the focus group. This provided the context and further clarity to the
farmers, which also generated ‘ease’ within the farmers and supported good interaction
and discussion. Time was then allocated to discuss each of the above nine topics as the
participants reported on their preferred green biorefinery models, feedstocks, scale and
product types. The analysis of the data gathered from all stakeholders was later used to
inform analysis and assumptions of the economic and GIS model.

2.2. Phase 2: Economic Analysis

2.2.1. Mass and Energy Balance
A mass and energy balance for a green biorefinery was developed to determine
the material and energy inputs and outputs of the model selected in Section 2.1. To do
this, several previous studies [42,43,49,50] were consulted, along with experts in green
biorefining research and demonstration, when designing the mass balance. Eventually the
mass and energy balance model described by Holtinger et al. [49] was chosen for adaption
based on its correlation to the preferred model selected by farmers in their focus group.
Following recommendations from the literature and consulting expert interviews, the scale
of production was set at 20t fresh weight (FW)/hr assuming 23% DM [43]. This would
represent a medium-scale, local biorefinery [51]. The hourly material and energy inputs
and outputs were then calculated. The two primary end products consisted of a fibre
insulation material and a protein co-product, while the residual brown juice could be used
to produce energy via anaerobic digestion which could be used to produce biogas for heat
and electricity (via CHP), or potentially converted to biomethane, replacing natural gas.
The annual mass balance was calculated by multiplying the hourly figures by the daily
hours the biorefinery would operate (assumed to be 10 h per day) and number of days
operated in the year (assumed at 280 days per year).

2.2.2. Biorefinery Economic Model

Using the mass and energy balance, the economic feasibility of the selected biorefinery
model was determined using an economic model adapted from Cristóbal et al. [52]. The
economic model was used to calculate the profitability of the biorefinery model in both
scenarios. Total capital expenditure including installation cost was estimated based on
learnings from the Danish Green Biorefinery industry [51]. All the cost estimates and
assumptions for the model are summarised in Table 1. The operational expenditures were
first determined and calculated following methods previously used by Zetterholm et al. [53].
The direct operational costs included the feedstock price (including purchase and transport
costs), binding materials for the insulation material co-product, cleaning solutions, heat
and electrical energy inputs, waste disposal, conditioning of products and distribution. The
indirect operational costs included the repair and maintenance of the biorefinery, insurance,
labour and overheads. Values were given to each operational cost following market and
literature research and annual costs calculated for each category. Indirect costs, repairs and
maintenance were assumed at 5% of the capital expenditure, insurance was predicted at
an annual rate and the overheads were assumed to be 10% of the labour costs, based on
the input of experts. The labour costs were calculated based on the annual salary of six
employees according to expert input [51]. All direct and indirect costs were then combined
to calculate the overall operational expenditure costs.
Grasses 2025, 4, 7 7 of 24

Table 1. Market values assigned to each product.

Market Value Cost (€) per Unit Reference

Grass-based insulation material 850/tDM [49], adjusting for inflation
Grass protein concentrate 499/tDM [37]
Biogas electricity 0.17/kWh [54]

Market values of end products were assigned based on the literature and are shown
in Table 1. The insulation material market value was based on the value assigned by
Holtinger et al. [49], adjusting for inflation. Market value for protein concentrate follows
the median market value for grass protein proposed previously as a non-GMO replacement
for soybean meal [37]. Energy prices were based on the latest Irish energy prices provided
by SEAI [54]. In the case of the sale of surplus electrical energy, a 30% discount against the
current counterfactual (e.g., electricity from the grid) is assumed. The potential to upgrade
part of the biogas to biomethane is not considered within the economic analysis, but this
may also represent a future opportunity. The other assumed input and output costs and
prices are presented in Table 2.

Table 2. Estimated costs and assumptions given to each input and output of the economic model.
References for each cost are also given.

Operational
Cost (€/Unit) Assumption References
Expenditure
Direct Expenses
The cost of the feedstock at the biorefinery, including
Grass Silage 150 [55]
transport from the farm and harvest costs.
Assumed at €1/unit due to previous studies,
Binding Materials 1.00 [56–58]
including the material in the overall fibre costs.
Overall cost of NaOH, HCL and H2 O cleaning
Cleaning Solutions 26.00 solutions listed by the mass balance carried out by [50,59]
Prieler et al. (2019).
Rates assumed to be akin to those of natural gas in
Heat Energy 0.07 Ireland and potential renewable energy costs at the [54]
time of this study.
Adapted from a previous biorefinery model
Waste Disposal 0.18 [60]
and literature.
Conditioning and Distribution 200,000.00 Figure adapted from previous biorefinery model. [55]
Indirect Expenses
Repairs and Maintenance 5% Assumed to be 5% of capital expenditure. [55]
Single annual figure based on estimates from
Insurance 50,000 [55]
previous demonstration.
Estimated to be the annual salary for 8 workers at
Labour 44,000 per Worker [51,55]
average national salary.
Assumed to be 10% of the labour costs. Includes
Overheads 10% [55]
costs such as administration and telephone.
Repairs and Maintenance 5% Assumed to be 5% of capital expenditure. [55]
Single annual figure based on estimates from
Insurance 50,000 [55]
previous demonstration.
Estimated to be the annual salary for 8 workers at
Labour 44,000 per Worker [51,55]
average national salary.
Assumed to be 10% of the labour costs. Includes
Overheads 10% [55]
costs such as administration and telephone.

Additional costs that were calculated include depreciation and loan repayment. The
depreciation was calculated based on the assumption of the cost of the item being 60% of
the CAPEX, a biorefinery lifetime of 20 years, a salvage value of 10% of cost price and the
machinery not considered. The loan repayment was calculated based on the assumption of
Grasses 2025, 4, 7 8 of 24

the loan amount at 80% of CAPEX, interest rate of 5%, a loan term of 10 years and a fixed
interest rate. The assumptions and calculation methods used are presented in Table 3, and
Equations (1) and (2).

Table 3. Assumptions to calculate the depreciation and loan costs of the biorefinery.

Assumptions
Cost of item assumed as 60% of CAPEX
Biorefinery lifetime assumed at 20 years
Depreciation
Salvage value assumed at 10% of cost price
Machinery not considered
Loan amount assumed at 80% of CAPEX
Interest rate 5%
Loan
Loan term 10 years
Assume interest rate is fixed

The straight-line formula for calculating the depreciation of a biorefinery [61]:

( asset cost − salvage value)

Depreciation = SLN (1)
use f ul li f e

Excel payment formula for calculating the loan repayment of a biorefinery [62]:

Loan repayment = PMT (rate, nper, pv, [fv], [type]) (2)

Rate—interest rate; nper—total number of loan payments; pv—current value of all

loan repayments; fv—(optional) the cash balance after the loans has been paid. type—when
the payments are due, the default used is 0.
The profitability of the biorefinery was then calculated using Equation (3), where the
costs of the model were subtracted from the revenues produced.

Profit/Loss = Total rev. − total opex − deprec. − loan (3)

Total rev.—total revenues; total opex—total operational expenditures; deprec.—

depreciation; loan—loan repayment.
The ROI was calculated from the annual net profit and total capital investment and
expressed as a percentage, as shown in Equation (4). The payback period for the green
biorefinery was calculated following Equation (5), which considers the interest and lifespan
of the project.
Return on investment [52]:

annual net profit

ROI (%) = (4)
total capital investment

Payback timeframe [52]:

total capital investment

Payback time (years) = (5)
annual net profit

The profitability was found by subtracting the total annual revenue from the total
expenditure. The ROI was calculated by dividing the annual net profit by the total capital
investment and represented as a percentage. A net positive ROI implies that the investment
is profitable. Finally, the payback period was calculated by dividing the total capital
investment by the annual net profit. The payback estimates the time frame required for an
Grasses 2025, 4, 7 9 of 24

investment to recoup its initial outlay, and the shorter the payback, the more desirable is
the investment.
A sensitivity analysis was also carried out considering the feedstock purchase costs
and insulation selling prices, as these were subject to change with market trends and
represent the main operational cost and the primary income source of the model. Feedstock
costs were given a parameter of €30/t from the original price, where cost would fall by
−€10/t, −€20/t and −€30/t and rise by the same parameters. For the insulation prices,
the parameter of €50/t was provided, beginning at the initial cost of €850/t. The prices
were reduced by €50/t until the cost of €700/t was reached, and increased to €1000/t.
The profitability, ROI and payback period were calculated from these parameters and are
presented in a graph format.

2.3. Phase 3: GIS Site Suitability Analysis

To determine potential locations for the selected biorefinery model in Ireland, a site
suitability analysis using multiple-criteria analysis (MCA) was implemented in ArcMap
version 10.8.1 [63]. MCA integrates and combines multiple datasets that are then used to
identify or assess geographic areas that satisfy a range of criteria [64]. Data representing
environmental, economic and infrastructure themes were selected following methods
similar to those of Valenti et al. [65], who utilised surveys and techno-economic analysis
to carry out data collection. The study by Jayarathna et al. [66] was used as a template
to categorise the collected geospatial data into three main themes, after which they were
further divided into sub-themed criteria (Table 4).

Table 4. Variables collected from stakeholder engagement in the co-design phase.

Theme Sub-Theme
Feedstock sites
Environmental Habitats and species to avoid
Land use
Farming incomes
Economic
Farming intensity
Distance to market
Protein feed market
Infrastructure Insulation market
Biogas market
End users

Findings from the stakeholder engagement and scoping review were used to identify
potential variables for use in the MCA. Using the sub-theme criteria, data were obtained
from various open geoportals, such as the Environmental Protection Agency (EPA), Central
Statistics Office (CSO) and other government data sites (see Table 5). When data layers
were not available, for example, in the case of the compound feed industry and biogas
injection points, spatial data were created. Potential market partners for each variable were
first obtained from public data sites [67,68] and then digitised. Once the variable layers had
been collected, they were further categorised into data that would best meet the criteria of
the analysis. Following the MCA undertaken by both Bell et al. [69] and Perpiña et al. [70],
the variables were listed under sub-theme headings, along with the justification of their
use, the source and a description of the variable (see Table 3). This method was used to
determine which variables closely matched the sub-themes listed in Table 2 and would be
included in the analysis.
Grasses 2025, 4, 7 10 of 24

Table 5. Buffer zone distances given to each feature layer, along with their criteria and reference.

Inclusion/
Variable Buffer Zone Criteria References
Exclusion
A buffer zone of this distance is recommended for
Rivers Exclusion 200 m [71]
rivers where pollution may occur.
Recommended distance for infrastructure or
Lakes Exclusion 20 m [72]
agriculture to take place from lake shorelines
Building cannot take place within areas that would
Unsuitable land Exclusion 1 km [70]
disturb protected areas
Safety area surrounding roads to ensure that the
Roads Inclusion 2 km infrastructure has access to them, increased to 2 km [70]
from previous study to ensure safe road access
This was selected to ensure access to a locally
available feedstock supply chain, since grass as a
Pastures Inclusion 10 km [33]
high moisture feedstock can suffer from
transportation issues.
This was selected based on ensuring that insulation
and surplus electricity had access to the local market.
Settlements Inclusion 10 km This is a conservative assumption since the protein is [49]
storable and can be transported over a longer
distance to national and international markets.
While biogas will be used to produce electricity in
the short term, the potential to upgrade this to
produce biomethane is a growing development area.
Gas network
Inclusion 5 km To achieve this, biogas grid injection points should be [45,73]
pipelines
located close to or within a short distance from
biorefineries producing the product, as biogas has a
shorter transport distance than natural gas.
Dried protein concentrate from grass is storable and
can be transported over longer distances (even
Agri feed partners Inclusion 10 km nationwide or internationally), but 10 km is kept as [55]
the distance to promote the best locations for local
biorefinery based on this product.

All layers were projected into Irish Transverse Mercator and then pre-processed using
the buffer tool to ensure that the layers incorporated a measurement of geographic distance.
Variables were classified as either inclusion or exclusion variables (Table 5), with exclusion
variables having a minimum distance that must be avoided in any development, such as a
minimum distance from water to ensure no pollution. For inclusion variables, this was a
maximum distance that was deemed suitable, supported by the economic analysis, such as
maximum distance from a grid injection point that is financially viable for transporting this
product. Buffer distances are provided in Table 5, along with their justification.
All exclusion layers (rivers, lakes and unsuitable land) were united to create a single
polygon layer containing any location that represented a single feature that needed to be
excluded from a final site. All inclusion layers were intersected to identify geographic
locations that satisfied all the inclusion criteria (roads, settlements, pastures, gas network
pipelines and Agri feed partners). This resulted in two layers, the united layer which
represents all locations where we cannot locate a new green biorefinery and the intersect
layer which represents all locations where we would ideally locate one. The final step
was to erase the unsuitable area from the suitable area, to identify locations that satisfied
all criteria.
Grasses 2025, 4, x FOR PEER REVIEW 11 of 24
Grasses 2025, 4, 7 11 of 24

3. Results
3. Results
3.1. Co‐Design
3.1. Co-Design Results
Results
3.1.1. Stakeholder Identification
From thetheinitial co-design
initial co-designandand
LegoLego ® SERIOUS
® SERIOUS PLAY®PLAY ® activities
activities with experts
with(Figure
experts2),
eight main
(Figure 2), stakeholders were identified
eight main stakeholders as having
were both as
identified a direct
havingandboth
indirect impact
a direct andonindirect
a green
biorefinery
impact on amodel. It was determined
green biorefinery model. Itthatwasfarmers, agricultural
determined cooperatives,
that farmers, academia
agricultural coop-
and research
eratives, and market
academia partnersand
and research would be directly
market partnersimpacted
would be bydirectly
the model. The remain-
impacted by the
ing stakeholders,
model. the policy
The remaining makers/regulators,
stakeholders, the policy consumers, funding consumers,
makers/regulators, bodies and the local
funding
community,
bodies and thewould
localhave an indirect
community, impact
would haveonantheindirect
model. impact
Due to ongreen
thebiorefineries
model. Duefo- to
cusingbiorefineries
green on utilising fresh grassonorutilising
focusing silage, livestock farmers
fresh grass that use
or silage, grasslands
livestock for that
farmers grazing
use
and silage,for
grasslands primarily
grazing beef and dairy
and silage, production,
primarily were
beef and considered
dairy production,as were
the main group as
considered of
farmers
the mainthat
groupwould be impacted
of farmers by thebe
that would biorefinery
impacted model, with tillagemodel,
by the biorefinery farmers, who
with pro-
tillage
duce green
farmers, whocrop residues,
produce greenalso being
crop impacted.
residues, also being impacted.

Figure 2.
Figure 2. Co-design
Co-design Lego
Lego®® SERIOUS
SERIOUSPLAY
PLAY®model
®
modelthat
that involved
involved all
all the identified stakeholders
the identified stakeholders
within the model.

3.1.2. Biorefinery Model

3.1.2. Biorefinery Model Selection
Selection
Interviews with identified
Interviews with identified key key stakeholders
stakeholders from
from the
the dairy
dairy sector,
sector, beef
beef sector,
sector, agri-
agri-
cultural
cultural cooperatives
cooperatives and and finance
finance sector,
sector, and
and policy
policy makers
makers as as well
well as
as market partners for
market partners for
eco-insulation animal protein feed products, indicated that knowledge
eco-insulation animal protein feed products, indicated that knowledge and education and education gaps
surrounding
gaps surrounding the bioeconomy
the bioeconomy and biorefineries would
and biorefineries first have
would to be to
first have addressed at farm
be addressed at
level to promote a biorefinery model amongst farmers. It was noted
farm level to promote a biorefinery model amongst farmers. It was noted from interviews from interviews that
there werewere
that there still concerns regarding
still concerns risk and
regarding riskuncertainty amongst
and uncertainty farmers,
amongst particularly
farmers, those
particularly
who had a more traditional farming background and who were
those who had a more traditional farming background and who were unfamiliar with unfamiliar with newer
technologies.
newer technologies. According to thetostakeholders,
According farmers
the stakeholders, may may
farmers be more inclined
be more to partake
inclined to par-
in a biorefinery if the technologies could integrate well with the existing
take in a biorefinery if the technologies could integrate well with the existing operations operations or
cause
or causelittle to no
little todisruption
no disruptionto their current
to their farming
current methods.
farming One stakeholder
methods. remarked
One stakeholder re-
that farmers would also need to be informed of the long-term viability
marked that farmers would also need to be informed of the long-term viability of the of the biorefinery,
Grasses 2025, 4, 7 12 of 24

particularly the requirements for farmer investment and the impact it may have on farmers’
income. Funding support and farmers mindsets were noted as the main challenges that
would need to be addressed to implement a green biorefinery model, along with a clear
market path for the potential products produced.
It was determined from the farmer focus group that a collaborative biorefinery model
would be suitable for Ireland, as this would present the opportunity for farmers and stake-
holders to be involved in the ownership of the model. A cooperative model engaging
farmers with technology providers and researchers was deemed a suitable model. This
approach would help the farmers to meet the required investment, knowledge, technology
and feedstock input for the model. Farmers noted that an incentive should come from the
government to support farmers’ participation, where a proportion of land could be set aside
for the biorefinery. A model which included tillage farmers as feedstock suppliers, with
access to appropriate crops, was also suggested as a possible solution for land expansion
issues, should economic support be available. All farmers agreed that the farming com-
munity would only be willing to participate in a biorefinery if a strong financial argument
could be made in favour of the green biorefinery. Therefore, the economic feasibility of the
biorefinery supported by an appropriate business model should be presented to farmers.
Six possible green biorefinery models were presented to the farmers, as shown in
Table 6. A mobile fresh grass unit was considered unsuitable for Irish agriculture due
to the intensive labour involved for farmers and the insufficient scale of the model. A
medium-scale fixed biorefinery model was selected as most suitable for Irish agriculture
by the farmer focus group. This model would be cooperatively owned and supplied by
a grass silage feedstock supply by a large group of farmers. Grass-based insulation was
selected as the most promising main output of this model, as it helped to meet a growing
local requirement for meeting Ireland’s retrofit targets, while providing the opportunity for
exportation. Grass silage was selected as the most suitable feedstock due to its year-round
storability and ability to transfer between different farms. Farmers were interested in
aligning this model with protein concentrate extraction and integrating it with a biogas
system to produce heat and electricity via CHP. Future development of biomethane gas
injection to the grid was also suggested as a possibility for biogas use, depending on the
available injection points.

Table 6. Green biorefinery scenarios presented in farmer focus group.

Biomass Feedstock End Product

Grass Silage Lactic acid, amino acids, biogas
Fresh Grass Ruminant feed, protein concentrate (pig/poultry feed), biogas
Grass Silage Insulation boards, protein concentrate, biogas
Grass Silage Fibre composites, biogas
Silage (roadside) Fibres for paper and packaging, biogas

3.2. Economic Analysis

3.2.1. Mass Balance
Operating at full capacity, the mass balance of the selected biorefinery model resulted
in an annual throughput of 12,880 tDM of grass silage, with 10,304 tDM separated into
the fibre press cake, and with 2576 tDM separated into the press juice [49]. A further
drying step along with the input or additives is used to convert the press cake to fibre
insulation material, which resulted in an output of 5796 tDM of grass insulation product.
The press juice is processed using a heat coagulation and a drying step to separate the
proteins, resulting in an output of 863 tDM animal feed protein. The mass flow is presented
in Figure 3 below. Converting the residual stream to biogas and heat and electricity via
Grasses 2025, 4, x FOR PEER REVIEW 13 of 24
Grasses 2025, 4, 7 13 of 24

3 below. Converting the residual stream to biogas and heat and electricity via CHP gen-
CHP generates
erates sufficientsufficient
electricityelectricity to meet
to meet the the needs
process process needs
but butin
results results
a heatindeficit
a heatfor
deficit
the
for the
process. process.

Process
Figure 3.flow
Figure 3. Process flow diagram
diagram of a biorefinery
of a biorefinery modelmodel operating
operating at full
at full capacity.
capacity.
3.2.2. Economic Model Scenario Analysis
3.2.2. Economic Model Scenario Analysis
An expenditure of €5.5 million for a 20 tFW/hr green biorefinery with anaerobic diges-
An expenditure of €5.5 million for a 20 tFW/hr green biorefinery with anaerobic diges-
tion and CHP was estimated for the biorefinery model [51]. The purchase of annual silage
tion and CHP was estimated for the biorefinery model [51]. The purchase of annual silage
feedstock represented the highest operational cost, reaching €1,932,000 per annum. The
feedstock represented the highest operational cost, reaching €1,932,000 per annum. The next
next highest direct operational cost comprised cleaning solutions for the green biorefinery
highest direct operational cost comprised cleaning solutions for the green biorefinery and
and AD plant, reaching a cost of €633,104 per annum, followed by the purchase of required
AD plant, reaching a cost of €633,104 per annum, followed by the purchase of required heat
heat energy, which had a deficit of 6,053,600 kWh and a cost of €423,702. Electrical energy
energy, which had a deficit of 6,053,600 kWh and a cost of €423,702. Electrical energy was
was not considered within the economic model, as the energy produced was found to be
not considered within the economic model, as the energy produced was found to be suffi-
sufficient to be recycled back into the system to meet the biorefinery’s electrical requirement.
cient to be recycled back into the system to meet the biorefinery’s electrical requirement.
The largest operational expense was found to be related to labour, at €352,000 per
The largest operational expense was found to be related to labour, at €352,000 per
annum for eight employees, followed by costs related to annual repairs and maintenance,
annum for eight employees, followed by costs related to annual repairs and maintenance,
at €275,000 per annum. The final economic model is presented in Table 7.
at €275,000 per annum. The final economic model is presented in Table 7.
Grasses 2025, 4, 7 14 of 24

Table 7. Biorefinery economic model.

Input/Output Total Annual €

Total Capital Expenditure 5,500,000.00
Grass Silage 1,932,000.00
Binding Materials 8144.82
Cleaning Solutions 633,104.64
Heat Energy 423,752.00
Waste Disposal 13,200.00
Conditioning and Distribution 195,750.00
Total Direct Expenses 3,203,951.46
Repairs and Maintenance 275,000.00
Insurance 50,000.00
Labour 352,000.00
Overheads 35,200.00
Total Indirect Expenses 712,200.00
Total Operational Expenditure 3,916,151.46
Insulation Material 4,926,600.00
Protein 430,617.04
Surplus Electrical Energy (Biogas) 315,302.40
Total Revenues 5,672,519.44
Total Operation Expenditure 3,916,151.46
Depreciation 148,500.00
Interest + Loan Repayment 569,820.13
Profit/Loss Before Tax 1,038,047.85
Return On Investment 21.57%
Payback Period 5 years

From the revenue streams produced, the insulation material provided the highest
revenue output, with a total output of 5796 tDM, resulting in a revenue of €4.9 million at
a rate of €850/t. Protein concentrate sold to the feed industry generated a total revenue
of €430,617.04 per annum at a selling price of €499 per tonne DM. Meanwhile, electricity
produced from surplus biogas and sold at a selling price €0.17c per kW generated an annual
income of €315,302 (Table 7). Once the profitability/loss, ROI and payback period of the
model had been calculated, it was determined that the biorefinery model had a profit of
€1,038,047.85, a high ROI of 21.57% and a payback period of 5 years.

3.2.3. Sensitivity Analysis

Fluctuations in the insulation material product and feedstock selling prices were
shown to have an impact on the profitability and return rate of the biorefinery model. A
linear increase in insulation price from €850/t to €1000/t resulted in a positive increase
in the profitability and investment return of the model. The profitability increased from
€1,038,047.85 to €1,907,448 with the increasing insulation selling price. The return on
investment also increased from 21.57% to 37.38%, as shown in Table 8. The payback period
was reduced to 2.68 years at the highest selling price of €1000/t. Consequently, a reduction
in insulation selling prices had a negative impact on the profitability and return rate of the
biorefinery model. The profitability of the model decreased significantly to €168,647, while
the return on investment was also reduced to 5.77%, and the payback period increased
to over 17 years. This highlights the importance of achieving a good selling price for the
insulation product.
Grasses 2025, 4, 7 15 of 24

Table 8. Sensitivity analysis for selling price of insulation material.

Unit Sell Price (Insulation) Revenues Profit/Loss ROI Payback

€700 €4,803,119 168,647 5.77% 17.34
€750 €5,092,919 458,448 11.04% 9.06
€800 €5,382,719 748,248 16.3% 6.13
€850 €5,672,519.44 1,038,047.85 21.57% 4.64
€900 €5,962,319 1,327,848 26.82% 3.73
€950 €6,252,119 1,617,648 32.11% 3.11
€1000 €6,541,919 1,907,448 37.38% 2.68

Increases in the feedstock price had a negative impact on the profitability and return
on investment of the biorefinery model. The profitability of the model decreased by up
to €651,648 with feedstock prices increasing to €180/t. The return on investment of the
scenario also decreased, to 14.55%. The payback period increased to 6.87 years at this
highest price of grass. On the other hand, lower feedstock costs had a more positive
impact on the profitability of the biorefinery model, with the profitability increasing to
€1,424,447, the return on investment increasing to 28.6% and the payback period reduced
to 3.5 years when grass was priced at €120/t. A representation of the payback period
sensitivity analysis of changes in feedstock cost prices is presented in Table 9.

Table 9. Sensitivity analysis for grass feedstock prices.

Unit Grass Cost (€/t) Profit/Loss ROI Payback

120 1,424,447 28.60% 3.50
130 1,295,647 26.26% 3.81
140 1,166,847 23.92% 4.18
150 1,038,047 21.57% 4.64
160 909,248 19.23% 5.2
170 780,447 16.89% 5.92
180 651,648 14.55% 6.87

Overall, higher insulation selling prices and lower feedstock costs would result in
a higher profitability and return on investment; however, the chart shows that a slight
change in either insulation selling price or grass cost does not result in a very big jump in
the profitability of the business.

3.3. GIS Site Suitability Analysis

Building on the results of Sections 3.1 and 3.2, and following the GIS analysis methods
and criteria presented in Section 2.3, twenty-eight suitable locations for green biorefineries
in Ireland were identified. These are presented in Figure 4 and were found within counties
Monaghan, Cavan, Meath, Louth, Kildare, Kilkenny, Wexford, Waterford, Tipperary, Cork,
Limerick, Clare, Mayo and Donegal. The overall area of each site differed due to the
removal of unsuitable land, and therefore sites within a 10 km radius were identified as
a single site. The site locations were limited by the availability of roads and distance to
market partners, particularly the availability of agricultural feed partners, as most feed
partners were located in the east and south of Ireland. The distance to settlements and
towns for insulation market partners was not seen as a limiting factor due to the large
area of coverage available at a 10 km buffer distance. The distance to the feedstock supply
or pastures was also not seen as a limiting factor due to the wide coverage of available
grassland biomass sites across Ireland.
 aghan, and three in county Meath (Figure 4), primarily due to the good availability of animal
feed protein market partners and access to gas network pipelines. Figure 4 shows that coun-
ties in the northeast, along with three counties in the south of Ireland, were found to be the
Grasses 2025, 4, 7 16 of 24
most suitable areas for green biorefineries to be implemented in Ireland.

Figure 4. Map of the suitable locations in Ireland for green biorefinery deployment.
Figure 4. Map of the suitable locations in Ireland for green biorefinery deployment.
Of the twenty-eight available sites, six were found in county Cork, four in county
In terms ofand
Monaghan, farmers’ incomes,
three in countyareas
Meath with lower4),
(Figure farming incomes
primarily due to would benefit
the good from the of
availability
green biorefinery
animal modelmarket
feed protein as an additional
partners income. Of the
and access suitable
to gas sites pipelines.
network located in Figure
Figure4,4 only
shows
twothat counties in the northeast, along with three counties in the south of Ireland,inwere
were found to be within a low-farming-income area. These sites were located County
found
Donegal, which had a median income of €11,655, and County Clare, with
to be the most suitable areas for green biorefineries to be implemented in Ireland. a median income
of €17,144. Theseof
In terms counties
farmers’were also found
incomes, areastowith
have the highest
lower farming coverage
incomesofwould
unsuitable land,
benefit from
which resulted in a low availability of suitable sites. While County Cork had
the green biorefinery model as an additional income. Of the suitable sites located in Figure 4,the highest
availability
only two of suitable
were found sites,
to bethewithin
average farming income was found
a low-farming-income to be higher
area. These thanlocated
sites were that
of other counties,
in County reaching
Donegal, €23,848
which had [74]. Largerincome
a median incomesofgenerated
€11,655, andfromCounty
existingClare,
grassland
with a
enterprises
median income of €17,144. These counties were also found to have the highest lower
may make access to grassland more challenging than in areas with coveragein- of
come generation.
unsuitable land, which resulted in a low availability of suitable sites. While County Cork
Farming intensity
had the highest also differed
availability between
of suitable counties
sites, the average containing
farming incomesuitable biorefinery
was found to be
sites. County Cork was identified as having the highest dairy and beef
higher than that of other counties, reaching €23,848 [74]. Larger incomes generated from cow numbers,
existing grassland enterprises may make access to grassland more challenging than in areas
with lower income generation.
Farming intensity also differed between counties containing suitable biorefinery sites.
County Cork was identified as having the highest dairy and beef cow numbers, reaching
397,000 and 69,100, respectively. County Monaghan had a dairy cow number of 39,300 and
a beef cow number of 29,800.
Grasses 2025, 4, 7 17 of 24

Based on environmental and infrastructure results, counties Cork and Monaghan were
found to have the highest availability of suitable biorefinery sites, though when socio-
economic factors such as farming income and farming intensity are considered, these sites
may be more competitive for green biorefinery development. Taking all these factors into
account, it was found that counties Louth, Kildare and Donegal could be very favourable
for a green biorefinery model deployment due to the presence of lower farming intensity
and incomes. A full compilation of all maps generated to inform the spatial analysis is
compiled in the Supplementary File S1.

4. Discussion
The model of green biorefinery development has significant replication for grassland
regions of Europe, which constitute 34% of utilised agriculture area [30]. The potential for
Ireland, the only country in Europe with over 50% of total land area comprising grassland,
is even more significant. These 4 million ha of grassland, which make up 90% of total
agricultural land use, are primarily used to sustain a predominant beef and dairy cattle
livestock sector. These sectors account for two-thirds of gross agricultural output and
similar proportions of agri-food exports [75]. However, a previous study has indicated
that there is an excess of grassland available of 1.7 million tonnes DM, which could be
increased to 12.2 million tonnes DM by adopting specific land management practices [76].
A significant resource could therefore be made available from underutilised or suboptimal
grasslands, while also sustaining existing livestock practices and food output. Furthermore,
as shown by this paper, the potential models for green biorefinery are diverse and can
be selected based on regional dynamics. While the current scenario is focused on the
production of eco-insulation, protein, and energy, alternative models of green biorefinery
can be targeted towards optimization of grasslands to increase protein efficiency per
hectare, thereby creating protein feeds for ruminants, monogastrics and humans from a
single farm [33,77,78]. In this sense, green biorefineries can help to unlock greater bio-
material potential from our grasslands, without compromising on food or feed output.
Additionally, the model of green biorefinery can bring potential environmental benefits,
with recent LCA studies showing a significant environmental footprint reduction in both
grass-based protein products and insulation products compared with current market
alternatives like soybean meal [56,79,80]. The co-production of such products along with
biogas could provide a mechanism to improve the sustainability of grassland management
in combination with other sustainable farming technologies, such as reduced fertiliser use
and animal dietary interventions [81].
While various bioeconomy and bio-based industry models are being developed at
research and demonstration levels, agreement on and selection of the most suitable model
requires expert knowledge from multiple local stakeholders. For green biorefinery, the
importance of obtaining the voice and input of the farmers that, in most cases, oversee
the grasslands, has been highlighted previously in the literature [30]. While literature and
consultation with experts can provide environmental, technical and economic information
regarding potential biorefinery models, they do not address challenges such as farmers’
willingness to participate in a biorefinery development, which requires direct engagement
with the farmers themselves. The multi-stakeholder involvement of farmers and other
value-chain actors in the decision-making process of a biorefinery value chain can help to
address knowledge gaps and ensure that the needs of each member are met, providing
a solid platform for the further development of the value chain. This approach can be
even more challenging in the bioeconomy, where stakeholders are often required from
traditionally distant sectors which do not have previous experience in collaborating [25].
Using a multi-actor approach which allows farmers and expert stakeholders to work
Grasses 2025, 4, 7 18 of 24

collectively on designing the model can be an appropriate structure for developing such
projects over a longer period [27]. At the European level, this multi-actor approach has
been promoted through the European Innovation Partnerships for Agriculture (EIP-Agri)
mechanism to support more bottom-up collaboration approaches in a variety of agricultural
sustainability and bioeconomy initiatives, including green biorefineries [27,82].
Primary producers such as farmers should be considered a central player in the design
of bio-based value chains. As highlighted within this paper, they can both supply feedstock
and benefit from the products produced. Farmers can also supply ground-level knowl-
edge of how farmlands are operated, knowledge that is particularly useful in determining
suitable areas for feedstock harvesting and solving logistical issues that may arise, not
to mention the level of commitment that farmers can provide to supplying a biorefinery.
Furthermore, farmers can play a central role as implementers of local biorefineries, ei-
ther by adopting small-scale biorefinery approaches [22] or through the development of
farmer-led biorefinery cooperatives which pool the resources of farmers to scale larger
regional facilities [21,24].
Emerging bioeconomy opportunities can potentially provide a diversification opportu-
nity for farmers, particular in sectors of agriculture which are struggling economically [27].
A recent study from Ireland, conducted by the Irish Cooperative Organisation Society,
found that over 80% of farmers would be open to trying new enterprises on their farms,
with over 60% agreeing that farmers should diversify their on-farm enterprises to protect
themselves for the future [83]. The study also found that green biorefinery (Biorefinery Glas
model), along with anaerobic digestion, was considered the bioeconomy diversification
opportunity with the highest level of interest among farmers [83]. The study also found a
strong willingness among farmers to pay in order to become members of a bioeconomy
cooperative [83]. However, the economic profitability of such opportunities needs to be
demonstrated to generate sufficient interest from farmers. While the economic model
contained within this study provides some initially positive findings, further work is re-
quired to assess the economic viability over time. As the project used a mixed-methods
approach involving technical feasibility, stake-holder preferences and GIS, the current
economic analysis was limited to the return on investment based on a fixed annual profit
and payback period, which only gives a snapshot of the economic viability of the project.
This approach was adopted following Cecelja et al. [84], who provided payback periods
for different feedstocks used in biorefineries. Depending on their needs and availability
in the current market, data stakeholders can use economic analyses such as net present
value and internal rate of return, which take into consideration the time value of money to
understand further the financial implications of any chosen value chain. The fluctuating
costs seen over recent years highlight the need to include sensitivity analysis, as included
in this paper. Furthermore, it is worth highlighting that the bioeconomy is evolving quickly,
and there are growing opportunities for implementing new cascading-value opportuni-
ties. For green biorefineries, for example, recent research demonstrating the production of
fructo-oligosaccharides from brown juice [85] or the extraction of food-grade protein from
the LPC fraction opens new economic opportunities from grasses [77].
GIS analysis also supports the process of assessing the viability of these new diversifi-
cation opportunities by considering environmental and socio-economic factors. This can
help to focus on potential location sites, which can balance between feedstock accessibility
and the market, benefiting from existing infrastructure and logistics while also avoiding
impacts on protected lands and waterways. We implemented the widely used GIS approach
of multiple-criteria analysis (MCA), which integrates and combines multiple datasets that
are then used to identify areas that satisfy a pre-specified range of criteria [64]. From the
suite of MCA, we used a combination of vector overlay techniques, commonly termed
Grasses 2025, 4, 7 19 of 24

the deductive inclusion–exclusion approach to decision making. Alternative methods

could have included ranking, weighting, or non-additive methods, although the approach
used in this research is considered the most risk-averse because all criteria must be met
to return a suitable site [86]. This provides decision makers with a range of low-risk sites
that would minimise the risk of losses for establishing a new biorefinery location. There
are various selection criteria that can be used in MCA, and in this current study, a local
biorefinery approach is taken in which feedstocks and products are assumed to be accessed
and used within a small radius. This is valid in the case of grass, which represents a
high-moisture feedstock which can be difficult to transport over long distances; however, it
is a conservative assumption in the case of protein concentrate and insulation, which can
be made as storable products and shipped nationally or internationally. In that scenario,
more sites may become available. The impact of scale in GIS and site suitability analyses
is well established [87], yet it highlights the importance of incorporating the co-creation
model to ascertain stakeholder engagement. Therefore, while we are confident that our
modelling decisions best suited the stakeholders we engaged with, the flexibility of the
model allows users to adapt variables on a case-by-case basis depending on value-chain
criteria and assumptions.
In order to bring this model to a commercial level, further work may be required
to secure market access for these new products regionally, nationally and internationally.
In the case of insulation materials, which were the primary income generator, a recent
study has found that 93% of Irish consumers would prefer to purchase bio-based products
as opposed to fossil-based products, with nearly half of consumers indicating that they
expect to purchase more bio-based insulation materials in the future [88]. From a policy
perspective, Ireland’s Climate Action Plan proposes to improve the energy ratings of
around 500,000 homes by 2030 to reduce energy consumption and carbon emissions [89].
This target could help to spur an indigenous eco-insulation sector. Several examples of
bio-based insulation from various sources like wood and hemp fibre are already on the
market. Grass-based insulation could generate a new diversification opportunity and
market for Ireland’s most abundant land-based resource. Moreover, with GIS increasingly
being utilised to map and monitor building status [64] and energy attributes [90] in Ireland,
subsequent site suitability analysis can be refined to account for the geographic variability
in the age of buildings, retrofits and energy efficiencies to reduce subsequent transportation
costs. Biorefinery co-products such as proteins and energy, along with future cascading
opportunity, can help to support the economic viability but can also help in displacement of
imported products, such as soybean from South America and natural gas from Russia, and
help to meet policies such as Renewable Energy Directive II, REPowerEUPlan, Ireland’s
National Biomethane Strategy 2024 and the forthcoming EU protein strategy.
Finally, support from and engagement with financial institutions and programmes will
be vital to support the wide-scale implementation of these rural bio-based business models.
A recent study highlighted access to finance as the most commonly cited barrier faced by
interviewees participating in the bioeconomy in Ireland [26]. This related both to setting
up and running operations. This factor becomes even more critical in the bioeconomy,
where financial risks can be high due to the large investment requirements, relatively new
technologies and collaboration models and the need to penetrate the market with new
products. To support such efforts, a number of financial institutions across Europe are
already placing a large emphasis on the need for sustainability as part of their pre-requisites
for capital investments. An example of this is the Nordic Investment Bank (NIB), which
promotes a Responsible Investment Framework by ensuring that all projects undergo a
mandate rating to make sure that the project fulfils NIB’s mandate of financing projects that
improve productivity and benefit the environment of the Nordic and Baltic countries [91].
Grasses 2025, 4, 7 20 of 24

At the producer level, the European Investment Bank announced a €3 billion financial
package to support loans for practitioners in the agricultural and bioeconomy sectors [92].
These loans will be matched by other participating financial institutions, unlocking close to
€8.4 billion of long-term investments for the bioeconomy sector. Specifically in the domain
of green biorefineries, the Ministry of Food, Agriculture and Fisheries of Denmark has
already taken ambitious steps to support the emergence of a commercial green biorefinery
sector in Denmark. Launched in 2021, with a budget of €35 million, the support scheme aims
to support the first commercial green biorefinery plants in Denmark. The scheme, which
has already helped to establish a fledgling green biorefinery sector in Denmark, included a
feasibility component, with successful projects co-funded to establish a commercial-scale
green biorefinery, which requires collaboration between a number of partners and must
also include farmers as part of the venture [93]. These types of mechanisms will be critical
for supporting farmer-centred green biorefinery approaches in other member states such
as Ireland.

5. Conclusions
Engaging with farmers at an early stage in the development of a bio-based business
model is essential in promoting and understanding the potential of these opportunities for
local regions. This engagement can help inform and build trust, but also obtain valuable
information on the preferred value chain to be selected and the operational model to be
adopted. Such engagement can also allow farmers to input into the model with practical
knowledge and identify the roles that the farmer can play, realizing opportunities for greater
empowerment and value generation. The identification and engagement of key stakeholders
across the value chain is also vital to gain insights on policy, markets and technologies relevant
for the value chain. The co-design approach of the current study supported the identification
of a medium-scale, locally focused, green biorefinery model for rural Ireland. The study has
found that such a model could be viable from an economic perspective, with a payback period
of 5 years. Meanwhile, 28 locations have been identified as suitable sites for such facilities,
mainly in the northeast and south of Ireland, though with varying levels of agricultural income
and farming intensity, which may also impact adoption.

Supplementary Materials: The following supporting information can be downloaded at:

https://www.mdpi.com/article/10.3390/grasses4010007/s1, File S1—Collection of GIS Maps devel-
oped within study.

Author Contributions: Conceptualization, A.H., H.M., B.O., P.H. and J.G.; methodology, A.H., B.O.,
P.H. and J.G.; software, A.H., E.M. and P.H.; validation, A.H., E.M., A.M., T.R., H.M., B.O., P.H. and
J.G.; formal analysis, A.H., E.M., A.M., T.R., H.M., B.O., P.H. and J.G.; investigation, A.H., H.M., B.O.,
P.H. and J.G.; resources, H.M., B.O., P.H. and J.G.; data curation, A.H.; writing—original draft, A.H.,
P.H. and J.G.; writing—review and editing, A.H., E.M., C.G.D., A.M., T.R., H.M., B.O., P.H. and J.G.;
visualization, A.H. and C.G.D.; supervision, T.R., H.M., B.O., P.H. and J.G. project administration,
P.H. and J.G.; funding acquisition, J.G. All authors have read and agreed to the published version of
the manuscript.

Funding: This research was supported through the Munster Technological University Postgraduate
Scholarship Programme.

Institutional Review Board Statement: Ethical approval was provided by the Ethics Committee of
Munster Technological University, Kerry.

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement: Data is contained within the article or Supplementary Materials.

Conflicts of Interest: The authors declare no conflicts of interest.

Grasses 2025, 4, 7 21 of 24

References
1. Clark, W.; Lenaghan, M. The Future of Food: Sustainable Protein Strategies Around the World; Zero Waste Scotland: Scotland, UK, 2020.
2. European Parliament. EU Agricultural Policy and Climate Change; European Parliament: Strasbourg, France, 2020.
3. EPA. Agriculture Sector Emissions Share 2022. Available online: https://www.epa.ie/our-services/monitoring--assessment/
climate-change/ghg/agriculture/ (accessed on 18 November 2024).
4. EPA. Ireland Will Not Meet Its 2020 Greenhouse Gas Emissions Reduction Targets. Action Is Needed Now to Meet 2030 EU Targets
in 2021. Available online: https://www.epa.ie/news-releases/news-releases-2021/ireland-will-not-meet-its-2020-greenhouse-
gas-emissions-reduction-targets-action-is-needed-now-to-meet-2030-eu-targets.php (accessed on 4 September 2024).
5. O’Brien, D.; Hennessy, T.; Moran, B.; Shalloo, L. Relating the carbon footprint of milk from Irish dairy farms to economic
performance. J. Dairy Sci. 2015, 98, 7394–7407. [CrossRef] [PubMed]
6. Climate Change Advisory Council. Carbon Budgets in Dublin, Ireland. 2023. Available online: https://www.climatecouncil.ie/
carbonbudgets/ (accessed on 14 November 2024).
7. Carlsson, I.Y. Denmark Will Be First to Impose CO2 Tax on Farms, Government Says. Reuters. 25 June 2024. Available online:
https://www.reuters.com/sustainability/denmark-will-be-first-impose-co2-tax-farms-government-says-2024-06-25/ (accessed
on 14 November 2024).
8. Eurostat. EU Agricultural Prices Continued to Rise in Q2 2022. Available online: https://ec.europa.eu/eurostat/web/products-
eurostat-news/-/ddn-20220930-3 (accessed on 12 November 2024).
9. Kiernan, A. Tillage Farmers’ Incomes Fall by 15% Due to Drop in 2020 Yields. Irish Examiner. 20 April 2021.
10. CSO. Agricultural Price Indices December 2021 in 2022. Available online: https://www.cso.ie/en/releasesandpublications/er/
api/agriculturalpriceindicesdecember2021/ (accessed on 10 November 2024).
11. Teagasc. Lower Production Costs and Exceptional Aid Measures Led to a Modest Increase in Average Farm Income in 2019; Teagasc:
Dublin, Ireland, 2020; Available online: https://www.teagasc.ie/news--events/news/2020/farm-income-in-2019.php (accessed
on 12 November 2024).
12. Bord Bia. Meat Market Review 2019; Bord Bia: Dublin, Ireland, 2019.
13. European Commission. EU Market Prices for Representative Products. 2020. Available online: https://agridata.ec.europa.eu/
extensions/DataPortal/prices.html (accessed on 10 November 2024).
14. CSO. Farm Ownership and Labour Input in 2016. Detailed Analysis Farm Structure Survey 2016—Central Statistics Office.
Available online: https://www.cso.ie/en/releasesandpublications/ep/p-fss/farmstructuresurvey2016/da/foli/ (accessed on
6 November 2024).
15. Teagasc CAP Provides Important Funds for Irish Farms. 2020. Available online: https://www.teagasc.ie/publications/2020/cap-
provides-important-funds-for-irish-farms.php#:~:text=The%20application%20process%20for%20the,it%20flows%20through%
20the%20economy (accessed on 15 November 2024).
16. IFA. Survey Shows Underlying Vulnerability of Farm Incomes 2021. Available online: https://www.ifa.ie/policy-areas/survey-
shows-underlying-vulnerability-of-farm-incomes/ (accessed on 15 November 2024).
17. AgriLand. Need for Off-Farm Income “a Fact of Life” for Drystock Farmers. 2018. Available online: https://www.agriland.ie/
farming-news/need-for-off-farm-income-a-fact-of-life-for-drystock-farmers/ (accessed on 15 November 2024).
18. Cadogan, S. Just 20% of Farm Families Rely on ag for Income. Irish Examiner. 9 November 2022. Available on-
line: https://www.irishexaminer.com/farming/arid-41002454.html#:~:text=Just%2020%25%20of%20farm%20families%20rely%
20fully%20on%20their%20farm,from%20pensions%20or%20social%20assistance. (accessed on 12 November 2024).
19. Dillon, E.; Donnellan, T.; Moran, B.; Lennon, J. Teagasc National Farm Survey 2020; Teagasc: Dublin, Ireland, 2021.
20. EU Commission. A Sustainable Bioeconomy for Europe: Strengthening the Connection Between Economy, Society and the Environment;
European Commission: Brussels, Belgium, 2018.
21. Allais, F.; Lescieux-Katir, H.; Chauvet, J.-M. The continuous evolution of the Bazancourt–Pomacle site rooted in the commitment
and vision of pioneering farmers. When reality shapes the biorefinery concept. EFB Bioecon. J. 2021, 1, 100007. [CrossRef]
22. Bruins, M.E.; Sanders, J.P. Small-scale processing of biomass for biorefinery. Biofuels Bioprod. Biorefin. 2012, 6, 135–145. [CrossRef]
23. De Visser, C.; van Ree, R. Small-Scale Biorefining; Wageningen University & Research: Wageningen, The Netherlands, 2016.
24. Lange, L. Business models, including higher value products for the new circular, resource-efficient biobased industry.
Front. Sustain. 2022, 3, 789435. [CrossRef]
25. Bröring, S.; Vanacker, A. Designing Business Models for the Bioeconomy: What are the major challenges? EFB Bioecon. J. 2022,
2, 100032. [CrossRef]
26. Pender, A.; Kelleher, L.; O’Neill, E. Regulation of the bioeconomy: Barriers, drivers and potential for innovation in the case of
Ireland. Clean. Circ. Bioecon. 2024, 7, 100070. [CrossRef]
27. Harrahill, K.; Macken-Walsh, Á.; O’Neill, E.; Lennon, M. An analysis of Irish dairy farmers’ participation in the bioeconomy:
Exploring power and knowledge dynamics in a multi-actor EIP-AGRI operational group. Sustainability 2022, 14, 12098. [CrossRef]
Grasses 2025, 4, 7 22 of 24

28. Department of Agriculture. Bioeconomy Action Plan 2023–2025. In Department of Agriculture; Department of Environment,
Climate and Communications: Dublin, Ireland, 2023.
29. Government of Ireland. Ireland’s National Biomethane Strategy. 2024. Available online: https://www.gov.ie/en/publication/d1
15e-national-biomethane-strategy/ (accessed on 18 November 2024).
30. Gaffey, J.; Rajauria, G.; McMahon, H.; Ravindran, R.; Dominguez, C.; Ambye-Jensen, M.; Souza, M.F.; Meers, E.; Aragonés, M.M.;
Skunca, D. Green Biorefinery systems for the production of climate-smart sustainable products from grasses, legumes and green
crop residues. Biotechnol. Adv. 2023, 66, 108168. [CrossRef]
31. Serra, E.; Lynch, M.; Gaffey, J.; Sanders, J.; Koopmans, S.; Markiewicz-Keszycka, M.; Bock, M.; McKay, Z.; Pierce, K. Biorefined
press cake silage as feed source for dairy cows: Effect on milk production and composition, rumen fermentation, nitrogen and
phosphorus excretion and in vitro methane production. Livest. Sci. 2023, 267, 105135. [CrossRef]
32. Gaffey, J.; O’Donovan, C.; Murphy, D.; O’Connor, T.; Walsh, D.; Vergara, L.A.; Donkor, K.; Gottumukkala, L.; Koopmans, S.;
Buckley, E. Synergetic Benefits for a Pig Farm and Local Bioeconomy Development from Extended Green Biorefinery Value
Chains. Sustainability 2023, 15, 8692. [CrossRef]
33. Gaffey, J.; Matinez, A.A.; Andrade, T.A.; Ambye-Jensen, M.; Bishop, G.; Collins, M.N.; Styles, D. Assessing the environmental
footprint of alternative green biorefinery protein extraction techniques from grasses and legumes. Sci. Total Environ. 2024,
949, 175035. [CrossRef] [PubMed]
34. Ecker, J.; Schaffenberger, M.; Koschuh, W.; Mandl, M.; Böchzelt, H.; Schnitzer, H.; Harasek, M.; Steinmüller, H. Green biorefinery
upper Austria–pilot plant operation. Sep. Purif. Technol. 2012, 96, 237–247. [CrossRef]
35. Rinne, M. Novel uses of ensiled biomasses as feedstocks for green biorefineries. J. Anim. Sci. Biotechnol. 2024, 15, 36. [CrossRef]
[PubMed]
36. Khoshnevisan, B.; Fog, E.; Baladi, S.; Chan, S.W.S.; Birkved, M. Using the product environmental footprint to strengthen the
green market for sustainable feed ingredients; Lessons from a green biomass biorefinery in Denmark. Sci. Total Environ. 2023,
877, 162858. [CrossRef]
37. Andrade, T.A.; Ambye-Jensen, M. Process integration and techno-economic assessment of a green biorefinery demonstration
scale platform for leaf protein production. In Computer Aided Chemical Engineering; Elsevier: Amsterdam, The Netherlands, 2022;
Volume 51, pp. 877–882.
38. de Souza, M.F.; Akyol, Ç.; Willems, B.; Huizinga, A.; van Calker, S.; Van Dael, M.; De Meyer, A.; Guisson, R.; Michels, E.; Meers, E.
From grass to gas and beyond: Anaerobic digestion as a key enabling technology for a residual grass biorefinery. Waste Manag.
2024, 182, 1–10. [CrossRef]
39. Nynäs, A.-L.; Newson, W.R.; Johansson, E. Protein fractionation of green leaves as an underutilized food source—Protein yield
and the effect of process parameters. Foods 2021, 10, 2533. [CrossRef]
40. Kamm, B.; Schönicke, P.; Hille, C. Green biorefinery–industrial implementation. Food Chem. 2016, 197, 1341–1345. [CrossRef]
41. Xiu, S.; Shahbazi, A. Development of green biorefinery for biomass utilization: A review. Trends Renew. Energy 2015, 1, 4–15.
[CrossRef]
42. Corona, A.; Ambye-Jensen, M.; Vega, G.C.; Hauschild, M.Z.; Birkved, M. Techno-environmental assessment of the green
biorefinery concept: Combining process simulation and life cycle assessment at an early design stage. Sci. Total Environ. 2018,
635, 100–111. [CrossRef]
43. O’Keeffe, S.M. Alternative Use of Grassland Biomass for Biorefinery in Ireland: A Scoping Study; Wageningen University and Research:
Wageningen, The Netherlands, 2010.
44. Mandl, M.G. Status of green biorefining in Europe. Biofuels Bioprod. Biorefin. Innov. A Sustain. Econ. 2010, 4, 268–274. [CrossRef]
45. Kamm, B.; Hille, C.; Schönicke, P.; Dautzenberg, G. Green biorefinery demonstration plant in Havelland (Germany).
Biofuels Bioprod. Biorefin. Innov. A Sustain. Econ. 2010, 4, 253–262. [CrossRef]
46. Santamaría-Fernández, M.; Lübeck, M. Production of leaf protein concentrates in green biorefineries as alternative feed for
monogastric animals. Anim. Feed Sci. Technol. 2020, 268, 114605. [CrossRef]
47. Grienitz, V.; Schmidt, A.-M. Scenario workshops for strategic management with Lego® serious play® . Probl. Manag. 21st Century
2012, 3, 26. [CrossRef]
48. Houston, K. Qualitative Data-Collection Methods; Jotform Blog: San Francisco, CA, USA, 2022.
49. Höltinger, S.; Schmidt, J.; Schönhart, M.; Schmid, E. A spatially explicit techno-economic assessment of green biorefinery concepts.
Biofuels Bioprod. Biorefin. 2014, 8, 325–341. [CrossRef]
50. Prieler, M.; Lindorfer, J.; Steinmüller, H. Life-cycle assessment of green biorefinery process options. Biofuels Bioprod. Biorefin. 2019,
13, 1391–1401. [CrossRef]
51. Ambye-Jensen, M. Discussion on Scale and Economics of Green Biorefinery Production in Denmark. Personal communication, 2021.
52. Cristóbal, J.; Caldeira, C.; Corrado, S.; Sala, S. Techno-economic and profitability analysis of food waste biorefineries at European
level. Bioresour. Technol. 2018, 259, 244–252. [CrossRef]
Grasses 2025, 4, 7 23 of 24

53. Zetterholm, J.; Bryngemark, E.; Ahlström, J.; Söderholm, P.; Harvey, S.; Wetterlund, E. Economic evaluation of large-scale
biorefinery deployment: A framework integrating dynamic biomass market and techno-economic models. Sustainability 2020,
12, 7126. [CrossRef]
54. SEAI. Energy Price Trends; SEAI: Dublin, Ireland, 2023; Available online: https://www.seai.ie/data-and-insights/seai-statistics/
prices (accessed on 18 November 2024).
55. Gaffey, J. Learning from Green Biorefinery Demonstration. Personal communication, 2024.
56. Franchi, C.; Brouwer, F.; Compeer, A. LCA Summary Report Grass Fiber Insulation Versus Stone Wool Insulation; Grasgoed:
Mechelen Belgium, 2020.
57. Annibaldi, V.; Cucchiella, F.; Rotilio, M. Economic and environmental assessment of thermal insulation. A case study in the
Italian context. Case Stud. Constr. Mater. 2021, 15, e00682. [CrossRef]
58. UK Green Building Council. Insulation Boards Made from Meadow Grass; UK Green Building Council: London, UK, 2019; Available
online: https://ukgbc.org/resources/insulation-boards-made-from-meadow-grass/ (accessed on 14 November 2024).
59. Reagent Good Manufacturing Practice. 2022. Available online: https://chemicals.ie/ (accessed on 14 November 2024).
60. Lindorfer, J.; Lettner, M.; Hesser, F.; Fazeni, K.; Rosenfield, D.; Annevelink, B.; Mandl, M. Technical, Economic and Environmental
Assessment of Biorefinery Concepts: Developing a Practical Approach for Characterisation; IEA bioenergy task; IEA: Paris, France, 2019.
61. Warnes, B. What Is Depreciation? 2022. Available online: https://bench.co/blog/tax-tips/depreciation/ (accessed on
15 November 2024).
62. Cheusheva, S. How to Make a Loan Amortization Schedule. 2023. Available online: https://www.ablebits.com/office-addins-
blog/create-loan-amortization-schedule-excel/ (accessed on 18 November 2024).
63. ESRI ArcGIS Desktop 10.8. 2021. Available online: https://desktop.arcgis.com/en/arcmap/latest/get-started/setup/arcgis-
desktop-system-requirements.htm (accessed on 15 November 2024).
64. Holloway, P. Understanding GIS Through Sustainable Development Goals: Case Studies with QGIS; CRC Press: Boca Raton, FL, USA, 2023.
65. Valenti, F.; Porto, S.M.; Dale, B.E.; Liao, W. Spatial analysis of feedstock supply and logistics to establish regional biogas power
generation: A case study in the region of Sicily. Renew. Sustain. Energy Rev. 2018, 97, 50–63. [CrossRef]
66. Jayarathna, L.; Kent, G.; O’Hara, I.; Hobson, P. Geographical information system based fuzzy multi criteria analysis for
sustainability assessment of biomass energy plant siting: A case study in Queensland, Australia. Land Use Policy 2022, 114, 105986.
[CrossRef]
67. Gas Networks Ireland. Pipeline Map. 2022. Available online: https://www.gasnetworks.ie/corporate/company/our-network/
pipeline-map/ (accessed on 17 November 2024).
68. Bord Bia. Feed Member List; Bord Bia: Dublin, Ireland, 2022; Available online: https://www.bordbia.ie/farmers-growers/member-
status/scheme-members/feed-member-list/ (accessed on 12 November 2024).
69. Bell, N.; Schuurman, N.; Hayes, M.V. Using GIS-based methods of multicriteria analysis to construct socio-economic deprivation
indices. Int. J. Health Geogr. 2007, 6, 17. [CrossRef] [PubMed]
70. Perpiña, C.; Martínez-Llario, J.C.; Pérez-Navarro, Á. Multicriteria assessment in GIS environments for siting biomass plants.
Land Use Policy 2013, 31, 326–335. [CrossRef]
71. Teagasc Riparian Buffer Zones. 2022. Available online: https://www.teagasc.ie/environment/water-quality/farming-for-water-
quality-assap/assap-factsheets/riparian-buffer-zones/ (accessed on 12 November 2024).
72. EPA Environmental Impact Statement March 2017—Appendix No. 8, Buffer Zones (No. 8). 2017. Available online: http:
//www.epa.ie/licences/lic_eDMS/090151b280609532.pdf (accessed on 4 November 2024).
73. Hengeveld, E.J.; Bekkering, J.; Van Gemert, W.; Broekhuis, A. Biogas infrastructures from farm to regional scale, prospects of
biogas transport grids. Biomass Bioenergy 2016, 86, 43–52. [CrossRef]
74. Dillon, E.; Moran, B.; Donnellan, T. Teagasc National Farm Survey Report; Teagasc: Dublin, Ireland, 2016.
75. Sustainable Food Systems Ireland, Agriculture and Food in Ireland. 2024. Available online: https://www.sfsi.ie/
agricultureandfoodinireland/#:~:text=Dairy%20and%20beef%20account%20for,food,%20drink%20and%20horticulture%
20exports (accessed on 12 November 2024).
76. McEniry, J.; Crosson, P.; Finneran, E.; McGee, M.; Keady, T.; O’Kiely, P. How much grassland biomass is available in Ireland in
excess of livestock requirements? Ir. J. Agric. Food Res. 2013, 52, 67–80.
77. Møller, A.H.; Hammershøj, M.; Dos Passos, N.H.M.; Tanambell, H.; Stødkilde, L.; Ambye-Jensen, M.; Danielsen, M.; Jensen, S.K.;
Dalsgaard, T.K. Biorefinery of green biomass– how to extract and evaluate high quality leaf protein for food? J. Agric. Food Chem.
2021, 69, 14341–14357. [CrossRef]
78. dos Passos, N.H.M.; Ambye-Jensen, M. Fractionation of proteins from alfalfa juice at demonstration scale: Effects of temperature
and centrifugation strategies on removal of green protein and recovery of the soluble white protein fraction. Sep. Purif. Technol.
2024, 330, 125461. [CrossRef]
79. Gaffey, J.; Collins, M.N.; Styles, D. Review of methodological decisions in life cycle assessment (LCA) of biorefinery systems
across feedstock categories. J. Environ. Manag. 2024, 358, 120813. [CrossRef]
Grasses 2025, 4, 7 24 of 24

80. Franchi, C.; Brouwer, F.; Compeer, A. LCA Summary Report Grass Protein Versus Soy Protein; GrasgoedL: Mechelen, Belgium, 2020.
81. Rubhara, T.; Gaffey, J.; Hunt, G.; Murphy, F.; O’Connor, K.; Buckley, E.; Vergara, L.A. A Business Case for Climate Neutrality in
Pasture-Based Dairy Production Systems in Ireland: Evidence from Farm Zero C. Sustainability 2024, 16, 1028. [CrossRef]
82. Ravindran, R.; Koopmans, S.; Sanders, J.P.; McMahon, H.; Gaffey, J. Production of Green biorefinery protein concentrate derived
from perennial ryegrass as an alternative feed for pigs. Clean Technol. 2021, 3, 656–669. [CrossRef]
83. Brosnan, J. The Evolving Bioeconomy in Ireland Activating Co-Operative Involvement; Irish Cooperative Organisation Society:
Dublin, Ireland, 2023.
84. Cecelja, F.; Yang, A.; Solda, M. Utilization of Biomass Feedstocks: A Case Study Based on Rice and Sugar Mills in Thailand. In
Computer Aided Chemical Engineering; Elsevier: Amsterdam, The Netherlands, 2012; Volume 30, pp. 217–221.
85. Menon, A.; Ravindran, R.; Koopmans, S.; Sanders, J.; Rai, D.K.; Gaffey, J.; Augustyniak, A.; McMahon, H. Purification,
characterisation, and determination of prebiotic potential of fructooligosaccharide from perennial rye grass. Int. J. Biol. Macromol.
2024, 279, 135031. [CrossRef]
86. Greene, R.; Devillers, R.; Luther, J.E.; Eddy, B.G. GIS-based multiple-criteria decision analysis. Geogr. Compass 2011, 5, 412–432.
[CrossRef]
87. Goodchild, M.F. Scale in GIS: An overview. Geomorphology 2011, 130, 5–9. [CrossRef]
88. Gaffey, J.; McMahon, H.; Marsh, E.; Vehmas, K.; Kymäläinen, T.; Vos, J. Understanding consumer perspectives of bio-based
products—A comparative case study from Ireland and The Netherlands. Sustainability 2021, 13, 6062. [CrossRef]
89. Government of Ireland. Climate Action Plan 2023; Government of Ireland: Dublin, Ireland, 2023.
90. Buckley, N.; Mills, G.; Fealy, R. An inventory of buildings in Dublin City for energy management. Ir. Geogr. 2020, 53. [CrossRef]
91. Nordic Investment Bank. Nordic Investment Bank In 2024. Available online: https://pub.norden.org/temanord2023-540/6-nib-
nordic-investment-bank.html (accessed on 8 January 2025).
92. European Investment Bank. €3 Billion of EIB Group Financing Announced for Farmers and Bioeconomy. 2024. Available online:
https://www.eib.org/en/press/all/2024-497-eur3-billion-of-eib-group-financing-announced-for-farmers-and-bioeconomy (ac-
cessed on 7 January 2025).
93. Ministry of Food, Agriculture and Fisheries of Denmark. Green Biorefining from Political Ambition to Implementation of New
Subsidies in Denmark. 2023. Available online: https://lbst.dk/Media/638527439483192746/Green_Biorefining_-_LBSTs_oplaeg_
paa_Go_Grass_event_24_maj_Supplerende_slides.pdf (accessed on 9 January 2025).

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.