Enhancing Sustainability: The Impact of Research and Development Expenditure on Future Environmental Innovation in European Firms

Abstract

Sustainability is key to our collective future, and environmental innovation is essential in integrating sustainability within organizations. Relatedly, theoretical insights from the resource-based view of the firm suggest that Research and Development (R&D) focused on environmental innovation is a key enabler of the development of unique knowledge-based assets. In this study, we integrate these insights to develop a hypothesis which predicts that current-period R&D expenditure is a significant driver of future environmental innovation. We test this prediction using a database comprising firms from eight European countries over the period 2003–2020. Our empirical findings, utilizing a random-effects estimation model employing controls for heterogeneity across firms, time, and countries, offers strong empirical support for this hypothesis and are strongly robust to alternative estimation methodologies. Furthermore, building on the absorptive capacity literature, we hypothesize and demonstrate that firm age positively moderates this relationship, indicating that experienced firms leverage R&D more effectively for future environmental innovation. Conversely, we hypothesize and demonstrate that relative R&D investment (measured using the R&D expenditure to capital expenditure ratio) negatively moderates the R&D–environmental-innovation relationship, highlighting the risk of R&D overinvestment in this domain. Our findings offer unique insights for C-level executives, policymakers, and researchers, demonstrating that while R&D is a key driver of future environmental innovation, its effectiveness is enhanced by firm experience, but can also be diminished by excessive R&D investment.

1. Introduction

The overarching motivation for this article is to investigate the role of investment in Research and Development (R&D) in fostering future environmental innovation, drawing on theoretical insights from a resource-based view of the firm. From the standpoint of the resource-based view, R&D focused on environmental innovation is a key enabler of the development of unique knowledge-based assets that have the potential to contribute directly to beneficial environmental outcomes [1,2].

Within the ambit of our overarching motivation, we have three primary research objectives. First, we explore the link between R&D investment in the current period and future—as distinct from the current period—environmental innovation. To date, studies that have explored the role of R&D in environmental innovation [3] have focused on the contemporaneous relationship between both variables [4]. However, work in other fields has shown a time lag before R&D manifests desired environmental outcomes [5,6]. Given the strategic importance of R&D in building sustainable business models and the recognized time lag before R&D efforts manifest, this study aims to address this gap by investigating the impact of current R&D expenditure on subsequent environmental innovation. Consequently, the first objective of our work is to explore the nature of the relationship between one-period-ahead environmental innovation and current-period R&D expenditure.

Second, prior theory suggests that, as firms age, they accumulate the hard-won experience of both R&D failures and R&D successes [7,8]. In addition, firms’ capacity to absorb external knowledge [9] typically increases with age [10,11]. Building on the concept of absorptive capacity, which suggests that firms’ ability to identify, assimilate, transform, and apply valuable external knowledge typically increases with age, we investigate whether firm age positively moderates the relationship between current R&D and future environmental innovation. Understanding this is of core relevance to our work, as it could mean that older, more experienced firms are better equipped to translate R&D into tangible environmental benefits, a crucial insight for long-term environmental innovation strategies. Consequently, we explore how the accumulated experience of firms—as reflected by firm age—can help make R&D investment more successful in driving environmental innovation. Therefore, our second research objective is to investigate whether firm age positively moderates the relationship between one-period-ahead environmental innovation and current period R&D expenditure.

Third, prior work shows that firms can actually invest too much of their scarce resources in R&D [12]. In effect, as firms invest more in R&D, the marginal benefit from an extra dollar of R&D investment can decline, leading to the R&D overinvestment phenomenon [13]. Naturally, the question arises as to whether the risk of R&D overinvestment impacts the role of R&D as a driver of environmental innovation. Recognizing that R&D investments are subject to diminishing marginal returns and the potential for overinvestment, we explore whether higher levels of R&D investment relative to capital expenditure negatively moderate the R&D–environmental-innovation relationship. This is pertinent because identifying potential overinvestment risks can help firms optimize resource allocation for sustainable innovation. Therefore, our third objective is to address this issue by investigating whether increasing levels of investment in R&D—relative to capital expenditure—negatively moderate the relationship between one-period-ahead environmental innovation and current-period R&D.

To explore our core research questions, we draw on a pan-European time-series cross-sectional (i.e., panel) database of firms spanning the 2003–2020 period. Our empirical work seeks to avoid a weakness in extant research in the environmental innovation space highlighted by del Río et al. [14], i.e., the absence of panel data work utilizing firm-level data. Prior research shows that a key benefit of panel data is that the researcher can control for a myriad of factors whilst isolating the impact of the specific variables under consideration [15]. Consequently, we aim to achieve optimal accuracy with respect to our key parameter estimates by employing state-of-the-art econometric estimations utilizing a relatively large multi-country panel dataset. While most of the extant research on the relationship between environmental innovation and R&D has focused on individual countries, we believe that important insights can be gleaned from employing a multi-country perspective [16] in our empirical analysis.

The remainder of our article is structured as follows. In Section 2, we integrate a discussion of our three hypotheses with a review of the prior literature. Section 3 of our work outlines our core empirical framework and presents the data utilized in our empirical analysis. Section 4 reveals our empirical findings, robustness tests, and extensions. The key contributions arising from this study are discussed in Section 5. Section 6 concludes this paper.

2. Literature Review and Hypotheses

2.1. The Prior Literature

Environmental innovation (which is also known as ‘green innovation’), (p. 1) “represents a deliberate shift towards eco-friendly technologies, practices, and processes within organizations” [17] and is of core importance to contemporary firms. This is because, in today’s business environment, firms are placing an ever-increasing emphasis on environmental innovation as pressures arising from industry regulators and environmentally conscious consumers are both compelling and incentivizing firms to direct their R&D efforts towards generating novel solutions in the environmental space [2,18]. Consequently, corporate investment in environmentally focused R&D is a strategic resource focused on building sustainable business models for the greater good of both business [10] and society [19]. Environmental innovation may lead to higher firm profits and can ultimately enhance firm value while simultaneously playing a key role in a firm’s efforts to meet its environmental objectives [12].

Consequently, the question naturally arises as to what the key enablers of environmental innovation are. While extant research demonstrates that the drivers of environmental innovation may be different from those for other forms of innovation [4], a critical area of commonality [3,20] is the potentially crucial role of a firm’s investment in R&D. Nonetheless, to date, there are important gaps in our understanding of the importance of R&D as a driver of environmental innovation.

Prior research highlights the role of R&D in environmental innovation contexts. Drawing on a sample of Spanish firms, Cainelli, De Marchi, and Grandinetti [3] find that internal resources—specifically R&D and training—are critical to environmental innovation. De Marchi [20] demonstrate the importance of inter-firm R&D cooperation strategies as a driver of environmental innovations for Spanish firms. Utilizing a panel dataset of German firms, Horbach [4] reports that (p. 163) “the improvement of the technological capabilities (“knowledge capital”) by R&D triggers environmental innovations”. Prior work has also shown that R&D investment has a positive impact on firms’ environmental, social, and governance (ESG) performance [21]. However, in common with other forms of R&D expenditures, R&D tailored towards environmental innovation does not necessarily lead to successful outcomes [22,23]. Although extant research has offered crucial insights, its focus has been on the contemporaneous relationship between R&D and environmental innovation. In contrast, in this study, we extend prior research by exploring the impact of R&D on future (as opposed to contemporaneous) environmental innovation. In addition, we employ a multi-country focus as opposed to the single-country studies presented in the prior literature [4,20]. We elaborate on these features of our work when presenting our first hypothesis in Section 2.2 below.

While R&D-led environmental innovation can influence the sustainability performance of firms globally, as older firms are subject to greater organizational learning and experience effects [24,25], firm age needs careful consideration as a potential moderating variable in our theoretical and empirical analysis. Although organizational learning and experience effects can yield considerable strategic advantages in environmentally led R&D contexts, younger firms can be more agile and less rigid [26]. Consequently, in the present work, we also focus on examining the moderating effect of firm age on the relationship between current-period R&D and future environmental innovation, and we draw on the notion of ‘absorptive capacity’ to better inform our framing of the underlying issues. We address these issues in more depth when presenting our second hypothesis in Section 2.3 below.

Relatedly, the question naturally arises as to whether the R&D investment in a specific period is too much or too little. While prior research has shown that the principle of diminishing returns applies to R&D investment [12], the difficulty for firms is discovering the level of R&D expenditure at which diminishing returns are likely to become significant. Determining the optimal R&D investment strategy is a complex and ongoing challenge for firms globally, regardless of their specific objectives or industries [27,28]. In Section 2.4 below, we elaborate on the issue of R&D overinvestment when presenting our third hypothesis.

2.2. Hypothesis One (H1): R&D Expenditure and Environmental Innovation

As already discussed, corporate R&D investment has the potential to transform an organization’s sustainability stance from both internal and external standpoints [18]. More specifically, R&D investment can play a key role in fostering and enhancing environmental innovation, which, in turn, can lead to significant advances in sustainability-relevant knowledge and technological capabilities [29,30]. Consequently, R&D investment in environmental innovation can transform sustainability concerns into real-world opportunities for the firm to meet its objectives across a range of sustainability-related objectives [20,31].

Below, we outline several reasons why we predict that R&D expenditure is a significant driver of future environmental innovation. First, R&D helps build the long-term innovation capacity [9] from which all forms of internal firm innovation—including future environmental innovation—ultimately emerge. Second, consumers increasingly demand green products [29,30]. As a result, investment in R&D is likely to be tailored to satisfy consumer demand. However, it usually takes some time for firms to adapt their R&D efforts to meet changes in consumer preferences. Third, regulators and governments now place stringent demands, constraints, and controls on firms’ environmental impacts [18]. Consequently, firms are increasingly directing their R&D budgets towards helping to achieve compliance with the multifarious environmental demands placed on them [32,33]. However, there is usually a lead time before the re-orientation of R&D budgets leads to successful innovations [5,6]. Fourth, managerial incentives are increasingly tied to environmental performance metrics [34,35,36]. This creates a natural incentive for senior executives to direct significant components of firms’ R&D budgets toward environmental innovation [37,38]. However, it typically takes time for R&D investment to yield results concordant with executives’ performance-related targets in the environmental innovation space [5,6].

Following the discussion presented above, our first hypothesis predicts that R&D expenditure in the current period impacts one-period-ahead environmental innovation. In our sensitivity analysis, we explore the impact of alternative lag lengths. Formally stated, our first hypothesis is as follows:

H1: 

One-period-ahead environmental innovation is positively related to a firm’s R&D expenditure in the current period.

2.3. Hypothesis Two (H2): The Moderating Role of Firm Age

The extant literature suggests that firms invest in R&D to both generate new information and improve the firm’s ability to utilize existing information [39]. The effectiveness of both components of R&D is potentially impacted by a firm’s absorptive capacity, defined (p. 625) as a “firm’s ability to identify, assimilate, transform, and apply valuable external knowledge” [40]. Absorptive capacity typically increases with the firm’s age [10,11] and is also a function of a firm’s interaction with its industry peers [7].

Building on these insights, there are several reasons why we expect firm age to have a positive influence on the relationship between environmental innovation and R&D expenditure. First, prior work suggests that, as firms age, they are better able to exploit their R&D absorptive capacity to drive environmental innovation [8,9,24,39]. Second, compared to other innovation activities, environmental innovation is subject to higher levels of risk [41]. As older firms cope better with innovation risks [42], we also expect that, as firms age, they will be more successful in managing the risk–reward tradeoffs associated with R&D investments targeted at environmental innovation. Third, a significant component of corporate environmental innovation is likely to be focused on implementing comparatively minor adjustments to existing products and production technologies to cope with changes attributable to developments in the environmental regulatory regime [41]. Relatedly, Curtis et al. [43] report that contemporary firms are focused on tailoring their R&D activities towards incremental or ‘safer’ innovations instead of more risky new projects. Consequently, we expect that a considerable portion of environmentally driven R&D investment will be tailored towards adapting existing technologies and offerings to ensure compliance with changes in environmental regulations. As firms age, they are likely to become more adept at tailoring their R&D to comply with regulatory demands, bringing knock-on benefits to environmental innovation. Fourth, older firms typically have a broader scope and are more likely to enter into successful research-based partnerships [44]. Building on these insights, we expect that, as they age, firms will become increasingly successful in ensuring that their R&D expenditure leads to successful environmental innovations in the future. This discussion leads to our second hypothesis, as follows:

H2: 

Firm age positively moderates the relationship between one-period-ahead environmental innovation and R&D expenditure in the current period.

2.4. Hypothesis Three (H3): The Moderating Influence of Relative R&D Investment

There are several reasons why firms may inadvertently overinvest in R&D [13] and, by extension, overinvest in R&D focused on environmental innovation. First, we note that the technologies associated with environmental innovation are comparatively new and complex [45]. Consequently, firms’ lack of organizational experience in dealing with these technologies may lead to overly optimistic predictions of future net cash inflows and other long-term benefits [46]. This complexity, in turn, enhances the risk of firms overinvesting in R&D focused on environmental innovation. Second, prior research demonstrates that the reputational benefits associated with corporate social responsibility (CSR) may cause executives to overinvest in CSR-related activities [47,48]. Relatedly, prior work also demonstrates that the personal values of senior executives can influence their approach to managing environmental innovations [49]. Consequently, because of social pressure, executives may encourage excessive investment in environmentally focused R&D. Third, environmental innovation is particularly influenced by regulatory activity [50] emerging from governments and industry regulators. Regulators may also incentivize firms to engage with environmental innovation [51]. Therefore, the requirements to meet the stringent environmental standards set by regulators and the desire to benefit from regulators’ incentives may lead firms to overinvest in those elements of R&D focused on environmental innovation [52].

Given that overinvestment in R&D focused on environmental innovation is a potential risk, the question arises as to how researchers can identify appropriate measures for this phenomenon. Extant research suggests that R&D overinvestment is more appropriately measured on a continuum rather than at a single point [13,53,54]. Building on this insight, one potentially useful way of identifying the R&D overinvestment continuum is to measure the ratio of a firm’s investment in R&D relative to its capital expenditure [55,56]. Capital expenditure (or CAPEX), which represents investment in tangible assets such as buildings, land, machinery, equipment, and the like, and R&D are typically the major components of a firm’s total investment expenditures [57]. Consequently, as investment in R&D relative to CAPEX increases, the likelihood of R&D overinvestment also increases [57,58,59,60]. We present our third hypothesis as follows:

H3: 

Relative R&D investment (i.e., the ratio of R&D to CAPEX) negatively moderates the relationship between one-period-ahead environmental innovation and R&D expenditure in the current period.

3. Materials and Methods

3.1. Estimation Models

To test our first hypothesis, we utilize the following equation (Equation (1)), where i and t subscripts represent firm and time periods, respectively:

FIRM_EIS_i,t+1 = f(FIRM_R&D_i,t, CONTROLS_i,t)

(1)

Our measure of firm-level environmental innovation is the ‘environmental innovation score’ (FIRM_EIS), which we will discuss in detail later. Equation (1) models a firm’s one-period-ahead environmental innovation score (FIRM_EIS_i,t+1) as a function of current-period R&D expenditure (FIRM_R&D_i,t,) and a set of control variables. Hypothesis 1, which predicts that current-period R&D expenditure has a positive and significant impact on future environmental innovation, implies a positive and significant coefficient on the R&D term in Equation (1).

Hypothesis 2 predicts that firm age (FIRM_AGE) positively moderates the relationship between one-period-ahead environmental innovation and current-period R&D expenditure. Our second equation (Equation (2)) extends Equation (1) by introducing the following interaction term:

FIRM_EIS_i,t+1 = f(FIRM_R&D_i,t, [FIRM_R&D_i,t × FIRM_AGE_i,t], FIRM AGE_i,t, CONTROLS_i,t)

(2)

The interaction term in Equation (2) [FIRM_R&D_i,t × FIRM_AGE_i,t] captures the moderating effect of firm age on the relationship between one-period-ahead environmental innovation and current-period R&D. We also include the current-period firm R&D and firm age variables in Equation (2) to measure the impact of the interaction term properly [16]. Hypothesis 2 predicts a positive and significant estimation coefficient on the [FIRM_R&D_i,t × FIRM AGE_i,t] interaction term in Equation (2).

Hypothesis 3 posits that relative R&D investment (i.e., the ratio of R&D to CAPEX) negatively moderates the relationship between one-period-ahead environmental innovation and current-period R&D expenditure. To test Hypothesis 3, we utilize the following equation (Equation (3)):

FIRM_EIS_i,t+1 = f(FIRM_R&D_i,t, [FIRM_R&D_i,t × FIRM_REL_INVEST_i,t], FIRM_REL_INVEST_i,t, CONTROLS_i,t)

(3)

In Equation (3), we extend Equation (1) by introducing a term to capture firm-level R&D investment relative to capital expenditure (FIRM_REL_INVEST). To compute FIRM_REL_INVEST, we divide a firm’s R&D expenditure in a period by its capital expenditure (i.e., CAPEX) in the same period [54,60]. The interaction between R&D expenditure and relative R&D investment is captured by the [FIRM_R&D_i,t × FIRM_REL_INVEST_i,t] term with the individual FIRM_REL_INVEST and FIRM_R&D variables also included to ensure the accurate estimation of interaction effects [61]. Hypothesis 3 predicts that the [FIRM_R&D_i,t × FIRM_REL_INVEST_i,t] interaction term in Equation (3) is negative and statistically significant.

3.2. Variable Measurement

Below, we discuss our approach to measuring the variables included in Equation (1) to Equation (3) above.

FIRM_EIS: The dependent variable in each of our estimation equations is the one-period-ahead (period t + 1) environmental innovation score (EIS). While several alternative measures of environmental innovation are used in the extant literature [62], here we focus on EIS as it is a relatively new—yet highly accurate—non-financial measure of firm-level environmental innovation. EIS, computed annually by LSEG Data & Analytics, specifically reflects a company’s propensity to reduce customers’ environmental costs through innovative technologies and related processes. The EIS is extracted directly from the Refinitiv Eikon (DataStream) database and has been included by Thomson Reuters (www.thomsonreuters.com, accessed on 10 November 2024) as part of their suite of ESG data. The EIS measure “reflects a company’s capacity to reduce the environmental costs and burdens for its customers, thereby creating new market opportunities through new environmental technologies and processes or eco-designed products” [63]. EISs can have a value between 0 and 100, with higher scores indicative of better environmental innovation performance. The EIS measure draws on relevant data from corporate disclosures and other publicly available sources to capture environmental innovation performance across a range of firm-specific indicators such as revenues from eco-innovative consumer offerings. These raw data are then transformed using proprietary techniques which employ the full set of data for all firms. Consequently, the EIS encapsulates a wide range of firm-specific, industry, and country-level data into a composite indicator which, although emerging from a stringently scientific methodology, is inherently limited, as it seeks to reflect multiple metrics for a given year in a single number. Nonetheless, despite this weakness, the EIS offers unique insights and is widely used by the investment community, academic researchers, and other professionals.

FIRM_R&D: We measure the firm R&D variable as the total Research and Development expenditure incurred by a firm during the fiscal year. This variable is converted into its natural log equivalent to control for scale-related heteroscedasticity. We note that, during our sample period, sample firms would have been required to adopt International Accounting Standard 38. Following the adoption of the aforementioned accounting standard, when development expenditure is deemed to be technically feasible and economically viable [64], that development expenditure must be treated as an asset on the firm’s balance sheet (i.e., the expenditure is ‘capitalized’). For firm years where firms capitalize development expenditure [16], we add back the amount capitalized to the R&D expenditure figure to ensure that R&D is measured on an equivalent basis across firms.

FIRM_AGE: We measure firm age as the number of years the company has been in existence [65], measured from the company formation date up until the end of the 2020 fiscal year.

FIRM_REL_INVEST: We measure relative R&D investment by dividing Research and Development in period t (as adjusted for capitalization) by a firm’s capital expenditure (i.e., CAPEX) in period t [59,60] (i.e., FIRM_REL_INVEST = Adjusted R&D Expenditure/CAPEX].

FIRM_SIZE: We measure firm size as the natural log of a firm’s sales revenue for period t [66] (i.e., FIRM_SIZE = ln(Sales Revenue).

FIRM_PERFORMANCE: We measure firm performance using the return on assets metric, which we calculate as a firm’s earnings before interest and tax for a period divided by a firm’s total assets at the end of that period [67] (i.e., FIRM_PERFORMANCE = EBIT/TOTAL ASSETS]. As discussed above, we adjust earnings and total assets for the firm’s capitalization of development expenditure in a given year

FIRM_LEVERAGE: We use a form of the debt to total assets ratio—defined as total liabilities divided by total assets to measure a firm’s leverage [68] (i.e., FIRM_LEVERAGE = TOTAL LIABILITIES/TOTAL ASSETS).

FIRM_LIQUIDITY: We measure a firm’s liquidity [69] as its cash (including short-term marketable securities) divided by its total current liabilities (i.e., FIRM_LIQUIDITY = CASH/TOTAL CURRENT LIABILITIES).

FIRM_MKT_BOOK: Following the prior literature, we measure a firm’s growth opportunities based on its market-to-book ratio, which we define as the firm’s market capitalization at the end of a period divided by the firm’s end-of-period book value of equity [61,70] (i.e., FIRM_MKT_BOOK = MARKET CAPITALIZATION/BOOK VALUE OF EQUITY).

3.3. Estimation Equations

We now present our applied estimation equations. Introducing β₀ and e to denote the regression intercept and error terms, respectively, we first recast Equation (1) in a form suitable for empirical estimation, as follows:

FIRM_EIS_i,t+1 = β₀ + β₁ FIRM_R&D_i,t + β₂ FIRM_AGE_i,t + β₃ FIRM_REL_INV_i,t + β₄ FIRM_SIZE_i,t + β₅ FIRM_PERFORMANCE_i,t + β₆ FIRM_LEVERAGE_i,t + β₇ FIRM_LIQUIDITY_i,t + β₈ FIRM_MKT_BOOK_i,t + e_i,t

(4)

Our first hypothesis, which predicts that R&D has a positive and significant impact on one-period-ahead environmental innovation, implies a positive and significant β₁ coefficient in Equation (4) (i.e., H1 predicts β₁ > 0).

To test our second hypothesis, we include the [FIRM_R&D × FIRM_AGE] interaction term and extend Equation (4) as follows:

FIRM_EIS_i,t+1 = β₀ + β₁ FIRM_R&D_i,t + β₂ FIRM_AGE_i,t + β₃ [FIRM_R&D_i,t × FIRM_AGE_i,t] + β₄ FIRM_REL_INV_i,t + β₅ FIRM_SIZE_i,t + β₆ FIRM_PERFORMANCE_i,t + β₇ FIRM_LEVERAGE_i,t + β₈ FIRM_LIQUIDITY_i,t + β₉ FIRM_MKT_BOOK_i,t + e_i,t

(5)

With reference to Equation (5), our second hypothesis posits that firm age positively moderates the relationship between one-period-ahead environmental innovation and current-period R&D expenditure (i.e., H2 predicts β₃ > 0).

To evaluate our third hypothesis, we include the [FIRM_R&D × FIRM_REL_INV] interaction variable and extend Equation (4) to derive the following estimation equation:

FIRM_EIS_i,t+1 = β₀ + β₁ FIRM_R&D_i,t + β₂ FIRM_AGE_i,t + β₃ FIRM_REL_INV_i,t + β₄ [FIRM_R&D_i,t × FIRM_REL_INV_i,t] + β₅ FIRM_SIZE_i,t + β₆ FIRM_PERFORMANCE_i,t + β₇ FIRM_LEVERAGE_i,t + β₈ FIRM_LIQUIDITY_i,t + β₉ FIRM_MKT_BOOK_i,t + e_i,t

(6)

Hypothesis 3, which predicts that relative R&D negatively moderates the relationship between one-period-ahead environmental innovation and current-period R&D expenditure, implies a negative and significant β₄ coefficient in Equation (6) (i.e., H3 predicts β₄ ˂ 0).

3.4. Sample and Data

Our sample firms are drawn from the EURO STOXX index (www.stoxx.com, accessed on 29 October 2024), which represents the sub-component of firms in the STOXX Europe 600 index from countries that have adopted the Euro as their currency. We focus on the constituents of this pan-European stock market index to assemble a diverse multi-country sample. In addition, as EURO STOXX index constituents are all located in the Eurozone, we remove the impact of currency fluctuation from our empirical analysis. An additional advantage is that the EURO STOXX index constituents are all highly tradeable stocks [71].

We collect data on our empirical measures for the sample firms over a comparatively long time series (from 2003 to 2020) to facilitate a detailed analysis over an extended time period. As the EURO STOXX index includes many financial firms, retailers, and other entities reporting zero R&D expenditure, we exclude those firms from our sample.

It is important to note that our sample exclusion criteria resulted in a focus on research-intensive firms exclusively. Because financial institutions, retailers, and related entities typically report zero expenditure on R&D, such firms are usually excluded from empirical work using R&D expenditure as a core variable. In this sense, our sample selection criteria are in harmony with related studies in the field.

In cases where a zero value is reported for the EIS metric, we exclude that observation from our sample. However, in our sensitivity analysis, we report results when we include observations with an EIS of zero. In our core empirical analysis, when we exclude firms with EISs equal to zero, we run the potential risk of introducing a sample selection bias, as such firms may well have unifying characteristics (in terms of size, industry, or sustainability strategies) which we inadvertently ignore. However, our reported sensitivity analysis, which includes observations with an EIS of zero, reveals that our findings are robust to the inclusion or exclusion of these observations.

Our final sample consists of an unbalanced (due to some missing data points) cross-sectional time-series dataset of 419 observations drawn from 37 firms from 2003 to 2020. There are eight countries represented in our overall sample, with Germany (36%), France (31%), and the Netherlands (9%) accounting for around three-quarters of the sample observations, and comparatively smaller representations from Italy (7%), Finland (7%) Belgium (6%), Spain (3%), and Ireland (1%).

The time-series and cross-sectional properties of our final sample are comparatively large, and our sample includes all firms in the EURO STOXX index which meet our stringent sample selection criteria. Each of the selected firms is a publicly quoted research-intensive enterprise with a long history of investing in R&D. The breadth of our sample firms results in representation from several industries and countries, while our coverage over the eighteen years of our sample period means that we also have significant depth of data observations. The extensive nature of our sample facilitates in-depth time-series cross-sectional analysis and facilitates the application of state-of-the-art panel-based econometric techniques [15]. Consequently, our empirical results will provide reliable estimates for our subsequent hypotheses tests.

While we make every effort to maximize the depth and breadth of our sample, the natural limitations to our sample size mean that we need to carefully limit our claims with regard to the overall representativeness of our research findings. For research-intensive firms in the European context, findings based on our sample data offer a comprehensive, reliable, and robust set of empirical insights. While we cannot claim that our work is representative of non-research-intensive European firms (such as financial institutions and retailers) and non-European firms, we strongly believe that the findings from our empirical work have the capacity to offer globally relevant insights. This is because many of those firms not included in our sample are concerned with achieving their own sustainability objectives and may ultimately consider launching bespoke Research and Development programs tailored towards harnessing the power of environmental innovation within the ambit of their unique circumstances.

4. Results

4.1. Summary Statistics

Our initial data analysis step was to check for potential outliers, and, following this analysis, we winsorized FIRM_REL_INV, FIRM SIZE, FIRM PERFORMANCE, FIRM_LEVERAGE, and FIRM_LIQUIDITY at the 1% and 99% levels. The results that we report later are robust to the use of non-winsorized variables.

The summary statistics for each of the variables used in our study are presented in

Table 1. Summary statistics.

	Variable	N	Mean	Std. dev.	Q1	Q3
1	FIRM_EIS (t + 1)	419	60.81	26.67	36.86	86.53
2	FIRM_R&D	419	12.24	1.93	11.21	13.66
3	FIRM_REL_INV	419	1.09	2.03	0.61	1.24
4	FIRM_AGE	419	30.78	11.33	24.00	35.00
5	FIRM_SIZE	419	16.36	1.48	12.12	18.66
6	FIRM_PERFORMANCE	419	0.07	0.06	0.04	0.11
7	FIRM_LEVERAGE	419	0.60	0.18	0.53	0.71
8	FIRM_LIQUIDITY	419	0.52	0.67	0.19	0.48
9	FIRM_MKT_BOOK	419	2.97	2.88	1.38	3.27

.

From Table 1, we see that the mean (or average) one-year-ahead environmental innovation score (FIRM_EIS (t + 1)) is 60.81, with most scores between Quartile 1 (Q1) of 36.86 and Quartile 3 (Q3) 62.84. The EISs are well distributed, and the high standard deviation (26.67) indicates a robust scoring process. The average age of firms (FIRM_AGE) in our sample during our sample period is 30.78 years, and firms earn, on average, a return on assets (FIRM_PERORMANCE) of around 7% per annum. This level of performance is consistent with other studies [16]. In a similar vein, the leverage (FIRM_LEVERAGE), liquidity (FIRM_LIQUIDITY), and market-to-book measures (FIRM_MKT_BOOK) are similar to those reported in the literature [72]. The FIRM_REL_INV measure, with its mean of 1.09, reveals that, on average, our sample firms invest slightly more in R&D than in CAPEX, but there is wide variation in this metric across our sample firms.

In

Table 2. Correlation matrix.

		1	2	3	4	5	6	7	8
1	FIRM_EIS (t + 1)	1
2	FIRM_R&D	0.38	1.00
3	FIRM_REL_INV	−0.04	0.19	1.00
4	FIRM_AGE	−0.28	−0.04	−0.21	1.00
5	FIRM_SIZE	0.34	0.53	−0.40	0.15	1.00
6	FIRM_PERFORMANCE	−0.12	−0.07	−0.04	−0.13	−0.15	1.00
7	FIRM_LEVERAGE	0.22	0.26	−0.24	−0.04	0.57	−0.24	1.00
8	FIRM_LIQUIDITY	−0.04	−0.15	0.32	−0.08	−0.55	−0.04	−0.63	1.00
9	FIRM_MKT_BOOK	−0.07	0.10	0.33	−0.31	−0.23	0.34	0.10	−0.01

, we show the Pearson correlation coefficients for the variables in our study.

In Table 2, a correlation greater, in absolute terms, than 0.13 is significant at the 1% significance level. Table 2 reveals a strong, statistically positive correlation of 0.38 between current-period R&D (FIRM_R&D) and one-period-ahead environmental innovation scores (FIRM_EIS (t + 1)). However, while the statistics in Table 2 are helpful from a preliminary analysis perspective, we cannot draw any insights concerning our hypothesis tests from correlation statistics [15].

4.2. Estimation Methods

As our data are from a cross-sectional time-series (i.e., panel) dataset, the first step in our analysis is to evaluate the appropriate estimation approach. Using the framework presented in O’Connell [15], we discover significant variation across firms, and further work employing the Hausman test [73,74] reveals that the firm random-effects estimation [16,73] is optimal in the present context. For our regression estimates, we undertake the variance inflation factor (VIF) test to check for potential multicollinearity issues [75]. However, the overall and individual coefficient VIFs are all under 3.59, which is well below the level indicative of potential multicollinearity problems [73]. The structure of our research design is likely to ensure that we avoid endogeneity issues, as our empirical framework utilizes a one-period-ahead dependent variable (i.e., EIS). Our statistical tests using a range of alternative approaches [73,76] confirm that endogeneity is not a concern in the present estimation context.

Our panel estimation has the inherent advantage of controlling for a wide range of omitted variables, and we include dummy variables for each sample year to deal with potential time-related heterogeneity. We also find no evidence of reverse causality when employing standard Granger causality tests [67]. Furthermore, our standard testing methodologies also reveal no omitted variable bias [15]. Future work building on our empirical approach could gainfully utilize instrumental variable (IV) estimation methodologies and dynamic panel data models such as the Generalized Method of Moments (GMM) [73] to extend our empirical insights in a broader set of empirical contexts.

4.3. Results of Hypotheses Tests

Our random-effects regression estimates of Equation (4) to Equation (6), which build on the conceptual models outlined in Equation (1) to Equation (3) above, are presented in

Table 3. Regression results for one-period-ahead environmental innovation scores.

Dependent Variable: Environmental Innovation Score (EIS) in Period t + 1
	(1)	(2)	(3)
FIRM_R&D	5.579	−4.435	6.244
	2.758 ***	−1.022	3.643 ***
FIRM R&D × FIRM AGE		0.304
		2.364 ***
FIRM_R&D × FIRM_REL_INV			−2.592
			−5.234 ***
FIRM_AGE	−0.513	−4.177	−0.612
	−2.003 **	−2.749 ***	−2.556 **
FIRM_REL_INV	0.41	0.719	33.355
	0.313	0.556	5.290 ***
FIRM_SIZE	3.151	4.64	5.187
	1.188	1.816 *	2.077 **
FIRM_PERFORMANCE	28.935	26.618	15.54
	0.941	0.858	0.479
FIRM_LEVERAGE	28.284	24.145	30.049
	1.557	1.413	1.659 *
FIRM_LIQUIDITY	4.454	4.784	3.206
	1.698 *	1.812 *	1.291
FIRM_MKT_BOOK	−1.075	−0.744	−0.892
	−1.341	−0.944	−1.227
N	419	419	419
Year-dummies	Yes	Yes	Yes
Country-dummies	Yes	Yes	Yes
Regression intercept	Yes	Yes	Yes
R-squared	0.431	0.441	0.483
Random Effects Panel Estimation	Yes	Yes	Yes

*** p < 0.01, ** p < 0.05, * p < 0.1.

. All the regression results reported in Table 3 are based on random-effects regression estimates. We also included dummy variables for each year of our study and each country in our sample to capture time-related and country-related sample variation. Our regression estimates also control for heteroscedasticity and allow for firm-level clustering of standard errors [15]. For each of the regression results in Table 3, the dependent variable is the one-period-ahead environmental innovation score (EIS) for each firm-year.

In Column (1) of Table 3, we present our findings for our estimates of Equation (4) earlier. Our first hypothesis—Hypothesis 1—predicts that current-period R&D positively and significantly impacts one-period-ahead environmental innovation scores. In Column (1) of Table 3, the regression estimate for the R&D coefficient is positive and significant at the 1% significance level (β_{FIRM_R&D} = 5.579; t-statistic = 2.758, p < 0.01). This finding offers strong support for our first hypothesis and, more generally, demonstrates that R&D investment is a key driver of future environmental innovation after controlling for a wide range of other firm-specific variables and time period, firm-related, and country-related heterogeneity. To the best of our knowledge, this result is the first to demonstrate a positive and statistically significant relationship between current R&D expenditure and subsequent environmental innovation.

Our second hypothesis—Hypothesis 2—predicts that firm age positively moderates the relationship between one-period-ahead environmental innovation and current-period R&D expenditure. In Column (2) of Table 3, we present the findings when we include the interaction between R&D and firm age (FIRM_R&D × FIRM_AGE) in our set of dependent variables, as per Equation (5) above. The results in Column (2) offer strong support for Hypothesis 2 at the 1% significance level (β_{[FIRM_R&D × FIRM_AGE]} = 0.304; t-statistic = 2.364, p < 0.01). This finding is an important and unique contribution to the literature, as it demonstrates that, as firms age, the relationship between current-period R&D investment and future environmental innovation grows significantly stronger.

Hypothesis 3 predicts that the ratio of R&D expenditure relative to CAPEX negatively moderates the relationship between one-period-ahead environmental innovation and R&D expenditure in the current period. For our tests of Hypothesis 3, we incorporate the interaction between R&D expenditure and our measure of relative R&D investment (i.e., (FIRM_R&D × FIRM_REL_INVEST) in our estimation equation. The results of our tests of Hypothesis 3, which are based on estimates of Equation (6) above, are presented in Column (3) of Table 3. Consistent with Hypothesis 3, the results in Column (3) show that the (FIRM_R&D × FIRM_REL_INVEST) interaction is negative and significant at the 1% significance level (β_{[FIRM_R&D × FIRM_REL_INVEST]} = −2.592; t-statistic = −5.394, p < 0.01). This finding reveals that, as relative investment in R&D increases, the role of R&D in generating environmental innovation outcomes decreases. This result offers a novel contribution to the extant literature as it provides clear empirical evidence of the risk of R&D overinvestment in the environmental innovation space.

4.4. Extensions and Sensitivity Analyses

Below are several sensitivity analyses concerning our core findings. First, our core results are based on the relationship between R&D expenditure and one-period-ahead environmental innovation. Acknowledging the importance of analyzing alternative lag lengths, we now extend our core analysis by exploring the relationship between current-period R&D expenditure and two-periods-ahead environmental innovation scores. These results—which are estimated on the same basis as those for the main findings reported above—are presented in

Table 4. Regression results for two-periods-ahead environmental innovation scores.

Dependent Variable: Environmental Innovation Score (EIS) in Period t + 2
	(1)	(2)	(3)
FIRM_R&D	6.011	−4.224	6.685
	2.830 ***	−0.955	3.603 ***
FIRM R&D × FIRM AGE		0.318
		2.781 ***
FIRM_R&D × FIRM_REL_INV			−2.514
			−4.103 ***
FIRM_AGE	−0.524	−4.33	−0.63
	−1.994 **	−3.253 ***	−2.556 **
FIRM_REL_INV	0.081	0.431	31.875
	0.051	0.275	4.143 ***
FIRM_SIZE	2.032	3.385	4.135
	0.75	1.28	1.604
FIRM_PERFORMANCE	22.25	20.18	8.306
	0.808	0.719	0.291
FIRM_LEVERAGE	33.803	29.14	32.88
	1.628	1.493	1.62
FIRM_LIQUIDITY	3.013	3.074	1.658
	1.028	1.056	0.59
FIRM_MKT_BOOK	−1.378	−1.053	−1.183
	−1.792 *	−1.411	−1.615
N	385	385	385
Year-dummies	Yes	Yes	Yes
Country-dummies	Yes	Yes	Yes
Regression intercept	Yes	Yes	Yes
R-squared	0.435	0.446	0.487
Random Effects Panel Estimation	Yes	Yes	Yes

*** p < 0.01, ** p < 0.05, * p < 0.1.

below.

The results in Column (1) of Table 4 show that current-period R&D expenditure is a positive and significant (at the 1% significance level) determinant of two-periods-ahead environmental innovation (β_{FIRM_R&D} = 6.011; t-statistic = 2.830, p < 0.01). Column (2) of Table 4 reveals that the interaction between current-period R&D expenditure and firm age is both positive and strongly statistically significant when employing two-periods-ahead environmental innovation scores as the alternate dependent variable (β_{[FIRM_R&D × FIRM_AGE]} = 0.318; t-statistic = 2.781, p < 0.01). The findings in Column (3) of Table 4 also show that the term capturing the interaction between current-period R&D expenditure and relative investment in R&D is negative and statistically significant at the 1% level (β_{[FIRM_R&D × FIRM_REL_INVEST]} = −2.514; t-statistic = −4.103, p < 0.01). Clearly, the results using two-periods-ahead environmental innovation (i.e., EIS in period t + 2) as the dependent variable offer continued strong support (at the 1% significance level) for each of our three hypotheses.

Second, we explain above that we excluded EIS firm-year observations equaling zero from the empirical analysis focusing on our hypotheses tests. In

Table 5. Regression results for one-period-ahead environmental innovation scores when the estimation sample includes firm-year EIS observations equal to zero.

Dependent Variable: Environmental Innovation Score (EIS) in Period t + 1
	(1)	(2)	(3)
FIRM_R&D	6.021	−1.329	6.312
	2.982 ***	−0.497	3.284 ***
FIRM R&D × FIRM AGE		0.274
		2.426 ***
FIRM_R&D × FIRM_REL_INV			−1.307
			−2.274 **
FIRM_AGE	0.169	−2.992	0.114
	0.494	−2.188 **	0.36
FIRM_REL_INV	0.129	0.28	16.703
	0.107	0.232	2.116 **
FIRM_SIZE	2.09	1.69	3.954
	0.768	0.635	1.438
FIRM_PERFORMANCE	10.004	12.696	4.829
	0.339	0.429	0.165
FIRM_LEVERAGE	35.995	39.439	34.701
	2.095 **	2.410 **	2.038 **
FIRM_LIQUIDITY	2.239	1.998	1.578
	0.937	0.852	0.68
FIRM_MKT_BOOK	0.492	0.519	0.579
	0.82	0.886	1.053
N	573	573	573
Year-dummies	Yes	Yes	Yes
Country-dummies	Yes	Yes	Yes
Regression intercept	Yes	Yes	Yes
R-squared	0.505	0.484	0.528
Random Effects Panel Estimation	Yes	Yes	Yes

*** p < 0.01, ** p < 0.05.

, we show the results when we include these observations.

These regressions, which use one-period-ahead EISs as the dependent variable, are based on a sample size of 573 firm-year observations. The findings in Table 5 offer continued strong support for our three hypotheses tests, as the FIRM_R&D variable in Column (1) and the (FIRM_R&D × FIRM_AGE) interaction in Column (2) are positive and significant at the 1% level, while the (FIRM_R&D × FIRM_REL_INVEST) interaction in Column (3) is negative and statistically significant at the 5% level.

Third, in unreported findings, we continue to find strong support for each of our hypotheses using the Driscoll–Kraay estimation methodology to control for first-order autocorrelation and heteroscedasticity simultaneously [77].

Fourth, in additional unreported findings, we find no significant difference in our core results across the three major country-level groupings in our sample (Germany, France, and a single group comprising the Netherlands, Italy, Belgium, Spain, Finland, and Ireland).

Fifth, we also found that, when we include both interaction terms in the same regression, our hypotheses continue to find statistically significant support using the one-period-ahead and two-periods-ahead environmental innovation scores as alternate dependent variables.

5. Discussion

We present a summary and overview of the key contributions of our work in Section 5.1 below. When discussing each of the three contributions of this study, we highlight the key extant empirical studies exploring issues closely linked to each of our contributions. We then outline the main policy (Section 5.2) implications of our study while also considering the primary limitations of our article (Section 5.3).

5.1. Key Contributions

The first contribution of our study is that, to the best of our knowledge, it is the only work which demonstrates that a firm’s expenditure on R&D in the current period has a strong positive and statistically significant impact on both one-period-ahead and two-periods-ahead environmental innovation. This is an important result, as it demonstrates that, in an era dominated by a global emphasis on sustainability [3,5], R&D can play a key role in driving successful environmental innovation. Our work is unique within the extant literature, as our article is, to the best of our knowledge, the first to focus on the ways in which R&D investment in the current period impacts subsequent (i.e., future) environmental innovation. This finding highlights the need for both internal and external stakeholders to place a renewed emphasis on the power and potential of their organizations’ R&D investments [4,6,18] to ensure future success in the environmental innovation space. In an era increasingly focused on global sustainability, this finding highlights the fact that R&D investments are not merely operational expenses, but are pivotal strategic investments that can cultivate future success in the crucial domain of environmental innovation. This calls for stakeholders to recognize and harness the power of organizational R&D to meet future environmental challenges and objectives.

Second, our results also highlight that, while R&D is a powerful driver of future environmental innovation, that impact is significantly magnified as firms accumulate more experience [9]. Our findings are consistent with a setting where, as their experience expands over time, firms become more adept at generating more successful environmental innovation-related outcomes. While this finding is concordant with predictions from the absorptive capacity—and the related literature [9,10,11]—our work is the only one of which we are aware that demonstrates the importance of firm experience in enhancing the positive impact of current-period R&D in driving future environmental innovation. Although prior work has discussed absorptive capacity generally, our study is unique in demonstrating the empirical significance of firm experience in enhancing the efficacy of R&D for future environmental innovation outcomes. This highlights that accumulated organizational learning is a key factor in maximizing the environmental innovation potential of R&D.

Third, given the theoretical ramifications of potential R&D overinvestment [13], a unique contribution of our work is that we explicitly estimate the significant negative moderating influence of higher relative R&D expenditures (i.e., R&D expenditure relative to CAPEX) on the relationship between R&D and future environmental innovation. Our results provide the first empirical evidence of which we are aware of what is ultimately an intuitively appealing result: namely, that C-level executives [78] need to ensure that they are fully aware of the potential risk of R&D overinvestment when allocating scarce investment funds to R&D projects focused on environmental innovation. Overall, this finding supports the theoretical premise of R&D overinvestment and suggests that, while R&D is beneficial, an excessive focus on R&D at the expense of other productive investments, or beyond a certain optimal threshold, can diminish its marginal impact on environmental innovation. As such, our work offers a crucial, actionable insight for C-level executives: a balanced investment strategy is paramount, and careful consideration must be given to the potential for diminishing returns when allocating scarce resources to R&D projects targeting environmental innovation.

5.2. Policy Implications

Our findings offer several policy-relevant insights. For politicians and regulators concerned with maximizing the sustainability impact of the European Green Deal, our work emphasizes the crucial impact of investment in R&D in attaining success across a range of desirable outcomes in the environmental innovation continuum. Our results highlight the crucial importance of firm age and experience in driving the success of R&D investment in generating environmental innovation highlight the need for policymakers to develop strategies designed to help younger firms enhance their own unique learning capabilities. One route may be to encourage knowledge-sharing platforms across sustainability-related innovations, while R&D collaboration schemes may also yield potential long-term benefits in terms of building absorptive capacity. Our results with respect to R&D overinvestment highlight the need for firms, politicians, and regulators to avoid the risks of excessive focus on R&D spending without adequate consideration of the potential risk of diminishing returns from such expenditures. As discussed earlier, we employ a state-of-the-art econometric analysis based on a multi-country panel dataset [14,15] to test our core hypotheses. Consequently, by focusing on a time-series cross-sectional sample of European firms and by finding strong econometric support for the three hypotheses discussed above, we provide important policy insights to governments and regulators concerned about the role of firm-led R&D in meeting the multifarious objectives of the European Green Deal [79].

We also highlight the importance of open innovation policies [80] in debates surrounding environmental innovation. The growth of open innovation is one of the most critical developments in the broader realm of corporate-led R&D innovation [21,22,23]. De Marchi [20] has shown that inter-firm R&D cooperation can lead to mutually beneficial outcomes concerning innovation. The literature has also addressed how open innovation strategies [24] lead to more efficient use of R&D resources in generating environmental innovation outcomes. Our finding that firm age positively moderates the influence of R&D on future environmental innovation suggests that, as they age, firms are better able to extract benefits from their R&D investments. One potential reason for this finding is that firms develop a wider nexus of inter-firm R&D partnerships and collaborations [81,82], including open innovation collaborations [83] as they age. Consequently, our findings highlight the need for younger firms [84] to develop open innovation R&D strategies to ensure that younger firms can compete with their older counterparts in environmental innovation. Relatedly, our findings highlighting the negative impact of R&D overinvestment suggest that firms aiming to succeed in the environmental innovation space should consider the potential advantages of fostering open-innovation R&D strategies as a means of reducing the risk to individual firms of excessive relative R&D investment [83].

Finally, while financial reporting [85] may often be seen as a peripheral ‘afterthought’ in debates about the potential roles of R&D in driving environmental innovation, the reality is that C-suite executives are likely to be highly cognizant of the myriads of implications of their expenditure on R&D from the perspective of impacting both short-term and long-term profitability [86]. Our work also highlights the urgent need for policies which encourage the financial reporting community to engage with professionals concerned with environmental innovation-led R&D research at the firm, inter-firm, and open-innovation levels. We reveal that our sample firms spend more on R&D than on CAPEX on average. Yet, while most CAPEX is treated as an asset in a firm’s balance sheet (i.e., statement of financial position), most R&D expenditure is treated as an expense in a firm’s income statements. While financial reporting regulators have made genuine efforts to improve financial reporting for intangibles, the tendency of the profession to continue to treat almost all R&D as an expense has important ramifications for how firms are governed, evaluated, and financed [16,61]. Given the clear relationship between R&D and future environmental innovation documented in our work, as well as the increasing importance of environmental innovation to society, our article places renewed emphasis on the question of whether the contemporary financial reporting model for R&D is fit for purpose in the twenty-first century [87,88].

5.3. Limitations

Notwithstanding the contributions of our work highlighted above, we also wish to consider the major weaknesses of our study. First, our work relies on the quality of the environmental innovation scores reported by Refinitiv Eikon (Datastream). However, prior work [62,89] has gainfully utilized this measure in empirical research. Second, as we cannot directly identify the component of a firm’s Research and Development investment focused specifically on objectives such as environmental innovation, we infer a relationship between R&D expenditure and future environmental innovation from our results. This type of inference is common in business research employing firm-level data [90]. This issue also highlights another weakness of conventional financial reporting systems in environmental innovation settings. While firms typically provide detailed breakdowns of their selling, general, and administrative expenses and their capital expenditures, equivalent details about the major components of R&D expenditure are rarely available from conventional financial reports. Third, although our sample comprises data from eight countries over a comparatively long time period, in common with most empirical studies of this type, there is always a risk of generalizing findings beyond the specific sample used [70].

6. Conclusions

In this article, utilizing a sample of firms from eight European countries over the 2003–2020 period, we show that R&D expenditure in the current period is a significant driver of future environmental innovation using both one-period-ahead and two-periods-ahead environmental innovation scores as alternate dependent variables. This finding is unique in the literature, in that it is the first of which we are aware which demonstrates a positive and statistically significant relationship between current-period R&D expenditure and subsequent environmental innovation. Drawing on prior theoretical work, we further hypothesize and show that firm age positively and significantly influences the relationship between current R&D expenditure and future environmental innovation. This finding is an important contribution to the literature, as it highlights that, while R&D is a powerful driver of future environmental innovation, its impact is significantly magnified as firms accumulate more experience. Finally, building on the extant literature on R&D overinvestment, we also predict and show that relative R&D investment (i.e., the ratio of R&D expenditure to capital expenditure) negatively moderates the relationship between current-period R&D and future environmental innovation. This result is impactful, as it provides clear empirical evidence of the risk of R&D overinvestment in the environmental innovation space.

Overall, our work informs academic debates surrounding the R&D–innovation nexus and has immediate and practical relevance for C-level executives, regulators, governments, and professionals in activity realms and policies focused on sustainability, environmental innovation, R&D, open innovation, and financial reporting.