The increasing awareness of stakeholders is leading firms to adopt strategies related to the reduction of their environmental impact, not necessarily linked to existing regulatory provisions (e.g. Corporate Social Responsibility strategies). We focus on the potential role of banks, when dealing with the credit merit of potential investors, and move a first step in the direction of understanding the consequences and trade-offs involved in the adoption of “green credit merit” (GCM) measurement tools. After providing a theoretical background for our investigation, we develop a descriptive analysis using firm level Spanish data. We show that in certain cases projects featuring a low environmental impact would gain access to credit according to green credit merit procedures but not according to standard credit merit indicators. Also, and remarkably, indicators focused on specific environmental problems (e.g. related to energy consumption) might prove much more effective and informative than wider ones.

1. A Note on a Green Credit Merit Rating
Abstract
The increasing awareness of stakeholders is leading firms to adopt strategies related to the reduction of their
environmental impact, not necessarily linked to existing regulatory provisions (e.g. Corporate Social Responsibility
strategies). We focus on the potential role of banks, when dealing with the credit merit of potential investors, and move
a first step in the direction of understanding the consequences and trade-offs involved in the adoption of “green credit
merit” (GCM) measurement tools. After providing a theoretical background for our investigation, we develop a
descriptive analysis using firm level Spanish data. We show that in certain cases projects featuring a low environmental
impact would gain access to credit according to green credit merit procedures but not according to standard credit merit
indicators. Also, and remarkably, indicators focused on specific environmental problems (e.g. related to energy
consumption) might prove much more effective and informative than wider ones.
Keywords: Environmental Management; Green finance; Credit merit; Corporate Social
Responsibility
1. Introduction
Environmental problems are pervasive. Just to have an idea of the environmental impacts at
stake, it is enough to cite the very recent evidence provided, on climate change, by the Fifth IPCC
Assessment Report1
or, concerning waste, the ever increasing trend in municipal solid waste
generation (MSW)2
. A substantial effort in research is addressing the measures to be undertaken to
reduce the environmental impact of production and consumption activities, both in terms of
regulatory design and in terms of firms’ and other economic actors’ behaviour. We put ourselves in
this second stream, by focusing on a yet underexplored issue, namely banks behaviour in granting
credit to firms, and its potential linkages with the environmental performance of financed projects.
We link to two strands of literature dealing respectively with Corporate Social
Responsibility (CSR) and the role of Environmental Management Practices (EMP). Under the first
respect, a substantial amount of literature investigates the links between CSR and firms’
profitability. Several authors suggest the relevance of a “demand driven” CSR (see, among others,
Waddock and Graves 1997, Poddi and Vergalli 2009, Barnett 2007), which highlights the
increasing attention of stakeholders towards the environmental performance of firms. Lyon and

Very Provisional and Incomplete – do not cite or quote without authors’ permission.
1
Available at: http://www.ipcc.ch/report/ar5/wg1/#.UtLS0vs6asM
2
See Hoornweg, D. and Bhada-Tata, P. (2012).

2. Maxwell (2008) also focus on a “supply side” CSR, mostly related to efficiency and costs
considerations.
The role of EMP is also investigated by several authors, mostly focusing on the impact of
firms’ adoption of “green” management strategies (such as ISO 14001 and other environmental
management systems) on their economic performance (see, among others, Konar and Cohen, 2001;
Telle, 2006; Darnall et al., 2008; Canon-de-Francia and Garcès-Ayerbe, 2009; Nishitani,
2010). A more recent contribution also assesses the link between EMPs adoption and environmental
innovation (Grolleau et al., 2013).
Despite a generally positive attitude and optimism toward CSR, periodic reviews have
challenged the validity of those studies assuming a straightforward, simple link between investing
in CSR and improving firm’s profitability, thus revitalizing the intense debate over the issue
(Perrini et al., 2011). In the debate, Pavie and Filho (2008) suggest that a positive correlation
between corporate social performance and financial performance can be found, but this relation
tends to be “bidirectional and simultaneous”.
The aim of our paper is to take stock of the debate on the determinants and consequences of
environmental management by firms and extend it to account for the financing stage of economic
activities. This is relevant for two main reasons:
1. First of all, banks themselves might be interested in adopting environmental management
practices in their activities, including credit merit measurement; this is compatible with the
views that firms gain from showing good environmental attitudes, shown by several papers
addressing EMS and CSR, as outlined above;
2. The incentive for banks to explicitly include environmental issues in determining credit
merit might boost firms’ environmental behaviours and enhance the positive links among
the latter and economic performances.
We move a first step in the analysis of possible “green” indicators of credit merit by analysing,
both theoretically and empirically, the potential impacts on investments financing and on social
welfare. Under a theoretical point of view, we adopt a barebones framework in which a
representative firm seeks credit in order to finance an investment project. Such project generates net
profits but also features an environmental impact, which can be reduced by additional investment
that is assumed to reduce the net profitability of the project. The firm applies for a loan to a local
bank featuring an exogenous fixed budget that, together with the degree of competition which is
present in the relevant market(s), affects the probability of firms to get the needed financial

3. resources. The bank can adopt a standard credit merit procedure, or can adjust the procedure to
account for the environmentally friendly investment of the firm. Our theoretical analysis suggests
that the explicit introduction of environmental sustainability criteria in measuring the credit merit of
firms implies a trade-off between environmental benefits and lost profits. A correct design of
environmental credit merit indicator then should account for such trade-off.
In the second part of the paper, our theoretical insights are tested by looking at data from a
sample of 22,877 Spanish firms, classified on the basis of their geographical location, dimension,
and sector of economic activity; each firm is assigned a standard credit rating score. This
information is then completed by deriving some indicators of credit merit based on sector specific
environmental quality. More precisely, in the absence of specific information about the
environmental impact of each firm, the average environmental impact for each sector (based on the
firm activity sector and the Life Cycle Assessment corresponding to that sector) has been adopted.
To assess if and to what extent the traditional credit merit indicator and our alternative measures
differ in determining credit access opportunities for firms, we compare the score rankings provided
by each indicator.
Results suggest fruitful hints concerning the impact of correcting the evaluation of credit
merit according to environmental impacts. First of all, the environmentally adjusted ranks seem to
produce outcomes which may differ significantly with respect to standard credit merit evaluation; in
several cases, the inclusion of environmental criteria may make the difference in terms of credit
accessibility. (e.g. some firms present very good performances according to standard credit merit
and bad performances according to green credit merit measurement, or vice versa). Also, and
finally, the chosen “environmental correction” instrument seems to matter for results.
Our conclusions, though preliminary, suggest that correcting credit merit evaluation tools to
account for environmental impacts might be a sensible strategy in the process of adoption of CSR
strategies by banks. On the other hand, significant care must be taken on how this correction takes
place.
The paper is organized as follows: the following section provides a theoretical framework
for the analysis, while section 3 performs the empirical investigation. Finally, section 4 provides
some concluding remarks.

4. 2. Theoretical Analysis
The aim of this section is to provide a theoretical background to the empirical evidence
performed in section 3. To this end, we model a representative firm seeking credit in order to
finance a new investment project or to get additional resources for an on-going project. Such
project generates net profits, labelled as b > 0. The project also features an environmental impact,
which can be reduced by paying a sum of money c; this might be interpreted, for example, as the
amount of money that must be spent to reduce the environmental impact of industrial plants, in case
the investment is indeed financed and implemented.
Financial resources needed to implement the investment project must be obtained by the
firm through credit; as an example, the firm might apply for a loan to a local bank. We assume that
the latter has allocated an (exogenous) budget to projects financing and can provide finance in the
limits of such budget. More precisely, the budget determines the threshold for each project to be
financed. To simplify matters as much as possible, we assume that a threshold G is implicitly
defined by the budget allocation process, so that, if only profitability matters, each firm’s project is
financed when the corresponding benefits exceed the threshold G. Clearly, the threshold also
depends on the degree of competition which is present in the relevant market. As a result, the firm
faces uncertainty concerning the possibility of getting the needed financial resources.
To explicitly account for uncertainty, we assume that the threshold G is a random variable
which is uniformly distributed over a range between 0 (implying that no competition and/or an
extremely large budget is allocated by the bank to projects financing) and a value H > 0, which
accounts for the smallest possible allocated budget and/or for the maximum possible competition in
running for obtaining a loan across firms in the relevant economic sector.
As a result, the project of the firm will indeed be financed with the following probability:
( )
where of course we assume 0 < b < H to guarantee that ( ) As a result,
represents in our very simple setting the probability that the project obtains a positive credit merit
evaluation.
So far, we have assumed that no role is played by environmental quality in determining the
credit merit of each project. In other words, the representative firm is financed if and only if its
profitability exceeds a threshold. Assume now, instead, that the investment related to environmental
quality is explicitly accounted for in determining the credit merit. We assume this is done by

5. completing the credit merit appraisal through a factor α(c) < 1 which, given profits and the bank’s
budget, decreases the threshold, which becomes α(c)G, implying that, ceteris paribus, the firm has a
higher likelihood to achieve the requested credit from the bank if the environmental quality
requirement is fulfilled. We assume that α’(c) < 0, i.e. a larger investment in environmental quality
implies that the relevant threshold is lower. This makes the credit merit evaluation procedure
sustainable, in the sense that it rewards environmental quality of proposed projects. Of course, the
investment whilst has a positive impact on credit rating, it also reduces the net benefits from the
investment, which are now b - c3
.
When sustainability is explicitly accounted for, the probability that the project of the i-th
firm will indeed be financed becomes as follows (recalling the uniform distribution for the credit
merit threshold G):
    
1 b c
prob c G b c
H c



  
The impact of such correction on the environmental features of credit merit is ambiguous: an
increase in c reduces the relevant credit merit threshold, but, at the same time, it decreases the net
profitability of the project. In order to avoid the uninteresting case where environmental credit merit
correction never improves the environmental quality of projects, we assume here that the function
α(c) is such that, at least for a subset of the relevant range of the parameter c , a positive value of the
same variable implies an improved probability that the investment is indeed granted the requested
funds. This amounts to assume that (at least over a range of c ):
  1 0b c c   .
An immediate consequence of this assumption is that a trade-off arises between including
environmentally sensible projects and financing economic initiatives featuring the largest profits.
In other words, there will be projects that would be financed in the absence of environmental
considerations that are no longer financed when the environment is accounted for. To understand
why this can be the case, assume a certain realization of G (label it as G1). Under the assumption
that   1b c c  , a project featuring benefits equal to b (without accounting for the
environmental impact) such that
( )
will not be financed under a standard credit merit
procedure, while it would be under a green credit merit procedure.
3
Our simplified setting neglects the possibility that the investment in environmental quality c affects also the profits of
the investment project (b). This is not expected to alter the qualitative features of our results.

6. An immediate consequence of the introduction of sustainability criteria in measuring the
credit merit of firms (implicitly) arise in our setting, in terms of environmental benefits and,
therefore, social welfare. On the other hand, such environmental benefits must be traded off against
an overall loss in profits by investing firms.
In the following section, we aim at testing the conclusions from our theoretical analysis
using data on Spanish firms.
3. Data Description, Simulation and Results.
To better understand the potential impact of the correction of credit merit indicators to
explicitly include a selection of non-financial parameters to the credit evaluation framework of a
bank, we use an original sample of 22,877 Spanish firms, distinguished according to their
dimension, sector of economic activity and geographical location4
. Starting from the credit rating
score provided by the dataset for each firm, we derive a simple index of environmental quality
based on the characteristics of the firm’s sector applying results from a Life Cycle Assessment
corresponding to that specific sector (see the Appendix for details). This integration is aimed at
providing a preliminary descriptive analysis and should be intended as a first step towards a full
understanding of the impact of the adoption of sustainability and environment related criteria in
determining the credit merit of firms.
We consider a subsample of the dataset which includes only firms with a turnover above
6.000.000 Euros, working in all the considered sector (see Table A1 in the Appendix), excluding
Real Estate services and retail markets. The resulting dataset is then complemented with
information on the environmental impact provided by IFC Esat through Life Cycle Assessment at
sector level 5
.
We adopt four different criteria to rank firms on the basis of: i) their standard credit merit
(CM), ii) an indicator based on the overall evaluation of the environmental impact of the investment
(labelled as Green Credit Merit - GCM); iii) an indicator of the investment pressure in terms of
energy consumption (GCMen); iv) an indicator of the investment pressure in terms of waste
generation (GCMw).
4
Data are provided by AIS - Aplicaciones de Inteligencia Artificial, S.A. www.ais-int.com
5
www.ifc-esat.org. See the Appendix for further details and examples for two selected economic sectors concerning LCA
and our environmental performance indicator.

7. In order to have a general picture of the degree of correlation among different orderings
obtained by adopting different credit merit criteria, we look at the Spearman rank correlation
between pair of variables. Specifically, we compare the “standard” credit merit indicator with each
“corrected” indicator accounting for the environmental pressure in turn. As it is well-known, the
Spearman correlation indicates the direction of association between two variables; a positive value
of the correlation coefficient indicates a positive relationship between the variables (e.g. both
variables tend to increase) whilst a value of zero signals absence of correlation. The higher the
value of the coefficient the stronger the correlation between the two variables. As Table 1 shows,
whilst the correlation between the score ranking provided adopting a standard credit merit indicator
and the one obtained by considering the overall Green Credit Merit indicator is quite strong,
meaning that there is a high probability that the same firms have access to banks’ financial
resources for their investments, much lower values can be found for the other two rankings. This
suggests that the design of green credit merit indicators might be more fruitful if a preliminary
analysis of the most relevant environmental impacts is performed before the index is implemented
(i.e. a specific indicator provides much more additional information than an aggregate one).
Table 1 - Spearman Rank Correlation for alternative credit merit rankings
To deepen previous analysis, different rankings can be compared by using contingency
matrices, where firms are grouped into five quintiles according to each indicator. Firms in higher
quintiles have a higher probability of being financed by the bank. In Table 2 a comparison is
performed between the ranking derived according to the standard credit merit scores and the
ranking where the credit merit is corrected to account for the environmental impact at sector level,
(Green Credit Merit – GCM).
Standard credit merit
Green credit merit 0.8525*
Green credit merit - energy 0.5435*
Green credit merit - waste 0.6465*
Note: Coefficient are statistically significant at conventional levels
(p-value <0.05).

8. Table 2 – Contingency Analysis: standard vs. green credit merit ordering
Each row in Table 2 accounts for one quintile of the ordering according to credit merit,
while each column accounts for a quintile resulting from the ordering according to GCM. Each
quintile features two rows (columns) accounting for the number of firms, the row frequency and the
cell frequency, respectively. So, for example, the three numbers on the first row, second column of
table 1 imply that 129 firms are in the first quintile (the lowest in terms of credit merit) while they
are in the second quintile according to GCM. They also suggest that 25,85% of firms in the first
quintile according to standard credit merit are in the second quintile according to GCM, and (third
line) they are 5.17% of all firms over all quintiles.
Table 2 suggests some interesting insights: there are indeed cases where the ordering
changes substantially: 2% of firms (i.e. 50 firms, 10% of firms in the 4th
quintile according to
standard CM) are in the 4th
quintile according to credit merit but only in the second quintile if the
environment is explicitly accounted for. A similar reasoning can be applied to firms which are in
the 4th
quintile according to GCM but in the second according to standard credit merit analysis,
albeit the dimensions involved are in this latter case less significant.
Pearson chi2(16) = 3.3e+03 Pr = 0.000
20.00 20.00 20.00 20.00 20.00 100.00
20.00 20.00 20.00 20.00 20.00 100.00
Total 499 499 499 499 499 2,495
0.04 0.08 0.72 3.41 15.75 20.00
0.20 0.40 3.61 17.03 78.76 100.00
5 1 2 18 85 393 499
0.04 2.00 2.81 11.42 3.73 20.00
0.20 10.02 14.03 57.11 18.64 100.00
4 1 50 70 285 93 499
1.72 3.17 10.26 4.45 0.40 20.00
8.62 15.83 51.30 22.24 2.00 100.00
3 43 79 256 111 10 499
3.77 9.58 5.81 0.72 0.12 20.00
18.84 47.90 29.06 3.61 0.60 100.00
2 94 239 145 18 3 499
14.43 5.17 0.40 0.00 0.00 20.00
72.14 25.85 2.00 0.00 0.00 100.00
1 360 129 10 0 0 499
t 1 2 3 4 5 Total
creditmeri 5 quantiles of gcm
of
quantiles
5

9. According to available data, we cannot differentiate between successful and unsuccessful
firms in getting credit (as we do not have information on the relevant thresholds). However, for the
sake of presentation, we here arbitrarily assume that project falling in the fourth and fifth quintiles
are those that obtain the requested funds. According to Table 2, we can note that 2% of firms
(belonging to the fourth quintile according to CM and to the second according to GCM) would get
access to credit only when the standard CM is applied.
We now turn to analyse whether the obtained results change by changing the environmental
dimension at hand. In Tables 3 and 4 we have corrected the standard credit merit indicator for a
single environmental variable (energy in Table 3 and waste in Table 4), to assess whether the
conclusions derived are sensible or not to the kind of indicator chosen and/or to the considered
environmental issue.
Table 3 – Contingency Analysis: standard vs. “energy corrected” credit merit ordering
By inspecting Table 3, we can note different results compared to the previous Table. A
relevant example: a significant number of firms in the fifth quintile according to standard CM
ordering are included in the second quintile according to GCMen (94 firms, 3,77% of total firms). It
Pearson chi2(16) = 2.2e+03 Pr = 0.000
20.00 20.00 20.00 20.00 20.00 100.00
20.00 20.00 20.00 20.00 20.00 100.00
Total 499 499 499 499 499 2,495
0.04 3.77 2.48 0.60 13.11 20.00
0.20 18.84 12.42 3.01 65.53 100.00
5 1 94 62 15 327 499
3.89 3.09 0.44 5.81 6.77 20.00
19.44 15.43 2.20 29.06 33.87 100.00
4 97 77 11 145 169 499
3.61 3.57 0.36 12.38 0.08 20.00
18.04 17.84 1.80 61.92 0.40 100.00
3 90 89 9 309 2 499
4.85 3.73 10.22 1.16 0.04 20.00
24.25 18.64 51.10 5.81 0.20 100.00
2 121 93 255 29 1 499
7.62 5.85 6.49 0.04 0.00 20.00
38.08 29.26 32.46 0.20 0.00 100.00
1 190 146 162 1 0 499
t 1 2 3 4 5 Total
creditmeri 5 quantiles of gcmen
of
quantiles
5

10. implies that a significant number of firms could indeed see its loan rejected due to energy related
considerations, while it is likely that the loan would be granted according to standard credit merit
reasoning. Also, notice that 162 firms (around 6,5% of total) which are ranked very low according
to credit merit, are instead in the third quintile according to GCMen: the inclusion of energy related
environmental impacts in the evaluation aimed at awarding credit could make a difference for these
firms. These results are specific of the indicator accounting only for energy related impacts.
Table 4 – Contingency Analysis: standard vs. “waste corrected” credit merit ordering
Considerations close to those obtained with reference to energy can be achieved by looking
at the green indicator only accounting for waste. In this case we have, for example, that several
firms (41, 1,64% of total) are ranked very high (4th quintile) according to GCMw, but very low (1st
quintile) with respect to standard CM. The opposite also seems to hold: 26 firms (around 1% of
total) are in the 5th
quintile according to “unadjusted” credit merit but rank poorly (second quintile)
according to GCMw.
Pearson chi2(16) = 1.9e+03 Pr = 0.000
20.00 20.00 20.00 20.00 20.00 100.00
20.00 20.00 20.00 20.00 20.00 100.00
Total 499 499 499 499 499 2,495
0.00 1.04 1.92 6.17 10.86 20.00
0.00 5.21 9.62 30.86 54.31 100.00
5 0 26 48 154 271 499
1.80 1.92 2.48 10.38 3.41 20.00
9.02 9.62 12.42 51.90 17.03 100.00
4 45 48 62 259 85 499
2.61 2.93 10.50 1.36 2.61 20.00
13.03 14.63 52.51 6.81 13.03 100.00
3 65 73 262 34 65 499
4.29 7.54 4.89 0.44 2.85 20.00
21.44 37.68 24.45 2.20 14.23 100.00
2 107 188 122 11 71 499
11.30 6.57 0.20 1.64 0.28 20.00
56.51 32.87 1.00 8.22 1.40 100.00
1 282 164 5 41 7 499
t 1 2 3 4 5 Total
creditmeri 5 quantiles of gcmw
of
quantiles
5

11. Overall, though still preliminary, our analysis suggests fruitful hints concerning the impact
of correcting the evaluation of credit merit according to environmental impacts. They can be
summed up as follows:
 Environmentally adjusted ordering seems to be in general not too far away from the one
resulting from the one stemming from standard credit merit analysis;
 Relevant exception arise; though it is not possible to derive precise conclusions concerning
the possibility of involved firms to be granted the financial resources (e.g. the requested
loan), we can anyway identify several cases where the chosen ordering makes a huge
difference in terms of inclusion in relatively “far” quintiles (e.g. very good performances
according to standard credit merit and bad performances according to green credit merit
measurement, or vice versa). This implies that the trade-off suggested by the theoretical
analysis can indeed arise in real life;
 The results seem somehow to depend on the chosen “environmental correction” instrument,
so that we achieve potentially different insights accounting for the overall environmental
impact rather than for specific environmental issues (e.g. energy or waste).
4. Concluding Remarks
Along the process of CSR strategies adoption, a significant role might be played by banks,
when dealing on the credit merit of potential investors. The benefits of such a strategy could be for
banks themselves, to adhere to the increasing environmental awareness of stakeholders, for firms,
providing a new channel through which environmentally friendly behaviour might improve their
economic performance, and for society as a whole, in terms of environmental benefits.
Adjusting credit merit procedures can indeed be one possible strategy to improve the signals
of environmental awareness by credit institutions. Our analysis moves a first step in the direction of
understanding the consequences and trade-offs involved in the adoption of “green credit merit”
measurement tools. We show, in particular, that the adopted tool might affect the final outcome in
terms of the successful investment projects, so that projects which are successful according to
standard credit merit measures might prove very unsuccessful when their merit is assessed through
environmentally adjusted indicators. Also, and remarkably, the design of the environmental credit
related index must be inspired by the objective: for example, if energy impacts reduction is the
objective, then a general indicator accounting for a wide range of environmental impacts might
prove not useful.

12. Of course, the paper features several weaknesses, and it is still in a preliminary version.
Additional research will imply the construction of a more detailed “environmentally adjusted”
credit merit indicator, together with a deeper discussion of welfare impacts and involved trade-offs
under a theoretical point of view. On the other hand, (in our view) promising results seem to arise
even in the very simple setting developed so far.
References
Barnett, M. L. (2007). Stakeholder influence capacity and the variability of financial returns to
corporate social responsibility. Academy of Management Review, 32(3), 794–816.
Canon-de-Francia, J., Garcés-Ayerbe, C., 2009. ISO 14001 Environmental Certification: A
Sign Valued by the Market?. Environmental and Resource Economics 44, 245-262.
Darnall, N., Henriques, I., Sadorsky, P., 2008. Do Environmental Management Systems
Improve Business Performance in the International Setting?. Journal of International
Management 14(4), 364-376.
Grolleau, G., Mzoughi, N and Pekovic, S. (2013), Environmental Management Practices: Good or
Bad News for Environmental Innovation? The Moderating Effect of Market Characteristics, paper
presented at the 2013 EAERE Conference, Toulouse, France.
Hoornweg, D. and Bhada-Tata, P. (2012), What a Waste. A Global Review of Solid Waste
Management, Urban Development Series Knowledge Papers, n.15, The World Bank.
Konar, S., Cohen, M.A., 2001. Does the Market Value Environmental Performance?, Review of
Economics and Statistics 83(2), 281-289.
Lyon, T. and J. Maxwell (2008), Corporate Social Responsibility and the Environment: A
Theoretical Perspective, Review of Environmental Economics and Policy 2(2), 240–260.
Nishitani, K., 2010. An Empirical Analysis of the Effects on Firm’s Economic Performance
of Implementing Environmental Management Systems. Environmental and Resources
Economics, 48, 569-586
Perrini, F., Russo, A., Tencati, A., and Vurro, C. (2011). Deconstructing the Relationship Between
Corporate Social and Financial Performance. Journal of Business Ethics, 102, 59-76
Poddi, L. and S. Vergalli (2009), Does Corporate Social Responsibility Affect the Performance of
Firms, Nota Di Lavoro 52.2009, 43.

13. Telle, K., 2006, It Pays to Be Green – A Premature Conclusion?, Environmental and
Resource Economics, 35, 195-220.
Waddock, S. A. and S. B. Graves (1997), The Corporate Social Performance - Financial
Performance Link, Strategic Management Journal 18(4), 303–319.
Appendix
We here provide some details and examples on the Life Cycle Analysis developed by IFC
Esat, on which our environmental performance indicators are developed (as illustrative examples
we consider the construction and cleaning sectors). Life Cycle Assessment (LCA) 67
is a tool to
evaluate the environmental and social aspects and performance of products, services and process
along their life cycle from the extraction of resource inputs to the eventual disposal of the product
or its waste. The International Organisation for Standardisation (ISO), a world-wide federation of
national standards bodies, has standardised this framework within the series ISO 14040 on LCA8
.
The ISO 14040 standards give guidelines on the principles and framework for LCA studies that
provide an organization with information on how to reduce the overall environmental impact of its
products and services. The basic concepts is to be utilized when laying the strategy for parameters
evaluation, to compare materials, products that are functionally equivalents, not just by the purchase
price of a product, but all future costs as well:
 Usage costs (energy/water consumption and other consumables)
 Maintenance costs
 Disposal costs/resale value
At this preliminary stage of the analysis, we adopted an environmental impact indicator
featuring a maximum value of 54, and assigned 6 points to high impacts, 3 points to medium
impacts and 0 points to low impact; then we used this index to reduce the Credit Merit indicator and
obtain the Green Credit Merit indicator. The corresponding indicators for energy and waste are
calculated by considering only the specific environmental impact.
6
UNEP/SETAC Life Cycle Initiative http://www.lifecycleinitiative.org/starting-life-cycle-thinking/life-cycle-approaches/
accessed 1/1/2014
7
Regional Action Center for Cleaner Production – UNEP Mediterranean Action Plan- Good Housekeeping Practices, Program
Design and Application in Industry (2000), page 70
8
International Organisation for Standardisation
http://www.iso.org/iso/home/news_index/news_archive/news.htm?refid=Ref1019 accessed 1/1/2014

14. Example 1 - Life Cycle Assessment of construction or demolition of infrastructure, buildings
and other installations sector:
The construction sector, which is related to the cement and concrete sectors as well as Wood
products and Recycling and Waste treatment, provides services such as the construction,
refurbishment and maintenance of buildings and infrastructure as roads. Demolition and wrecking
of buildings and infrastructure is also supplied by this sector.
Operations and construction processes include:
• Clearing and groundwork such as excavation and land filling, levelling and land drainage. Some
additional operations e.g. drilling, tunnelling etc.
• Building of foundations and the construction of the building itself
• Finishing such as electrical installations, painting, joinery etc.
• Finally, the construction site is reinstated and its surroundings restored.
The following table reports the corresponding LCA assessment, and the value of the environmental
impact indicator we built up.
M Overall - Constructions
L Energy
L Water Use
L Emissions to Water
M Waste
M Emissions to Air
M Ecosystems
H Workplace Health & Safety
L Disaster Risk
M Site Contamination
18 Environmental Impact

15. Example 2 - Cleaning services
Commercial laundry services include the washing, bleaching, drying and pressing of textiles. Dry
cleaning is very similar to washing except that textiles are washed in liquid cleaning solvent instead
of water, as some fabrics are harmed when washed with water. The term "dry cleaning" refers to the
fact that no water is used in the washing process.
M
Overall - Laundry & Dry
Cleaning
L Energy
M Water Use
H Emissions to Water
M Waste
M Emissions to Air
L Ecosystems
M Workplace Health & Safety
L Disaster Risk
H Site Contamination
24 Environmental Impact
Table A1: Sectors of economic activity
Sector of economic activity Number of projects
CNAE 21
Agriculture 306
Fishery 53
Mining and quarrying 190
Utilities 189
Tourism 739
Services 3629
Food and beverages 633
Manufacture of w ood and cork 423
General industry 1563
Minerals industry and metallurgy 1374
Machinery industry 466
Transportation 1456
Manufacture of furniture 294
Textile 192
Clothing 316
Manufacture of other non-metallic mineral products 484
Construction 2531
Wholesale trading 5303
Retail trading 1759
Real estate 956
Total 22877