Question:
You are given the full text of a scientific research paper.

    Summarize the **research methods** used in the study in a way that is informative for policy audiences. Your response should:
    - Describe the overall study design, data sources, tools, and models used
    - Avoid unnecessary technical details or equations
    - Use accessible but precise language

    Write a short paragraph suitable for the “Scientific Research Study Methods” section of a policy brief.

    Scientific Research Paper:
    Analysis
https://doi.org/10.1038/s41560-019-0546-4
1Tyndall Centre for Climate Change Research, Department for Mechanical, Aerospace and Civil Engineering, Faculty of Science and Engineering,
University of Manchester, Manchester, UK. 2Department of Economics, School of Social Sciences, University of Manchester, Manchester, UK.
3Hunter Centre for Entrepreneurship, University of Strathclyde Business School, Glasgow, UK. 4Grantham Institute, Imperial College London, London, UK.
*e-mail: timothy.braunholtz-speight@manchester.ac.uk; maria.sharmina@manchester.ac.uk; edward.manderson@manchester.ac.uk
L
ocal energy projects delivered by community groups could
potentially play a pivotal role in realizing the transition to a
low-carbon energy future. Community energy schemes offer
an alternative to large-scale energy provision, with various forms
of community energy already found across Europe, North America
and elsewhere1–5. In the UK, the term ‘community energy' is gener-
ally associated with small civil society organizations and/or social
enterprises running projects that encourage energy saving and
efficiency, or that generate renewable electricity. These projects are
typically grounded in the motivation to accelerate decarbonization
through both decentralization and democratization of the energy
system, and address issues such as fuel poverty and energy justice6–11.
The growth of the sector in the UK has been driven by a combi-
nation of the decreasing cost of renewable energy technologies and
government policies1,4 (see Table 1). However, more recently, gov-
ernment support for small-scale renewables has been substantially
scaled back1,12. Most notably, Feed-in Tariff scheme (FITs) rates
fell more than 50% from 2015 to 2016 for many technologies (see
Supplementary Table 2) and the scheme is now closed to new proj-
ects. In this challenging low-subsidy environment, project develop-
ment and investment have slowed significantly13.
These recent developments emphasize the importance of under-
standing how the community energy sector is financed. However,
there is currently very limited empirical evidence on the financing
of community energy activities6,14. Studies in Germany and Belgium
note the mixture of motivations reported by citizens investing in
community energy15,16; further studies in Germany note the sub-
stantial size of the renewable energy cooperative sector there17 and
its success in raising finance from cooperative members18. Much of
the literature on the UK community energy sector focusses on the
qualitative exploration of the definitions, motivations and challenges
for projects, and how they engage with questions of justice and pov-
erty6–8,10,11,19–27. Nevertheless, some studies have examined the sec-
tor’s finance and business models9,13,28–33. In particular, sector-wide
surveys have played an important role in establishing the size and
structure of the sector, gathering some data on finance; however,
these surveys do not offer project-level analysis on financial perfor-
mance9,13,33. A government-convened working group on community
energy finance offered insight into the difficulties faced by com-
munity projects, but did not present an analysis of empirical data29.
We are aware of only one previous quantitative study of community
energy business models at project level, which compared the costs of
community- and commercially-owned wind and hydro energy proj-
ects in Scotland30,31. The study provides valuable detailed evidence
on the distinctiveness of community energy, finding that commu-
nity projects face additional risks and transaction costs compared
with commercial projects. However, its scope does not include other
aspects of business models (such as finance or revenue), other busi-
ness model types and projects in other parts of the UK.
In this analysis, we fill an important gap in the community
energy literature by providing a UK-wide analysis of the financing
mechanisms and financial performance of individual community
energy projects. We also systematically characterize community
energy business models using quantitative methods. We perform
our analysis using a dataset on the financial characteristics of com-
munity energy projects, collected in a survey of the UK community
energy sector undertaken by the authors in 2017−2018.
The Financing Community Energy survey
We use data from the new Financing Community Energy survey
of the UK community energy sector conducted in 2017−2018. The
survey structure is based on the Business Model Canvas34, which
analyses organizations’ value propositions and associated activities,
customers, resources, and costs and revenues (see Supplementary
Methods for the survey questions). For each project’s energy gen-
eration and financial flows (costs and revenues), we collected data
for only the most recent 12-month period for which they were
available, to minimize the administrative burden on participants
Business models and financial characteristics of
community energy in the UK
Tim Braunholtz-Speight   1*, Maria Sharmina   1*, Edward Manderson   2*, Carly McLachlan1,
Matthew Hannon3, Jeff Hardy   4 and Sarah Mander1
Community energy projects take a decentralized and participatory approach to low-carbon energy. Here we present a quantita-
tive analysis of business models, financing mechanisms and financial performance of UK community energy projects, based on
a new survey. We find that business models depend on technology, project size and the fine-tuning of operations to local con-
texts. Although larger projects rely more on loans, community shares are the most common and cheapest financial instrument
in the sector. Community energy has pioneered low-cost citizen finance for renewables, but its future is threatened by reduc-
tions, and instability, in policy support. Over 90% of the projects in our sample make a financial surplus during our single-year
snapshot, but this falls to just 20% if we remove income from price guarantee mechanisms, such as the Feed-in Tariff scheme.
Renewed support and/or business model innovations are therefore needed for the sector to realize its potential contribution
to the low-carbon energy transition.
Nature Energy | VOL 5 | FebruarY 2020 | 169–177 | www.nature.com/natureenergy
169

Analysis
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and maximize the number of projects included in the dataset. Our
analysis of the community energy sector is therefore cross-sectional
rather than longitudinal (see Methods for further discussion).
We received substantive responses to our survey on 145 projects
from 48 organizations. To obtain more data on certain technologies,
we supplemented the survey data with information on a further
eight projects from organizations’ published financial statements
and reports. This extra data give a total of 153 projects and 56 orga-
nizations in our dataset. However, as published documents provide
less extensive data than our survey, the data on the additional eight
projects are used only when we provide summary statistics on proj-
ect characteristics by technology.
Of the 153 projects, the majority (139) are electricity generation
projects, with a total capacity of about 41 MW. Two of these projects
also involved heat generation, and there are a further five that are
exclusively heat generation projects. Nine projects involve no direct
energy generation. Other surveys of the sector found 228 commu-
nity energy organizations in England, Wales and Northern Ireland
in 2018, of which 204 were engaged in electricity generation, with
a total of 168 MW operational capacity13. In Scotland, there was a
total of 81 MW of community-owned renewable electricity gen-
erating capacity in 201728. Our survey dataset therefore captures
approximately one-sixth of the UK community energy sector in
terms of installed generation capacity.
Our analysis first provides a taxonomy of the project business
models employed in the community energy sector. We then anal-
yse the financial characteristics of community energy projects,
the mechanisms by which they have been financed, the price they
charge their customers and the importance of FITs and other incen-
tive schemes to project revenues.
A taxonomy of community energy projects
To shed further light on the structure of the sector we applied cluster
analysis to produce the first ever quantitative data-driven taxonomy
of community energy business models in the UK. A cluster analysis
of the survey results helps to identify similarities and differences
between community energy projects, to complement case study
research. The clusters also present a ‘menu’ of business models that
can be used to inform the design of new projects.
Our taxonomy has two parts, based on two runs of the cluster
analysis. The first run used project-only variables, omitting the
project location and all the variables that related to the organization
running the project. This analysis produced three broad clusters
(Table 2) shaped largely by the type of energy activity undertaken
(generation versus demand-side activities) and by the type of tech-
nology employed.
The second cluster analysis run used all the variables, includ-
ing those relating to the organization as a whole (such as turnover,
number of members and legal structure) and the project location.
Here, the three broad clusters splintered into many smaller ones
producing 12 clusters in total (Table 3).
The two runs of the cluster analysis map closely on to one another
(Table 3). Taken together, they suggest that while technology and
activity are important drivers of business models, within the three
broad clusters, community energy organizations have fine-tuned
their business models. This fine-tuning includes different means of
accessing finance and other resources (such as varying reliance on
community shares, loans and grants), and offering a range of value
propositions to different customers (such as funding other local
projects, providing educational opportunities, cutting CO2 emis-
sions and reducing energy bills).
Of the 12 clusters, the ‘demand-side services’ and ‘energy as a
sideline’ projects stand out as significantly different from the others,
and they also form the third cluster in the project-level cluster anal-
ysis. The other ten clusters are differentiated partly by technology,
with a clear divide between solar rooftop and other generation tech-
nologies, also reflected in the project-level cluster analysis. Other
variables, such as whether the organization runs multiple projects,
has paid staff or is entirely volunteer-run, the type of customers it
deals with and how it finances its projects, are part of the fine-tun-
ing we noted above.
We find that the most common aspects across the current busi-
ness models in the community energy sector include a predomi-
nance of electricity generation particularly through solar PV, not
having charitable status and not being linked to a charitable ‘parent’
body, employing three full-time staff at most on average (although
there are rare cases of employing up to ten staff), relying on at least
some volunteers (and up to 90 in some projects), relying on FITs for
revenue and community shares for finance, mainly working with
one type of customer only and typically emphasizing environmental
value propositions over social and economic value propositions.
Costs, revenues and performance of energy generation
projects
Our sample includes 84 solar rooftop, 15 wind, 12 hydro, 4 solar
ground-mount and 2 biomass projects with sufficient data to calcu-
late financial performance. Table 4 presents summary statistics on
the average project characteristics by technology. (The table does
not include data on the two biomass heat projects due to the risk of
compromising data confidentiality.)
We find that there is substantial heterogeneity in the size, costs
and revenues of community energy projects across the different
technologies. Wind and solar ground-mount projects tend to be
substantially larger than others in terms of generation capacity and
performance, costs and revenues. The mean solar rooftop project is
smaller, at 74 kW capacity, but this size remains much larger than
typical UK domestic solar rooftop capacity35 ( <4 kW).
Table 4 also presents two measures of the financial performance
of the projects. Annual costs per unit generated are highest for wind
projects and lowest for solar rooftop and biomass. In contrast, the
return on capital expenditure (CAPEX) is higher for the average
wind project than the average solar or hydro project. With the caveat
that the sample is very small, biomass heat compares very favourably
in terms of return on capital with other technologies (21 pence per £
CAPEX). The differences in performance observed across technolo-
gies may reflect various factors, including project-specific charac-
teristics such as age and size, both of which may have a significant
impact on original capital expenditure figures, organization-specific
characteristics, such as the expertise of personnel and learning by
doing, as well as the features of the technologies themselves.
We find that the average annual financing costs across all proj-
ects is £46,500 per annum (excluding projects with zero financing
costs). The average total CAPEX across these projects is £865,900.
Therefore, community energy projects on average face annual
financing costs equal to about 5% of their initial total CAPEX.
Table 1 | Principal forms of government-mandated price
support for small-scale low-carbon energy provision referred
to in this paper
Scheme
Dates open to
new projects
Energy scope
Renewables obligation (RO)
2002–2017
Electricity generation
Feed-in Tariff scheme
2010–2019
Electricity generation
Renewable heat incentive (RHI)
2014–2021
Heat generation
Notes: These schemes offer eligible projects a guaranteed price for the electricity or heat they
generate. In some cases the schemes operate differently across the UK’s devolved nations,
and each scheme involves a complex set of scale and technology bandings, and eligibility and
administrative requirements. Further information on these schemes can be found on the Ofgem
website67 and in previous work by the authors1.
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Analysis
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Community energy project financing
Data on financing mechanisms are available for 136 projects (89%
of the total). Around three-quarters of projects (77%) use just one
or two external financing instruments to fund their projects. Over
one-third (37%) of projects also use the organization’s pre-existing
funds to undertake a project. Community shares36 are the most
Table 2 | Taxonomy of UK community energy from cluster analysis of survey data using only project-level variables
Cluster name
No. of projects
No. of organizations
Key characteristics of clusters
Standalone renewables
20
16
Wind, hydro and ground-mounted solar electricity generation projects,
and one biomass heat network
On-site customer renewables
85
22
Almost entirely rooftop solar photovoltaic (PV) projects, but also one
solar thermal, one hydro, one wind and one biomass boiler. Electricity
sold to customer in building (solar rooftop) or by private wire
Demand-side activities
14
10
A mixture of energy efficiency and fuel poverty advice projects, and
renewable energy generated for own use (rather than selling)
Table 3 | Taxonomy of UK community energy from cluster analysis of survey data using project, organization and location variables
Project-level cluster
(Table 2)
Cluster name
No. of
Projects
Energy activities
Organization
Finance and resources
Revenues and
customer sectors
Standalone renewables
Multi-financed
hydro and wind
4
Hydro and wind
electricity generation
on small/medium–
large scale
Co-ops and other
companies, single
projects only
Mix of financing
instruments; often
pay for sites and other
resources
FITs only
Standalone renewables
and on-site customer
renewables
Large wind selling
to grid
5
Wind with mean
capacity of >2 MW
Co-ops and other
companies with
part-time staff
All use loans, some
community shares
RO, energy sales to
wholesalers or local
grid
On-site customer
renewables
Medium-scale
generation with
mixed financing
9
Wind, hydro, solar
ground-mount and
biomass heat – mean
capacity >1 MW
Co-ops and other
companies with
some paid staff
All use loans, some
community shares
FITs and sales to
energy companies
Small/medium
solar rooftop
9
Solar rooftop
PV – mix of scales
Volunteer-run
co-ops
Community shares,
most resources free
FITs and energy sales
to mix of sectors
Multi-site solar on
public sector roofs
35
Solar rooftop PV
mostly <50 kW
capacity – sometimes
with energy efficiency
also
All co-ops with some
paid employment,
running multiple
projects
Community shares;
sites free, some
resources free or
in-house (for example,
legal services)
FITs and energy
sales, mostly to
public sector
Professionalized
solar rooftop
co-ops
13
Solar rooftop
PV – mix of scales
Co-ops with multiple
projects and paid
staff
Community shares
main financing
instrument; many
resources paid for
FITs and energy sales
to mix of sectors
Small multi-project
generation for third
sector groups
12
Mostly solar rooftop
PV (but some heat)
Volunteer co-ops
running multiple
projects
Mix of financing
instruments
FITs and energy sales
to third sector
Small solar rooftop
9
Solar rooftop PV
mostly <50 kW
capacity
Mostly volunteer-
run co-ops
Finance mostly
community shares;
mix of free and paid
resources
FITs and energy sales
to mix of sectors
Smaller-scale
multi-project
co-ops
4
Multiple solar rooftop
PV <50 kW capacity
Volunteer-run
co-ops
All using community
shares, some loans
and grants also
Mix of public, private
and third sector
customers
Demand-side activities
Demand-side
services
6
Energy efficiency
advice, installation
and fuel poverty
reduction work
Mostly co-ops, paid
staff and volunteers
Grants and loans
Customer fees,
energy services
contracts, some work
free of charge
Energy as a sideline
7
Rooftop solar PV, heat
and electricity storage
Small-scale third
sector sports and
leisure clubs
Grants and self-
financing
FITs and savings on
own energy bills
All three project-level
clusters
Multi-tech
generation including
partnerships
6
Hydro, wind and solar
rooftop PV
Companies with paid
staff
Grant and loan finance;
sites free, other
resources paid or
in-house
Hydro and wind sold
to grid, solar to local
customer
Note: For further explanation of the terms used, see Methods section on data gathering.
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frequently used instrument, with 102 issues of community shares in
our dataset. In addition, there are 73 loans, 54 grants and 9 ‘other’
instruments, mostly bonds.
Focusing on operational energy generation projects (121 of the
136 with financing data), the size of the CAPEX is related to how
the finance is raised: larger projects rely more heavily on loans, and
smaller projects rely more on community shares. There seems to
be a threshold around a CAPEX of £200,000: 88% of generation
projects above this threshold use some loan finance, but only 17%
of projects below this threshold reported using loans. However,
community shares still account for a significant proportion of the
total capital raised for all but the largest scales of project, as Fig. 1
shows. Community shares account for almost all the finance raised
by projects with a CAPEX of less than £200,000 (the majority of
projects), but a much smaller proportion of the total finance raised
by projects costing over £1.5 million. Grants, such as the Rural and
Urban Community Energy Funds37,38, form a relatively small part of
total capital for all project scales. However, they may play a signifi-
cant role in de-risking projects39, as they are often used to finance
the earliest – and riskiest – stages of project development. They are
also important sources of funding for projects in the demand-side
services cluster.
We use regression analysis to consider whether there is a sta-
tistical relationship between the cost of finance and the instru-
ment type (see Methods for details). From this analysis, we find
that community shares charge an interest rate that is 2 percentage
points lower than loans on average. (Community shares typically
pay interest rather than dividends, see Supplementary Note 1.) To
put this finding into perspective, the average size of the financing
instruments in the regression sample is about £306,000; therefore,
the first year’s interest payments on this would be about £6,200
lower if financed by community shares rather than loans (see
Methods section for details).
These findings are striking because, unlike conventional equity,
community shares are neither saleable to third parties for profit,
nor do they necessarily give the holder a claim to the proceeds of
a sale of the issuing company’s assets36. Therefore, the prospect
of capital gains, which might encourage conventional sharehold-
ers to accept lower interest payments, is not available to commu-
nity shareholders. Further, there do not appear to be many cases
of community shares refinancing risky early-stage loans: most
projects that issued community shares did not use loans at all. We
explore alternative explanations for the interest rate difference in
the Discussion section.
We now turn to the marketing mechanisms employed to attract
funds. Despite the growth of online alternative finance platforms,
such as Ethex or Crowdfunder, that can reach potential investors
across the UK, around half the community share issues in our data-
set were made using local marketing only, for example, through
local newspapers and the organization’s own website. Many others
were marketed nationally, but through community energy networks
rather than general alternative finance online platforms. There is a
clear gap between the scale of funds raised by these different mecha-
nisms, with general large-scale marketing raising the largest sums
(see Table 5). However, our data show that local marketing has the
lowest mean interest rates, with rates on average 0.8% lower than
energy-specific UK-wide marketing (a significant difference at the
1% significance level). It is also notable that locally marketed com-
munity shares raised enough to cover project CAPEX for 32 of the
43 projects in the table (74% of these projects). This suggests that
many community energy projects have succeeded in raising the
capital they need through relatively cheap local finance.
Importance of price guarantee schemes to project finances
As the FIT and RO schemes are now closed to new projects, we
examine the importance of revenue from these schemes to commu-
nity energy business models. The overwhelming majority of genera-
tion projects in our dataset accessed FITs, RHI or RO revenues (only
two projects did not). Of these, we used 110 projects with sufficient
detail on annual costs and revenues to perform a simple calculation
to examine their dependency on these schemes (note that existing
projects are not affected by cuts to FITs rates and the closure of the
FIT and RO schemes, see Methods for details). We find that 92% of
these projects (101 projects) were in financial surplus (that is, total
annual revenues exceeded total annual costs) for the year for which
data were provided; however, after removing the price scheme rev-
enues, only a fifth of the projects (22 projects) were in surplus. As
these projects were designed to draw on FITs or similar revenue
streams, it is not surprising that removing those revenues would
push many projects into deficit. Yet, it is notable that 22 projects do
not suffer this fate in our exercise, and so in the rest of this section
we examine their characteristics in more detail.
Of the 22 projects, 5 were commissioned in the 2 years prior to
the survey date and were financed primarily by community shares
but reported no financing costs. Quite often, community shares are
Table 4 | Energy generation project characteristics by
technology
Hydro
Wind
Solar
ground
Solar
roof
Number of projects in sample
12
15
4
84
Capacity (kW)
163
1,862
3,428
74
(162)
(2,741)
(2,304)
(168)
Total capital expenditure
(£ thousands)
1,097
3,255
5,992
87
(979)
(4,187)
(6,387)
(170)
Annual operating costs
(£ thousands)
25
136
172
3
(32)
(182)
(205)
(13)
Annual financing costs
(£ thousands)
29
161
318
4
(37)
(219)
(370)
(9)
Annual generation (MW h)
472
4,317
3,385
56
(458)
(7,512)
(2,289)
(111)
Annual revenue (£ thousands)
91
552
730
11
(93)
(818)
(900)
(25)
Annual surplus (£ thousands)
37
256
240
4
(39)
(489)
(328)
(7)
Capacity factor
0.36
0.27
0.11
0.09
(0.15)
(0.14)
(0.01)
(0.03)
Annual cost per kW h (£)
0.12
0.15
0.12
0.11
(0.05)
(0.18)
(0.10)
(0.09)
Return on capital costs (£)
0.11
0.18
0.10
0.12
(0.05)
(0.10)
(0.03)
(0.05)
Notes: Table shows mean characteristics with standard deviations (the square root of the variance)
in parentheses. Only data from fully operational projects are included. Some projects for which
revenue data are missing are excluded. Data for eight projects taken directly from organizations’
published financial statements and reports are only used in this table. The table does not include
data on the two biomass heat projects due to the risk of compromising data confidentiality.
Operating costs refer to expenditure on running the project. Financing costs include all repayments
of borrowing and payments to shareholders. Operating costs do not include financing costs.
Annual surplus = (annual revenue – annual operating costs – annual financing costs). Capacity
factor = [annual generation/(365 × 24 × capacity)]. Annual cost per kW h = (annual operating
costs + annual financing costs)/annual generation. See Methods section for a discussion of this
metric and a comparison with the levelized cost of energy (LCOE) metric. Return on capital
costs = (annual revenue/total capital costs). Costs, revenues and generation figures are based on a
single year of project data. See Methods section for further discussion of the implications.
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issued with the stipulation that they pay no interest for the first year
or two of generation36, which may be the case with these projects.
Subtracting community share interest payments, at the rate given
for each project, from these project revenues leaves three out of five
of them making a loss without FITs revenue.
Four projects, including one of the five just mentioned, had addi-
tional revenue that was not directly linked to levels of energy gen-
eration (for example, environmental grant funding or land rental
to a commercial partner); two other projects gained revenue in the
form of savings on the organization’s own energy costs. The remain-
ing 12 projects were all solar rooftop projects that sold electricity to
the owners or occupiers of the building where the solar panels were
located. For 10 out of these 12 projects, the customer used at least
80% of the electricity generated, at a price between wholesale and
typical retail prices (see the next section).
This analysis suggests only a small number of existing projects,
those selling most of the electricity they generate to on-site custom-
ers, could generate a financial surplus without price support. Further,
the 22 projects highlighted above are mostly small scale (less than
50 kW generation capacity), and 18 were left with an annual surplus
of less than £3,000 without price support revenues. In the context
of such small surpluses, year-on-year variation in weather condi-
tions and operational costs can have a significant bearing on project
financial performance. The long-term price guarantee offered by
schemes such as the FITs plays a significant role in de-risking proj-
ects and attracting finance40,41.
Prices paid by community renewable energy customers
Community energy generation projects sell to a range of custom-
ers, including energy companies, other companies, community
and third sector organizations, and public sector bodies. We find
that energy companies pay the lowest rates on average, equal to
just 5.03 pence per kW h in our sample (Table 6). This low rate is
to be expected, as projects selling to energy companies are compet-
ing with wholesale rather than retail prices. Of community energy’s
retail customers, 6 out of 25 community or third sector customers
in our dataset receive energy for free (‘zero rate’ customers); the
remaining 19 customers pay an average rate equal to 6.33 pence per
kW h. Private sector companies that are not energy companies pay
a slightly higher rate equal to 6.87 pence per kW h. Public sector
organizations pay 2.28 pence per kW h or 45% more on average for
their energy than energy companies (and 0.99 pence per kW h more
than community or third sector customers). However, this rate may
still represent a significant saving on retail market electricity prices:
average non-domestic electricity prices were over 10 pence per kW h
for most of the period (2015–2017) to which these data relate42.
120
100
80
60
40
20
0
Percentage of CAPEX (%)
<200
(90 projects)
200–1,499
(11 projects)
>1,500
(10 projects)
Project CAPEX range (£ thousands)
Bonds
Loans
Grants
Community
shares
Fig. 1 | Percentage of capital raised by different instruments in relation
to the scale of project capital expenditure. For each size category of
project CAPEX, the chart shows the proportion of total finance raised for
all projects in that CAPEX range by different instruments (namely, loans,
community shares and grants). Where less than 100% of CAPEX is shown
as being raised, this is due to some instruments that raised only relatively
small sums being omitted from the figure. Where more than 100% of
CAPEX is raised, these organizations retain surplus funds for reinvestment
in future projects, in agreement with investors. The chart is based on 111
energy generation projects with sufficient data on financing and CAPEX to
perform the analysis.
Table 5 | Community share issues analysed by marketing
mechanism
No. of
share
issues
% of
total
Total
amount
raised (£)
Mean
amount
raised
per share
issue (£)
Mean
interest
rate on
shares (%)
General online
platforms – UK wide
9
10
6,362,856
706,984
4.33 (1.00)
Energy-specific
marketing – UK
wide
38
42
7,881,930
207,419
5.06 (0.33)
Local marketing
43
48
4,248,498
98,802
4.26 (0.83)
Total
90
100
18,493,284 205,481
4.60 (0.79)
Notes: We find that the difference in mean interest rate between energy-specific marketing and
local marketing is statistically significant at the 1% level (t-statistic of 5.64). The difference in
mean interest rate between energy-specific marketing and general online platforms is statistically
significant at the 10% level (t-statistic of 2.14). The difference in mean interest rate between
local marketing and general online platforms is insignificant. For the mean interest rate on shares,
standard deviations (the square root of the variance) are given in parentheses.
Table 6 | Energy prices charged by community renewable
generators by type of customer
Customer type
Average
customer
rate (pence
per kW h)
No. of
customers
Excluding recipients of free
energy
Average
customer rate
(pence per
kW h)
No. of
customers
Energy company 5.03 (0.66)
12
5.03 (0.66)
12
Other private
sector company
6.24 (3.48)
11
6.87 (2.95)
10
Community or
third sector
4.81 (3.39)
25
6.33 (2.28)
19
Public sector
7.32 (1.54)
51
7.32 (1.54)
51
Notes: Table shows mean customer rates by customer type, with standard deviations (the square
root of the variance) in parentheses.
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Discussion
This paper sheds light on how community energy organizations
have developed small-scale energy projects, often with significant
citizen funding, in an energy system dominated by large-scale
actors and commercial finance that is the UK energy system32. We
find that, while organizations fine-tune the details of their business
models to their context, the sector is dominated by renewable elec-
tricity generation for which two basic project business models have
been developed. First, larger projects have become increasingly pro-
fessionalized and ‘bankable’, as shown by their ability to raise com-
mercial loans alongside citizen finance. Second, many organizations
run rooftop solar PV projects that supply an on-site customer as
well as the grid, and are small enough to be mainly funded through
community share issues. Whilst international comparisons are lim-
ited by the scarcity of literature, we note that these UK community
solar projects appear to be similar to German renewable energy
cooperatives in terms of financing structure and the cost of capital,
although German cooperatives tend to be larger in terms of capi-
tal raised17,18. We further find evidence that UK community energy
projects benefit from a local discount in fund-raising, with locally
marketed community share issues the cheapest category of finance
in our dataset (other than grants).
Our analysis shows that bringing social finance approaches into
the renewables sector has helped community energy to pioneer
innovations that can make a significant contribution to the energy
transition. In the field of social finance, innovations such as crowd-
funding and community shares have emerged as a response to the
difficulties that social enterprises have with accessing finance from
traditional lenders43,44. Meanwhile, in the energy sector, it is argued
that progress towards a low-carbon transition is hampered by domi-
nant actors being ‘locked-in’ to the existing system by short-term
economic pressures45–47. However, expanding and diversifying the
energy investor base can increase the flow of finance into renew-
ables and other transition technologies48, because different actors
invest according to different criteria49,50.
We suggest that, through its emphasis on environmental and
social value propositions, community energy has developed alter-
native investment criteria that have successfully lowered financing
costs for small-scale renewables through diversifying the investor
base. Previous research in the UK51, Belgium15 and Germany16,18
finds that people invest in community energy projects for a mix of
financial and non-financial reasons, and local investors may invest
larger sums15. Our analysis shows that, while it is clear from previ-
ous research that community projects can face additional costs and
challenges29–31, a community approach may also bring some finan-
cial advantages.
However, most of the business models in our data were built
in an energy market where revenue was substantially de-risked by
price guarantee schemes40 such as FITs. While citizens’ investment
motivations may be mixed, the financial security offered by such
schemes was likely particularly important for people investing their
own money41. What can our study say about future prospects for
community energy in contexts like the UK (and also Germany52,53),
where FIT schemes are now closed?
First, we note that renewable heat and self-financing demand-
side projects are currently a rarity in the UK sector13. Yet the proj-
ects of this type in our dataset show a financial surplus. The growing
availability of technologies (for example, light-emitting diode (LED)
lights for energy efficiency) and the continued financial support for
renewable heat (RHI for heat generation), coupled with the reduc-
tion in support for renewable electricity, may lead to growth in these
activities as community energy groups seek new business models54.
Second, even discounting those with special circumstances, we
find that 11% of renewable energy projects still showed an annual
surplus without FITs (or other price scheme) revenues. This finding
suggests that some new renewable electricity projects may be viable
in a post-FITs world. Our analysis indicates the key elements of
post-FITs renewable energy business models to be rooftop solar PV
as the generation technology, a building with high energy demand
as the site and a customer willing to pay. Our data show public sec-
tor bodies pay, on average, the highest prices for community-gener-
ated electricity, but we also find projects on private sector rooftops
that show a surplus without FITs revenues.
However, without some form of price stabilizer it is hard to see
the number of projects using community renewable energy business
models returning to its previous rate of growth in the short term.
One source of price stability could be a floor price for exported
electricity, as suggested by community energy sector associations55.
Another mechanism might be Contracts for Difference auctions.
The UK already runs such auctions for large-scale renewables and
has opened them to remote island wind56. The auctions may benefit
some future community projects using the standalone renewables
business model, but could benefit many more if other technologies
were also able to participate.
Third, policy could encourage, or even mandate, public sector
bodies to purchase community-generated energy on long-term con-
tracts. Given the growth of the community energy sector to date,
the low cost of community capital and the wider social benefits it
offers, these three measures would appear to be promising routes
forward for both expanding renewable generation capacity and
supporting the delivery of positive social impacts through the
energy transition.
Methods
Survey design and data collection. This survey formed part of the Financing
Community Energy research project led by Professor McLachlan, which was
funded as part of the UK Energy Research Centre (UKERC) research programme.
In the early stages of this research project, Community Energy England (CEE)
and Community Energy Wales (CEW) launched their State of the Sector Survey
2017 (SOTS 2017), which addressed some of the same topics. The Financing
Community Energy project signed a Memorandum of Understanding with CEE
to share survey data where possible, to maximize the benefit from the two data
collection exercises.
The survey questionnaire covered characteristics of community energy
organizations, and of the projects they run. With regard to organizations,
it included the legal structure, annual turnover, numbers of paid staff and
volunteers, and numbers of members. In relation to each project, topics included
energy activities (including electricity or heat generation and energy efficiency),
ownership (sole or partnership type), financing (for example, details of each
instrument type, value, terms), resources employed (including sites, technical,
financial and legal services, general administration), costs (operating and
financing), revenues (values and sources), value propositions (a range of economic,
social and environmental propositions), customers (for example, types and rates
paid) and other beneficiaries.
These categories were based on the Business Model Canvas approach to
analysing business models34, adjusted to take account of the project’s particular
interest in financing mechanisms, and the characteristics of the community energy
sector as the project team understood it.
The format of some of the questions was designed to complement the SOTS
2017 survey to facilitate data sharing. Pre-set multiple choice formats were used
as far as possible to facilitate data coding and quantitative analysis. Some free-text
qualitative questions were also included, particularly in relation to organizations’
future plans. The responses to these questions have been fed into other parts of the
overall research project and are not reported in this paper.
The survey sample was constructed with reference to the SOTS 2017
respondents list, data on community energy organizations in Scotland held by the
social enterprise consultancy SCENE, and through internet searches, searching
attendance lists at sector events, and Local Energy Scotland sending a survey link
to their members through their newsletter.
The survey received research ethics approval from the University of
Manchester in October 2017. Informed consent was obtained in writing from all
survey participants. The questionnaire was piloted in October/November 2017
with three community energy organizations. Only minor changes were made after
the pilot process, and the pilot data form part of the survey dataset analysed in
this paper. The full survey was launched in November 2017 and closed in May
2018. During January and February 2018 it was suspended in England, Wales and
Northern Ireland to avoid an overlap with the 2018 iteration of the SOTS.
The survey was available to complete online or by telephone interview with
the project team. Two methods of completing the survey were offered because the
team were conscious that community energy is a heavily surveyed sector. Allowing
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research participants to choose the most convenient participation method ensured
the survey achieved sufficient responses for a meaningful quantitative data analysis,
while also reducing the administrative burden on research participants. Ideally, we
would use only one method of data collection, because using different methods
may affect the quality of the data. Although we attempted to minimize this concern
by ensuring that the online and telephone data followed a standardized framework,
we cannot rule out that inconsistent data collection methods have resulted in
measurement error in our data. This is a limitation of the study.
In total the researchers contacted 280 organizations, of which 83 responded
and 48 completed the survey, providing data on 145 projects. Not all projects are
included in all the analysis presented here. Complete data were not available for
some projects, limiting the kinds of analysis that could be performed. Further,
some projects were classified as ‘stalled or on hold’, and so by their nature did
not have complete data. Data were collected on an additional eight projects using
published accounts and reports only. These data are presented in Table 4, to
provide greater coverage of the hydro, wind and solar ground-mount technologies,
but are not otherwise used in the analysis.
Cluster analysis. We used data on 119 projects for cluster analysis, as the projects
that had missing values related to any of the business model aspects had to be
excluded. Missing values were predominantly due to respondents not having access
to some of the information required in the survey, for example, because of lost
paperwork or limited documenting.
Unlike in the performance and financial analysis (please see the next section),
the two bioenergy projects were not excluded from the cluster analysis, as their
anonymity would be preserved when aggregated into clusters. However, we did
re-run the cluster analysis without these two projects to test whether the results
would change. The exclusion of these two projects did not affect the composition of
the clusters in any of the runs (that is, either in the run with all variables, including
those related to the organizational level and location, or in the run with only
project-level variables). Excluding those two projects also had little effect on the
silhouette coefficients and the shapes of the clusters on the t-distributed stochastic
neighbour embedding (t-SNE) plots. As the presence or otherwise of the bioenergy
projects did not affect our cluster analysis results, we kept those projects in the
sample. The analysis runs with those projects excluded are available on request
from the authors.
The cluster analysis was performed using R 3.6.157 and the packages dplyr58,
cluster59, factoextra60, ggplot261, Rtsne62, dbscan63, fpc64 and clustMixType65.
Partitioning around medoids (PAM), hierarchical agglomerative clustering (HAC),
density-based clustering and k-prototypes clustering were the four clustering
methods applied to the dataset.
We included different combinations of organization-level variables (for
example, the legal structure of the organization) and project-level variables (for
example, the type of energy activity and type of customer) in several analysis
runs. The first run used all 48 variables, the second run omitted the variables
for organization turnover and project location, and the third run omitted all the
organization-level variables (such as turnover, number of members, number of
volunteers, number of staff employed, ownership structure, charitable status and
year of foundation) and project location, and used only the 40 variables relating to
the operation of individual projects.
Before running the analyses, we created a heatmap of the dissimilarity in the
community energy dataset using the daisy function with Gower distance that can
handle mixed types of variables. The heatmap (Supplementary Fig. 1) demonstrated
that the dataset did contain patterns compared with a heatmap of random data. We
then performed a sanity check on the dissimilarity matrix through outputting the
most and the least similar pairs of projects, with the expected results.
We used two key types of validation statistics to compare the results of the
four clustering methods: the within-cluster sum of squares (WSS) and the average
silhouette width (see Supplementary Table 1). The WSS was significantly better
for PAM (1.8901) than for the next best method using this metric, HAC (4.5846).
The average silhouette width for PAM (0.3058) was slightly lower than for HAC
(0.3798), but significantly better than for the next best method using this metric,
density-based clustering (0.1485). The validation results were similar for the
analysis run that included only project variables, that is, with organization and
location variables excluded. In this run, density-based clustering showed somewhat
better average silhouette width (0.2910) than PAM (0.2690). However, in this
analysis run, the density-based clustering method only yielded one cluster of 68
projects, with the remaining 51 projects designated as outliers, which did not
provide meaningful insights. On the basis of these validation statistics (reported in
Supplementary Table 1), the PAM clustering results were not a statistical artefact,
and hence we selected PAM as our main clustering method.
For PAM clustering, we calculated and plotted silhouette width (Supplementary
Fig. 2) to select the optimal number of clusters. Twelve clusters corresponded
to the highest silhouette width (0.4054) for the first analysis run in which all
variables were used, with thirteen and three clusters for the second and third runs,
respectively (with the highest silhouette widths of 0.4327 and 0.4026, respectively).
The results of the first two analysis runs were very similar to each other, therefore
we omitted the second analysis run with 13 clusters as it did not add any extra
insights to the results.
We then visualized the clustering using the t-SNE technique. The t-SNE
technique decreases the number of dimensions while preserving the structure of
the dataset. The resulting figures for both the twelve- and three-cluster runs are
presented in Supplementary Fig. 3. The figures illustrate which business models
are well defined and distinct (for example, clusters 4, 11 and 12 in Supplementary
Fig. 3a) and which business models are more diffuse and might share similarities
with other types (for example, clusters 6, 8 and 9 in Supplementary Fig. 3a). Similar
plots for other clustering methods (Supplementary Fig. 4a,b) give a less clear-cut
allocation of projects into clusters.
In relation to the variables present within the clusters, it is important to
explain the value proposition variable. We constructed a list of value propositions
potentially offered by community energy organizations to their customers based
on a review of the wider community energy literature. Survey participants were
asked to say which of these value propositions they felt were important in their
customers’ decision to use their services (for example, to buy electricity). In
the cluster analysis, the value propositions were categorized as environmental,
economic or social, and projects were coded according to whether participants
selected environmental, economic or social propositions (or a mixture) as
important. Environmental value propositions included providing renewable
electricity, reducing CO2 emissions and tackling climate change. Economic value
propositions included electricity generation regardless of origin, reducing energy
bills, dealing with a known trusted organization, benefiting local economy,
enabling customer to meet planning requirements and enhancing customer
reputation. Finally, social value propositions included bringing a community
together, generating community benefit and providing educational benefits.
In further research customer perspectives of the value propositions offered by
community energy organizations could be investigated.
Performance and financial analysis. This paper uses data collected for a single
year of project operation. Therefore, we provide a cross-sectional analysis that
involves looking at the sector at a moment in time rather than assessing how it
changes over time. It is particularly important to bear this in mind for the project
performance characteristics presented in Table 4: because generation, revenue and
operating costs may vary considerably from one year to the next, these data may
not be representative of project performance in other years. Future research may
wish to address these issues by collecting survey data on project performance over
a number of years to construct a panel dataset.
Although data were collected between November 2017 and May 2018, as noted
above, the data do not relate to the performance of projects during the months the
survey was open. Rather, organizations reported data that relate to a 12-month
period, more specifically, the most recent financial year for which data on the
project were available. As the data measure project performance over a 12-month
period, they will reflect project performance over a sustained period of time rather
than during an individual month or season of the year. Furthermore, we do not
typically expect that community energy projects will vary that much in terms of
their performance from one year to the next, especially in a systematic way (such
that variations over time would not average out across projects when performing
statistical tests, for example, when performing t-tests of means). Nonetheless, we
cannot be sure that information during one 12-month period is representative of a
different 12-month period. This is an issue with any cross-sectional dataset.
The absence of data with a time dimension means we also do not aim to assess
performance over the lifetime of a project, for example, by measuring the internal
rate of return. Likewise, we analyse costs in terms of cost per unit of kW h of
generation rather than the LCOE. Cost per kW h is a similar metric to an LCOE in
that it involves dividing operating costs and a capital cost recovery component (in
the form of an annual financing cost) by electricity generation. However, unlike the
LCOE, it provides an annual snapshot rather than discounting the predicted costs
and generation over a project’s entire lifetime.
To better understand the importance of financing characteristics for
community energy projects, we explore whether there is a statistical relationship
between the interest rate (cost of finance) and the instrument type. We are
particularly interested in comparing community shares with loans because the
majority of community energy projects are financed using these instruments.
(Grants are also a common source of finance but do not charge interest.) To do
this we first note that the mean interest rates for community shares and loans in
our sample are 4.58% and 5.58%, respectively. The difference in means is 1.01
percentage points. Performing a t-test on the equality of the mean rates, we find
that the means are statistically different at the 1% significance level (t-statistic of
3.03). Thus, community shares charge on average a statistically lower interest rate
than loans.
A comparison of means may, however, be misleading because the size of the
finance obtained and the financing term (duration) may also influence the interest
rate. We therefore compare the difference in interest rate between community
shares and loans while holding these other characteristics constant. We do this
by estimating a linear regression model. We proceed by defining three dummy
variables that capture the instrument type:
CommunityShare = 1 if the financing instrument is community shares, and
CommunityShare = 0 if it is not community shares.
Bonds = 1 if the financing instrument is bonds, and Bonds = 0 if it is not bonds.
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Loans = 1 if the financing instrument is loans, and Loans = 0 if it is not loans.
Although all three instrument types are included in our model, we need
to include only two of these three dummy variables in the regression equation
(further explanation can be found in ref. 66). We choose to include the
CommunityShare and Bonds dummy variables. Therefore, Loans is the base group
(or benchmark or omitted group) and is the group against which comparisons are
made. We choose loans as the base group because we are especially interested in
looking at the difference in interest rate between community shares and loans.
We then estimate the linear regression model given by equation (1):
IRi ¼ β1 þ β2CommunitySharei þ β3Bondsi þ β4Sizei þ β5Durationi þ ϵi
ð1Þ
where the dependent variable IR is the financing interest rate of financing source i.
IR is a continuous variable that can take non-integer values. CommunityShare and
Bonds are defined above. As explained previously, we compare how these financing
instruments are associated with the interest rate relative to the omitted category,
which is loans. In equation (1) we also include the variables Size and Duration
to control for the size and duration of the financing instrument, respectively.
Size is defined as the monetary value of the financing source (in £ millions) and
Duration is a dummy variable equal to 1 if the finance term is 240 months or more,
or indefinite/not specified, and 0 if a relatively short-term duration (less than
240 months). β1 to β5 are coefficients to be estimated. Finally, ε is an error term. We
estimate regression equation (1) using ordinary least squares.
Each observation on financing source i belongs to an organization that may
use one or more sources of finance for its community energy project(s). Outcomes
for different financing sources within organizations are likely to be correlated.
As we cannot assume that the error term is independently distributed within
organizations, we cluster standard errors at the organization level.
In equation (1), the continuous variables (IR and Size) enter in levels. An
alternative approach that allows for a non-linear relationship between the
dependent and explanatory variables is to enter the continuous variables in
logarithms. We find our results are robust if we use a logarithmic functional form
(the results are available on request). However, here we present the results with
variables in levels because in this case the coefficients have a percentage
point interpretation.
We now present the results from the estimation of regression (1). Here
we report the estimated coefficients with cluster-robust standard errors in
parentheses. For each estimated coefficient, we also report the t-statistic that
we calculate to test the null hypothesis that the population coefficient equals
zero. We find, ^β1 ¼ 5:124 ð0:585Þ
I
 and t-statistic = 8.76, ^β2 ¼ 2:016 ð0:706Þ
I
 and
t-statistic = –2.85, ^β3 ¼ 0:653 ð0:667Þ
I
 and t-statistic = –0.98, ^β4 ¼ 0:185 ð0:091Þ
I

and t-statistic = 2.02, and ^β5 ¼ 1:452 ð0:777Þ
I
 and t-statistic = 1.87. Finally, the
R-squared from the regression is 0.2457 and there are 118 observations.
The estimated coefficient for the dummy variable CommunityShare (–2.016)
indicates that there is a difference in the interest rate between community shares
and loans of 2.016 percentage points on average in our sample while holding
constant the size and duration of the finance. The t-statistic indicates that this
difference is statistically significant at the 1% level. To put this finding into
perspective, the average size of an individual financial instrument (that is, a
single loan or share issue) in the regression sample is about £306,000. Therefore,
for the average project, the annual interest payment for the first year would be
on average lower by £6,168.96 (2.016% of £306,000) if financed by community
shares rather than loans. This does not take into account compound interest and
repayments in later years of a project; it is simply intended to illustrate what the
interest rate differential between loans and community shares means, in terms of
actual amounts a community energy project might pay in interest on the initial
principal sum. In the paper, the figures given are rounded for greater readability:
thus, we mention a “2 percentage points” difference in interest rates and an average
repayment differential of “about £6,200”.
We also find that projects financed by bonds do not have an interest rate that is
significantly different from loans. In addition, we find that instruments that have a
longer duration and larger value have higher interest rates on average.
We investigate whether these results are sensitive to outliers. We do not find
any evidence of observations with large estimated residuals that may affect the
estimates. We also investigate the distribution of the dependent and explanatory
variables by inspecting the raw data and by using a leverage-versus-squared-
residual plot. From this analysis we identify two observations with large leverage
on the estimated coefficients due to outlying values for the explanatory variables.
However, our central findings on the difference in the interest rate between
community shares and loans are robust to dropping these observations from the
analysis. Therefore, they do not affect our conclusions.
The impact of the removal of price guarantee schemes is calculated by simply
subtracting all price guarantee scheme revenue (FITs, RHI or RO) from total
project revenue, project by project, for the single year of revenue data that we
collected. It is important to note that, for the FITs, projects retain the tariff rate for
which they initially qualified for the rest of their lifetime, including an inflation
adjustment; unlike the RO, the FITs is not subject to annual variations in price due
to market conditions. The RO scheme revenues are affected by year-to-year market
variation, but this variation is not itself affected by the scheme being closed to new
entrants. Therefore, the data do not only reflect the performance of community
energy under the tariff rates available to new projects at the time of data collection.
This analysis allows an appreciation of the extent to which actual projects
are reliant on price scheme revenues. There is no consideration for how projects
might have been designed if the schemes had not been available, which is a more
complex question. Therefore, these results do not in themselves show that it would
be impossible to design a future project to make a financial surplus without a price
guarantee scheme; nor, given that we have just one year’s data, do they test the
‘viability' of a project over its lifetime.
To investigate whether different types of customers pay different rates for
community-generated energy, we calculate mean rates paid by the four different
types of customer (energy companies, other private sector, public sector,
community and third sector). As noted in the main body of the text (Table 5), we
find that the mean rates differ, with the mean rate lowest for energy companies and
highest for public sector customers. Performing a t-test on the equality of the mean
rates paid by energy companies and public sector organizations, we find that the
means are statistically different at the 1% significance level (t-statistic of 3.69).
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
Source data for Fig. 1 are provided with the paper. The data gathered in the
Financing Community Energy survey can be found at https://doi.org/10.5286/
ukerc.edc.000007, or by searching ‘Financing Community Energy’ on the UK
Energy Research Centre’s Energy Data Centre: https://ukerc.rl.ac.uk/
Due to the terms under which the data were collected, individual project records
cannot be made public and are therefore not be available. However, aggregated
records of small numbers of similar projects are available.
Received: 10 May 2019; Accepted: 20 December 2019;
Published online: 10 February 2020
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Acknowledgements
This research was funded as part of the UK Energy Research Centre Phase 3 research
programme (grant number EP/L024756/1). C. Birch and C. Walsh, doctoral researchers
at the Tyndall Centre for Climate Change Research, University of Manchester, also
provided research assistance for the survey. We are grateful for comments from seminar
participants at several universities and conferences. Any remaining errors are our own.
We would like to thank the survey participants for their time and data, and Community
Energy England, in particular E. Bridge and J. Hall, for their practical support and for
sharing their State of the Sector data with the research team (further details on this is in
the Methods section). We would like to thank SCENE (and in particular J. Harnmeijer
and S. Robinson) for additional data which, although they are not analysed in this paper,
were very helpful to us in framing our survey work, as noted in the Methods section. We
would also like to thank Local Energy Scotland for assistance with marketing the survey.
Author contributions
C.M. led the research project. C.M., S.M., M.S., E.M., J.H., M.H. and T.B-S. contributed
to the conception, framing and design of the survey research. T.B-S. conducted the
survey and supervised the work of C.B. and C.W.; he also conducted the analysis of
the composition of project finances and the impact of price support mechanisms. M.S.
designed and conducted the cluster analysis. E.M. conducted the econometric analyses
of financing instrument interest rates and provided descriptive statistical analysis. All
authors jointly wrote the paper: T.B-S. led the writing; M.S., E.M., C.M., M.H., J.H. and
S.M. contributed text and extensive comments on the structure and content of several
drafts of the paper.
Competing interests
C.M. is Chair of the Trustees of the climate change charity Possible (formerly 10:10),
and a director of Community Energy North. Both of these roles are unpaid. M.H. is an
unpaid Trustee of South Seeds, Glasgow, a community environmental charity with a
focus on energy. J.H. is a non-executive director of Public Power Solutions Limited, a
renewable energy developer that has worked with community groups.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41560-019-0546-4.
Correspondence and requests for materials should be addressed to T.B.-S., M.S. or E.M.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2020
Nature Energy | VOL 5 | FebruarY 2020 | 169–177 | www.nature.com/natureenergy
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Quantitative survey of activities, structures and financial characteristics of community energy companies in the UK.
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Data were excluded from the analyses when there was not sufficient data on a particular project to perform the analysis, or where the
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were selected for analysis, is included in the text of the paper. Data were collected on 20 projects that were not fully operational - either
in active development, or 'on hold' - but these were excluded from most analyses in the paper which focus on the characteristics and
performance of operational projects. (These data will be used for other parts of the overall research project.)

The details of data exclusions for specific analyses presented in the paper are:
Cluster analysis (Tables 2 and 3): 35 projects were excluded for insufficient data.
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analysis focussed on energy generation projects only.
Project characteristics (Table 4): data from projects that are not fully operational were excluded.
Community share marketing mechanisms (Table 5): 63 projects were excluded, as they did not use community shares to raise funds.
Project reliance on FITs, RHI or RO revenues: 43 projects were excluded, either because they did not use these revenue schemes, or
there was insufficient data to perform the analysis.
Regression analysis on cost of finance (equation (1)): projects with missing data were excluded.
Non-participation
Response rate was 17%. 280 organisations were contacted and 48 organisations completed the survey. A further 18 declined to
participate, 18 responded expressing interest in the survey but did not complete the questionnaire, and the remaining 196 did not
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    Scientific Research Study Methods:

Answer: Little is known about how community energy projects raise finance, so we conducted a new UK-wide survey of the sector. Our survey structure used the Business Model Canvas to analyse organisations’ value propositions (what they offer the customer) and associated activities, customers, resources, costs and revenues. We collected data on up to 200 variables per project, paying particular attention to financing mechanisms. We received substantive responses to our survey on 145 projects from 48 organizations. We conducted cluster analysis to identify groups of similar business models. Descriptive statistical analysis allowed us to examine financial performance, the impact of removing price guarantee schemes on project revenues, and the prices different customers pay for community energy. We also used econometric analysis to examine the relationship between the cost of finance and financing mechanisms.