# Is Green Public Procurement a deterrent for SMEs?
## Empirical evidence from France

<figure><img src="image_1.png" /></figure>

► To cite this version:

Adrien Deschamps. Is Green Public Procurement a deterrent for SMEs? Empirical evidence from France. Small Business Economics, 2025, 10.1007/s11187-025-01034-1. hal-04999776

HAL Id: hal-04999776

https://hal.science/hal-04999776v1

Submitted on 21 Mar 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
This version of the article has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://link.springer.com/article/10.1007/s11187-025-01034-1. Use of this Accepted Version is subject to the publisher's Accepted Manuscript terms of use https://www.springernature.com/fr/open-science/policies/accepted-manuscript-terms".

# Is Green Public Procurement a deterrent for SMEs?

## Empirical evidence from France

**Author information:**

Adrien Deschamps

Avignon Université, JPEG laboratory, Avignon, France

adrien.deschamps@univ-avignon.fr

0009-0002-1302-5269

**Abstract:**

Public procurement can be defined as the process by which public contracting authorities purchase goods, services, and works from private suppliers, including small and medium enterprises (SMEs). The access of SMEs to public contracts is one of the objectives of public procurement policies, as they suffer from complex procedures and high transaction costs. At the same time, public procurement is increasingly being used as an instrument for environmental policies, through green clauses (i.e. mandatory specifications in the contract performance) and award criteria (i.e. the dimensions of the offers the contracting authority assesses). The objective of this empirical work is to determine whether environmental clauses and criteria have an impact on SME participation in award procedures. This paper assesses for the first time the compatibility between SME accessibility and environmental issues in public procurement with a dataset encompassing approximately 10,000 award procedures in France between 2022 and 2023. The findings indicate that green public procurement stimulates SME participation. The intensity of this effect may vary between environmental clauses and criteria across sectors, but there is no evidence of a deterrent effect of green public procurement on SMEs.
**Keywords:**

Green Public Procurement; Sustainable Development; SME; Participation; Public Procurement;
Sustainable Public Procurement

**JEL:**

D22, H57, L25, Q01, Q56
# 1. Introduction

Public procurement refers to all the contracts through which national and local governments purchase supplies, services, and works from private companies. It represents approximately 12% of GDP and one-third of public spending in OECD countries (OECD, 2018). Traditionally, contracting authorities seek to obtain the best value for money in the intrinsic features of the contract matter. However, due to its weight in national activity, public procurement is increasingly being used as a lever for public policies. In addition to economic efficiency, the access of small and medium enterprises (SMEs) has long been a core objective of public procurement policies. Indeed, SMEs have historically been underrepresented in public procurement (MacManus, 1991). In response, most governments in the world have implemented policies designed to support the access of SMEs to public procurement (OECD, 2018), both directly and through subcontracting. However, social inclusion, the development of innovation, and the protection of the environment are also becoming key objectives of modern public procurement policies.

Green public procurement is the utilization of public procurement as a tool for environmental policies, as opposed to economic instruments such as taxes or tradable permits (Lundberg & Marklund, 2018). In practice, green public procurement is implemented through environmental clauses and award criteria. A green clause is a mandatory standard related to environmental issues set by the contracting authority in the call for tenders. Any offer failing to comply with the clause is ineligible for the contract award. For instance, a printing contract might include a green clause that requires the use of recycled paper. In contrast, a green criterion partially bases the award procedure on the environmental dimension of submitted tenders. It is an incentive to adopt greener practices since the score assigned to each offer is partly based on the environmental features of the latter. When the contracting authority utilizes several award criteria, each criterion receives a specific weight depending on its relative importance in the assessment of the offers. In France, the *Climat et Résilience* law of 2021 is about to generalize the implementation of green clauses and criteria to all the public contracts.
The report *SME needs analysis in public procurement* of the European Commission (2021) highlights the lack of analysis on SME access to public procurement. The report claims that public procurement is a major lever to promote both sustainable development and SME growth. Yet, due to a paucity of impact assessments, it remains unclear whether these two goals are in conflict, compatible, or even mutually reinforcing.

The objective of this paper is to address the dearth in empirical works on green public procurement, especially considering the potential impact of green public procurement on SME participation in award procedures. While this effect has never been assessed so far due to a lack of relevant data, this paper utilizes a new comprehensive dataset on French public procurement (Deschamps & Potin, 2025) to overcome this challenge. The issue of SME participation in green public procurement is decisive for the consistency of public procurement policies in France, as the *Climat et Résilience* law is about to generalize the implementation of environmental clauses and criteria without prior impact assessment.

More precisely, three research questions will be addressed.

First, it is crucial to determine whether sustainable development and SME access to public procurement are compatible (R.Q.1: Does green public procurement influence the participation of SMEs in public procurement?). While they are two key objectives of public procurement policies, the related literature remains scarce. It is difficult to predict the potential effect of green clauses and criteria on SME participation based on the existing works. On the one hand, environmental constraints increase the cost of participation in award procedures, and SMEs are more sensitive to transaction costs than larger firms. On the other hand, SMEs may be more receptive to environmental requirements due to their innovative and flexible nature.

Second, green clauses and criteria may have different impacts, given their different economic natures (R.Q.2: Do environmental award criteria and clauses have the same impact on SME participation?). Green clauses require firms to employ specific technologies, while green criteria reward environmental innovations through a trade-off between price and quality, without necessarily indicating which technology to implement. The difference between environmental clauses and award criteria is not considered by the existing literature on green
public procurement. Clauses and criteria are cited as drivers of green public procurement, but
there has been no examination of potential discrepancies between their respective impacts,
due to a lack of accurate data. The dataset utilized in the empirical analysis allows to distinguish
clauses from criteria for the first time.

Finally, SMEs are present in various sectors, including public works, services, and supplies.
However, the effect of green public procurement on SMEs may differ depending on the
industry (R.Q.3: *Is the impact of green public procurement on SMEs the same across business
sectors?*). The proportion of SMEs among companies, the potential for green innovation, and
the extent to which green clauses and criteria are implemented vary across sectors.

The empirical findings indicate that green public procurement has a positive impact on SME
participation. While the intensity of this effect may differ across sectors, as well as the
respective roles of green clauses and criteria, no sign of a deterrent effect for SMEs emerges
from the sample.

The rest of the paper is organized as follows. Section 2 highlights the issues raised by green public procurement for SME-friendly policies. Section 3 provides an overview of the regulatory framework in France. Section 4 provides a detailed description of the dataset and the econometric strategy. Section 5 presents the results of the empirical analysis at three distinct levels: public procurement as a whole, large sectors, and specific industries. Finally, Section 6 discusses the findings and their potential implications for public procurement policies.

## 2. Related literature

### 2.1 SME participation and public procurement policies

Firms tend to believe that public procurement is less fair and efficient than the private sector,
making it less attractive and competitive (MacManus, 1991). This seems to be particularly true
for small organizations, as an increase in the overall quality of public procurement is associated
with higher SME participation (Hoekman & Taş, 2022). In an extensive literature review,
Mahuwi & Israel (2023) conclude that limited access to information, complex tendering
procedures, financial constraints, limited capacity and skills, and unequal competition with
larger firms seem to be the most recurrent hindrances raised by the literature on SME
participation. Flynn (2017) highlights that the bigger the firm, the more important the
tendering resources and capabilities of the latter to face complex tender processes. In
particular, the firm's resources are associated with the number and value of the contracts it
bids on. Since SMEs typically bid for a few small contracts, transaction costs weigh more heavily
on SMEs than they do on larger firms.

In response, governments tend to implement policies aiming at supporting both the
participation and success of SMEs in the award procedures of public contracts. Although the
primary aim of these policies is to stimulate business growth, they can also pursue objectives
of social justice and integration (Orser et al., 2019). Regarding SME participation, the OECD
(2018) distinguishes between the policies that aim to reduce the size of the contract through
allotment (Morand, 2003), the simplification and standardization of procedures (Hoekman &
Taş, 2022), the alleviation of SME financial capacities (Schäfer et al., 2024), and e-procurement
(Saastamoinen et al., 2018). The report also identifies preference programs (Lewis, 2017) and
subcontracting (Maréchal & Morand, 2012) as the two main tools to enhance their success
rate. However, the implementation of SME-friendly public procurement policies faces some
limitations. Flynn & Davis (2015) show that the compliance of public buyers with SME policies
is imperfect. Compliance seems high for open tendering, feedbacks and financial capacities,
while it is lower for allotment and consortium bidding. Additionally, local institutions do not
fully implement the national measures. Concerning the effectiveness of these policies, Flynn
& Davis (2016) find that SME-friendly policies may increase SME success rate in award
procedures more than their frequency of tendering.

## 2.2 SMEs and green public procurement

A literature reviewed conducted by Zahra et al. (2023) highlights that most papers related to SMEs and green public procurement use a qualitative approach based on interviews and questionnaires. Actually, research has mostly focused on the way SMEs implement green public procurement, rather than the direct impact of green considerations on their participation. To the best of our knowledge, there is no theoretical model analyzing SME
participation in green public procurement, nor econometric works challenging the results of the qualitative research with large-scale data.

At first glance, green public procurement may have a negative impact on SMEs by increasing their participation costs. Loader (2015) identifies poorly written specifications and overly prescriptive award criteria as impediments to SME participation. Masron et al. (2024) inventory the potential hindrances to SME implementation of green public procurement under five categories: "financial, legal, people, knowledge, and organizational challenges". SMEs may lack expertise to handle green requirements, and it may be more costly for them to call on external skills. Green public procurement implies more complex award procedures, with more documentation related to sustainable requirements and more award criteria to consider. Irrespective of the technology to be used in the event of the contract award, dealing with environmental clauses and criteria represents a fixed cost at the bidding stage, which weighs more heavily on SMEs as they tend to bid for smaller contracts.

Conversely, there are reasons to believe that SMEs may be attracted to green public procurement. SMEs are flexible and innovative organizations. These features can be leveraged to implement green public procurement. For instance, in 2018, SMEs accounted for 90% and 70% of clean technology businesses in the United Kingdom and Finland, respectively (OECD, 2018). Environmental issues have a specific nature and cannot be analyzed only as a transaction cost. Indeed, participating in green public procurement directly impacts the firm, encouraging the adoption of sustainable production practices. Krieger & Zipperer (2022) sought to measure the overall impact of being awarded a contract with environmental criteria on green innovation. Their paper asserts that green criteria increase the probability of introducing greener products within SMEs. Interestingly, this result does not hold for larger firms. Green innovation can contribute to cost minimization and waste reduction in the production process, thereby enhancing productivity for both public and private contracts (Dzikriansyah et al., 2023). Additionally, the implementation of greener production processes may improve the general corporate image of the firm (Liargovas et al., 2017).
### 3. Legal background

There are approximately 160,000 SMEs in France. They are characterized by a workforce of fewer than 250 employees. SMEs account for 30% of the national employment and 25% of the added value. SMEs are awarded 60% of the public contracts, but these contracts represent only 27% of the total value of public procurement.

France has been a pioneer in the field of green public procurement. As early as 2008, a ministerial circular encouraged government institutions to integrate sustainable issues into their purchasing. This phenomenon has now spread to all public purchasers and is becoming increasingly mandatory. The *Climat et Résilience* law of 2021 states that by August 2026, all public procurement contracts must include an environmental clause and award criterion. This objective represents a major challenge for contracting authorities, as the proportion of contracts including green considerations must be at least quadrupled between 2024 and 2026.

More generally, French public procurement is governed by three principles, derived from the European Union law: open competition, equal treatment, and transparency. These principles are reflected in the procedure a public contract usually follows. A public institution first issues a call for tenders in which it describes its needs and the rules of the award procedure, including clauses and award criteria. Companies then have a limited advertising period to submit an offer. Once this period is over, the contracting authority evaluates any bids submitted, and decides which company will be awarded the contract, based on the award criteria and clauses set out in the call for tenders. This decision is communicated in an award notice. If the contract value exceeds the legal thresholds, the call for tenders and award notice must be published in a centralized official journal that is accessible online. The sample used in the following empirical analysis comes from these online notices.
# 4. Materials and methods

## 4.1 Data description

### 4.1.1 Sample

The empirical investigation is based on the *BeauAMP* dataset (Deschamps & Potin, 2025), which describes all the public contracts advertised in France between 2015 and 2023, with more than one hundred variables. Since the dataset results from the conversion of online notices, some information is missing when contracting authorities did not describe every aspect of the contract. The sample that will be utilized contains contracts awarded between January 2022 and December 2023 in metropolitan France. It is available on the following repository: https://github.com/AdrienDeschampsAU/GPP_and_SMEs.

The sample includes 9,834 observations, with 4,809 for service contracts, 3,882 for supplies, and 1,143 for works. One observation corresponds to a lot (i.e. a subdivision of a contract that can be awarded independently). The observations come from 3,990 different contracts. The sample gathers the observations where the relevant variables are available, and it only encompasses contracts formed under the open procedure, which corresponds to the main tender procedure whereby any firm can make an offer to the contracting authority. Additionally, the total number of offers will never exceed ten, to avoid the few extreme values reflecting input errors from contracting authorities. 1,237 different contracting authorities made these contracts. Authorities are categorized depending on their status. The observations are divided across the different types of institutions as follows: 2,038 observations come from 477 different municipalities, 1,847 observations come from 277 different specialized local institutions (such as hospitals or high schools), 1,600 observations come from 240 different municipal federations, 1,352 observations come from 59 different *départements* (the second level of administrative division under the national government), 432 observations come from 9 different *régions* (the first level of administrative division under the national government), and 1,754 observations come for 175 different national institutions.
### 4.1.2 Variables

The variable *Participation* indicates whether at least one SME participated in the award procedure (1) or not (0). As shown in Table 1, 77% of the lots in the sample received at least one offer from an SME. More precisely, contracting authorities receive on average 1.78 offers from SMEs (Table 2).

<table><caption>Table 1: Description of the binary variables</caption><thead><tr><td>Variable</td><td>True</td><td>False</td><td>Content</td></tr></thead><tbody><tr><td>Participation</td><td>0.77</td><td>0.23</td><td>Whether one SME is participating in the award procedure.</td></tr><tr><td>Green</td><td>0.37</td><td>0.63</td><td>Whether a green clause or an award criterion is being implemented.</td></tr><tr><td>G_clause</td><td>0.29</td><td>0.71</td><td>Whether a green clause is being implemented.</td></tr><tr><td>G_criterion</td><td>0.22</td><td>0.41</td><td>Whether a green criterion is being implemented.</td></tr><tr><td>Framework</td><td>0.53</td><td>0.47</td><td>Whether the contract is a framework agreement.</td></tr><tr><td>Social_clause</td><td>0.10</td><td>0.90</td><td>Whether a social clause is being implemented.</td></tr><tr><td>Allotment</td><td>0.78</td><td>0.22</td><td>Whether the contract is divided into several lots.</td></tr></tbody></table>

The participation of small businesses in public procurement varies significantly across regions (Figure 1 in Appendix 9.1). SMEs tend to be more represented in the regions where major metropolises are located, such as Île-de-France with Paris or Auvergne-Rhône-Alpes with Lyon. In addition, the representation of SMEs among bidders varies depending on the industry. There is no significant difference between works, services, and supplies in general. However, Figure 2 highlights strong sectoral heterogeneity by using the CPV divisions (i.e. the first two digits of the *Common Procurement Vocabulary*, which is a European classification of sectors in public procurement). As expected, SME participation is not frequent in sectors with high fixed costs such as financial services, energy, or telecommunications. Conversely, they are much more represented in sectors such as transport services, training, or printing, which allow for smaller investments and workforce. The contracting authority's status exhibits less heterogeneity (Figure 3 in Appendix 9.2). Interestingly, SMEs are not over-represented in local procurement.
<table><caption>Table 2: Description of the continuous variables</caption><thead><tr><th>Variable</th><th>Count</th><th>Mean</th><th>Std</th><th>Min</th><th>Q1</th><th>Med</th><th>Q3</th><th>Max</th><th>Content</th></tr></thead><tbody><tr><td>G_weight</td><td>9,834</td><td>2.81</td><td>6.98</td><td>0</td><td>0</td><td>0</td><td>0</td><td>60</td><td>The weight of the environmental criterion on the score assigned to each offer.</td></tr><tr><td>Price_weight</td><td>9,834</td><td>52.04</td><td>19.80</td><td>0</td><td>40</td><td>50</td><td>60</td><td>100</td><td>The weight of the price criterion on the score assigned to each offer (after subtracting G_weight).</td></tr><tr><td>Award_price</td><td>9,834</td><td>12.01</td><td>1.93</td><td>0.69</td><td>10.99</td><td>12.18</td><td>13.17</td><td>19.41</td><td>The logarithm of the award price (in euros).</td></tr><tr><td>Advertising</td><td>9,834</td><td>37.12</td><td>10.67</td><td>14</td><td>31</td><td>33</td><td>40</td><td>174</td><td>The duration of the advertising period (in days).</td></tr><tr><td>Offers_other</td><td>9,834</td><td>1.11</td><td>1.56</td><td>0</td><td>0</td><td>0</td><td>2</td><td>10</td><td>The number of offers the contracting authority received from firms larger than SMEs.</td></tr><tr><td>Offers_SME</td><td>9,834</td><td>1.78</td><td>1.72</td><td>0</td><td>1</td><td>1</td><td>3</td><td>10</td><td>The number of offers the contracting offers received from SMEs.</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>Figure 2: SME participation by CPV division</figcaption></figure>
There appears to be a segmentation of participation in public procurement between SMEs and larger firms. The correlation matrix in Appendix 9.3 shows a negative correlation between *Offers_SME* and *Offers_other*. Figure 4 confirms this global trend: for a given number of offers from larger organizations, the absence of SMEs is always the most likely outcome. Similarly, even when there are three or four offers from small businesses, the most frequent value of *Offers_other* is zero.

<figure><img src="image_4.png" /><figcaption>Figure 4: Correlation between the participation of SMEs and larger firms</figcaption></figure>

Concerning green public procurement, 37% of the lots in the sample include an environmental clause or award criterion (*Green* = 1). Green clauses (*G_clause*) are slightly more prevalent than green criteria, which are encapsulated by the variable *G_criterion* (29% vs 22%). Furthermore, 14% of the observations include both a green clause and criterion. Figure 5 in Appendix 9.4 plots the distribution of *G_weight*, which corresponds to the weight (in %) of the environmental criterion on the total score assigned to the offers. The most frequent weight of the environmental criterion is 10%, and contracting authorities tend to assign weights in multiples of ten. While on average the environmental features of an offer account for slightly less than 3% of its score, the average weight of the environmental criterion is 13% once it is implemented. In contrast, the price competitiveness of the offer (*Price_weight*) usually accounts for half the score. To avoid the creation of a correlation between the weights of
environmental matters and price competitiveness, *Price_weight* is computed on the basis of 100 – *G_weight*. In other words, *Price_weight* indicates the respective importance of price competitiveness as opposed to qualitative award criteria, except those related to environmental issues. While the weights of the environmental criterion and the price criterion remain negatively correlated (Appendix 9.3), this correlation is not endogenous as it reflects the contracting authority's choice to opt for a procedure mostly based on price competitiveness or qualitative aspects, regardless of environmental considerations. By reducing the total number of criteria whereby an offer will be assessed, an increase in the weight of the price criterion is expected to have a positive impact on the SME participation, as the procedure will be simpler.

The implementation of green public procurement varies across several dimensions. First, there are significant regional discrepancies (Figure 6 in Appendix 9.5). The region around Paris is the one where firms face green clauses and criteria the most: almost 50% of the procedures include environmental considerations in this region. Second, the proportion of contracts integrating a green clause or criterion varies significantly across sectors. Figure 7 illustrates that the furniture sector is the one where green public procurement is implemented the most. Conversely, green matters are rare in training contracts and financial services. Overall, services show less propensity for implementing green public procurement compared to works and supplies (31% of the lots against 45% and 43%, respectively). While Figure 8 in Appendix 9.6 is a scatter plot demonstrating a clear correlation between the implementation of environmental clauses and environmental criteria within a given sector, the three shapes on each bar in Figure 7 indicate the respective frequence of clauses and criteria for each CPV division. Furthermore, there is no clear correlation between the scale of the contracting authority and the share of contracts including a green clause or criterion in the sample (Figure 3 in Appendix 9.2).
<figure><img src="image_5.png" /><figcaption>Figure 7: Green public procurement by CPV division</figcaption></figure>

It is noteworthy that there appears to be a positive correlation between the implementation of green public procurement and SME participation. Figure 9 highlights that the average value of *Participation* is significantly higher when *Green*, *G_clause*, and *G_criterion* are not null.

<figure><img src="image_6.png" /><figcaption>Figure 9: Average SME participation and implementation of green public procurement</figcaption></figure>
However, this correlation must be challenged in regressions including additional control variables. To do so, there are four more control variables in the sample. First, *Award_price* corresponds to the logarithm of the award price of the lot (in euros). Utilizing a logarithmic scale is essential for handling the wide range of values, as the lot price ranges from a few euros to almost 200 million euros. The impact of the award price on SME participation may be positive once the industry is taken into account, since it increases the expected return of tendering. Second, the advertising period (*Advertising*) indicates the duration (in days) of the period when firms can prepare to submit offers. This period generally lasts for one month. At first sight, a longer advertising period might increase the participation of small businesses, as they have more time to deal with procedures. However, a longer advertising period may also indicate a complex procedure. Third, the variable *Framework* indicates whether the lot belongs to a framework agreement (i.e. a contract setting the terms and conditions for future repeated subcontracts, without needing to organize a new award procedure). In the sample, 53% of the observations are framework agreements. This type of contract may be particularly appealing to SMEs, as it reduces transaction costs in the long term and allows for smaller repeated operations. Finally, the variable *Allotment* is a binary variable indicating whether the contract was subdivided into several lots (1) or not (0). The allotment is intended to be advantageous to SMEs. As contracting authorities are required to resort to allotment when feasible, 78% of observations come from subdivided contracts. 47% of contracts were subject to allotment, and the average number of lots is 3.86.

## 4.2 Econometric issues

### 4.2.1 Models

The participation of at least one SME will be considered as a binary outcome variable in a logistic regression model (or logit). This approach is probably preferable to considering participation as a count variable, given that the number of offers from SMEs (and firms in general) is typically low. What matters the most is to determine whether at least one SME decided to participate in the award procedure. In this case, the contract can be deemed as accessible for small organizations. Additionally, a logit model requires less observations for estimating significant associations than count regressions, which facilitates the incorporation
of more control variables, fixed effects, and robustness checks. It is possible to consider green matters in general or clauses and criteria distinctly as treatment variables.

Furthermore, the number of offers from larger firms can be incorporated into the analysis. Indeed, if SMEs and other firms are attracted to the same contracts, *Offers_other* has to be taken into account to control for the general effect of green considerations on the attractiveness of a contract, as this paper focuses on the specific effect of green public procurement on SMEs. Conversely, if there is a segmentation of the participation between SMEs and larger firms, which seems to be the case in the sample, controlling for the participation of other firms provides further information on the contract features: SMEs may not be participating not because of environmental clauses and criteria, but because the contract is more suitable for large organizations. These observations lead to three distinct logit models.

The first configuration in Equation 1 is the most elementary. It only looks for the impact of green public procurement in general on the likelihood of observing at least one SME among the bidders.

$$ (1) \log \left( \frac{P(\text{Participation} = 1 | X)}{1 - P(\text{Participation} = 1 | X)} \right) = \beta_0 + \beta_1 \cdot \text{Green} + \beta \cdot \text{Control} + \varepsilon $$

$\varepsilon$ is a random error term and *Control* is a matrix of control variables, namely: the duration of the advertising period, the weight of the price criterion, the logarithm of the award price, the allotment of the contract, the presence of a framework agreement, and the implementation of a social clause. *Control* also includes fixed effects: the industry with a variable level of accuracy thanks to CPV codes, the type of contracting authority, and the quarter and region the contract was issued.

$$ (2) \log \left( \frac{P(\text{Participation} = 1 | X)}{1 - P(\text{Participation} = 1 | X)} \right) = \beta_0 + \beta_1 \cdot G_{\text{clause}} + \beta_2 \cdot G_{\text{criterion}} + \beta_3 \cdot G_{\text{weight}} + \beta \cdot \text{Control} + \varepsilon $$

The model in Equation 2 differentiates between the effect of environmental criteria, including their weight on the total score, and the impact of green clauses. It is also possible to consider
the participation of larger organizations in the regression. The third model in Equation 3 is the most sophisticated and probably the more precise one, as it controls for potential discrepancies in the impact of environmental clauses and award criteria as well as the participation of larger firms.

$$ (3) \log \left( \frac{P(\text{Participation} = 1 | X)}{1 - P(\text{Participation} = 1 | X)} \right) = \beta_0 + \beta_1 \cdot G_{\text{clause}} + \beta_2 \cdot G_{\text{criterion}} + \beta_3 \cdot G_{\text{weight}} + \beta_4 \cdot \text{Other\_offers} + \beta \cdot \text{Control} + \varepsilon $$

Negative binomial regressions will sometimes be employed as a robustness check, as outlined in Equation 4. Negative binomial regressions are justified by the overdispersion in the sample, which is evidenced by a significantly positive α coefficient in the tables summarizing the results in Section 5. Overdispersion would lead to biased estimates when employing a simple Poisson model. If environmental considerations influence SMEs, the association between green public procurement and SME participation should be observed not only between zero and strictly positive values, but also for non-strictly positive values.

$$ (4) \log(\text{Offers\_SME}) = \beta_0 + \beta_1 \cdot \text{Green} + \beta_2 \cdot \text{Other\_offers} + \beta \cdot \text{Control} + \varepsilon $$

### 4.2.2 Standard errors

Even though the different lots of a given contract share certain characteristics, they can be awarded independently. The logit models will consider each lot as an observation (considering a non-subdivided contract as one observation), thereby negating the assumption of independence among observations. The models will then use clustered standard errors, with each cluster corresponding to a contract.

### 4.2.3 Sample weighting

The number of observations from each contracting authority varies depending on the size of the latter. Consequently, the representation of the purchasers is heterogeneous, which may influence the outcome of the model. To address this issue, each observation will be weighted by the inverse of the number of occurrences of the corresponding contracting authority in the
sample. This ensures that the estimated impact of green clauses and criteria is not due to an
overrepresentation of some contracting authorities.

4.3 Scale issues

The nature and content of environmental clauses and criteria are likely to vary across sectors,
while the dataset does not provide the exact features of green clauses and criteria. To address
this, resorting to sectoral subsamples may be useful. However, focusing on specific sectors will
result in econometric regressions being based on fewer contracts, which complicates
identifying significant relationships. Considering this trade-off, it may be relevant to replicate
the econometric analysis at various levels of public procurement and assess whether the
results remain consistent.

5. Results

Given the trade-off between the sample size and the homogeneity of environmental clauses and criteria, the econometric approach will be threefold. First, the models will be estimated at a macro level (i.e. public procurement as a whole). Second, intermediate-level models will be estimated, which significantly reduce the potential sectoral heterogeneity while allowing for a comfortable sample size. Finally, to check whether the results found on the first two levels are not biased by the sectoral diversity of environmental considerations, it is possible to perform econometric regressions on specific industries.

5.1 Macro-level: public procurement in general

Table 3 provides the results for public procurement as a whole. Green clauses and criteria seem to have a favorable impact on SME participation in public procurement. Given the mean of *Participation* (0.77), the positive impact of green public procurement on the likelihood of SME participation can be approximated between +5 and +10 percentage points. While distinguishing green clauses from green criteria, it seems that they both have a positive effect. These initial findings provide evidence that addresses the first two research questions. The results so far indicate that the implementation of green public procurement stimulates SME
participation in award procedures (R.Q.1). Additionally, environmental clauses and criteria
may have a similar effect (R.Q.2).

The weight assigned to the price criterion has a positive impact on SME participation.
Interestingly, both the importance of price competitiveness and the implementation of green
criteria have a positive impact on SME participation, indicating that green award criteria have
a specific effect compared to other qualitative criteria. Likewise, an increase in the contract
size and framework agreements both have a positive impact on SME participation in award
procedures.

As suggested by the descriptive statistics, the number of offers from larger firms has a negative and very significant impact on the SME participation. However, the introduction of this further control variable does not have an impact on the significant coefficients related to green public procurement. The most notable impact of introducing *Offers_other* is an increase in the pseudo-R² of the estimations, confirming that there is a segmentation in the participation in public procurement depending on the size of the firms.

To ensure the reliability of these results, several robustness checks are performed. Different
logit models are estimated in Table 8 (Appendix 9.7). First, conditional logit models based on
the first three digits of the CPV code confirm that green matters in general, as well as the
implementation of green clauses and criteria separately, have a positive impact on SME
participation. Conditional models are relevant because they allow to take into account the
stratification of the sample across the miscellaneous sectors of public procurement. They
implicitly control for the unobservable characteristics of each industry with a higher degree of
precision than sectoral fixed effect in standard logistic regression (first three digits of the CPV
code instead of the first two ones). Even though these conditional models also identify a
negative effect of the weight assigned to the environmental criterion, suggesting that SMEs
may be primarily attracted when the importance of the environmental characteristics of the
offer remains moderate, this result must be interpreted with caution, as the absence of
clustered standard errors results in larger confidence intervals. This result may be overly
influenced by the features of the few contracts where the environmental criterion is assigned
a high weight, as these observations are assumed to be independent.
<table><caption>Table 3: Results for the general sample</caption><thead><tr><th>Dependent variable</th><th>Participation</th><th>Participation</th><th>Participation</th></tr><tr><th>Model type</th><th>Logit (1)</th><th>Logit (2)</th><th>Logit (3)</th></tr></thead><tbody><tr><td>Green</td><td>0.443***</td><td>-</td><td>-</td></tr><tr><td>G_clause</td><td>-</td><td>0.407**</td><td>0.421**</td></tr><tr><td>G_criterion</td><td>-</td><td>0.484**</td><td>0.596**</td></tr><tr><td>G_weight</td><td>-</td><td>-0.021</td><td>-0.021</td></tr><tr><td>Offers_other</td><td>-</td><td>-</td><td>-0.817***</td></tr><tr><td>Advertising</td><td>-0.006</td><td>-0.006</td><td>-0.002</td></tr><tr><td>Price_weight</td><td>0.016***</td><td>0.017***</td><td>0.011***</td></tr><tr><td>Award_price</td><td>-0.007</td><td>-0.003</td><td>0.012</td></tr><tr><td>Allotment</td><td>-0.080</td><td>-0.074</td><td>-0.163</td></tr><tr><td>Framework</td><td>-0.074</td><td>-0.085</td><td>0.025</td></tr><tr><td>Social_clause</td><td>-0.446</td><td>-0.505</td><td>-0.447</td></tr><tr><td>Constant</td><td>1.427**</td><td>1.426**</td><td>2.143***</td></tr><tr><td>Quarter</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Status</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>CPV</td><td>Two digits</td><td>Two digits</td><td>Two digits</td></tr><tr><td>Sample weighting</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Standard errors</td><td>Clustered</td><td>Clustered</td><td>Clustered</td></tr><tr><td>Clusters</td><td>3,986</td><td>3,986</td><td>3,986</td></tr><tr><td>Observations</td><td>9,829</td><td>9,829</td><td>9,829</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.22</td><td>0.22</td><td>0.37</td></tr><tr><td colspan="4">* 10% significance<br>** 5% significance<br>*** 1% significance</td></tr></tbody></table>

A double robust approach is also applied. More precisely, the Equation 1 model is adjusted (while also controlling for *Offers_other*) with an inverse probability weighting of the treatment variable *Green*. In other words, the average treatment of *Green* on *Participation* is computed while estimating simultaneously a logistic regression of *Green* with the other independent variables and fixed effects of the model. This method provides an unbiased estimator of *Green* if one of these two models is correctly specified (Funk et al., 2011). While the observed decrease in the significance of the estimated coefficient for *Green* is probably attributable to the size of the sample and the simultaneous computation of two models, the *p-value* remains
under 5%. Unfortunately, the sample size does not allow to compute a double robust estimator
for *G_clause* and *G_criterion*.

SME participation can also be estimated by considering the number of offers from SMEs as a
count variable. Table 9 in Appendix 9.8 features three negative binomial regressions with
*Offers_SME* as a dependent variable. These alternative models corroborate the results found
for environmental matters so far, even though the coefficients for green award criteria are less
significant than before.

## 5.2 Meso-level: CPV divisions

There are at least two reasons to replicate this general approach to more specific sectors of
public procurement. First, it is important to ensure that the general results are not biased by
a poor handling of the sectoral diversity in the sample. Second, R.Q.3 cannot be addressed
while considering public procurement as a whole. Unfortunately, as fixed effects, clustering,
and sample weighting require relatively large samples, it is not possible to conduct the analysis
on the dozens of CPV divisions. Instead, this subsection will focus on the two largest ones:
public works (construction, equipment, refurbishment, etc.) and environmental services
(cleaning, recycling, garbage collection, etc.). Each of these two sectors accounts for around
10% of observations.

### 5.2.1 Works

Table 4 presents a summary of the results obtained with the three models outlined in
Equations 1 to 3 for public works (i.e. the division "45" of the CPV classification). This time,
sectoral fixed effects are based on the first three digits of the CPV code. These results partially
align with the previous findings by indicating a segmentation of participation between SMEs
and larger firms and a higher propensity for SMEs to submit offers as the weight of the price
criterion rises.

However, the findings indicate that neither binary nor count models can identify a significant
effect of environmental matters on SME participation with this subsample. This could be
indicative of variability in the impact of green public procurement on SMEs across sectors (R.Q.3). However, it is also possible that the absence of a significant result is due to limitations in the sample or the model's features. Unfortunately, it is not possible to replicate these models for sub-sectors of the CPV division "45".

<table><caption>Table 4: Results for public works (CPV = 45)</caption><thead><tr><th>Dependent variable</th><th>Participation</th><th>Participation</th><th>Participation</th><th>Offers_SME</th></tr><tr><th>Model type</th><th>Logit (1)</th><th>Logit (2)</th><th>Logit (3)</th><th>Negative binomial</th></tr></thead><tbody><tr><td>Green</td><td>0.325</td><td>-</td><td>-</td><td>-</td></tr><tr><td>G_clause</td><td>-</td><td>0.567</td><td>0.460</td><td>-0.117</td></tr><tr><td>G_criterion</td><td>-</td><td>0.283</td><td>0.330</td><td>0.172</td></tr><tr><td>G_weight</td><td>-</td><td>-0.028</td><td>-0.042</td><td>-0.003</td></tr><tr><td>Offers_other</td><td>-</td><td>-</td><td>-1.264***</td><td>-0.334***</td></tr><tr><td>Advertising</td><td>-0.002</td><td>0.001</td><td>-0.011</td><td>0.004</td></tr><tr><td>Price_weight</td><td>0.042***</td><td>0.044***</td><td>0.043***</td><td>0.001</td></tr><tr><td>Award_price</td><td>0.291***</td><td>0.287***</td><td>0.392***</td><td>0.052*</td></tr><tr><td>Allotment</td><td>-0.692</td><td>-0.696</td><td>-0.612</td><td>-0.140</td></tr><tr><td>Framework</td><td>-0.705</td><td>-0.722</td><td>0.387</td><td>0.043</td></tr><tr><td>Social_clause</td><td>-0.506</td><td>-0.617</td><td>-0.444</td><td>0.147</td></tr><tr><td>Constant</td><td>0.638</td><td>0.523</td><td>0.292</td><td>1.174**</td></tr><tr><td>Quarter</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Status</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>CPV</td><td>Three digits</td><td>Three digits</td><td>Three digits</td><td>Three digits</td></tr><tr><td>Sample weighting</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Standard errors</td><td>Clustered</td><td>Clustered</td><td>Clustered</td><td>Clustered</td></tr><tr><td>95% interval of α</td><td>-</td><td>-</td><td>-</td><td>[0.03, 0.17]</td></tr><tr><td>Clusters</td><td>334</td><td>334</td><td>334</td><td>337</td></tr><tr><td>Observations</td><td>1,116</td><td>1,116</td><td>1,116</td><td>1,143</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.36</td><td>0.37</td><td>0.58</td><td>0.15</td></tr><tr><td colspan="5">* 10% significance</td></tr><tr><td colspan="5">** 5% significance</td></tr><tr><td colspan="5">*** 1% significance</td></tr></tbody></table>
5.2.2 Environmental services

<table><caption>Table 5: Results for environmental services (CPV = 90)</caption><thead><tr><th>Dependent variable</th><th>Participation</th><th>Participation</th><th>Participation</th><th>Offers_SME</th></tr><tr><th>Model type</th><th>Logit (1)</th><th>Logit (2)</th><th>Logit (3)</th><th>Negative binomial</th></tr></thead><tbody><tr><td>Green</td><td>0.513</td><td>-</td><td>-</td><td>-</td></tr><tr><td>G_clause</td><td>-</td><td>0.664</td><td>1.002**</td><td>0.225*</td></tr><tr><td>G_criterion</td><td>-</td><td>0.097</td><td>0.610</td><td>0.157</td></tr><tr><td>G_weight</td><td>-</td><td>0.002</td><td>-0.024</td><td>-0.010</td></tr><tr><td>Offers_other</td><td>-</td><td>-</td><td>-1.203***</td><td>-0.237***</td></tr><tr><td>Advertising</td><td>-0.007</td><td>-0.006</td><td>-0.009</td><td>-0.001</td></tr><tr><td>Price_weight</td><td>-0.006</td><td>-0.008</td><td>-0.012</td><td>0.002</td></tr><tr><td>Award_price</td><td>-0.160*</td><td>-0.171**</td><td>-0.085</td><td>0.041</td></tr><tr><td>Allotment</td><td>0.337</td><td>0.321</td><td>0.319</td><td>0.060</td></tr><tr><td>Framework</td><td>-0.217</td><td>-0.192</td><td>0.135</td><td>0.060</td></tr><tr><td>Social_clause</td><td>-0.759</td><td>-0.895*</td><td>-0.840</td><td>-0.186</td></tr><tr><td>Constant</td><td>6.316***</td><td>6.369***</td><td>8.504***</td><td>1.290**</td></tr><tr><td>Quarter</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Status</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>CPV</td><td>Three digits</td><td>Three digits</td><td>Three digits</td><td>Three digits</td></tr><tr><td>Sample weighting</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Standard errors</td><td>Clustered</td><td>Clustered</td><td>Clustered</td><td>Clustered</td></tr><tr><td>95% interval of α</td><td>-</td><td>-</td><td>-</td><td>[0.01, 0.37]</td></tr><tr><td>Clusters</td><td>418</td><td>418</td><td>418</td><td>423</td></tr><tr><td>Observations</td><td>889</td><td>889</td><td>889</td><td>895</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.20</td><td>0.20</td><td>0.38</td><td>0.11</td></tr><tr><td colspan="5">* 10% significance<br>** 5% significance<br>*** 1% significance</td></tr></tbody></table>

A similar approach is replicated for environmental services (i.e. the division "90" of the CPV classification) in Table 5. The smaller size of the sample likely explains the lack of significant estimates among the control variables, except for the participation of larger firms, which confirms the segmentation of participation between SMEs and larger companies.
Interestingly, *Green* and *G_Criterion* have no significant effect, but environmental clauses have a positive impact on SME participation in logistic and negative binomial regressions, once *Offers_other* is considered. This result still answers R.Q.1 positively, by finding a new positive effect of green public procurement on SME participation, but it also implies that the effect of green clauses and criteria within a given sector may have different magnitudes (R.Q.2). Additionally, the effect of green public procurement on SME participation seems different for environmental services and public works (R.Q.3).

### 5.3 Micro level: specific sectors

It is possible to zoom in even further on public procurement and focus on a specific industry. As the sample size is significantly narrowing, it is no longer possible to conduct negative binomial regressions. Engineering services (i.e. CPV codes starting with "713") and cleaning services (i.e. CPV codes starting with "9091") are the two sectors that have both enough observations and sufficient homogeneity to not use sector fixed effects.

#### 5.3.1 Engineering services

Despite the limited size of the sample for engineering services, it is still possible to identify a strong segmentation of participation depending on the size of the firms in Table 6. Framework agreements seem to attract SMEs. The duration of the advertising period appears to have a negative effect on SME participation in this industry. It may be indicative that the contract is complex, requiring more time for companies to formulate offers.

Regarding the impact of green public procurement on SME participation for engineering services, a rather significant effect of green clauses is identified (the *p-value* is equal to 6%). Even though a negative effect of green criteria on SME participation is identified in the second model, it is barely significant (the *p-value* is almost equal to 10%), and this model does not control for the segmentation in public procurement, which appears to be a key factor. This result should probably be discarded.
<table><caption>Table 6: Results for engineering services (CPV = 713)</caption><thead><tr><th>Dependent variable</th><th>Participation</th><th>Participation</th><th>Participation</th></tr><tr><th>Model type</th><th>Logit (1)</th><th>Logit (2)</th><th>Logit (3)</th></tr></thead><tbody><tr><td>Green</td><td>0.275</td><td>-</td><td>-</td></tr><tr><td>G_clause</td><td>-</td><td>0.382</td><td>2.671*</td></tr><tr><td>G_criterion</td><td>-</td><td>-1.646*</td><td>-0.715</td></tr><tr><td>G_weight</td><td>-</td><td>0.006</td><td>-0.074</td></tr><tr><td>Offers_other</td><td>-</td><td>-</td><td>-1.056***</td></tr><tr><td>Advertising</td><td>-0.074**</td><td>-0.068**</td><td>-0.113***</td></tr><tr><td>Price_weight</td><td>-0.009</td><td>-0.008</td><td>-0.017</td></tr><tr><td>Award_price</td><td>0.071</td><td>0.078</td><td>0.131</td></tr><tr><td>Allotment</td><td>0.124</td><td>0.173</td><td>0.130</td></tr><tr><td>Framework</td><td>0.253</td><td>0.321</td><td>1.548**</td></tr><tr><td>Social_clause</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Constant</td><td>5.897**</td><td>5.735**</td><td>11.321***</td></tr><tr><td>Quarter</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Status</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>CPV</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Sample weighting</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Standard errors</td><td>Clustered</td><td>Clustered</td><td>Clustered</td></tr><tr><td>Clusters</td><td>194</td><td>194</td><td>194</td></tr><tr><td>Observations</td><td>323</td><td>323</td><td>323</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.38</td><td>0.39</td><td>0.54</td></tr><tr><td colspan="4">* 10% significance</td></tr><tr><td colspan="4">** 5% significance</td></tr><tr><td colspan="4">*** 1% significance</td></tr></tbody></table>

### 5.3.2 Cleaning services

Although cleaning contracts are part of environmental services, which showed no significant impact of environmental criteria in Subsection 5.2.2, the last model in Table 7 identifies a positive impact of green award criteria on SME participation. Conversely, green clauses may have no significant effect on SME participation in this industry. It is the first time a positive effect is found for environmental criteria only. It tends to confirm that green public procurement has a boosting effect on SME participation (R.Q.1), while the intensity of the respective impacts of green clauses and criteria may vary (R.Q.2). These variations appear to
depend on the sector (R.Q.3). Neither green clauses nor green criteria seem systematically more significant. The only consistent finding is the absence of a negative impact of green public procurement on SME participation.

<table><caption>Table 7: Results for cleaning contracts (CPV = 9091)</caption><thead><tr><th>Dependent variable</th><th>Participation</th><th>Participation</th><th>Participation</th></tr><tr><th>Model type</th><th>Logit (1)</th><th>Logit (2)</th><th>Logit (3)</th></tr></thead><tbody><tr><td>Green</td><td>1.715</td><td>-</td><td>-</td></tr><tr><td>G_clause</td><td>-</td><td>-0.490</td><td>-0.216</td></tr><tr><td>G_criterion</td><td>-</td><td>3.042</td><td>5.009***</td></tr><tr><td>G_weight</td><td>-</td><td>-0.008</td><td>-0.044</td></tr><tr><td>Offers_other</td><td>-</td><td>-</td><td>-1.575***</td></tr><tr><td>Advertising</td><td>-0.034</td><td>-0.058</td><td>-0.071*</td></tr><tr><td>Price_weight</td><td>-0.015</td><td>-0.023</td><td>-0.038*</td></tr><tr><td>Award_price</td><td>-0.203</td><td>-0.127</td><td>0.136</td></tr><tr><td>Allotment</td><td>-1.144</td><td>-1.668</td><td>-2.632</td></tr><tr><td>Framework</td><td>-1.515*</td><td>-2.271</td><td>-2.000</td></tr><tr><td>Social_clause</td><td>-0.670</td><td>-0.718</td><td>-0.242</td></tr><tr><td>Constant</td><td>7.365**</td><td>8.449**</td><td>10.365**</td></tr><tr><td>Quarter</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Status</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>CPV</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Sample weighting</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Standard errors</td><td>Clustered</td><td>Clustered</td><td>Clustered</td></tr><tr><td>Clusters</td><td>94</td><td>94</td><td>94</td></tr><tr><td>Observations</td><td>218</td><td>218</td><td>218</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.36</td><td>0.38</td><td>0.58</td></tr><tr><td colspan="4">* 10% significance</td></tr><tr><td colspan="4">** 5% significance</td></tr><tr><td colspan="4">*** 1% significance</td></tr></tbody></table>
# 6. Discussion

## 6.1 Reliability of the findings

The credibility of the results is first supported by the utilized dataset. The sample includes a rather high number of control variables and fixed effects. These variables comprehensively address the various features of a contract, its contracting authority, and its geographical and temporal context. As highlighted by Figure 9, the positive effect of green public procurement on SME participation is already found with simple descriptive statistics. Concerning the risk of an omitted variable bias, the pseudo-R² of the models are quite high, which indicates that the most important variables are already being considered. Moreover, controlling for the participation of other firms with *Offers_other* seems to bring more information on the contract by increasing the predictive power of the models, while not significantly affecting the estimated coefficients. Thus, the variables that could not be directly controlled in the models do not seem to induce a bias in the estimations.

Despite the diversity of subsamples and econometric models, the findings are quite consistent. Green public procurement seems to have a positive impact on SME participation in award procedures. When focusing on specific branches of public procurement, the results are less straightforward, but they do not contradict the thesis of a positive impact of environmental matters on SME participation. The intensity of this positive effect may vary between green clauses and criteria depending on the sector, but there is no sign of a negative impact on small firms. Firms may prefer the direct requirement of a given technology through clauses in certain industries, and the more general inducement to adopt greener practices through award criteria in other sectors. For instance, this hypothesis is supported by the heterogeneous findings between environmental services in general and the specific cleaning contracts. In the context of public works, the lack of significance of the results for *Green* could be attributed to the diversity of this sector. As the heterogeneity of the sectoral findings remains difficult to explain, further research could focus on specific industries, using more precise data or qualitative approaches. Other very significant results are estimated in this subsample (e.g. the award price), and these effects are probably less sector-dependent than the respective impacts of green clauses and criteria.
Concerns regarding a detrimental effect of the potential heterogeneity of green clauses and criteria are mitigated by the stability of the estimations for *Green*. In addition to fixed effects logistic regressions based on two-digit CPV codes, conditional logistic regressions based on three-digit CPV codes identify a positive impact of green clauses and criteria while considering the unobserved features of each sector in the sample. As a result, the findings demonstrate a substantial resilience to sectoral heterogeneity, providing confidence in their reliability and generalizability. However, the exact content of green clauses and criteria remains unknown. While it does not prevent from getting significant and robust results, it may explain why it is difficult to find significant results for the weight of the environmental criterion. The weight in itself has little meaning without further details on the exact scope of the criterion.

The four additional conservative strategies employed in the empirical analysis (i.e. clustered standard-errors, sample weighting, double robust estimators, and negative binomial regressions) also contribute to the credibility of the results. First, standard errors must be clustered to address the strong similarities among the different lots of a given contract, thereby making the standard errors less optimistic. Unfortunately, the conditional logit models do not allow clustering due to the diversity of CPV codes among the different lots of a contract. Larger standard errors may explain why more significant estimates emerge with this kind of model, even though these results seem sensible (e.g. the positive impact of framework agreements of SME participation).

Secondly, as illustrated in Table 3, the implementation of sample weighting has no noteworthy impact on the estimated coefficient of the variable *Green*. It suggests that the estimations are not overly dependent on the heterogeneous sizes of the contracting authorities. While sample weighting acts as a conservative strategy to avoid a bias induced by the overrepresentation of some contracting authorities in the sample, it does not appear to be an artificial distortion of the data.

Third, it may be argued that the implementation of green public procurement is not a random event. The presence of environmental clauses and criteria appears to be correlated with the status of the contracting authority, its location, or the affected industry. To address a potential
endogeneity problem, a double robust estimator is computed in Table 8 (Appendix 9.7). The positive and significant effect estimated for the variable *Green* withstands the incorporation of a logistic regression of the treatment variable within the general logit model, suggesting that the data is not critically affected by endogeneity.

Finally, while the participation of at least one SME remains the main point of interest, the finding of equivalent results with count variable models corroborates the existence of a positive impact of green public procurement on SME participation. This effect holds regardless of whether SME participation is considered as a binary or count variable.

## 6.2 Result interpretation

The results tend to indicate that the rise in transaction costs due to environmental concerns may be more than counterbalanced by the aspiration of small firms to contribute to the shift in public procurement towards more environmentally friendly practices. The fact that both the presence of a green criterion and a higher weight of the price criterion have a positive impact on SME participation implies that the environmental dimension of award criteria specifically matters. Although SMEs tend to submit more offers as the weight of the price criterion increases (i.e. as the number of qualitative criteria decreases), the introduction of an environmental criterion has a positive impact on their participation. As a result, the results do not reflect a general SME preference for qualitative criteria, but a specific preference for sustainable issues. Further research could challenge this conclusion by integrating the firm's innovation and examining whether innovative SMEs are more attracted to green public procurement, for example.

An increase in the participation of small firms could be interpreted as a potential sign of favoritism. However, the positive effect of green requirements on SME participation holds for values greater than one in count models, indicating that green clauses and criteria are not used to restrict the procedure to a specific firm. More generally, Maréchal & Morand (2022) find that green public procurement does not appear to be used as a tool for favoritism in France.
The findings are encouraging for the consistency of public procurement polices in France. Even though the *Climat et Résilience* law will generalize the implementation of green clauses and award criteria by 2026, very little was known so far about the impact of green public procurement on SME access to public procurement. The results suggest that these objectives are not in conflict, at least at the participation stage. Further research could extend the analysis to the award decision stage. It should be noted that the *Climat et Résilience* law impacts public procurement in general, irrespective of the contract size, while the sample in this study is limited to contracts exceeding advertising thresholds. Smaller contracts, which represent most transactions, might not experience the same impact from green public procurement.

## 7. Conclusion

In the context of an accelerated implementation of green public procurement, this paper contributes to reducing the dearth in empirical works by assessing for the first time the direct impact of environmental clauses and award criteria on SME participation in award procedures. The results indicate that green public procurement may encourage SME participation. However, this positive effect may be found only for green clauses or award criteria when focusing on specific industries. No effect could be found for public works. These discrepancies remain difficult to explain with the available data, but no negative effect emerges, regardless of the industry and model specifications. Additionally, this study highlights a clear segmentation of participation in public procurement between SMEs and larger firms, as well as a SME preference for a low number of qualitative criteria (except those related to environmental issues).

The results indicate that there is no trade-off between the implementation of green public procurement and SME participation in award procedures. Consequently, the *Climat et Résilience* law may have a positive effect on SMEs. The latter are innovative firms that can be leveraged by public purchasers to make their contracts greener. The most significant challenge for contracting authorities is the increase in the frequency of green clauses and criteria. To comply with the requirements of the *Climat et Résilience* law, the implementation of environmental clauses and criteria will have to be at least quadrupled by August 2026. This
quantitative increase should not result in poorly written environmental specifications that
would penalize small businesses the most.

This paper paves the way for further research on green public procurement. While it may
refute the notion that green public procurement has a negative impact on SME participation,
this quantitative approach is insufficient to fully explain the sectoral and instrumental (i.e.
clauses vs. criteria) differences in the sensitivity of SMEs to green public procurement. The
findings cannot conclude whether the sectoral discrepancies between green award criteria
and clauses stem from industry-specific characteristics or are attributable to the limited
sample size. Further research, including qualitative studies in specific sectors such as public
works and environmental services, could provide valuable insights that might refine the results
and explain the observed sectoral heterogeneity. Focusing on a limited number of contracts or
using more precise data could challenge the analysis, especially regarding the exact content of
environmental clauses and criteria.

8. References

Deschamps, A., & Potin, L. (2025). Processing and consolidation of open data on public procurement in France (2015-2023). *Data in Brief*, 111277.

[https://doi.org/10.1016/j.dib.2025.111277](https://doi.org/10.1016/j.dib.2025.111277)

Dzikriansyah, M. A., Masudin, I., Zulfikarijah, F., Jihadi, M., & Jatmiko, R. D. (2023). The role of green supply chain management practices on environmental performance: A case of Indonesian small and medium enterprises. *Cleaner Logistics and Supply Chain, 6*, 100100.

[https://doi.org/10.1016/j.clscn.2023.100100](https://doi.org/10.1016/j.clscn.2023.100100)

European Commission, Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs. (2021). *SME needs analysis in public procurement: Final report*. Office des publications de l'Union européenne. [https://doi.org/10.2873/86199](https://doi.org/10.2873/86199)
Flynn, A. (2017). Re-thinking SME disadvantage in public procurement. *Journal of Small Business and Enterprise Development*, 24(4), 991–1008. https://doi.org/10.1108/JSBED-03-2017-0114

Flynn, A., & Davis, P. (2015). The policy–practice divide and SME-friendly public procurement. *Environment and Planning C: Government and Policy*. https://doi.org/10.1177/0263774X15614667

Flynn, A., & Davis, P. (2016). Firms’ experience of SME-friendly policy and their participation and success in public procurement. *Journal of Small Business and Enterprise Development*, 23(3), 616–635. https://doi.org/10.1108/JSBED-10-2015-0140

Funk, M. J., Westreich, D., Wiesen, C., Stürmer, T., Brookhart, M. A., & Davidian, M. (2011). Doubly Robust Estimation of Causal Effects. *American Journal of Epidemiology*, 173(7), 761–767. https://doi.org/10.1093/aje/kwq439

Hoekman, B., & Taş, B. K. O. (2022). Procurement policy and SME participation in public purchasing. *Small Business Economics*, 58(1), 383–402. https://doi.org/10.1007/s11187-020-00414-z

Krieger, B., & Zipperer, V. (2022). Does green public procurement trigger environmental innovations? - *ScienceDirect*. *Research Policy*, 51(6). https://doi.org/10.1016/j.respol.2022.104516

Lewis, G. H. (2017). Effects of federal socioeconomic contracting preferences. *Small Business Economics*, 49(4), 763–783. https://doi.org/10.1007/s11187-017-9860-z

Liargovas, P., Apostolopoulos, N., Pappas, I., & Kakouris, A. (2017). SMEs and Green Growth: The Effectiveness of Support Mechanisms and Initiatives Matters. In S. Renko & A. Pestek (Eds.), *Green Economy in the Western Balkans* (pp. 79–108). Emerald Publishing Limited. https://doi.org/10.1108/978-1-78714-499-620171004
Loader, K. (2015). SME suppliers and the challenge of public procurement: Evidence revealed by a UK government online feedback facility. *Journal of Purchasing and Supply Management*, 21(2), 103–112. https://doi.org/10.1016/j.pursup.2014.12.003

Lundberg, S., & Marklund, P.-O. (2018). Green public procurement and multiple environmental objectives. *Economia e Politica Industriale, 45*(1), 37–53. https://doi.org/10.1007/s40812-017-0085-6

MacManus, S. A. (1991). Why Businesses are Reluctant to Sell to Governments. *Public Administration Review*, 51(4), 328–344. https://doi.org/10.2307/976748

Mahuwi, L., & Israel, B. (2023). A review on participation of SMEs in public procurement: Opportunities, challenges, and policy implications. *New Applied Studies in Management, Economics & Accounting, 6*(4), 18–33. https://doi.org/10.22034/nasmea.2023.176305

Maréchal, F., & Morand, P.-H. (2012). Small business participation procurement policy: Subcontracting vs Allotment. *Recherches économiques de Louvain*, 78(2), 5–23. https://doi.org/10.3917/rel.782.0005

Maréchal, F., & Morand, P.-H. (2022). Are social and environmental clauses a tool for favoritism? Analysis of French public procurement contracts. *European Journal of Political Economy*, 73, 102140. https://doi.org/10.1016/j.ejpoleco.2021.102140

Masron, N. A., Ismail, S., & Zainol, Z. (2024). Barriers to the involvement in green public procurement among small and medium enterprises (SMEs) government suppliers. *Journal of Public Procurement*, 24(2), 210–231. https://doi.org/10.1108/JOPP-03-2023-0016

Morand, P.-H. (2003). SMEs and public procurement policy. *Review of Economic Design*, 8(3), 301–318. https://doi.org/10.1007/s10058-003-0104-0
Orser, B., Riding, A., & Weeks, J. (2019). The efficacy of gender-based federal procurement policies in the United States. *Small Business Economics*, 53(2), 491–515.
https://doi.org/10.1007/s11187-018-9997-4

Saastamoinen, J., Tammi, T., & Reijonen, H. (2018). E-procurement and SME involvement in public procurement of innovations: An exploratory study. *International Journal of Procurement Management*. https://doi.org/10.1504/IJPM.2018.092768

Schäfer, D., Stephan, A., & Fuhrmeister, S. (2024). The impact of public procurement on financial barriers to general and green innovation. *Small Business Economics*, 62(3), 939–959.
https://doi.org/10.1007/s11187-023-00790-2

OECD. (2018). *SMEs in Public Procurement: Practices and Strategies for Shared Benefits*. Organisation for Economic Co-operation and Development.
https://doi.org/10.1787/9789264307476-en

Zahra, F., Hadi, S., Ngemba, H. R., & Latowale, M. I. (2023). *SMEs Participation in Green Public Procurement: An Academic Literature Review*. 215–223. https://doi.org/10.2991/978-94-6463-140-1_21
9. Appendix

9.1

<figure><img src="image_7.png" /><figcaption>Figure 1: Average SME participation by **région** (% of the observations)</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 3: SME participation and green public procurement by type of contracting authority</figcaption></figure>
9.3

<table><thead><tr><th></th><th>Participation</th><th>Offens_SME</th><th>Offens_other</th><th>Green</th><th>G_clause</th><th>G_criterion</th><th>G_weight</th><th>Price_weight</th><th>Social_clause</th><th>Award_price</th><th>Framework</th><th>Advertising</th><th>Allocation</th></tr></thead><tbody><tr><td>Participation</td><td>1.00</td><td>0.57</td><td>-0.49</td><td>0.14</td><td>0.13</td><td>0.09</td><td>0.05</td><td>0.09</td><td>0.05</td><td>0.05</td><td>0.11</td><td>-0.04</td><td>-0.03</td></tr><tr><td>Offens_SME</td><td>0.57</td><td>1.00</td><td>-0.33</td><td>0.10</td><td>0.11</td><td>0.06</td><td>0.04</td><td>0.01</td><td>0.06</td><td>0.06</td><td>0.08</td><td>-0.01</td><td>-0.07</td></tr><tr><td>Offens_other</td><td>-0.49</td><td>-0.33</td><td>1.00</td><td>-0.04</td><td>-0.02</td><td>-0.02</td><td>-0.03</td><td>-0.12</td><td>0.01</td><td>0.03</td><td>-0.01</td><td>0.04</td><td>-0.01</td></tr><tr><td>Green</td><td>0.14</td><td>0.10</td><td>-0.04</td><td>1.00</td><td>0.83</td><td>0.68</td><td>0.22</td><td>0.05</td><td>0.28</td><td>0.14</td><td>0.13</td><td>-0.01</td><td>0.04</td></tr><tr><td>G_clause</td><td>0.13</td><td>0.11</td><td>-0.02</td><td>0.83</td><td>1.00</td><td>0.39</td><td>0.29</td><td>0.03</td><td>0.36</td><td>0.13</td><td>0.10</td><td>-0.01</td><td>0.02</td></tr><tr><td>G_criterion</td><td>0.09</td><td>0.06</td><td>-0.02</td><td>0.68</td><td>0.39</td><td>1.00</td><td>0.77</td><td>0.03</td><td>0.16</td><td>0.09</td><td>0.15</td><td>0.03</td><td>0.02</td></tr><tr><td>G_weight</td><td>0.05</td><td>0.04</td><td>-0.03</td><td>0.52</td><td>0.29</td><td>0.77</td><td>1.00</td><td>0.13</td><td>0.12</td><td>0.05</td><td>0.09</td><td>0.03</td><td>0.02</td></tr><tr><td>Price_weight</td><td>0.09</td><td>0.01</td><td>-0.12</td><td>0.05</td><td>0.03</td><td>0.03</td><td>0.13</td><td>1.00</td><td>-0.01</td><td>0.08</td><td>0.01</td><td>-0.05</td><td>0.08</td></tr><tr><td>Social_clause</td><td>0.05</td><td>0.06</td><td>0.01</td><td>0.28</td><td>0.35</td><td>0.15</td><td>0.12</td><td>-0.01</td><td>1.00</td><td>0.15</td><td>0.03</td><td>0.03</td><td>0.03</td></tr><tr><td>Award_price</td><td>0.05</td><td>0.06</td><td>0.03</td><td>0.14</td><td>0.13</td><td>0.09</td><td>0.05</td><td>0.08</td><td>0.16</td><td>1.00</td><td>0.08</td><td>0.05</td><td>-0.28</td></tr><tr><td>Framework</td><td>0.11</td><td>0.08</td><td>-0.01</td><td>0.13</td><td>0.10</td><td>0.15</td><td>0.09</td><td>0.01</td><td>0.03</td><td>0.08</td><td>1.00</td><td>-0.06</td><td>-0.00</td></tr><tr><td>Advertising</td><td>-0.04</td><td>-0.01</td><td>0.04</td><td>-0.01</td><td>-0.01</td><td>0.03</td><td>0.03</td><td>-0.05</td><td>0.03</td><td>0.05</td><td>-0.06</td><td>1.00</td><td>0.02</td></tr><tr><td>Allocation</td><td>-0.03</td><td>-0.07</td><td>-0.01</td><td>0.04</td><td>0.02</td><td>0.02</td><td>0.02</td><td>0.08</td><td>0.03</td><td>-0.28</td><td>-0.00</td><td>0.02</td><td>1.00</td></tr></tbody></table>
<figure><img src="image_9.png" /><figcaption>Figure 5: Distribution of the weight of the environmental criterion</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Figure 6: Implementation of a green clause or criterion by **région** (% of the observations)</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Figure 8: Scatter plot of the implementation of green clauses and criteria by sector (CPV divisions)</figcaption></figure>
9.7

<table><caption>Table 8: Further robustness checks for the general logit model</caption><thead><tr><th rowspan="2">Dependent variable</th><th>Participation</th><th>Participation</th><th>Participation</th><th>Participation</th></tr><tr><th>Conditional logit</th><th>Conditional logit</th><th>Conditional logit</th><th>Double robust logit</th></tr></thead><tbody><tr><td>Green</td><td>0.282*</td><td>-</td><td>-</td><td>0.047**</td></tr><tr><td>G_clause</td><td>-</td><td>0.256**</td><td>0.384***</td><td>Yes</td></tr><tr><td>G_criterion</td><td>-</td><td>0.576***</td><td>0.668***</td><td>Yes</td></tr><tr><td>G_weight</td><td>-</td><td>-0.037***</td><td>-0.037***</td><td>Yes</td></tr><tr><td>Offers_other</td><td>-</td><td>-</td><td>-0.870***</td><td>Yes</td></tr><tr><td>Advertising</td><td>-0.005</td><td>-0.004</td><td>-0.001</td><td>Yes</td></tr><tr><td>Price_weight</td><td>0.013***</td><td>0.015***</td><td>0.010***</td><td>Yes</td></tr><tr><td>Award_price</td><td>0.046</td><td>0.046</td><td>0.102***</td><td>Yes</td></tr><tr><td>Allotment</td><td>-0.045</td><td>-0.033</td><td>-0.090</td><td>Yes</td></tr><tr><td>Framework</td><td>0.262*</td><td>0.253*</td><td>0.492***</td><td>Yes</td></tr><tr><td>Social_clause</td><td>-0.211</td><td>-0.245</td><td>-0.267</td><td>Yes</td></tr><tr><td>Quarter</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Status</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>CPV</td><td>Three digits</td><td>Three digits</td><td>Three digits</td><td>Two digits</td></tr><tr><td>Sample weighting</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Standard errors</td><td>Robust</td><td>Robust</td><td>Robust</td><td>Clustered</td></tr><tr><td>Clusters</td><td>-</td><td>-</td><td>-</td><td>3,869</td></tr><tr><td>Observations</td><td>9,366</td><td>9,366</td><td>9,366</td><td>9,308</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.11</td><td>0.11</td><td>0.31</td><td>-</td></tr><tr><td colspan="5">* 10% significance</td></tr><tr><td colspan="5">** 5% significance</td></tr><tr><td colspan="5">*** 1% significance</td></tr></tbody></table>
9.8

<table><caption>Table 9: General regression with the number of SME candidates as a count variable</caption><thead><tr><th>Dependent variable</th><th>Offers_SME</th><th>Offers_SME</th><th>Offers_SME</th></tr><tr><td></td><td>Negative binomial</td><td>Negative binomial</td><td>Negative binomial</td></tr></thead><tbody><tr><td>Green</td><td>0.140***</td><td>-</td><td>-</td></tr><tr><td>G_clause</td><td>-</td><td>0.126**</td><td>0.130***</td></tr><tr><td>G_criterion</td><td>-</td><td>0.117*</td><td>0.117*</td></tr><tr><td>G_weight</td><td>-</td><td>-0.004</td><td>-0.004</td></tr><tr><td>Offers_other</td><td>-</td><td>-</td><td>-0.278***</td></tr><tr><td>Advertising</td><td>0.002</td><td>0.001</td><td>0.002</td></tr><tr><td>Price_weight</td><td>0.002**</td><td>0.003**</td><td>0.001</td></tr><tr><td>Award_price</td><td>-0.009</td><td>-0.008</td><td>-0.002</td></tr><tr><td>Allotment</td><td>-0.092**</td><td>-0.089**</td><td>-0.093**</td></tr><tr><td>Framework</td><td>0.027</td><td>0.020</td><td>0.045</td></tr><tr><td>Social_clause</td><td>-0.052</td><td>-0.073</td><td>-0.027</td></tr><tr><td>Constant</td><td>0.750***</td><td>0.747***</td><td>0.872***</td></tr><tr><td>Quarter</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Status</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>CPV</td><td>Two digits</td><td>Two digits</td><td>Two digits</td></tr><tr><td>Sample weighting</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Standard errors</td><td>Clustered</td><td>Clustered</td><td>Clustered</td></tr><tr><td>95% interval of α</td><td>[0.17, 0.26]</td><td>[0.17, 0.26]</td><td>[0.09, 0.17]</td></tr><tr><td>Clusters</td><td>3,990</td><td>3,990</td><td>3,990</td></tr><tr><td>Observations</td><td>9,834</td><td>9,834</td><td>9,834</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.06</td><td>0.06</td><td>0.10</td></tr><tr><td colspan="4">* 10% significance</td></tr><tr><td colspan="4">** 5% significance</td></tr><tr><td colspan="4">*** 1% significance</td></tr></tbody></table>
**Conflict of Interest:**

The author declares that he has no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Data availability:**

The data used in this paper as well as the script to replicate the paper's results are available on the following repository: https://github.com/AdrienDeschampsAU/GPP_and_SMEs

**Funding:**

This research was partially funded by the French national research agency (Agence Nationale de la Recherche, ANR), within the framework of the following project: "Détecter la Corruption dans les Marchés Publics Grant ANR-19-CE38-0004".

**Author contributions:**

The sole author of this article is Adrien Deschamps.

**Ethical statements:**

This research was partially funded by the French national research agency (Agence Nationale de la Recherche, ANR), within the framework of the following project: "Détecter la Corruption dans les Marchés Publics Grant ANR-19-CE38-0004". The author declares that he has no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Acknowledgements:**

The author would like to express gratitude to the two anonymous reviewers for their truly relevant comments which helped improve the quality of the paper, as well as Avignon Université and the JPEG Laboratory for their support, and Pierre-Henri Morand and François Maréchal for their proofreading of the manuscript.