Examining the relationship between green innovation dynamics and firm performance: a comparative study of BRICS and G7 countries

Abstract

Green innovation entails the production assimilation or exploitation of unique methods of doing business that result in a decrease in environmental risks. Green innovation is a complex and multifaceted process, encompassing product, process, and organizational dimensions, each with unique factors influencing firm performance, business efficiency, and sustainable development. This research investigates the impact of eco-innovation on the financial performance of firms across 12 G7 and BRICS countries from 2013 to 2022. Considering 2100 listed firms, the study explores how process, product, and organizational eco-innovation dimensions affect business performance. Utilizing Feasible Generalized Least Square (FGLS) regression and System Generalized Method of Moments (GMM) models, the results indicate a positive impact of environmental innovation on firm performance. Further, in BRICS countries, process, and organizational eco-innovations have greater impacts, while in G7 countries, product eco-innovation gets precedence. The varying impacts reflect the unequal stages of development and priorities between the two groups. It also signifies that G7 countries possess more resources for green innovation, while BRICS countries are still evolving. The findings therefore underscore the importance of tailored regulations, monitoring, and support to facilitate the integration of eco-innovations into business processes. Moreover, there is a need for firms to adopt and execute green innovation-based economic activities that would make them financially efficient, and strengthen the growth imperatives of both the groups of countries. Collaboration between developed and emerging economies would go a long way fostering common goals across them.

Keywords:

• Firm performance
• green innovation
• organizational eco-innovation
• product eco-innovation
• process eco-innovation
• BRICS
• G7

Reviewing Editor:

• Jeffrey Muldoon, Emporia State University, United States

Subjects:

• Economics
• Environmental Economics
• Business, Management, and Accounting
• Finance

1. Introduction

Of late, academics and corporate professionals widely agree on the need for considerable alterations to how businesses establish relationships with the natural and social environments that are integral to their operations and products (Cai & Li, 2018; Franceschini & Pansera, 2015; Lee & Min, 2015; López & Montalvo, 2015; Segarra-Oña et al., 2015; Vivanco et al., 2015). Amidst global ecological crises caused by pollution, environmental degradation, and resource scarcity, firms have started exploring diverse strategies to gain a competitive advantage and enhance performance (Cai & Li, 2018) while ensuring environmental sustainability. Amongst all, green innovation stands apart as a way forward for many firms across countries.

Green innovation, often called environmental or eco-innovation, entails the production, assimilation, or exploitation of unique methods of doing business that result in a decrease in environmental risks (Kemp & Pearson, 2007). It is a firm’s considered response to external demands, which differs from the Schumpeterian notion of innovation that originates from within a firm and enters the marketplace to exploit or discover new markets (Bocken et al., 2014; Santos et al., 2017). Green innovation involves the potential disruption of an already existing production model, contributing to reducing adverse environmental and social impacts (Kuo & Smith, 2018). Through its life cycle, environmental risks associated with resource use are also minimized (Kemp & Pearson, 2007). From a sustainability perspective, green innovation can be construed as a strategy for firms to sustain their long-term survival while enhancing their performance. It leads to the reconfiguration of resources to develop green capabilities of the firms to realize competitive advantages.

Green innovation encompasses diverse forms (OECD, 2005b), including product, process, organizational, and marketing. Evidently, different organizations seem to have adopted different eco-innovation programs with a common goal of becoming environmentally more responsible. Some such programs are service innovation, policy innovation, product service innovation, etc. (Santos et al., 2017).

For green innovation to proliferate, it is imperative that the firms espousing it stay financially sustainable and improve their performance. However, the impacts of green innovation are decidedly mixed. There is inconclusive evidence concerning the relationship between green innovation and firm performance across countries. It may be attributed, inter alia to varying regulatory and institutional frameworks across regions or industries, with numerous countries experiencing different stages of development (Nishitani et al., 2017; Santos et al., 2017). While there is evidence of eco-innovation improving firm performance (Atalay et al., 2013), many firms still have a fear of experiencing the possibility of market failure (Chiesa & Frattini, 2011), an imminent increase in production cost, and a decline in price-cost margins (Katsikeas et al., 2016). In emerging markets, though, there is no denying that increasing environmental risks tend to generate market opportunities for products and services, the low purchasing power amongst consumers and poor knowledge about eco-products limit the scope for their proliferation as people are extremely sensitive to high prices (Biswas & Roy, 2015).

Interestingly, the efforts to go green and the impacts thereof are not uniform across developed and developing countries. To be specific, the G7 countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the USA) pioneer the advocacy for net-zero emissions. They have shown their commitment to diversifying their energy portfolios to include green energy sources in their conventional energy basket (Murshed, 2020). Still, their dependence on non-renewable energy sources continues to remain high. The newly industrializing countries, on the other hand, are reportedly responsible for one-third of environmental technology innovations in recent years (Walz et al., 2017). The BRICS (Brazil, Russia, India, China, and South Africa) economies, in particular, are gradually moving toward green growth with a growing share of renewable energy sources in their energy mix (Zhao et al., 2021). However, the key to the sustenance of such efforts lies in cross-country collaborations. Of late, the G7 countries have started extending their support for green innovations in developing countries (Herman & Xiang, 2022).

Against this backdrop, this study examines the impact of green innovation on firm performance across G7 (developed) and BRICS (emerging economies) nations. Additionally, it empirically investigates separately the impact of three major dimensions of eco-innovation, namely process, product, and organizational, on firms’ financial performances in these countries. We consider the firms operating in these countries, covering a span of ten years from 2013 to 2022. The basic objective of this research is to explore if the impact of green innovation varies according to the country groups and whether different forms of eco-innovation have differing roles in different countries. As the chosen groups of countries experience varying stages of development, their efforts toward green innovation are not necessarily uniform, and so could be their outcomes. Although empirical studies exist on green innovation and firm performance (Aldieri et al., 2020; Gonenc & Scholtens, 2017; Song et al., 2017), to the best of our knowledge, studies focusing on the roles of product, process, and organizational innovation in the context of both developed and emerging countries are limited. Moreover, there are also conflicting findings. This empirical study, while offering an insight into the dynamics of the G7 and BRICS countries, thus tries to fill this gap.

The study carries significance from the following standpoints. First, the emerging economies, in general, and the BRICS countries in particular, hold strategic significance due to their crucial roles in the global production chain, thanks to their economic and business predominance. They have become major contributors to global growth and, consequently, key players in international trade and investment. Their ever-expanding consumer base, natural resources, and growing industries make them attractive business destinations (Farooq et al., 2024; Gyedu et al., 2021; Stiglingh, 2015). Consequently, understanding the dynamics of eco-innovation and its concomitant impact on business performance in these economies carries significance, especially for those seeking to navigate these evolving global business landscapes. Second, the developed and industrialized countries, and specifically, the G7 countries, have become frontrunners in innovations and shown a strong desire for eco-innovation (Gyedu et al., 2021). However, mere domestic eco-innovation is not enough. There is a need for technology transfer from developed countries to the rest, which entails cross-country collaborations on green growth and innovations in environmental technology (Herman & Xiang, 2022). In the absence of such collaborations, the impact of green innovation on firm performance may vary between developed and developing countries. Finding differential impacts on firm performance between G7 and BRICS would help one understand such dynamics better.

This firm-level study provides a more granular and detailed examination of the shared relationship between eco-innovation and firm performance. It allows for a closer examination of specific strategies, practices, and innovations implemented at the firm level and their direct impacts on performance outcomes. This empirical research can help refine existing frameworks regarding underlying mechanisms of eco-innovation and firm performance linkage (Cai & Zhou, 2014; Mondejar-Jim Enez et al., 2015). It also creates the scope for a more targeted and actionable approach to identifying the mechanisms that link green innovation to positive business outcomes.

While previous studies have largely focused on country-level surveys to explore the association between eco-innovation and firm performance, this study employs a formative construct to measure eco-product, eco-process, and eco-organization and analyzes their influence on firm performance across different countries. Employing the feasible generalized least squares (FGLS) model, the findings ascertain the positive impact of green innovation on firm performance across all countries. However, the impact seems to vary between G7 and BRICS nations according to the dimensions. The findings contribute to the existing empirical research by ascertaining the significance of the stage of development of a country upon the strength of the association between eco-innovation and its impact on firm performance. Besides, the importance of the firm-specific characteristics as control, viz., age, size, social pillar score, and governance pillar score, is also highlighted.

Accordingly, the remainder of the paper is organized as follows: In section 2, we review the extant literature highlighting the theoretical and empirical underpinning. In section 3, we present a conceptual framework supporting the basis of this study. Consequently, the methods of data analysis are outlined, highlighting sample country selection, variable identification, model specification, etc. An integrated model is employed to assess simultaneously the effects of eco-innovation at product, process, and organizational levels on the financial performance of firms. In the penultimate section, the empirical results are presented and discussed. The final section concludes the study with necessary implications.

2. Literature review

2.1. Theoretical literature

Green innovation has gained prominence since the time of the third Industrial Revolution of the 1970s, with a focus on innovation and sustainability. The ‘Limits to Growth’ as propounded by the ‘Club of Rome’ and ‘Our Common Future’ by the UN have reaffirmed the need for sustainability while vouching for the effective utilization of limited resources for economic progress and well-being of future generations (Brundtland, 1987). Over time, the notion of green innovation has evolved, with a shift in its focus from mere enhanced economic growth to an improved quality of life for the present and future generations (Levidow et al., 2016).

The notion of eco-innovation involves innovation with a thrust on sustainability (Rennings, 2000). It is characterized by the creation of something new directed toward reducing adverse environmental impacts. There is also a predisposition to influence social attitudes and cultural and institutional norms (de Oliveira Brasil et al., 2016; OECD, 2005a). Eco-innovation is said to reduce the use of natural resources and decrease the discharge of harmful substances across the life cycle. From this standpoint, two pertinent points emerge. First, all the novel processes that involve efficient resource use can be called eco-innovation. Second, eco-innovation manifests itself as a critical assessment of environmental impacts and risks. It is also a major candidate for innovation policies that address societal challenges of climate change, resource efficiency, and energy/resource scarcity (Kemp & Oltra, 2011). The development of environmental technologies, viz. pollution control technologies, cleaner process technologies, waste management equipment, etc., organizational innovations for the environment, viz. environmental management and auditing systems, chain management, etc., product and service innovation offering environmental benefits and green system innovation are some of the traits of eco-innovation (Kemp & Pearson, 2007).

The significance of green innovation has already been increasingly acknowledged in a growing body of literature. To operate in an ecologically friendly manner, all the business houses need to embrace green innovation practices at the organizational, process, and product levels (Hojnik & Ruzzier, 2016). A consensus on this has been arrived at in at least 11 out of the 17 United Nations (UN) sustainable development goals (SDGs) (Moratis & Melissen, 2019). The UN has already taken initiatives to endorse and promote environmental-friendly innovations through the 2030 plan for SDGs.

Green innovation is usually understood as a firm’s response to external demands, and it may be in contrast to innovation as understood by Schumpeter. He contends that innovation primarily originates from within a firm and gradually explores new markets (Bocken et al., 2014; Santos et al., 2017). As Kemp and Pearson (2007) argue, green innovation can be attained when a business uses a product or resource in a manner that reduces its contributions to pollutants, thereby reducing environmental risk. The notion of green innovation seems to have evolved to demonstrate that innovation may be not only sustainable but also ecological (Hellström, 2007; Levidow et al., 2016).

From the perspective of the firms, eco-innovation may have mixed impacts on their performance. As the neoclassical theory suggests, green innovation, if tied to environmental regulations, may cause an increase in cost (Palmer et al., 1995; Walley & Whitehead, 1994). However, there are positive effects as well that may offset such impacts (Porter & van der Linde, 1995). According to the natural resource-based view, there are many competitive advantages a pro-active environmental behavior can generate, yielding positive economic returns. As it ascertains, a positive financial effect can result from the development of distinct bundles of resources and capabilities within the firm (Hart & Dowell, 2011). A firm could find it profitable to invest in eco-innovation if it can efficiently associate its internal skills (e.g. physical, financial, labor, and organizational resources) with environmental objectives. These efforts could mitigate future regulatory costs and improve the effectiveness of the production processes (Kemp & Pearson, 2007).

Further, it may be important to state that familiarity with the eco-innovation types that complement one another is critical to achieving higher productivity and greater competitiveness. At the same time, as the theory of socio-technical systems argues, the execution of innovations must include social issues and management systems so that corporate performance is optimized (de Oliveira Brasil et al., 2016). The association between eco-innovation and performance signifies not just a mere reduction in environmental risks but in cost reduction, an increase in sales with product differentiation, and a rise in profit margins, brand value, and firm reputation (Klewitz et al., 2012).

There is a need for a robust policy directed toward promoting green innovation. The need for such a policy emanates from two important problems: market failure and system failure. The former is a neoclassical approach, which suggests that eco-innovation has to address the public good nature of knowledge leading to an appropriability problem. There are also uncertainties about the benefits and costs of eco-innovation. Further, the external costs of environmentally unfriendly alternatives are usually not internalized. Rather, they are transferred to the society and environment (Kemp & Oltra, 2011). System failure involves problems, such as infrastructure constraints and technological rigidities, knowledge and institutional barriers, and competition with old products, which hinder innovation. There is a need for proper policy interventions to address these twin problems (Nelson, 2009).

2.2. Empirical literature

To add to the debate over the relationship between green innovation and firm performance, several empirical studies have been carried out in different countries and contexts (Bocken et al., 2014; Lin et al., 2014). Recently, there has been an emphasis on eco-innovation among the firms of the G7 countries, which has significantly contributed to enhancing their performance. These countries have made substantial investments in research and development, trademarks, and patents, demonstrating their commitment to innovation (Sesay et al., 2018; Stiglingh, 2015). They are also recognized for their knowledge production, driven by historical developments and high growth rates (Inglesi-Lotz & Pouris, 2013).

A firm’s financial performance is considered a key indicator when one examines its green innovation efforts. Investors analyze a firm’s current and future financial performance to make investment decisions. Existing studies have focused on evaluating a firm’s financial performance, particularly its bottom-line results (Bitencourt et al., 2020; Hojnik & Ruzzier, 2016; Przychodzen & Przychodzen, 2013). These studies have primarily focused on accounting-based metrics, such as Return on Assets (ROA) and Return on Equity (ROE), and found that investing in green innovation leads to improved ROA and ROE. The proactive approach to spending is construed as an important prerequisite as companies need to be constructive rather than reactive to benefit from green innovation (Przychodzen & Przychodzen, 2013).

The empirical findings on the impact of green innovation on firm performance are, however, distinctly mixed. While some of the studies have established a positive relationship (Aldieri et al., 2020; Song et al., 2017; Wagner, 2010), some others have observed a negative relationship (Gonenc & Scholtens, 2017; Rassier & Earnhart, 2010). Many existing studies have yielded mixed findings (Cheng et al., 2014) or indicated only specific categories of green innovation having effects on financial performance (de Oliveira Brasil et al., 2016). There is also evidence of an inverted U-shaped relationship between green innovation and firm performance (Misani & Pogutz, 2015; Wagner, 2010). A positive linkage between green innovation and firms’ financial performance is established in Poland and Hungary. Following the accounting-based metrics, it is proven that investing in green innovation leads to improved ROE and ROA. A recent report based on 153 surveyed companies ascertains an increase in sales and a reduction in manufacturing costs (Pure Strategies Report, 2017).

The studies establishing a positive relationship provide a broad range of explanations, including the increased demand for ‘green firm products’ due to product differentiation and market expansion (García-Granero et al., 2018), cost reduction due to pollution-reducing measures or increasing efficiency in resource use (Dangelico et al., 2017), and increased competitiveness attributable to an improved corporate image (Testa et al., 2011). Dangelico et al., 2017). A positive relationship can also emanate if environmental innovation is tied to robust regulations (Tsireme et al., 2012). Arguably, strong financial resources are critical to fostering green innovation, and a hands-on approach to monitoring spending patterns helps firms benefit from green innovation (Przychodzen & Przychodzen, 2013).

Those studies establishing a negative relationship between green innovation and firm performance attribute it inter alia to the absence of necessary information (Berchicci & King, 2007) and difficulties in comprehending the cost-saving potential of green innovation (Horbach, 2008). The possibilities of a non-linear relationship may signify that initially, environmental measures become expensive, but after a threshold, the costs are compensated (Barnett & Salomon, 2012).

It may be significant to state that the firm performance could vary according to the types of eco-innovation. Both process and product environmental innovations have markedly different positive effects on a firm’s environmental performance (Cai & Li, 2018). However, there is inadequate evidence regarding the impact of organizational innovation (Chiou et al., 2011). The contrasting findings according to countries and contexts, and inadequate focus on various dimensions of eco-innovation, warrant the need for examining the effects of eco-innovation in all its stages - from product development to the process and beyond.

Nonetheless, emerging economies reportedly face several challenges to sustainable economic growth. They need to find a balance between the current utilization of natural resources and preserving them for future generations. Simultaneously, they must prioritize the well-being of their citizens alongside economic development. By adopting sustainable practices and incorporating eco-innovation into their growth strategies, these countries can work toward achieving a harmonious integration of economic growth, environmental sustainability, and societal well-being (Davidescu et al., 2015; Jabbour et al., 2015; Przychodzen & Przychodzen, 2015).

Compared to emerging economies, advanced economies often exhibit significant institutional differences. The varying institutional contexts of the latter concerning regulatory framework, governance structure, and market dynamics shape the priorities of their businesses and governments. Addressing these differences could be critical while designing effective strategies concerning their specific needs and opportunities (Hojnik & Ruzzier, 2016; Kijek, 2015; Lee & Min, 2015).

Given a lack of empirical consensus and limited studies, there is perhaps a need for further investigation concerning eco-innovation in both developed and emerging economies. This would help broaden our understanding of the role of eco-innovation in different country contexts (Díaz-García et al., 2015). To the best of our knowledge, studies examining the impact of green innovation on firm performance in emerging economies, especially BRICS, and a comparison between the advanced and emerging countries are seldom found. There are also limited cross-country analyses, focusing specifically on green innovation at the firm level. It is important to recognize that eco-innovation is context- and country-driven. Hence, understanding the context while delving into specific countries can provide valuable insights into the challenges, opportunities, and dynamics associated with eco-innovation in different country settings (Kemp & Oltra, 2011). The comparisons whatsoever made so far have predominantly focused on aggregate indicators rather than examining individual dimensions and firms (Davidescu et al., 2015).

3. Data and methodology

3.1. Data

The present study is based on secondary data collected from two major sources, namely Refinitiv database (Thomson Reuters) and Bloomberg database. Our analysis considers the publicly listed firms of G7 and BRICS countries. The sample comprises 2100 firms across twelve countries covering a period of ten years from 2013 to 2022. The original dataset had 4000 listed firms from which 2100 firms having a market capitalization of above US$3 billion were chosen for the present purpose. Details of variables, their sources, and their operationalization are furnished in Table 1.

Table 1. Operationalization of the variables.

Dependent variable	Operationalization	Data source	References
TQ—Tobin’s Q (FPE)	Equity market value + Liabilities market value/Equity book value + Liabilities book value	Bloomberg database	McNamara & Bromiley, 1999; Rahman et al., 2022
Independent variable
Product eco-innovation (PDEI) Index formed using PCA of three indicators.	Environmental products/services/activities	Thomson Reuters (Refinitiv) database	García-Granero et al., 2020; Johl & Toha, 2021
	Environmental design of the product	Thomson Reuters (Refinitiv) database
	Clean energy product	Thomson Reuters (Refinitiv) database
Process eco-innovation (PCEI) Index formed using PCA	Reduce the use of energy (Resource reduction score)	Thomson Reuters (Refinitiv) database	García-Granero et al., 2020; Johl & Toha, 2021
	Keep energy use to a minimum (target energy efficiency score)	Thomson Reuters (Refinitiv) database
	Policy energy efficiency sore	Thomson Reuters (Refinitiv) database
	Renewable energy (Resource Use score)	Thomson Reuters (Refinitiv) database
Organizational eco-innovation (OGEI) Index formed using PCA	EMS or ISO 14000 certified	Thomson Reuters (Refinitiv) database	García-Granero et al., 2020; Johl & Toha, 2021
	Environmental management training	Thomson Reuters (Refinitiv) database
	Equator Principle	Thomson Reuters (Refinitiv) database
Overall eco-innovation (OVEI)	TRESGENPIS or Environmental Innovation Score	Thomson Reuters (Refinitiv) database	Thomson Reuters (Refinitiv) database
Control variable
Social Pillar Score (SPS)	Social Pillar Score data from the ESG statistics	Thomson Reuters (Refinitiv) database	Grund & Westergaard-Nielsen, 2008; Ergun et al., 2022
Governance Pillar Score (GPS)	Governance Pillar Score data from the ESG statistics.	Thomson Reuters (Refinitiv) database
Firm size (SIZE)	Natural log of each company’s total assets	Bloomberg database
Firm age (AGE)	Years of firms’ existence (current year–year of incorporation)	Bloomberg database

PCA: principle component analysis was used to construct different indices.

3.2. Conceptual framework

There are two theoretical dispositions that offer insights into the interconnection between a firm’s resources and its competitive advantage. They are the resource-based view and the new resource-based view. The former emphasizes internal resources and capabilities, highlighting their roles in gaining a long-term comparative edge over competitors, while the latter extends this perspective by recognizing the importance of rare, inimitable, and non-substitutable strategic resources for sustained competitive advantage. In essence, both frameworks provide a theoretical foundation for understanding the relationship across resources, environmental innovation, and firm performance (García-Granero et al., 2020; Munodawafa & Johl, 2019). They are instrumental in comprehending the role of resources in driving innovation, analyzing green capabilities, and creating competitive advantages. By directing managerial attention toward internal resources, both approaches aim to identify a firm’s strengths, skills, and competencies that can give it a comparative edge.

Accordingly, three main dimensions of green innovation, namely product, process, and organization, emanate. These dimensions cover both internal and external eco-innovations. Product eco-innovation (PDEI) focuses on designing and developing goods and services that minimize their overall environmental impact. It puts a thrust on eco-design, introducing novel or substantially enhanced products. Process eco-innovation (PCEI) involves the implementation of new or improved production and delivery methods in terms of techniques, equipment, and software to minimize adverse environmental impacts. It aims to decrease material use, lower risks, and achieve cost savings. Compared to the above, organizational eco-innovation (OGEI) involves investments in eco-friendly equipment and embracing innovative production technologies. It requires firms to develop specific resources and capabilities to implement technological eco-innovations successfully. Overall, firms need to invest in green innovation across these dimensions to address environmental challenges and make effective progress toward sustainability (Cai & Li, 2018; Cai & Zhou, 2014; García-Granero et al., 2020).

3.3. Variable description

The present study aims to examine how green innovation affects firms’ financial performance across G7 and BRICS nations. While delving into that, we examine the impact of overall green innovation as well as its individual dimensions on the financial performance of the firms (Figure 1). While analyzing the relationship, we consider other relevant firm-level variables as control. A brief discussion regarding the dependent and independent variables is made as follows:

Figure 1. A conceptual framework depicting the relationship between environmental innovation and firm performance.

Source: By author.

3.3.1. Firm performance

The firm performance is measured by Tobin’s Q. Tobin’s Q provides insights into how the market perceives a firm’s green innovation efforts and assigns values to them (Atan et al., 2018; Saygili et al., 2022). While ROA is relevant in considering the costs associated with environmental innovation, Tobin’s Q can reflect the subsequent market responses, if any. While some researchers consider ROA more reliable, we consider Tobin’s Q as a performance indicator for the present purpose as it is a measure to capture market trends and the value attributed to firms. The data for the estimation of Tobin’s Q is collected from the Bloomberg database. Tobin’s Q can be estimated as $$\mathit{\text{Tobi}}{n}^{\prime }s\hspace{0.17em}Q=\frac{\mathit{\text{Equity Mark}}\text{e}t\hspace{0.17em}\mathit{\text{Value}}+\mathit{\text{Liabilities Market}}\hspace{0.17em}\mathit{\text{Value}}}{\mathit{\text{Equity Book}}\hspace{0.17em}\mathit{\text{Value}}+\mathit{\text{Liabilities Book}}\hspace{0.17em}\mathit{\text{Value}}}$$

3.3.2. Green innovation

Green innovation relates to green practices and policies the firms implement. In the present study, we formulate an index of green innovation (García-Granero et al., 2020). The purpose is to check how green innovation affects firm performance and how each dimension matters. Drawing upon previous studies (García-Granero et al., 2020; Kuo & Smith, 2018; Marcon et al., 2017; OECD, 2005a), we consider three different dimensions: (a) PDEI, (b)PCEI, and (c) OGEI. PDEI comprises three indicators, namely environment-friendly products/services/activities, clean energy products, and environment-friendly design products (García-Granero et al., 2020). PCEI comprises four indicators. They are the resource use score, resource reduction score, policy energy efficiency score, and target energy efficiency score (Dalhammar, 2015; Johnstone et al., 2010; Rodriguez & Wiengarten, 2017). OGEI considers three items viz. environmental management system (EMS) or ISO 14000 certified, environmental management training, and equator principle (De Jesús Pacheco et al., 2016; Frosch, 1994). We have formulated individual indices for the above three dimensions as well as an overall index of green innovation considering all three dimensions together. To construct the index, we have employed the principal component analysis (PCA). We have employed the environmental innovation score data of Thomson Reuters for the said purpose.

3.3.3. Control variables

While examining the effects of green innovation on firm performance, following earlier studies, we consider four firm-level variables as control (Ergun et al., 2022; Grund & Westergaard-Nielsen, 2008). They are firm size, age, social pillar score (SPS), and governance pillar score (GPS). Firm-specific features seem to affect the magnitude and/or thematic content of ecological exposure provided in business financial reports. To prudently detect whether changes in these disclosures are related to changes in ecological performance, we control for these additional features. More precisely, bigger and older firms are inclined to release more extensive financial and environmental information. Similarly, larger corporations tend to exhibit greater transparency and prioritize societal welfare (Ergun et al., 2022; Grund & Westergaard-Nielsen, 2008; Pattern, 1992, 2002). In addition, environmental social governance (ESG) of individual forms also has a bearing on their performance. ESG comprises two different scores, namely the social pillar score (SPS) and the governance pillar score (GPS). While SPS signifies how socially responsible a firm is, the GPS represents the governance structure of a firm (Senadheera et al., 2021) For the present purpose, the data on size and age are sourced from Bloomberg and the data on ESG is sourced from Thomson Reuters.

3.4. Methodology

To examine the association between green innovation and firms’ financial performance across ten selected countries, we employ the feasible generalized least squares (FGLS) model. Prior research has utilized the pooled ordinary least squares (OLS), fixed effect, or random effect panel regressions to evaluate how different variables affect firm performance. The stochastic disturbance regression term in these regression methods must conform to the assumptions of spherical disturbance perturbation. In the likely presence of group-wise heteroscedasticity, cross-sectional correlation, and autocorrelation within panels in the stochastic disturbance term, the FGLS model is the preferred choice for parameter estimation. By adjusting the stochastic disturbance term, the FGLS model allows for a more accurate estimation of the parameters (Greene, 2018). It is utilized to estimate the parameters by modifying the stochastic disturbance term to enhance the efficiency of the estimators. It effectively mitigates multicollinearity problems and considers cross-sectional dependence. Compared to OLS regression, the FGLS model is considered unbiased and asymptotically normal (Aitken, 1936; Miller & Startz, 2018). For FGLS, (1) $$\widehat{{\beta }_{\mathit{\text{GLS}}}\hspace{0.17em}}={\left(X\widehat{{\Sigma }^{-1}}X\right)}^{-1}X{\Sigma }^{-1}Y$$(1)

The explained variable is denoted as Y, representing the firm performance. The vector X encompasses a set of explanatory variables, including different eco-innovations and control variables. The transpose of X indicates its rearranged form. Σ is the Kronecker product, which is expressed as (2) $$\Sigma ={\Omega }_{m\hspace{0.17em}\times \hspace{0.17em}m}\otimes I_{T_i\times T_j}$$(2)

The quantity of observations is symbolized by m, and T_i indicates the duration of observation. The estimated variance matrix is obtained by replacing the estimator $\widehat{{\Omega }_{i,j}}$ with Ω in the corresponding position. Accordingly, the specific FGLS models for the present study are expressed as follows: (3) $$\mathit{\text{FP}}{E}_{\mathit{\text{it}}}=\alpha +{\beta }_1\mathit{\text{PDE}}{I}_{\mathit{\text{it}}}+{\gamma }_1\mathit{\text{sp}}{s}_{\mathit{\text{it}}}+{\gamma }_2\mathit{\text{gp}}{s}_{\mathit{\text{it}}}+{\gamma }_3\mathit{\text{fsiz}}{e}_{\mathit{\text{it}}}+{\gamma }_4\mathit{\text{fag}}{e}_{\mathit{\text{it}}}+{\mathit{ϵ}}_{\mathit{\text{it}}}$$(3) (4) $${\mathit{\text{FPE}}}_{\mathit{\text{it}}}=\alpha +{\beta }_1{\mathit{\text{PCEI}}}_{\mathit{\text{it}}}+{\gamma }_1{\mathit{\text{sps}}}_{\mathit{\text{it}}}+{\gamma }_2{\mathit{\text{gps}}}_{\mathit{\text{it}}}+{\gamma }_3{\mathit{\text{fsize}}}_{\mathit{\text{it}}}+{\gamma }_4{\mathit{\text{fage}}}_{\mathit{\text{it}}}+{ϵ}_{\mathit{\text{it}}}$$(4) (5) $${\mathit{\text{FPE}}}_{\mathit{\text{it}}}=\alpha +{\beta }_1\mathrm{ }{\mathit{\text{OGEI}}}_{\mathit{\text{it}}}+{\gamma }_1{\mathit{\text{sps}}}_{\mathit{\text{it}}}+{\gamma }_2{\mathit{\text{gps}}}_{\mathit{\text{it}}}+{\gamma }_3{\mathit{\text{fsize}}}_{\mathit{\text{it}}}+{\gamma }_4{\mathit{\text{fage}}}_{\mathit{\text{it}}}+{ϵ}_{\mathit{\text{it}}}$$(5) (6) $${\mathit{\text{FPE}}}_{\mathit{\text{it}}}=\alpha +{\beta }_1{\mathit{\text{OVEI}}}_{\mathit{\text{it}}}+{\gamma }_1{\mathit{\text{sps}}}_{\mathit{\text{it}}}+{\gamma }_2{\mathit{\text{gps}}}_{\mathit{\text{it}}}+{\gamma }_3{\mathit{\text{fsize}}}_{\mathit{\text{it}}}+{\gamma }_4{\mathit{\text{fage}}}_{\mathit{\text{it}}}+{ϵ}_{\mathit{\text{it}}}$$(6)

Where ${\mathit{\text{FPE}}}_{\mathit{\text{it}}}$ represents performance for the $i^{\mathit{\text{th}}}$ firm at time $t$. OVEI is the overall eco-innovation. The terms age and size represent firm age and firm size, respectively. All other terms are already defined earlier.

To ensure the consistency of our findings, we employ the Breusch-Pagan (BP) test and the White test to detect the possibility of heteroscedasticity. The BP test assesses the hypothesis that error variances are equal (homoscedastic) against the alternative hypothesis that they exhibit a multiplicative pattern (heteroskedastic). Though the White test serves a similar purpose, it is particularly effective when we deal with non-linear heteroscedasticity and have errors that do not follow a normal distribution. The results of the Breusch-Pagan and White tests are presented in Table 2. Subsequently, we investigate the possibility of serial correlation and cross-sectional dependency. The Wooldridge SE F-Test is employed to detect serial correlation, and the results are reported in Table 3. Similarly, Pesaran’s test is employed to examine cross-sectional dependency, if any (Table 4).

Table 2. Results of the heteroscedasticity test.

Dependent variable firm performance	Model 1 (PDEI)	Model 2 (PCEI)	Model 3 (OGEI)	Model 4 (OVEI)
Breusch-Pagan test
Chi^2	297738.21	536.12	584.38	164.36
Prob > Chi^2	0.000	0.000	0.000	0.000
Chi^2	19969.56	1325.52	1180.43	1396.74
White test
Prob > Chi^2	0.0000	0.0000	0.0000	0.0000

Source: Authors’ estimates.

Table 3. Results of the serial correlation test (White test).

Dependent variable firm performance	Model 1 (PDEI)	Model 2 (PCEI)	Model 3 (OGEI)	Model 4 (OVEI)
Chi^2	2061.784	2158.755	2131.654	2000.017
Prob > Chi^2	0.0000	0.0000	0.0000	0.0000

Source: Authors’ estimates.

Table 4. Results of the cross-sectional dependence.

	FPE	PDEI	PCEI	OGEI	OVEI	SPS	GPS	AGE	size
Pesaran’s test of CD	1502.353	148.710	251.744	114.559	210.290	1152.182	420.416	3935.389	647.402
Prob	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

Source: Authors’ estimates.

The results presented in Table 2 reveal the presence of heteroscedasticity in the model. This is evident as the null hypothesis is rejected at a significance level of 1% for the measures of firm performance. Besides, the model also exhibits serial correlation, as indicated by the results of the Wooldridge F-test, rejecting the null hypothesis of no serial correlation at a 1% significance level (Table 3). Similarly, Pesaran’s test supports the concerns regarding cross-sectional dependency, which also rejects the null hypothesis at a 1% significance level (Table 4). Consequently, the FGLS approach is chosen as the appropriate method based on the above diagnostic tests, which addresses these problems adequately. Needless to say, FGLS is widely employed in various studies investigating the relationship between sustainability and financial deepening (Akintande et al., 2020; De Pascale et al., 2020; Le & Nguyen, 2019; Obiora et al., 2020).

3.5. Robustness check

Additionally, to check the robustness of the results, the two-step system generalized method of moments (GMM) model is used to examine the influence of explanatory variables on firm performance. Some important variables in our model might be endogenous. The problems of heteroscedasticity and endogeneity associated with panel data can also be addressed by GMM. It is also considered a suitable model for a dataset having small time spans (T) and big cross-sections (N) (Bhattacharya et al., 2017). GMM comprises two subtypes: difference GMM and system GMM. The employment of instruments that are valid under the presumption that the disturbance terms are actually independent and serially uncorrelated is what makes both difference and system GMM approaches so attractive. The system GMM is more resistant to autocorrelation and heteroscedasticity. It turns out to be better than difference GMM if the dependent variable is significantly more persistent (Blundell & Bond, 1998). So we have used system GMM as a robustness check mechanism in our work.

4. Results and discussion

As mentioned earlier, we have employed FGLS with robust standard errors to study the effects of green innovation on firms’ financial performance in G7 and BRICS countries. The findings (Table 5) reveal that OVEI and all its three individual dimensions exert positive impacts on firm performance, corroborating earlier studies (Bossle et al., 2016; Cai & Li, 2018; de Oliveira Brasil et al., 2016; Larbi-Siaw et al., 2022). A one percent increase in OVEI leads to an 11% increase in firms’ financial performance in these countries. While all the individual dimensions do influence positively, the impacts of PCEI (1.92) (Marcon et al., 2017; Rodriguez & Wiengarten, 2017) followed by OGEI (1.27) (Hojnik & Ruzzier, 2016) turn out to be much greater than that of PDEI (0.41).

Table 5. FGLS with robust standard errors results regarding the relationship between green innovation and firm performance.

	Model 1 FPE	Model 2 FPE	Model 3 FPE	Model 4 FPE
PDEI	0.41***
PCEI		1.92***
OGEI			1.27***
OVEI				0.11***
SPS	0.59***	0.52***	0.57***	0.55***
GPS	0.33***	0.32***	0.33***	0.33***
Age	1.43***	1.04***	1.35***	1.01***
Size	0.52***	0.47***	0.53***	0.28***

Source: Authors’ estimates.

***p < 0.01.

We have also carried out a separate sub-sample analysis for the BRICS and G7 countries (Table 6). Interestingly, the coefficients of the OVEI for both the sub-samples turn out to be more or less the same. However, the coefficient of PDEI (2.11) in the G7 countries is very high, and there seem to be significant differences in the impacts between the two groups of countries. There is also a visible difference in the impacts of OGEI between them. However, the impacts of PCEI tend to be, by and large, the same between the two groups. Interestingly, both overall and sub-sample analyses indicate that the coefficients of all the control variables are positive and significant.

Table 6. Sub-sample analysis.

	Model 1 FPE		Model 2 FPE		Model 3 FPE		Model 4 FPE
	BRICS	G7	BRICS	G7	BRICS	G7	BRICS	G7
PDEI	0.08***	2.10***
PCEI			1.83***	1.80***
OGEI					1.35***	1.04***
OVEI							0.10***	0.11***
SPS	0.59***	0.55***	0.52***	0.52***	0.57***	0.56***	0.55***	0.54***
GPS	0.33***	0.33***	0.32***	0.32***	0.32***	0.33***	0.32***	0.33***
Age	6.30***	1.50***	4.57***	1.27***	5.47***	1.82***	4.65***	1.43***
Size	1.52***	1.09*	1.44***	1.19***	1.72***	1.49**	2.13***	0.83*

Source: Authors’ estimates.

***p < 0.01, **p < 0.05, *p < 0.1.

To test the robustness of the results, we have also employed the System GMM for the overall sample as well as the two sub-samples (Tables 7 and 8). Interestingly, these results, by and large, indicate a similar association between green innovation and firm performance. The signs of coefficients remain the same, with a little variation in the values across dimensions. In all the models, the coefficients of the control variables are positive and significant, implying that with the increase in age and size and improvement in SPS and GPS, the firms tend to perform better financially.

Table 7. System GMM (two-step).

	Model 1 FPE	Model 2 FPE	Model 3 FPE	Model 4 FPE
L.FPE	0.24***	0.10***	0.15***	0.06*
PDEI	0.07**
PCEI		1.44***
OGEI			1.23***
OVEI				0.10***
SPS	0.51***	0.57***	0.51***	0.56***
GPS	0.22***	0.28***	0.26***	0.30***
Age	0.81***	0.79***	0.86***	0.61***
Size	0.10***	0.06***	0.53***	0.13*
AR(1)	0.001	0.031	0.001	0.000
AR(2)	0.322	0.114	0.335	0.224
Hansen test	0.124	0.159	0.115	0.105

Source: Authors’ estimates.

***p < 0.01, **p < 0.05, *p < 0.1.

Table 8. System GMM country wise (two-step).

	Model 1 FPE		Model 2 FPE		Model 3 FPE		Model 4 FPE
	BRICS	G7	BRICS	G7	BRICS	G7	BRICS	G7
L.FPE	0.31***	0.08**	0.10***	0.24***	0.15***	0.25***	0.24***	0.10**
PDEI	1.01**	1.93***
PCEI			1.39***	0.91***
OGEI					1.62***	1.04***
OVEI							0.10***	0.11***
SPS	0.45***	0.55***	0.55***	0.50***	0.44***	0.52***	0.55***	0.54***
GPS	0.24***	0.30***	0.29***	0.25***	0.27***	0.32***	0.32***	0.33***
Age	0.43*	0.77***	0.90***	0.06*	0.06*	1.27***	4.65***	1.43***
Size	0.01*	0.01*	0.04*	0.04*	0.02*	1.19***	2.13***	0.83*
AR(1)	0.001	0.000	0.067	0.021	0.041	0.000	0.012	0.000
AR(2)	0.221	0.369	0.445	0.778	0.665	0.692	0.517	0.492
Hansen test	0.214	0.125	0.183	0.121	0.441	0.831	0.227	0.371

Source: Authors’ estimates.

***p < 0.01, **p < 0.05, *p < 0.1.

The findings support the notion that environmental innovation, encompassing product, process, and organizational dimensions, exerts positive impacts on firms’ financial performances. These dimensions play a critical role in influencing firm performance in both BRICS and G7 countries, though with varying proportions. The results closely align with the findings of Cheng et al. (2014), who focused on 121 electronics manufacturing companies in Thailand. The results also seem to partially agree with the models proposed by de Oliveira for textile manufacturing firms originating in Brazil (de Oliveira Brasil et al., 2016) and manufacturing firms in Ghana (Larbi-Siaw et al., 2022).

One interesting revelation is that in BRICS countries, the primary drivers of firm performance are process and organizational innovation, while product innovation carries lesser importance. In contrast, in G7 countries, product innovation takes precedence, followed by process and organizational innovations. These results seem to provide interesting implications. BRICS countries are in the process of developing organizational and process innovations (Amores-Salvadó et al., 2015; Cheng et al., 2014; de Oliveira Brasil et al., 2016; Larbi-Siaw et al., 2022). They need to capitalize on these dimensions further with a renewed focus while ensuring that product innovation gets due attention. On the contrary, G7 countries seem to benefit more from their product innovations. While encouraging further innovations in that dimension, they must pay attention to process and organizational innovations. Nevertheless, product innovation puts thrust on eco-design, eco-product, and clean energy products, contributing to firms’ sustainability (Mazzoni, 2020). According to Tseng and Bui (2017), green-design or environmental friendly product design is an improved process of manufacturing that takes into account the environmental, social, and economic factors associated with a product’s life cycle. Its aim is to minimize the ecological footprint and prioritize the efficient use of resources (Ivascu, 2020). Another crucial aspect that requires attention is the continuous evolution or revolution of eco-products, including a reduction in raw material intensity, and increasing renewable energy concentration, product recyclability, and strength (Mazzoni, 2020; Stankevičienė & Nikanorova, 2020). It will foster a firm’s participation in a sustainable economy.

5. Conclusion and policy implications

The objective of this study was to examine how environmental innovation affects firm performance in countries experiencing different levels of development. For the said purpose, BRICS and G7 countries were considered. Employing FGLS and system GMM models, the analysis revealed that environmental innovation has a positive impact on firm performance. Additionally, firms in BRICS countries seem to gain more from PCEI and OGEI compared to firms in G7 countries. Contrarily, in G7 countries, PDEI carries greater significance compared to other dimensions, indicating possibly a more advanced stage of eco-innovation in these countries. These findings partially support the notion of divergent eco-innovation outcomes between G7 and BRICS countries. Further, G7 countries perhaps are already endowed with the necessary resources for green innovation, whereas BRICS countries are still evolving in this area.

One major takeaway of this study is that eco-innovation, regardless of dimensions, can help firms perform better financially. As the latter’s performance has ramifications for the overall growth of an economy, it may be pertinent to promote green innovation in all spheres of economic activities. Efforts to encourage green innovation in products, processes, and organizations can go a long way toward enhancing the growth prospects of both G7 and BRICS countries.

From the perspective of policy implications, one may advocate for robust regulations, monitoring, and support for firms across both developed and emerging economies as steps toward incorporating eco-innovations into their processes. Adoption of eco-innovation technologies and processes could enhance resource efficiency, reduce waste generation, and minimize environmental impact. By investing in energy-efficient equipment, optimizing production processes, and implementing sustainable supply chain practices, firms can achieve cost savings over time (Chiarvesio et al., 2015; Chiou et al., 2011). Managers can play a crucial role in identifying opportunities for cost-saving innovations, allocating resources effectively, and overseeing the implementation of green practices across different processes. Embracing green innovation would also allow firms to differentiate themselves in the marketplace by offering environmental-friendly products and services. Consumers are increasingly seeking eco-friendly alternatives, and firms prioritizing sustainability can take advantage of this (Bossle et al., 2016; de Oliveira Brasil et al., 2016).

The firms are required to engage themselves in effective marketing and communication strategies to highlight their organizational commitment to environmental responsibility, thereby enhancing brand reputation and loyalty (Bossle et al., 2016). By adopting sustainable practices, they can reduce their environmental footprint and, consequently, minimize possible regulatory penalties (Cheng et al., 2014). Additionally, integrating eco-innovation into management frameworks would allow firms to identify and mitigate supply chain disruptions and resource scarcity. It would also help them gain access to government incentives, grants, or subsidies wherever available. Moreover, sustainable business practices can attract socially responsible investors who prioritize environmental, social, and governance (ESG) criteria in their investment decisions. Embracing green innovation can also enhance productivity, and retention by aligning organizational values with employees’ personal beliefs and aspirations (Grund & Westergaard-Nielsen, 2008).

The varying impacts of various dimensions of eco-innovation between two groups of countries reflect varying stages of their development and priorities toward eco-innovation. From a policy perspective, these findings have important ramifications for policymakers and practitioners alike. First, in BRICS countries may consider implementing targeted initiatives to promote environmental innovation and enhance firms’ capacity to leverage eco-friendly practices for improved performance (Gyedu et al., 2021; Hojnik & Ruzzier, 2016). This could involve investing in research and development, providing financial incentives, and fostering collaboration between government, academia, and industry. Additionally, G7 countries should continue to support and incentivize firms to engage in environmental innovation, particularly in areas where there may be untapped potential for growth and competitiveness. This may involve strengthening regulatory frameworks, promoting knowledge sharing and best practices, and encouraging investments in sustainable technologies and practices. There is also a need for greater collaboration between G7 and BRICS countries so that all can synergistically grow toward addressing a common goal (Murshed, 2020; Testa et al., 2011).

However, the study is still not free from limitations. As it is based on firm-level analysis, the generalization of the findings for the entire economy may be made with caution. Moreover, the model estimation is based only on ten years of data. The data for a longer period would help understand the short-run and long-run dynamics separately. This study can also be broadened by incorporating both listed and un-listed firms. Notwithstanding these limitations, it may be concluded that green innovation can be a way forward to attain and maintain a sustainable future as it ensures better firm performance for both developed and emerging economies alike.

Authors contributions

Nirupam Mukhopadhyay: conceptualization, methodology, data and analysis, writing original draft. Narayan Chandra Nayak: conceptualization, writing review and editing, supervision.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data can be made available on request.

Additional information

Funding

No funding received.

Notes on contributors

Nirupam Mukhopadhyay

Nirupam Mukhopadhyay is a research scholar at the Department of Humanities and Social Sciences, Indian Institute of Technology Kharagpur, India. His research interests include Green growth, Green innovation and diffusion of innovation at firm as well as Macroeconomic level.

Narayan Chandra Nayak

Narayan Chandra Nayak is senior professor of Economics at the Department of Humanities and Social Sciences, Indian Institute of Technology Kharagpur, India. His research interests include Applied Macroeconomics, Public policy, Food security and Financial Inclusion and growth dynamics.