3.1. Database and Descriptive Statistics
The data source used for this work is the Flash Eurobarometer 498 (FLE498) survey on “SMEs, Resource Efficiency and Green Markets”, wave 5. It was conducted between November 8th and 10 December 2021, and follows earlier Eurobarometer waves (FLE342 in 2012, FLE381 in 2013, FLE426 in 2015 and FLE456 in 2017) (As suggested by a referee, Flash Eurobarometer 315: “Attitudes of European entrepreneurs towards eco-innovation” is the European Commission’s most important research on the topic). The database includes the 28 member states of the European Union, plus Albania, Macedonia, Montenegro, Serbia, Turkey, Iceland, Moldova, Norway, the UK and the US (For additional information, see
https://europa.eu/eurobarometer/surveys/detail/2287, accessed on 1 January 2023).
In the Flash Eurobarometer Survey 498, a total of 17,662 managers (14,482 from the EU28) were selected using a stratification procedure according to the dimensions of the firm (1–9 employees, 10–49 employees, 50–249 employees and 250 employees or more) and sector (manufacturing, retail, services and industry). As in any survey, the reliability of data strictly depends on how the participating managers interpret and answer the relevant questions. Although some questions are subjective, we are confident that the overall data collected represent the general attitude of top management of firms involved in resource-efficiency innovations [
60].
Unlike other empirical environmental databases that offer only aggregate data at the country level, the Flash Eurobarometer 498 survey includes four micro-dimensions: country, industry, age and size. This is a relevant strength of the present work as it allows us to investigate at the most disaggregated level with information on individual firms.
Due to the main focus of our analysis, i.e., to study the relationship between access to various financial and non-financial resources and eco-efficiency actions of European firms, and the data cleaning procedure (in order to remove observations with missing values for selected variables), our final sample comprises 9158 companies. Notably, these include EU28 firms that are actively taking measures to be more resource-efficient and have invested in these actions in the last two years.
Table 1 provides an overview of the sample by country, sector and firm size.
Table 1 shows that the most represented countries are Sweden, Romania and Greece (with a total of 423–440 firms each), while the least represented ones are Cyprus, Luxembourg and Malta (with less than 200 firms). The sample is dominated by three industries, namely wholesale and retail trade, manufacturing and construction. Together, these industrial sectors account for roughly 67% of the firms, reflecting the actual aggregate composition of the EU economy [
61]. In terms of size, the firms in our sample are 36% micro-enterprises (≤9 employees), 38% small enterprises (10–49), 19% medium-sized companies (50–249) and only the remaining 7% are large-sized firms (≥250). Finally, as far as age is concerned, most of the firms are less than 50 years old (88%), and many of them are relatively young (the share of firms under the age of 25 alone reaches 50% of the total). Only about 13% of the firms are historical (i.e., older than 50).
For the purpose of this study, resource efficiency is defined as the use of natural resources in a sustainable and environmentally sound manner at different stages of the firm’s supply chain, from sourcing and production to, for example, waste management [
62].
Business managers were asked to answer the following question Q1: “
What actions is your company undertaking to be more resource efficient?”. As shown in
Figure 2, the most common resource efficiency actions undertaken by the firms of the sample are: minimizing waste (16%), saving energy (15.9%), saving materials (14.4%), recycling by reusing material or waste within the company (11.4%) and saving water (11.3%). Other relevant actions include switching to greener suppliers of materials (9.4%), selling your residues and waste to another company (8.7%), designing products that are easier to maintain, repair or reuse (7.2%), using predominantly renewable energy (e.g., including own production through solar panels) (5.7%).
For our empirical analysis, we clustered eco-actions according to the number implemented by each firm. To this aim, we used the three-category classification proposed by Eurobarometer. Specifically, firms that implemented one or two eco-efficiency actions were classified in the group “few actions”, those that implemented three to four actions in the group “some actions”and finally, those with more than four actions were included in the group “many actions”. The result of this grouping is shown in
Figure 3. Overall, the first cluster contains 4731 enterprises (51.7% of the total), the second cluster 2550 (27.8% of the total) and the third cluster 1877 (20.5% of the total). Thus, the new variable obtained represents our dependent variable and captures the intensity of a firm’s resource-efficiency innovation [
63].
The last step to prepare the dataset for estimation involves identifying the type of resources (internal vs. external and financial vs. non-financial) used by firms to invest in eco-innovations. To this aim, we rely on question Q5: “Which type of support does your company rely on in its efforts to be more resource efficient?”. The possible answers to Q5 are: (1) own financial resources; (2) own technical expertise; and (3) external support. Then, only for firms that answered “external support” (that we identify as external resources), we also retrieve information from Q6: “More precisely, which type of external support is it?”. The possible answers are: (1) Public funding such as grants, guarantees, or loans; (2) Private funding from a bank, an investment company, or venture capital fund; (3) Private funding from friends and relatives; (4) Advice or other non-financial assistance from public administration; (5) Advice or other non-financial assistance from private consulting and audit companies; (6) Advice or other non-financial assistance from business associations and clusters; (7) Advice or other non-financial assistance from supply chain partners. While we take question Q5 in its original form, as for Q6, due to the heterogeneity of possible answers by the interviewed managers, we group external finance (including both private and public funding) and external non-financial assistance (mainly advices and other forms of assistance). The descriptive statistics of these variables (in dummies) are shown in
Table 2.
What emerges from Panel (a) is that the vast majority of firms in the sample (90%) relied on their internal resources that include own financial resources and/or own technical expertise, while only 30% of firms relied on external resources that include external finance and/or external non-financial assistance. Breaking down the 8239 firms that employed internal resources, we observe that 82% of them used own financial resources, while own technical expertise covered 64% of the firms. Of course, several firms benefited from both of them in their eco-efficiency actions.
In Panel (b), we further break down the type of external resources received by the 2765 firms that specified using it. Specifically, 60% of them benefited from external finance from both public funding and private funding (in the form of grants, guarantees or loans), while 75% received external non-financial assistance (in the form of advice or other non-financial assistance from governments, private consulting and auditing firms, business associations and supply chain partners). Finally, the last distinction concerns the 1673 firms that received external finance. Of these, we distinguish between public funding (e.g., grants, guarantees or government loans) for 72% of firms, while private funding from banks, lending institutions, investment companies or venture capital funds, friends and relatives accounts for 55% of firms. It is important to remember that these types of assistance are not mutually exclusive: several firms may benefit from both public and private funding, as well as from different forms of internal and external resources. Indeed, as we show in the results section, the most eco-innovative firms are likely to make use of more types of resources.
3.2. Empirical Models
We estimate an ordered logit model (ologit in Stata) (The command “ologit” (Ordered logistic regression) fits ordered logit models of ordinal variable on the independent variables. Estimation using Stata 17, where we compare the impact of different internal and external (financial and non-financial) resources on the number of actions undertaken by the firms to be more resource efficient (variable Q1): few actions, some actions and many actions. As is known, models for ordinal outcomes can be described in terms of a latent variable [
64]. The basic structure is as follows:
where
is the latent variable (number of resource efficiency actions undertaken by firm
i),
X is a vector of explanatory and control variables, and
is the idiosyncratic error term. The latent variable can be split into
N ordinal categories, so that the observed variable is
and the probabilities of observing
are
where
F denotes the logistic cumulative distribution function. The three categories for our “
” dependent variable
—How many actions is your company undertaking to be more resource efficient?—are: few (
), some (
), and many (
).
To test our first hypothesis (H1a), we include our ordered variable with the three categories indicating the number of resource efficiency actions undertaken by each firm
i and the use of internal (“
”) or external resources (“
”) to support these actions (Equation (
4) below). Then, following the argument that it is necessary to distinguish between different types of support to assess their effect on firms’ resource efficiency strategies (H2), we specify Equation (
5), separating proprietary financial resources (“
”) from in-house technical expertise (“
”). A relevant issue discussed in the literature concerns the degree of complementarity among different resources used by firms for eco-innovation (H3). To answer this question, we use the specification in Equation (
6), which traces Equation (
5), but includes interactions among the variables.
In the second stage of our analysis, we shift our focus to the relationship between external resources and firm’s eco-innovations (Note that this sample represents a subsample compared to that used to test H1a, as not all firms rely on external resources (see
Table 2)). This is relevant considering that the literature has identified different effects of external financial and non-financial resources to stimulate eco-innovation [
65]. To this end, we introduce a dummy that captures the firm
i use of external financial (“
”) and non-financial (“
”) resources, using “
” as control. This is performed in Equation (
7). Then, following the literature, we try to disentangle the effect of different sources of external finance, distinguishing between public (“
”) and private funding (“
”) (H1b), as specified in Equation (
8). Finally, as in the previous exercise, we use the specification outlined in Equation (
9) to capture the extent of complementarity between external financial (public and private funding) and non-financial resources (“
”) (H3).
To minimize any estimation bias due to potential omitted variables, we include a large set of controls in all the specifications presented above [
66]. As illustrated in the theoretical framework outlined in
Figure 1, these aim to eliminate all potential confounding factors (at market or company level) that could distort the relationship between the financial resources used by the firm and its eco-innovations. To select the relevant variables, we follow the literature already cited and the availability of variables surveyed by the Eurobarometer. Specifically, to account for the observable characteristics of the firms, we included: firm size—captured by the number of employees (in log), age—as a proxy for experience (in log), the growth of the firm’s annual turnover in the past two years, whether the firm is in a B2B, B2C o B2P business, whether it offers products or services (categorical variables) and whether it sells green products or plans to sell them in the future (“
Green prod. Yes” or “
Green prod. Planned”, respectively). Finally, as is usually the case, we include industry (manufacturing, retail, services, etc.) and country dummies.