Knowledge flows and ecological efficiency: A regional analysis of open innovation in the European Union

Knowledge flows and ecological efficiency: A regional analysis of open innovation in the European Union | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Knowledge flows and ecological efficiency: A regional analysis of open innovation in the European Union Amir Hossein Nimjerdi, Mohammad Rahsepar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8074689/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Open innovation is acknowledged as a dual paradigm that can improve ecological efficiency (Eco-Efficiency) via knowledge dissemination and advanced technologies, while simultaneously escalating environmental pressure through intensified economic activities and resource consumption. This paper analyzes the dual effects of intellectual property payments and receipts as proxies for open innovation on Eco-Efficiency, and examines the heterogeneity of these effects between Eastern and Western European Union (EU) countries from 2011 to 2020. Eco-Efficiency was measured using data envelopment analysis (DEA) under an output-oriented variable returns to scale (BCC) model. To estimate causal relationships, a panel regression model with fixed effects and a feasible generalized least squares (FGLS) estimator was employed to address heteroskedasticity and autocorrelation issues. Results for the entire EU indicated that both payment and receipt variables had a positive and significant impact on Eco-Efficiency. However, regional analysis revealed a clear duality: in Western countries, receipts had a small positive effect while payments were insignificant, whereas in Eastern countries, payments had a strong negative effect and receipts showed a strong positive effect on Eco-Efficiency. These findings, which held robust even when tested with the generalized method of moments (GMM) estimator and an alternative dependent variable, highlight the need for region-specific and intelligent policy approaches in the management of open innovation flows to attain sustainable development goals. Ecological efficiency Open innovation Intellectual property Regional Heterogeneity Sustainable Development 1. Introduction In the modern world, global economic systems face a fundamental paradox: the pursuit of continuous economic growth without exacerbating environmental crises (Ekonomou & Halkos, 2023 ; Guterres, 2020 ). Climate change, resource depletion, and rising pollution pose significant threats to the health of current and future generations (Mir et al., 2023 ; Nguyen et al., 2023 ; WEF, 2024 ). In response to these widespread problems, the European Union (EU), by adopting of European Green Deal as an innovative growth strategy, has committed to transforming the continent into the world's first climate-neutral economy by 2050 (Silander, 2019 ). Central to this monumental transition is achieving higher ecological efficiency (Eco-Efficiency) (De Pascale et al., 2020 ; Mhatre et al., 2021 )—a concept emphasizing the generation of greater economic output with lower inputs of material and energy and reduced pollution (Kuosmanen & Kortelainen, 2005 ; Luo et al., 2021 ). Consequently, a deep understanding of the factors that can enhance Eco-Efficiency at the national level has become a critical subject in academic and policy-making literature. Within this context, open innovation has become a key strategic paradigm that focuses on the purposeful use of inbound and outbound knowledge flows to accelerate internal innovation and expand markets for external exploitation (Díaz-Díaz & de Saá Pérez, 2014 ; Leitão et al., 2020 ). On a practical level, these knowledge flows are often measured by transactions involving intellectual property (Dubbert et al., 2019 ). These flows have a dual role in achieving sustainability goals (Kusi-Sarpong et al., 2022 ; S. Liu & Zhong, 2024 ). On the one hand, receipts for the use of intellectual property show successful commercialization of domestic knowledge (Virchenko et al., 2021 ) and access to advanced, potentially more resource-efficient technologies (L. Li & Chen, 2023 ), which can directly contribute to enhanced Eco-Efficiency (L. Li & Chen, 2024 ; Yasmeen et al., 2020 ). On the other hand, payments for the use of intellectual property can signify technological dependence on external sources and an outflow of financial capital from the national economy (Khrustalev & Slavyanov, 2019 ; Maskus, 2000 ). This resource drain might constrain the funding necessary for domestic investment in research and development and the deployment of green technologies (S. Liu & Zhong, 2024 ; S. Wang, 2025 ), thereby creating a barrier to improving Eco-Efficiency. This inherent duality necessitates a meticulous examination of the net effects of these flows. Although the existing literature increasingly examines the relationship between innovation and environmental performance, a prominent gap remains. Most prior studies treat open innovation as a unified and homogeneous construct, primarily focusing on aggregate indicators such as R&D expenditures (De Backer et al., 2008 ) or international patent counts (Yun et al., 2016 ). This one-dimensional approach overlooks the inherent dual and contrasting nature of open innovation flows, particularly their financial dimension manifested in intellectual property payments and receipts. Furthermore, scant research has delved into how the economic-technological context of a region can moderate the role of these flows. In other words, the hypothesis that the effects of open innovation on Eco-Efficiency may systematically differ across countries with varying levels of economic development, industrial structure, and technological capabilities has remained largely unexplored. This study is designed with the primary aim of analyzing the dual role of open innovation in enhancing Eco-Efficiency, while accounting for the component of regional heterogeneity across the EU. To achieve this overarching goal, four specific objectives are pursued, which collectively represent the innovation and contribution of this research: First, to measure the Eco-Efficiency of 27 EU member states from 2011 to 2020 using a comprehensive data envelopment analysis (DEA) framework based on an output-oriented variable returns to scale (BCC) model. This model simultaneously incorporates multiple inputs, including labor, capital, and energy consumption, as well as both desirable outputs, including GDP and municipal waste recycling rate, and undesirable outputs, including ecological footprint and municipal waste generation. Second, to implement an econometric analysis of the separate and distinct effects of the two main flows of open innovation—namely, payments and receipts for the use of intellectual property as proxies for this paradigm (Andersen & Rossi, 2011 ; Michelino et al., 2015 ) on the calculated levels of Eco-Efficiency, thereby providing a more nuanced understanding of the concept's dual nature. Third, constituting the primary innovation of the paper, to investigate the critical role of regional heterogeneity by dividing the sample into two distinct groups of Eastern and Western European countries, and to reveal potential dualities in the direction, magnitude, and significance of causal relationships within these two regions with distinct economic-technological contexts. Fourth, to ensure the reliability and robustness of the findings by conducting a series of robustness checks, which encompasses the utilization of an alternative dependent variable for Eco-Efficiency featuring a distinct combination of outputs, and the employment of the generalized method of moments (GMM) estimator to address potential econometric challenges such as endogeneity. After this introduction, the paper is structured as follows. Section 2 reviews the relevant theoretical and empirical literature, focusing on existing gaps to be used as a basis for developing the research hypotheses. Section 3 discusses in detail the research methodology, which includes the measurement model of Eco-Efficiency, the panel regression model, variable definitions, and econometric estimation protocols. Section 4 presents and describes the empirical results of model estimations. Section 5 interpreting these findings within the context of the previous literature, discussing their theoretical and policy implications. Finally, Section 6 concludes by summarizing the main points of the research, stating its limits, and providing suggestions for future studies. 2. Literature review and hypothesis 2.1. Ecological efficiency (Eco-Efficiency) Eco-Efficiency is a key paradigm in sustainable development (Jin et al., 2024 ) that addresses the relationship between economically valuable added performance and environmental pressures caused by economic activities (Sun & Wang, 2022 ; WBCSD, 2000 ). For the objective measurement of this concept, DEA has been widely used due to its ability to assess multiple inputs and outputs at the same time, without needing for an a priori functional form (Collier et al., 2011 ; Zhou et al., 2006 ). This method is particularly suitable for assessing Eco-Efficiency at the country and regional levels, as it can integrate both desirable outputs and undesirable outputs (Rashidi & Saen, 2015 ; Yılmaz, 2025 ). The methodology is categorized into radial and non-radial models, in which radial models assume that changes in inputs and outputs are proportional (Charnes et al., 1978 ; Sueyoshi et al., 2017 ). In contrast, non-radial models account for slack variables (Zubir et al., 2024 ). Eco-Efficiency scores have risen internationally over time, although growth has been inconsistent among countries and regions (Fang et al., 2024 ; C.-N. Wang et al., 2020 ). According to (Sadorsky, 2021 ), the significant gap in Eco-Efficiency growth is largely attributable to the fact that developed and developing countries have different economic models based on their technological capabilities and environmental policies. Numerous studies have applied DEA to measure Eco-Efficiency in the EU. The study by (Puertas et al., 2022 ) employed DEA to assess the Eco-Efficiency of 20 countries of the EU between 2014–2018. Their model utilized consumption of renewable energy, environmental protection investment, employment in the environmental sector, and recycling rate of waste as inputs, whereas the outputs were pollution-attributable mortality and GDP. The findings revealed that nations in Eastern Europe demonstrated the most significant potential for enhancement in their environmental performance. Other studies focusing on EU member states concluded that investment in clean technologies and renewable energy is positively associated with Eco-Efficiency (Cicea et al., 2014 ; Majid et al., 2023 ). 2.2. Open innovation Open innovation is defined as an innovation paradigm that permeates organizational boundaries, emphasizing the purposeful use of inbound and outbound knowledge flows to accelerate internal innovation and expand external markets (Chesbrough, 2003 ; Díaz-Díaz & de Saá Pérez, 2014 ; Leitão et al., 2020 ). At the macroeconomic level, measuring these knowledge flows presents a fundamental challenge. Within this context, charges for the use of intellectual property, payments and receipts have been increasingly accepted as valid and quantitative proxies for inbound and outbound knowledge flows in the Open Innovation paradigm (Andersen & Rossi, 2011 ; Michelino et al., 2015 ). Payments indicate inbound knowledge flows and reliance on external technologies and knowledge (Khrustalev & Slavyanov, 2019 ; Maskus, 2000 ), whereas receipts reflect outbound knowledge flows and the successful commercialization of intellectual property assets in global markets (Li & Chen, 2023 ; Virchenko et al., 2021 ). Numerous studies have used these indicators to measure open innovation at the national level. Some research has found a positive relationship between these flows and economic performance. For instance, it has been demonstrated that intellectual property receipts are correlated with higher total factor productivity levels (Leogrande, n.d.; Yang & Cai, 2025 ). Similarly, current studies state that bidirectional knowledge flows (both inbound and outbound) lead to a more dynamic innovation ecosystem (Cassiman & Valentini, 2016 ; Robertson et al., 2023 ). However, contradictory evidence exists concerning the impact of these flows on broader outcomes like environmental performance. overarching outcomes such as environmental performance. Some research indicates that inbound knowledge flows via payments can enhance access to more efficient green technologies, resulting in increased Eco-Efficiency (Sanchez-Garcia et al., 2024 ; Tengö & Andersson, 2021 ; Yin et al., 2025 ). Conversely, other studies warn that over-reliance on external technology might undermine domestic innovation capacity and create a "technology dependency trap" that hinders the development of indigenous green technologies (Khrustalev & Slavyanov, 2019 ; Maskus, 2000 ). This contradiction in findings highlights the need for a more meticulous examination of the net effects of these flows in different contexts, particularly concerning sustainability goals. 2.3. The relationship between open innovation and Eco-Efficiency The examination of relationship between open innovation and Eco-Efficiency constitutes a nascent research domain characterized by complex mechanisms. In theory, open innovation can improve Eco-Efficiency via several ways. First, inbound knowledge flows can facilitate access to advanced, less polluting technologies that were developed in other countries by transferring green technologies (Sanchez-Garcia et al., 2024 ; Tengö & Andersson, 2021 ; Yin et al., 2025 ). Second, knowledge diffusion can help spread the best practices and standards for managing the environment by connecting with global innovation networks (Phonthanukitithaworn et al., 2024 ; Worakittikul et al., 2025 ). Third, innovations that derived from open collaborations can improve resource productivity, facilitating process optimization and decreasing consumption of energy and material (Mairesse et al., 2025 ; Phonthanukitithaworn et al., 2024 ). Empirical evidence on this relationship is still changing and sometimes contradictory. Numerous studies substantiate the beneficial effects of open innovation initiatives on environmental performance. For example, a study by (Caputo et al., 2016 ) found that open collaborations led to better environmental performance. A provincial-level study similarly identified relationship between knowledge intensity and decreased emission intensity (Y. Zhao et al., 2023 ). Nonetheless, there is also contradictory evidence, highlighting the dual aspects of open innovation. Some studies contend that inbound knowledge flows may not consistently target green technologies and could potentially result in lock-in into unsustainable technological trajectories (Khrustalev & Slavyanov, 2019 ; Sanchez-Garcia et al., 2024 ; Yin et al., 2025 ). Moreover, the financial expenses linked to intellectual property payments may redirect resources away from domestic investments in green research and development, potentially negating the beneficial impacts of knowledge flows (S. Liu & Zhong, 2024 ; S. Wang, 2025 ). A study on transition economies showed that depending too much on foreign technology without enough absorptive capacity can slow down progress on environmental issues (D. Li et al., 2022 ). These contradictions imply that the relationship between open innovation and Eco-Efficiency may depend on context, the nature of knowledge flow (inbound versus outbound), and the degree of regional development. 2.4. Control variables To isolate the net impact of open innovation on Eco-Efficiency, a group of control variables from the literature based on accessibility and their impact on Eco-Efficiency is added to the model. GDP per capita was included to examine the Environmental Kuznets Curve hypothesis, which asserts an inverted U-shaped correlation between per capita income and environmental degradation (Dinda, 2005 ). Numerous studies have corroborated the existence of the Environmental Kuznets Curve (EKC) (X. Liu et al., 2007 ); however, conflicting evidence suggests that economic growth may result in increased environmental pressure (Molaei & Basharat, 2015 ). The Porter Hypothesis states that well designed environmental regulations can stimulate innovations that not only reduce pollution but also incite resource productivity improvements and alleviating compliance costs (M. Porter, 1996 ; M. E. Porter & Linde, 1995 ). Several empirical research has emphasized the positive impact of regulations on efficiency and green innovation (Y. Xu et al., 2022 ; J. Zhang et al., 2020 ). On the other hand, some have shown that regulations can hurt businesses financially and make them less competitive (Breuer et al., 2025 ; Tu & Shi, 2022 ), which could have a negative impact on economic growth (X. Zhao et al., 2022 ). The industrial sector's share of the economy is a key factor because it uses more resources and causes more pollution (Usman & Balsalobre-Lorente, 2022 ). Countries with a lot of heavy industries, like steel and cement, usually have more environmental problems (B. Xu & Lin, 2020 ). Some studies, on the other hand, argue that moving toward high-tech, less polluting industries in the industrial sector can moderate this negative relationship (B. Xu & Lin, 2018 ). An educated workforce is essential for the assimilation, adaptation, and implementation of advanced technologies, including green technologies (Hondroyiannis et al., 2022 ; Mehta & Khan, 2024 ). Human capital is associated with more absorptive capacity and domestic innovation, which can improve Eco-Efficiency (Bye & Fæhn, 2022 ; C. Zhang & Lin, 2012 ). A direct way to lower level of ecological footprint is to switch from fossil fuels to renewable energy sources (Sharma et al., 2021 ). Research consistently demonstrates a positive correlation between the integration of renewable energy and environmental performance metrics (Q. Wang et al., 2023 ). 2.5. Research gap and hypothesis development The literature review indicates that the primary deficiency in the existing research is the concurrent neglect of the duality of open innovation (differentiating between intellectual property payments and receipts) and the moderating influence of regional heterogeneity within the EU. To fill this gap, the following hypotheses are developed: H1: Payments and receipts for intellectual property positively and significantly influence Eco-Efficiency at the aggregate EU level. H2: The correlation between open innovation and Eco-Efficiency differs between Eastern and Western EU countries. H2a: In Western, intellectual property receipts have a significant positive impact on Eco-Efficiency. H2b: In Eastern, intellectual property payments have a significant negative impact on Eco-Efficiency, whereas receipts positively influence it. 3. Research methodology 3.1. Research design This study utilizes a quantitative and causal-descriptive research design, employing panel data. The research population includes 27 EU states from 2011 to 2020. The choice of this timeframe was predicated on two principal factors: firstly, the comprehensive and uniform availability of data for all study variables across all countries during this interval; and secondly, the inclusion of critical policy periods, notably the execution of the Europe 2020 strategy and the official commencement of the European Green Deal. To evaluate the impact of regional heterogeneity, the countries were divided into two distinct sectors based on geographic and economic criteria: Western country group (13 countries) comprising advanced and stable economies, and Eastern country group (14 countries). The analytical framework in this study is organized into two successive and complementary steps. The first step is to use the non-parametric DEA method to calculate the Eco-Efficiency of each country for each year. In the second stage, econometric models using panel data regression is applied to identify determining factors and estimate the net effects of the independent variables. 3.2. Eco-Efficiency measurement model The Eco-Efficiency was measured using the non-parametric DEA method under the BCC assumption with an output-oriented approach, whose mathematical model is presented in Eq. 1. $$\:\text{max}\beta\:$$ $$\:s.t.\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ $$\:\sum\:_{j=1}^{n}{\lambda\:}_{j}{x}_{ij}^{p}\le\:{x}_{io}^{p},\:\:\sum\:_{j=1}^{n}{\lambda\:}_{j}{y}_{rj}^{p}\ge\:\beta\:{y}_{ro}^{p},\:\:\sum\:_{j=1}^{n}{{\lambda\:}_{j}\stackrel{-}{y}}_{pj}^{b}\le\:\beta\:{\stackrel{-}{y}}_{po}^{b},\:\:\sum\:_{j=1}^{n}{\lambda\:}_{j}=1,\:\:{\lambda\:}_{j}\ge\:0,\:\:j=1,\dots\:,n\:\:$$ In this model, \(\:j=1,\:\dots\:,n\) represents the set of decision-making units (DMUs), the variable \(\:{\lambda\:}_{j}\) specifies the contribution share of the j-th DMU in forming the efficient frontier, and the efficiency score for each unit is calculated as the inverse of β \(\:(Eco-Efficiency=1/\beta\:)\) . In this model, energy consumption, labor, and capital are regarded as input variables ( \(\:{x}^{p}\) ), while the desirable outputs ( \(\:{y}^{p}\) ) comprise GDP and recycling rate of municipal waste, and the undesirable outputs ( \(\:{\stackrel{-}{y}}^{b}\) ) encompass ecological footprint and municipal waste generation. All of these input and output variables required for calculating Eco-Efficiency, along with their measurement units and sources, are presented in Table 1 . Table 1 Inputs and outputs in the calculation of ecological efficiency. Variable Unit Source Inputs: Energy consumption Thousand tons of oil equivalent Eurostat Total number of labors % population World Bank Net investment in non-financial assets current US $ World Bank Desirable outputs: Gross Domestic Product (GDP) current US $ World Bank Recycling rate of municipal waste % total waste Eurostat Undesirable outputs: Ecological footprint index Global hectares per capita Global Footprint Network Annual amount of municipal wastes Ton Eurostat Note : All data used in this study pertain to 27 European Union member states for the period 2011–2020. The capital variable is measured using the net investment in non-financial assets indicator. The selection of this model is due to its capability to simultaneously handle multiple inputs and outputs without requiring parametric assumptions or a specific functional form (Collier et al., 2011 ; Zhou et al., 2006 ), making it apt for measuring Eco-Efficiency as a multivariate dimension. All computations related to the Eco-Efficiency for the period 2011–2020 were performed using LINGO 19. 3.3. Econometric model To quantitatively analyze the impact of open innovation on Eco-Efficiency, a two-way fixed effects panel regression model was employed. The basic econometric model of this research is presented in Eq. 2. $$\:Eco-Efficienc{y}_{i,t}=\:{\beta\:}_{0}+\:{\beta\:}_{1}IP.\:Paymen{t}_{i,t}+\:{\beta\:}_{2}IP.\:Receip{t}_{i,t}+\:\sum\:{\beta\:}_{j}{X}_{i,t}+\:{\alpha\:}_{i}\:+\:{\lambda\:}_{t}\:+\:{\epsilon\:}_{i,t}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ In this model, the dependent variable ( \(\:Eco-Efficienc{y}_{i,t}\) ) is the calculated Eco-Efficiency index for country i in year t. The main independent variables include intellectual property payments ( \(\:IP.\:Paymen{t}_{i,t}\) ) and intellectual property receipts ( \(\:IP.\:Receip{t}_{i,t}\) ). The vector \(\:{X}_{i,t}\) represents a set of control variables including GDP per capita, environmental regulations, industrial structure, human capital, and share of renewable energy. \(\:{\alpha\:}_{i}\) and \(\:{\lambda\:}_{t}\) denote country-fixed effects and time-fixed effects respectively, while \(\:{\epsilon\:}_{i,t}\) is the error term. Detailed operational definitions, abbreviation, and data sources for all variables used in the model, including the dependent, independent, and control variables, are provided in Table 2 . Table 2 Details of regression variables. Variable Abbreviation Measurement/Description Source Dependent variable: Ecological efficiency Eco-Efficiency The optimal ratio of the difference between desirable and undesirable outputs to inputs. Calculated by author Independent variables: Payments for IP Use IP. Payment Charges for the use of intellectual property, payments (current US $ ). World Bank Receipts from IP Use IP. Receipt Charges for the use of intellectual property, receipts (current US $ ). World Bank Control variables: GDP per capita GDP. Pc Gross Domestic Product divided by population (current US $ ) World Bank Environmental regulations ER Number of environmental regulations and policies (Wuepper et al., 2024 ) Industrial structure IS Industry value added (% of GDP) World Bank Human capital HC Number of workers with tertiary education (ISCED 2011) Eurostat Share of Renewable Energy R. new Share of primary energy consumption that comes from renewables. OurWorldinData Note : All variables listed above are utilized in the regression analysis. The ecological efficiency score is a unitless index ranging from 0 to 1, calculated by the author using the DEA model described in Section 3.2 . 3.4. Estimation methods Before model estimation, to confirm that there are no spurious regression in the model, the stationarity of the research variables was examined using both the Im-Pesaran-Shin (IPS) and Fisher-type panel unit root tests, the results of which indicated that all variables were stationary at the level. Then, to reject the null hypothesis according to the Hausman test (p-value < 0.05), the fixed effects model was considered as a better specification than the random effects model. Subsequently, to detect econometric problems, tests for autocorrelation using the xtserial command and heteroskedasticity using the xttest3 command were conducted. Since both tests were statistically significant (p-value < 0.05), the feasible generalized least squares (FGLS) method was utilized to obtain efficient and unbiased estimates. To confirm the robustness and reliability of findings, two other alternative methods as robustness checks were adopted: (1) recalculating the dependent variable using a different combination of outputs (only GDP and ecological footprint), and (2) estimating the model using the GMM to address potential endogeneity concerns. 4. Empirical results 4.1. Descriptive statistics Prior to presenting the model estimation results, the descriptive statistics of the research variables are presented in Table 3 . The data refer to a balanced panel including 270 observations, which included the mean, standard deviation, minimum, and maximum values for the entire sample. Table 3 Descriptive Statistics. Variable Obs Mean Std. Dev. Min Max Eco-Efficiency 270 .778 .246 .327 1 IP. Payment 270 6.585e + 09 1.600e + 10 30999229 9.837e + 10 IP. Receipt 270 4.351e + 09 8.957e + 09 173942.07 4.876e + 10 GDP. Pc 270 33389.266 23048.197 6951.739 123734.31 ER 270 1.293 1.901 0 10 IS 270 19.841 6.56 6.4 39 HC 270 34.143 8.373 17.9 51.6 R. new 270 14.566 10.014 1.97 51.056 Note : Measurement units are specified in Table 2 . According to the results, the mean Eco-Efficiency is 0.778, indicating the average performance of the sample countries in environmental productivity. The range of this index from 0.327 to 1, and its standard deviation of 0.246, suggest relative dispersion and the existence of heterogeneity in the Eco-Efficiency levels of the studied countries. Regarding the open innovation variables, the mean of IP. Payment at 6.585 billion USD is, on average, higher than the mean of IP. Receipt at 4.351 billion USD. The very high standard deviation of both variables, exceeding their respective means, indicates a significant disparity in the level of countries' participation in open innovation flows. This heterogeneity is also clearly observed in the GDP per capita variable. 4.2. Unit Root test As mentioned in section 3.4 , to avoid spurious regression, the stationarity of the variables was examined using two reliable panel unit root tests—IPS and Fisher-type. The results of these tests, presented in Table 4 , clearly indicate that the statistics for all variables are significant at the 5% and 1% level (p-value < 0.05; p-value < 0.01) in both tests. This result implies that all variables are stationary at level [I(0)]. Consequently, it can be assured that the possibility of spurious regression in subsequent model estimations is ruled out, and the data are suitable for more reliable econometric analysis. Table 4 Results of panel unit root Tests. Variable IPS-Statistic p-value Fisher-Statistic p-value Stationarity Eco-Efficiency -5.876 0.000 155.669 0.000 Yes IP. Payment -2.487 0.006 163.134 0.000 Yes IP. Receipt -2.784 0.000 156.728 0.000 Yes GDP. Pc -2.123 0.016 124.199 0.000 Yes ER -5.626 0.000 154.677 0.000 Yes IS -2.649 0.004 135.909 0.000 Yes HC -11.721 0.000 125.265 0.000 Yes R. new -1.796 0.036 142.394 0.000 Yes Note : This table reports the results of the Im-Pesaran-Shin (IPS) and Fisher-type panel unit root tests. Both tests' null hypothesis is that the panel contains a unit root (non-stationary). The IPS test allows for heterogeneous autoregressive parameters of panels, and the Fisher test combines p-values of individual unit root tests. 4.3. Regression results The results of the model estimation using the FGLS method for the entire EU and the two Eastern and Western regions are presented in Table 5 . For the entire EU, both IP. Payment (0.381, p-value < 0.01) and IP. Receipt (0.154, p-value < 0.05) show a positive and significant impact on Eco-Efficiency at the 99% and 95% confidence levels, respectively. Among the control variables, GDP per capita, environmental regulations, and the share of renewable energy have a positive and significant effect on Eco-Efficiency, while industrial share and human capital have a negative and significant impact on it. Table 5 Results of FGLS regression. Variable European Union Western Eastern IP. Payment 0.381*** (0.101) -0.021 (0.018) -9.100*** (2.812) IP. Receipt 0.154** (0.076) 0.057*** (0.011) 5.93** (2.720) GDP. Pc 0.000*** (0.000) 0.000*** (0.000) 0.000* (0.000) ER 0.013** (0.005) 0.000 (0.001) 0.006 (0.012) IS -0.017*** (0.002) 0.002*** (0.001) -0.005 (0.007) HC -0.003* (0.002) -0.001* (0.000) 0.010 (0.008) R. new 0.005*** (0.001) -0.001*** (0.000) -0.006 (0.005) Constant 0.966*** (0.078) 0.949*** (0.021) 0.348 (0.376) Obs. 270 130 140 Note : The table reports FGLS coefficients with robust standard errors in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. All models include country and year fixed effects. To prevent dispersion of IP. Payment and IP. Receipt data, they are normalized to the maximum and minimum of this variable. The results for Western countries reveal a different pattern. In this region, IP. Payment has insignificant effect, whereas IP. Receipt has a small but highly significant positive impact (0.057, p-value < 0.01). This finding may indicate the maturity of the innovation ecosystem in Western countries, where knowledge commercialization plays a more effective role in enhancing Eco-Efficiency. The most striking results pertain to the Eastern EU countries. In this region, IP. Payment has a strong negative impact (-9.10, p-value < 0.01) on Eco-Efficiency, while IP.Receipt has a strong positive effect (5.93, p-value < 0.05). This significant duality may stem Eastern nations' dependence on importing obsolete and energy-intensive technologies on one hand, and the essential role of developing indigenous innovation capabilities and exporting advanced technologies on the other. 4.4. Robustness checks As mentioned, to validate the findings, robustness checks were conducted by utilizing the GMM to control endogeneity issues and alternative dependent variable (Alt. Eco-Efficiency) with a different combination of outputs. The results of these tests, presented in Table 6 , indicate a remarkable stability of the study's main findings. Table 6 Results of robustness tests. Variable European Union Western Eastern GMM Alt. Eco-Efficiency GMM Alt. Eco-Efficiency GMM Alt. Eco-Efficiency IP. Payment 0.381*** (0.103) 0.382*** (0.104) -0.020 (0.077) -0.014 (0.079) -9.097*** (2.813) -9.089*** (2.934) IP. Receipt 0.155** (0.077) 0.152* (0.077) 0.056** (0.026) 0.054** (0.025) 5.928** (2.722) 5.923** (2.785) GDP. Pc 0.000*** (0.000) 0.000*** (0.000) 0.000** (0.000) 0.000* (0.000) 0.000* (0.000) 0.000 0.000 ER 0.014*** (0.005) 0.013** (0.006) 0.001 (0.001) 0.000 (0.002) 0.006 (0.012) 0.006 (0.010) IS -0.018*** (0.002) -0.017*** (0.002) 0.001 (0.002) 0.001 (0.002) -0.005 (0.007) -0.005 (0.006) HC -0.003* (0.002) 0.002 (0.002) -0.002 (0.002) -0.001 (0.002) 0.010 (0.008) 0.010** (0.005) R. new 0.004*** (0.001) 0.005*** (0.001) -0.002** (0.001) -0.002** (0.001) -0.006 (0.005) -0.006 (0.005) Constant 0.958*** (0.079) 0.960*** (0.080) 0.976*** (0.100) 0.983*** (0.104) 0.348 (0.376) 0.348 (0.260) Obs. 270 270 130 130 140 140 Note : Robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The GMM estimator uses appropriate instruments for the lagged dependent variable and endogenous regressors. Alt. Eco-Efficiency denotes the alternative ecological efficiency measure using only GDP and ecological footprint as outputs. To prevent dispersion of IP. Payment and IP. Receipt data, they are normalized to the maximum and minimum of this variable. As observed, across all three samples (the entire EU, Western, and Eastern countries), the sign, significance level, and even the approximate magnitude of the coefficients for the key variables—IP. Payment and IP. Receipt—remain entirely consistent with the FGLS results presented in Table 5 , under both the GMM and the alternative dependent variable model. The negative and significant impact of IP. Payment in Eastern region and the positive impact of IP. Receipt across all three regions are confirmed by both robustness tests. This strong consistency in the result pattern significantly enhances the internal validity of the model and the robustness of the key findings of this study. 5. Discussion The findings of this study overall support the hypotheses proposed in Section 2.5 . The positive and significant impact of both IP. Payment and IP. Receipt on Eco-Efficiency at the aggregate EU level (H1) can be interpreted within the framework of an integrated knowledge network and technology spillover effects at the regional level (Cassiman & Valentini, 2016 ; Robertson et al., 2023 ). In such an ecosystem, bidirectional knowledge flows, regardless of direction, can generally contribute to improved environmental performance by facilitating the diffusion of more efficient technologies and best practices. However, the analysis of regional heterogeneity (H2) reveals more complex layers. The results for Eastern EU countries are particularly noteworthy. The strong negative impact of IP. Payment (H2b) aligns with the technology dependency trap theory (Khrustalev & Slavyanov, 2019 ; Maskus, 2000 ). This finding suggests that payments in these countries might primarily be for importing older, more energy-intensive technologies from more advanced economies, a phenomenon sometimes described as pollution haven effect. Furthermore, these significant financial costs could divert resources needed for domestic investment in green R&D (S. Liu & Zhong, 2024 ; S. Wang, 2025 ). Conversely, the strong positive impact of IP. Receipt in the same region indicates the vital role of developing indigenous innovation capabilities and commercializing domestic green technologies, which directly leads to increased Eco-Efficiency (L. Li & Chen, 2023 ; L. Li & Chen 2024 ; Virchenko et al., 2021 ; Yasmeen et al., 2020 ). This dual pattern in Eastern countries partially aligns with findings from (D. Li et al., 2022 ) in transition economies, emphasizing the importance of absorptive capacity and intern al development. In contrast, the pattern observed in Western countries only a small and significant positive effect from IP. Receipt (H2a) is more consistent with findings from (Leogrande, n.d.; Yang & Cai, 2025 ), who emphasize the association of IP. Receipt with higher productivity. The insignificance of payments in the West might indicate a mature innovation ecosystem and less reliance on external knowledge for addressing advanced environmental challenges. Therefore, by providing empirical evidence on the dual nature of open innovation, the findings of this study help resolve existing contradictions in the prior literature and demonstrate that the economic-technological context of a region plays a decisive role in shaping the environmental outcomes of open innovation flows. This study expands its contributions to theoretical literature in a multitude of ways. First, by providing empirical evidence of the inherent duality of open innovation, this study extends the classical theoretical framework of open innovation, which often emphasizes its integrated benefits. The results demonstrate that merely having open innovation boundaries is insufficient; rather, the direction of knowledge flows (inbound vs. outbound) and, specifically, the economic-institutional context in which these flows occur, lead to vastly different environmental outcomes. This bridges open innovation theory with the literature on environmental economics and regional development, emphasizing the necessity of considering context as a key variable in modeling this relationship. This study shows that a single model cannot explain the relationship between open innovation and Eco-Efficiency across all regions. The results of this study state significant implications for policymakers both at the EU level and within national governments. For European Commission and other supranational organizations, these results suggest that adoption of same policies (one-size-fits-all) in innovation and environment may be ineffective or even counterproductive. Instead, innovation strategies should be tailored to each country cluster's level of development, industrial structure, and technological capabilities. For Western EU countries, policies should focus on encouraging the development and export of advanced and high-efficiency green technologies, reinforcing their position as global leaders and, more importantly, the collective interest of the Union’s Eco-Efficiency. For Eastern EU countries, there is an urgent need for a dual-pronged smart policy approach: first, guiding intellectual property payments towards clean and green technologies rather than obsolete and polluting ones through tools like tax incentives or preferential financing; and second, strengthening domestic innovation capabilities through targeted support for green R&D, human capital enhancement, and green industrial clusters, aiming to increase the share of high-value-added intellectual property receipts. 6. Conclusion This study intended to examine the dual effect of open innovation on Eco-Efficiency and the moderating role of regional heterogeneity within the EU. The findings indicated that, at the EU level as a whole, both payments and receipts for intellectual property had a positive and significant effect on Eco-Efficiency. However, analysis at the regional level uncovered a distinct duality: in Eastern countries, payments had a strong negative effect and receipts a strong positive effect, whereas in Western countries, payments were insignificant and only receipts exhibited a small positive impact. These results, which remained robust for various checks, underlined the critical role of considering the economic-technological context in the design of innovation and environmental policies. Despite its contributions, this paper has several limitations that must be taken into consideration when interpreting results, such as the fact that its relatively short time span-2011-2020-may miss long-term trends. Second, the use of intellectual property payments and receipts as proxies for open innovation, although common, does not capture all dimensions of this complex paradigm. Third, the DEA method has several challenges, including sensitivity to outliers and an inability to conduct classical statistical hypothesis testing. Furthermore, it is noteworthy that the quantitative indicator of the number of environmental regulations does not necessarily reflect the stringency or enforcement effectiveness of these regulations. The limitations discussed above and the results of this study indicate promising directions for future research. First, the investigation of a similar relationship within other economic blocs (e.g., ASEAN or NAFTA) could assist in confirming the generalizability of the findings. Secondly, employing alternative or complementary proxies for open innovation, like co-patent data or international research collaboration indices, could highlight different facets of the issue. Third, the examination of the moderating role of significant variable, such as institutional quality, plays in the relationship between open innovation and Eco-Efficiency could provide a greater understanding of the underlying causal mechanisms. Declarations Declaration of Competing Interest The authors declare that they have no relevant financial or non-financial competing interests. Ethical Approval This study did not involve human participants or animals as subjects of research. Therefore, ethical approval from an institutional review board was not required. Consent to Participate As this research is based on regional and aggregated data analysis without any direct involvement of human subjects, individual consent to participate was not applicable. Consent to Publish Given that the manuscript does not contain any individual personal data, images, or case studies, consent for publication is not applicable. Clinical Trial Registration This research is an economic and regional analysis based on secondary data and is not a clinical trial. Therefore, clinical trial registration is not applicable. Mohammad Rahsepar Data curation, Resources, Writing - original draft preparation. Funding This study has not received any sort of funding. Author Contribution **Amir Hossein Nimjerdi:** Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration.**Mohammad Rahsepar:** Data curation, Resources, Writing - original draft preparation. Data Availability Data will be made available from corresponding author on reasonable request. References Andersen B, Rossi F. UK universities look beyond the patent policy discourse in their intellectual property strategies. Sci Public Policy. 2011;38(4):254–68. Breuer M, Leuz C, Vanhaverbeke S. (2025). Reporting regulation and corporate innovation. J Account Econ, 101769. Bye B, Fæhn T. The role of human capital in structural change and growth in an open economy: Innovative and absorptive capacity effects. World Econ. 2022;45(4):1021–49. Caputo M, Lamberti E, Cammarano A, Michelino F. Exploring the impact of open innovation on firm performances. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8074689","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558122477,"identity":"b29a855e-b471-4534-a107-f034905e9ad5","order_by":0,"name":"Amir Hossein Nimjerdi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYFACxgZmMA0mDRjkYOI8RGsxhrLwaYEpgYLEBlQ+JuCfdrjtc0FFnT0/O+8B5oKCben9M/IPMPyoYZAxb8CuReJ2YvPsGWcOJ85s5ktgnmFwO3fGjWQGxp5jDDwyB3BYA9TCzNt2IMHgMI8BMw9QSwNQCwNvAwOPBA4d8hAtdfb2UC3p8iBb/uLRYgDRwsy4gRmiJcEAqIUZny2GIC08QL/MANoCRLcNN555bHBY5pgETi1yt9MfM/OAQqz/jOFjnj+35eWOJz58+KbGxh6XFhRwAIlBlIZRMApGwSgYBTgAAKMZTqWap3i8AAAAAElFTkSuQmCC","orcid":"","institution":"Amirkabir University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Amir","middleName":"Hossein","lastName":"Nimjerdi","suffix":""},{"id":558122479,"identity":"e4ec513e-29f8-43b1-9e0c-fce2d8d676d7","order_by":1,"name":"Mohammad Rahsepar","email":"","orcid":"","institution":"Amirkabir University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Rahsepar","suffix":""}],"badges":[],"createdAt":"2025-11-10 08:38:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8074689/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8074689/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98203552,"identity":"c8d0bbc6-eb2e-4146-be05-e59872b49586","added_by":"auto","created_at":"2025-12-15 08:19:37","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":177866,"visible":true,"origin":"","legend":"","description":"","filename":"paper.docx","url":"https://assets-eu.researchsquare.com/files/rs-8074689/v1/d82c05c6f685817679182e6d.docx"},{"id":98203548,"identity":"8197771b-20d5-463b-923c-5926db0bb023","added_by":"auto","created_at":"2025-12-15 08:19:37","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4516,"visible":true,"origin":"","legend":"","description":"","filename":"92192c42593140b1a3c455cfe9f0431c.json","url":"https://assets-eu.researchsquare.com/files/rs-8074689/v1/93cd90e4091f1bd743a1362f.json"},{"id":98203550,"identity":"eae4457b-2527-4a71-ac85-826d67fbbca8","added_by":"auto","created_at":"2025-12-15 08:19:37","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":168959,"visible":true,"origin":"","legend":"","description":"","filename":"92192c42593140b1a3c455cfe9f0431c1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8074689/v1/72ab745c8f50e409d2b6544d.xml"},{"id":98203549,"identity":"9252b3f5-747e-487a-be75-a2b444579a60","added_by":"auto","created_at":"2025-12-15 08:19:37","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":168958,"visible":true,"origin":"","legend":"","description":"","filename":"92192c42593140b1a3c455cfe9f0431c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8074689/v1/e5e6b583a7ce21bfa35f18a3.xml"},{"id":98203551,"identity":"d49017bd-ba7c-4464-9311-781b9b1590cb","added_by":"auto","created_at":"2025-12-15 08:19:37","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174403,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8074689/v1/ba7a739097f6947694cf646f.html"},{"id":98432107,"identity":"4b2c2b7c-b42d-40ec-b1c8-9600381b610e","added_by":"auto","created_at":"2025-12-17 16:48:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1030252,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8074689/v1/803cd234-6d1b-4518-9b7d-d94facdf6b54.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Knowledge flows and ecological efficiency: A regional analysis of open innovation in the European Union","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the modern world, global economic systems face a fundamental paradox: the pursuit of continuous economic growth without exacerbating environmental crises (Ekonomou \u0026amp; Halkos, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Guterres, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Climate change, resource depletion, and rising pollution pose significant threats to the health of current and future generations (Mir et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; WEF, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In response to these widespread problems, the European Union (EU), by adopting of European Green Deal as an innovative growth strategy, has committed to transforming the continent into the world's first climate-neutral economy by 2050 (Silander, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Central to this monumental transition is achieving higher ecological efficiency (Eco-Efficiency) (De Pascale et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mhatre et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u0026mdash;a concept emphasizing the generation of greater economic output with lower inputs of material and energy and reduced pollution (Kuosmanen \u0026amp; Kortelainen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, a deep understanding of the factors that can enhance Eco-Efficiency at the national level has become a critical subject in academic and policy-making literature.\u003c/p\u003e\u003cp\u003eWithin this context, open innovation has become a key strategic paradigm that focuses on the purposeful use of inbound and outbound knowledge flows to accelerate internal innovation and expand markets for external exploitation (D\u0026iacute;az-D\u0026iacute;az \u0026amp; de Sa\u0026aacute; P\u0026eacute;rez, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Leit\u0026atilde;o et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On a practical level, these knowledge flows are often measured by transactions involving intellectual property (Dubbert et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These flows have a dual role in achieving sustainability goals (Kusi-Sarpong et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; S. Liu \u0026amp; Zhong, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the one hand, receipts for the use of intellectual property show successful commercialization of domestic knowledge (Virchenko et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and access to advanced, potentially more resource-efficient technologies (L. Li \u0026amp; Chen, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which can directly contribute to enhanced Eco-Efficiency (L. Li \u0026amp; Chen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yasmeen et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the other hand, payments for the use of intellectual property can signify technological dependence on external sources and an outflow of financial capital from the national economy (Khrustalev \u0026amp; Slavyanov, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Maskus, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This resource drain might constrain the funding necessary for domestic investment in research and development and the deployment of green technologies (S. Liu \u0026amp; Zhong, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; S. Wang, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), thereby creating a barrier to improving Eco-Efficiency. This inherent duality necessitates a meticulous examination of the net effects of these flows.\u003c/p\u003e\u003cp\u003eAlthough the existing literature increasingly examines the relationship between innovation and environmental performance, a prominent gap remains. Most prior studies treat open innovation as a unified and homogeneous construct, primarily focusing on aggregate indicators such as R\u0026amp;D expenditures (De Backer et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) or international patent counts (Yun et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This one-dimensional approach overlooks the inherent dual and contrasting nature of open innovation flows, particularly their financial dimension manifested in intellectual property payments and receipts. Furthermore, scant research has delved into how the economic-technological context of a region can moderate the role of these flows. In other words, the hypothesis that the effects of open innovation on Eco-Efficiency may systematically differ across countries with varying levels of economic development, industrial structure, and technological capabilities has remained largely unexplored.\u003c/p\u003e\u003cp\u003eThis study is designed with the primary aim of analyzing the dual role of open innovation in enhancing Eco-Efficiency, while accounting for the component of regional heterogeneity across the EU. To achieve this overarching goal, four specific objectives are pursued, which collectively represent the innovation and contribution of this research: First, to measure the Eco-Efficiency of 27 EU member states from 2011 to 2020 using a comprehensive data envelopment analysis (DEA) framework based on an output-oriented variable returns to scale (BCC) model. This model simultaneously incorporates multiple inputs, including labor, capital, and energy consumption, as well as both desirable outputs, including GDP and municipal waste recycling rate, and undesirable outputs, including ecological footprint and municipal waste generation. Second, to implement an econometric analysis of the separate and distinct effects of the two main flows of open innovation\u0026mdash;namely, payments and receipts for the use of intellectual property as proxies for this paradigm (Andersen \u0026amp; Rossi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Michelino et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) on the calculated levels of Eco-Efficiency, thereby providing a more nuanced understanding of the concept's dual nature. Third, constituting the primary innovation of the paper, to investigate the critical role of regional heterogeneity by dividing the sample into two distinct groups of Eastern and Western European countries, and to reveal potential dualities in the direction, magnitude, and significance of causal relationships within these two regions with distinct economic-technological contexts. Fourth, to ensure the reliability and robustness of the findings by conducting a series of robustness checks, which encompasses the utilization of an alternative dependent variable for Eco-Efficiency featuring a distinct combination of outputs, and the employment of the generalized method of moments (GMM) estimator to address potential econometric challenges such as endogeneity.\u003c/p\u003e\u003cp\u003eAfter this introduction, the paper is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the relevant theoretical and empirical literature, focusing on existing gaps to be used as a basis for developing the research hypotheses. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3\u003c/span\u003e discusses in detail the research methodology, which includes the measurement model of Eco-Efficiency, the panel regression model, variable definitions, and econometric estimation protocols. Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents and describes the empirical results of model estimations. Section \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003e5\u003c/span\u003e interpreting these findings within the context of the previous literature, discussing their theoretical and policy implications. Finally, Section \u003cspan refid=\"Sec19\" class=\"InternalRef\"\u003e6\u003c/span\u003e concludes by summarizing the main points of the research, stating its limits, and providing suggestions for future studies.\u003c/p\u003e"},{"header":"2. Literature review and hypothesis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Ecological efficiency (Eco-Efficiency)\u003c/h2\u003e\u003cp\u003eEco-Efficiency is a key paradigm in sustainable development (Jin et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) that addresses the relationship between economically valuable added performance and environmental pressures caused by economic activities (Sun \u0026amp; Wang, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; WBCSD, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). For the objective measurement of this concept, DEA has been widely used due to its ability to assess multiple inputs and outputs at the same time, without needing for an a priori functional form (Collier et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This method is particularly suitable for assessing Eco-Efficiency at the country and regional levels, as it can integrate both desirable outputs and undesirable outputs (Rashidi \u0026amp; Saen, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yılmaz, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe methodology is categorized into radial and non-radial models, in which radial models assume that changes in inputs and outputs are proportional (Charnes et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Sueyoshi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In contrast, non-radial models account for slack variables (Zubir et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Eco-Efficiency scores have risen internationally over time, although growth has been inconsistent among countries and regions (Fang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; C.-N. Wang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to (Sadorsky, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the significant gap in Eco-Efficiency growth is largely attributable to the fact that developed and developing countries have different economic models based on their technological capabilities and environmental policies.\u003c/p\u003e\u003cp\u003eNumerous studies have applied DEA to measure Eco-Efficiency in the EU. The study by (Puertas et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) employed DEA to assess the Eco-Efficiency of 20 countries of the EU between 2014\u0026ndash;2018. Their model utilized consumption of renewable energy, environmental protection investment, employment in the environmental sector, and recycling rate of waste as inputs, whereas the outputs were pollution-attributable mortality and GDP. The findings revealed that nations in Eastern Europe demonstrated the most significant potential for enhancement in their environmental performance. Other studies focusing on EU member states concluded that investment in clean technologies and renewable energy is positively associated with Eco-Efficiency (Cicea et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Majid et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Open innovation\u003c/h2\u003e\u003cp\u003eOpen innovation is defined as an innovation paradigm that permeates organizational boundaries, emphasizing the purposeful use of inbound and outbound knowledge flows to accelerate internal innovation and expand external markets (Chesbrough, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; D\u0026iacute;az-D\u0026iacute;az \u0026amp; de Sa\u0026aacute; P\u0026eacute;rez, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Leit\u0026atilde;o et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). At the macroeconomic level, measuring these knowledge flows presents a fundamental challenge. Within this context, charges for the use of intellectual property, payments and receipts have been increasingly accepted as valid and quantitative proxies for inbound and outbound knowledge flows in the Open Innovation paradigm (Andersen \u0026amp; Rossi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Michelino et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Payments indicate inbound knowledge flows and reliance on external technologies and knowledge (Khrustalev \u0026amp; Slavyanov, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Maskus, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), whereas receipts reflect outbound knowledge flows and the successful commercialization of intellectual property assets in global markets (Li \u0026amp; Chen, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Virchenko et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNumerous studies have used these indicators to measure open innovation at the national level. Some research has found a positive relationship between these flows and economic performance. For instance, it has been demonstrated that intellectual property receipts are correlated with higher total factor productivity levels (Leogrande, n.d.; Yang \u0026amp; Cai, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, current studies state that bidirectional knowledge flows (both inbound and outbound) lead to a more dynamic innovation ecosystem (Cassiman \u0026amp; Valentini, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Robertson et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, contradictory evidence exists concerning the impact of these flows on broader outcomes like environmental performance. overarching outcomes such as environmental performance. Some research indicates that inbound knowledge flows via payments can enhance access to more efficient green technologies, resulting in increased Eco-Efficiency (Sanchez-Garcia et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Teng\u0026ouml; \u0026amp; Andersson, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Conversely, other studies warn that over-reliance on external technology might undermine domestic innovation capacity and create a \"technology dependency trap\" that hinders the development of indigenous green technologies (Khrustalev \u0026amp; Slavyanov, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Maskus, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This contradiction in findings highlights the need for a more meticulous examination of the net effects of these flows in different contexts, particularly concerning sustainability goals.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. The relationship between open innovation and Eco-Efficiency\u003c/h2\u003e\u003cp\u003eThe examination of relationship between open innovation and Eco-Efficiency constitutes a nascent research domain characterized by complex mechanisms. In theory, open innovation can improve Eco-Efficiency via several ways. First, inbound knowledge flows can facilitate access to advanced, less polluting technologies that were developed in other countries by transferring green technologies (Sanchez-Garcia et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Teng\u0026ouml; \u0026amp; Andersson, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Second, knowledge diffusion can help spread the best practices and standards for managing the environment by connecting with global innovation networks (Phonthanukitithaworn et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Worakittikul et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Third, innovations that derived from open collaborations can improve resource productivity, facilitating process optimization and decreasing consumption of energy and material (Mairesse et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Phonthanukitithaworn et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEmpirical evidence on this relationship is still changing and sometimes contradictory. Numerous studies substantiate the beneficial effects of open innovation initiatives on environmental performance. For example, a study by (Caputo et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that open collaborations led to better environmental performance. A provincial-level study similarly identified relationship between knowledge intensity and decreased emission intensity (Y. Zhao et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNonetheless, there is also contradictory evidence, highlighting the dual aspects of open innovation. Some studies contend that inbound knowledge flows may not consistently target green technologies and could potentially result in lock-in into unsustainable technological trajectories (Khrustalev \u0026amp; Slavyanov, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sanchez-Garcia et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, the financial expenses linked to intellectual property payments may redirect resources away from domestic investments in green research and development, potentially negating the beneficial impacts of knowledge flows (S. Liu \u0026amp; Zhong, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; S. Wang, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A study on transition economies showed that depending too much on foreign technology without enough absorptive capacity can slow down progress on environmental issues (D. Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These contradictions imply that the relationship between open innovation and Eco-Efficiency may depend on context, the nature of knowledge flow (inbound versus outbound), and the degree of regional development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Control variables\u003c/h2\u003e\u003cp\u003eTo isolate the net impact of open innovation on Eco-Efficiency, a group of control variables from the literature based on accessibility and their impact on Eco-Efficiency is added to the model.\u003c/p\u003e\u003cp\u003eGDP per capita was included to examine the Environmental Kuznets Curve hypothesis, which asserts an inverted U-shaped correlation between per capita income and environmental degradation (Dinda, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Numerous studies have corroborated the existence of the Environmental Kuznets Curve (EKC) (X. Liu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e); however, conflicting evidence suggests that economic growth may result in increased environmental pressure (Molaei \u0026amp; Basharat, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Porter Hypothesis states that well designed environmental regulations can stimulate innovations that not only reduce pollution but also incite resource productivity improvements and alleviating compliance costs (M. Porter, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; M. E. Porter \u0026amp; Linde, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Several empirical research has emphasized the positive impact of regulations on efficiency and green innovation (Y. Xu et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; J. Zhang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the other hand, some have shown that regulations can hurt businesses financially and make them less competitive (Breuer et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tu \u0026amp; Shi, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which could have a negative impact on economic growth (X. Zhao et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe industrial sector's share of the economy is a key factor because it uses more resources and causes more pollution (Usman \u0026amp; Balsalobre-Lorente, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Countries with a lot of heavy industries, like steel and cement, usually have more environmental problems (B. Xu \u0026amp; Lin, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Some studies, on the other hand, argue that moving toward high-tech, less polluting industries in the industrial sector can moderate this negative relationship (B. Xu \u0026amp; Lin, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAn educated workforce is essential for the assimilation, adaptation, and implementation of advanced technologies, including green technologies (Hondroyiannis et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mehta \u0026amp; Khan, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Human capital is associated with more absorptive capacity and domestic innovation, which can improve Eco-Efficiency (Bye \u0026amp; F\u0026aelig;hn, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; C. Zhang \u0026amp; Lin, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). A direct way to lower level of ecological footprint is to switch from fossil fuels to renewable energy sources (Sharma et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Research consistently demonstrates a positive correlation between the integration of renewable energy and environmental performance metrics (Q. Wang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Research gap and hypothesis development\u003c/h2\u003e\u003cp\u003eThe literature review indicates that the primary deficiency in the existing research is the concurrent neglect of the duality of open innovation (differentiating between intellectual property payments and receipts) and the moderating influence of regional heterogeneity within the EU. To fill this gap, the following hypotheses are developed:\u003c/p\u003e\u003cp\u003eH1: Payments and receipts for intellectual property positively and significantly influence Eco-Efficiency at the aggregate EU level.\u003c/p\u003e\u003cp\u003eH2: The correlation between open innovation and Eco-Efficiency differs between Eastern and Western EU countries.\u003c/p\u003e\u003cp\u003eH2a: In Western, intellectual property receipts have a significant positive impact on Eco-Efficiency.\u003c/p\u003e\u003cp\u003eH2b: In Eastern, intellectual property payments have a significant negative impact on Eco-Efficiency, whereas receipts positively influence it.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Research methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Research design\u003c/h2\u003e\u003cp\u003eThis study utilizes a quantitative and causal-descriptive research design, employing panel data. The research population includes 27 EU states from 2011 to 2020. The choice of this timeframe was predicated on two principal factors: firstly, the comprehensive and uniform availability of data for all study variables across all countries during this interval; and secondly, the inclusion of critical policy periods, notably the execution of the Europe 2020 strategy and the official commencement of the European Green Deal. To evaluate the impact of regional heterogeneity, the countries were divided into two distinct sectors based on geographic and economic criteria: Western country group (13 countries) comprising advanced and stable economies, and Eastern country group (14 countries).\u003c/p\u003e\u003cp\u003eThe analytical framework in this study is organized into two successive and complementary steps. The first step is to use the non-parametric DEA method to calculate the Eco-Efficiency of each country for each year. In the second stage, econometric models using panel data regression is applied to identify determining factors and estimate the net effects of the independent variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Eco-Efficiency measurement model\u003c/h2\u003e\u003cp\u003eThe Eco-Efficiency was measured using the non-parametric DEA method under the BCC assumption with an output-oriented approach, whose mathematical model is presented in Eq.\u0026nbsp;1.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{max}\\beta\\:$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:s.t.\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\sum\\:_{j=1}^{n}{\\lambda\\:}_{j}{x}_{ij}^{p}\\le\\:{x}_{io}^{p},\\:\\:\\sum\\:_{j=1}^{n}{\\lambda\\:}_{j}{y}_{rj}^{p}\\ge\\:\\beta\\:{y}_{ro}^{p},\\:\\:\\sum\\:_{j=1}^{n}{{\\lambda\\:}_{j}\\stackrel{-}{y}}_{pj}^{b}\\le\\:\\beta\\:{\\stackrel{-}{y}}_{po}^{b},\\:\\:\\sum\\:_{j=1}^{n}{\\lambda\\:}_{j}=1,\\:\\:{\\lambda\\:}_{j}\\ge\\:0,\\:\\:j=1,\\dots\\:,n\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn this model, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j=1,\\:\\dots\\:,n\\)\u003c/span\u003e\u003c/span\u003e represents the set of decision-making units (DMUs), the variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}_{j}\\)\u003c/span\u003e\u003c/span\u003e specifies the contribution share of the j-th DMU in forming the efficient frontier, and the efficiency score for each unit is calculated as the inverse of β \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(Eco-Efficiency=1/\\beta\\:)\\)\u003c/span\u003e\u003c/span\u003e. In this model, energy consumption, labor, and capital are regarded as input variables (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}^{p}\\)\u003c/span\u003e\u003c/span\u003e), while the desirable outputs (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}^{p}\\)\u003c/span\u003e\u003c/span\u003e) comprise GDP and recycling rate of municipal waste, and the undesirable outputs (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{y}}^{b}\\)\u003c/span\u003e\u003c/span\u003e) encompass ecological footprint and municipal waste generation. All of these input and output variables required for calculating Eco-Efficiency, along with their measurement units and sources, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInputs and outputs in the calculation of ecological efficiency.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInputs:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergy consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThousand tons of oil equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEurostat\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal number of labors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e% population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWorld Bank\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNet investment in non-financial assets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecurrent US\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWorld Bank\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDesirable outputs:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGross Domestic Product (GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecurrent US\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWorld Bank\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecycling rate of municipal waste\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e% total waste\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEurostat\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUndesirable outputs:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEcological footprint index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlobal hectares per capita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGlobal Footprint Network\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual amount of municipal wastes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEurostat\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eNote\u003c/b\u003e: All data used in this study pertain to 27 European Union member states for the period 2011\u0026ndash;2020. The capital variable is measured using the net investment in non-financial assets indicator.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe selection of this model is due to its capability to simultaneously handle multiple inputs and outputs without requiring parametric assumptions or a specific functional form (Collier et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), making it apt for measuring Eco-Efficiency as a multivariate dimension. All computations related to the Eco-Efficiency for the period 2011\u0026ndash;2020 were performed using LINGO 19.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Econometric model\u003c/h2\u003e\u003cp\u003eTo quantitatively analyze the impact of open innovation on Eco-Efficiency, a two-way fixed effects panel regression model was employed. The basic econometric model of this research is presented in Eq.\u0026nbsp;2.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:Eco-Efficienc{y}_{i,t}=\\:{\\beta\\:}_{0}+\\:{\\beta\\:}_{1}IP.\\:Paymen{t}_{i,t}+\\:{\\beta\\:}_{2}IP.\\:Receip{t}_{i,t}+\\:\\sum\\:{\\beta\\:}_{j}{X}_{i,t}+\\:{\\alpha\\:}_{i}\\:+\\:{\\lambda\\:}_{t}\\:+\\:{\\epsilon\\:}_{i,t}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn this model, the dependent variable (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Eco-Efficienc{y}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e) is the calculated Eco-Efficiency index for country i in year t. The main independent variables include intellectual property payments (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IP.\\:Paymen{t}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e) and intellectual property receipts (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IP.\\:Receip{t}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e). The vector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e represents a set of control variables including GDP per capita, environmental regulations, industrial structure, human capital, and share of renewable energy. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e denote country-fixed effects and time-fixed effects respectively, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e is the error term. Detailed operational definitions, abbreviation, and data sources for all variables used in the model, including the dependent, independent, and control variables, are provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetails of regression variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbbreviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMeasurement/Description\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependent variable:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEcological efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEco-Efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe optimal ratio of the difference between desirable and undesirable outputs to inputs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCalculated by author\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndependent variables:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePayments for IP Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIP. Payment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCharges for the use of intellectual property, payments (current US\u003cspan\u003e$\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWorld Bank\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReceipts from IP Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIP. Receipt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCharges for the use of intellectual property, receipts (current US\u003cspan\u003e$\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWorld Bank\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl variables:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP per capita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDP. Pc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGross Domestic Product divided by population (current US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWorld Bank\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironmental regulations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of environmental regulations and policies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Wuepper et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustrial structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndustry value added (% of GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWorld Bank\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHuman capital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of workers with tertiary education (ISCED 2011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEurostat\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShare of Renewable Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR. new\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShare of primary energy consumption that comes from renewables.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOurWorldinData\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote\u003c/b\u003e: All variables listed above are utilized in the regression analysis. The ecological efficiency score is a unitless index ranging from 0 to 1, calculated by the author using the DEA model described in Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Estimation methods\u003c/h2\u003e\u003cp\u003eBefore model estimation, to confirm that there are no spurious regression in the model, the stationarity of the research variables was examined using both the Im-Pesaran-Shin (IPS) and Fisher-type panel unit root tests, the results of which indicated that all variables were stationary at the level. Then, to reject the null hypothesis according to the Hausman test (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the fixed effects model was considered as a better specification than the random effects model. Subsequently, to detect econometric problems, tests for autocorrelation using the xtserial command and heteroskedasticity using the xttest3 command were conducted. Since both tests were statistically significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the feasible generalized least squares (FGLS) method was utilized to obtain efficient and unbiased estimates. To confirm the robustness and reliability of findings, two other alternative methods as robustness checks were adopted: (1) recalculating the dependent variable using a different combination of outputs (only GDP and ecological footprint), and (2) estimating the model using the GMM to address potential endogeneity concerns.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Empirical results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Descriptive statistics\u003c/h2\u003e\u003cp\u003ePrior to presenting the model estimation results, the descriptive statistics of the research variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The data refer to a balanced panel including 270 observations, which included the mean, standard deviation, minimum, and maximum values for the entire sample.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEco-Efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIP. Payment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.585e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.600e\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30999229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.837e\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIP. Receipt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.351e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.957e\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e173942.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.876e\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP. Pc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33389.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23048.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6951.739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e123734.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR. new\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e: Measurement units are specified in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAccording to the results, the mean Eco-Efficiency is 0.778, indicating the average performance of the sample countries in environmental productivity. The range of this index from 0.327 to 1, and its standard deviation of 0.246, suggest relative dispersion and the existence of heterogeneity in the Eco-Efficiency levels of the studied countries.\u003c/p\u003e\u003cp\u003eRegarding the open innovation variables, the mean of IP. Payment at 6.585\u0026nbsp;billion USD is, on average, higher than the mean of IP. Receipt at 4.351\u0026nbsp;billion USD. The very high standard deviation of both variables, exceeding their respective means, indicates a significant disparity in the level of countries' participation in open innovation flows. This heterogeneity is also clearly observed in the GDP per capita variable.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Unit Root test\u003c/h2\u003e\u003cp\u003eAs mentioned in section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e, to avoid spurious regression, the stationarity of the variables was examined using two reliable panel unit root tests\u0026mdash;IPS and Fisher-type. The results of these tests, presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, clearly indicate that the statistics for all variables are significant at the 5% and 1% level (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in both tests. This result implies that all variables are stationary at level [I(0)]. Consequently, it can be assured that the possibility of spurious regression in subsequent model estimations is ruled out, and the data are suitable for more reliable econometric analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of panel unit root Tests.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIPS-Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFisher-Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStationarity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEco-Efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-5.876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e155.669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIP. Payment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e163.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIP. Receipt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e156.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP. Pc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e124.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-5.626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e154.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e135.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-11.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e125.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR. new\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e142.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e: This table reports the results of the Im-Pesaran-Shin (IPS) and Fisher-type panel unit root tests. Both tests' null hypothesis is that the panel contains a unit root (non-stationary). The IPS test allows for heterogeneous autoregressive parameters of panels, and the Fisher test combines p-values of individual unit root tests.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Regression results\u003c/h2\u003e\u003cp\u003eThe results of the model estimation using the FGLS method for the entire EU and the two Eastern and Western regions are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. For the entire EU, both IP. Payment (0.381, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and IP. Receipt (0.154, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) show a positive and significant impact on Eco-Efficiency at the 99% and 95% confidence levels, respectively. Among the control variables, GDP per capita, environmental regulations, and the share of renewable energy have a positive and significant effect on Eco-Efficiency, while industrial share and human capital have a negative and significant impact on it.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of FGLS regression.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEuropean Union\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWestern\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEastern\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIP. Payment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.381***\u003c/p\u003e\u003cp\u003e(0.101)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003cp\u003e(0.018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-9.100***\u003c/p\u003e\u003cp\u003e(2.812)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIP. Receipt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.154**\u003c/p\u003e\u003cp\u003e(0.076)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.057***\u003c/p\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.93**\u003c/p\u003e\u003cp\u003e(2.720)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP. Pc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.013**\u003c/p\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.017***\u003c/p\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002***\u003c/p\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.003*\u003c/p\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.001*\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR. new\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.005***\u003c/p\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.001***\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.966***\u003c/p\u003e\u003cp\u003e(0.078)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.949***\u003c/p\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.348\u003c/p\u003e\u003cp\u003e(0.376)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote\u003c/b\u003e: The table reports FGLS coefficients with robust standard errors in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. All models include country and year fixed effects. To prevent dispersion of IP. Payment and IP. Receipt data, they are normalized to the maximum and minimum of this variable.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results for Western countries reveal a different pattern. In this region, IP. Payment has insignificant effect, whereas IP. Receipt has a small but highly significant positive impact (0.057, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This finding may indicate the maturity of the innovation ecosystem in Western countries, where knowledge commercialization plays a more effective role in enhancing Eco-Efficiency.\u003c/p\u003e\u003cp\u003eThe most striking results pertain to the Eastern EU countries. In this region, IP. Payment has a strong negative impact (-9.10, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) on Eco-Efficiency, while IP.Receipt has a strong positive effect (5.93, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This significant duality may stem Eastern nations' dependence on importing obsolete and energy-intensive technologies on one hand, and the essential role of developing indigenous innovation capabilities and exporting advanced technologies on the other.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Robustness checks\u003c/h2\u003e\u003cp\u003eAs mentioned, to validate the findings, robustness checks were conducted by utilizing the GMM to control endogeneity issues and alternative dependent variable (Alt. Eco-Efficiency) with a different combination of outputs. The results of these tests, presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, indicate a remarkable stability of the study's main findings.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of robustness tests.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eEuropean Union\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eWestern\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eEastern\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGMM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAlt. Eco-Efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGMM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAlt. Eco-Efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGMM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAlt. Eco-Efficiency\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIP. Payment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.381***\u003c/p\u003e\u003cp\u003e(0.103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.382***\u003c/p\u003e\u003cp\u003e(0.104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.020\u003c/p\u003e\u003cp\u003e(0.077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003cp\u003e(0.079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-9.097***\u003c/p\u003e\u003cp\u003e(2.813)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-9.089***\u003c/p\u003e\u003cp\u003e(2.934)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIP. Receipt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.155**\u003c/p\u003e\u003cp\u003e(0.077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.152*\u003c/p\u003e\u003cp\u003e(0.077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.056**\u003c/p\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.054**\u003c/p\u003e\u003cp\u003e(0.025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.928**\u003c/p\u003e\u003cp\u003e(2.722)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.923**\u003c/p\u003e\u003cp\u003e(2.785)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP. Pc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000**\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.014***\u003c/p\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013**\u003c/p\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.018***\u003c/p\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.017***\u003c/p\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.003*\u003c/p\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.010**\u003c/p\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR. new\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.004***\u003c/p\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005***\u003c/p\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.002**\u003c/p\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.002**\u003c/p\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.958***\u003c/p\u003e\u003cp\u003e(0.079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.960***\u003c/p\u003e\u003cp\u003e(0.080)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.976***\u003c/p\u003e\u003cp\u003e(0.100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.983***\u003c/p\u003e\u003cp\u003e(0.104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.348\u003c/p\u003e\u003cp\u003e(0.376)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.348\u003c/p\u003e\u003cp\u003e(0.260)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: Robust standard errors are in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The GMM estimator uses appropriate instruments for the lagged dependent variable and endogenous regressors. Alt. Eco-Efficiency denotes the alternative ecological efficiency measure using only GDP and ecological footprint as outputs. To prevent dispersion of IP. Payment and IP. Receipt data, they are normalized to the maximum and minimum of this variable.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs observed, across all three samples (the entire EU, Western, and Eastern countries), the sign, significance level, and even the approximate magnitude of the coefficients for the key variables\u0026mdash;IP. Payment and IP. Receipt\u0026mdash;remain entirely consistent with the FGLS results presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, under both the GMM and the alternative dependent variable model. The negative and significant impact of IP. Payment in Eastern region and the positive impact of IP. Receipt across all three regions are confirmed by both robustness tests. This strong consistency in the result pattern significantly enhances the internal validity of the model and the robustness of the key findings of this study.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe findings of this study overall support the hypotheses proposed in Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e2.5\u003c/span\u003e. The positive and significant impact of both IP. Payment and IP. Receipt on Eco-Efficiency at the aggregate EU level (H1) can be interpreted within the framework of an integrated knowledge network and technology spillover effects at the regional level (Cassiman \u0026amp; Valentini, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Robertson et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In such an ecosystem, bidirectional knowledge flows, regardless of direction, can generally contribute to improved environmental performance by facilitating the diffusion of more efficient technologies and best practices.\u003c/p\u003e\u003cp\u003eHowever, the analysis of regional heterogeneity (H2) reveals more complex layers. The results for Eastern EU countries are particularly noteworthy. The strong negative impact of IP. Payment (H2b) aligns with the technology dependency trap theory (Khrustalev \u0026amp; Slavyanov, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Maskus, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This finding suggests that payments in these countries might primarily be for importing older, more energy-intensive technologies from more advanced economies, a phenomenon sometimes described as pollution haven effect. Furthermore, these significant financial costs could divert resources needed for domestic investment in green R\u0026amp;D (S. Liu \u0026amp; Zhong, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; S. Wang, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Conversely, the strong positive impact of IP. Receipt in the same region indicates the vital role of developing indigenous innovation capabilities and commercializing domestic green technologies, which directly leads to increased Eco-Efficiency (L. Li \u0026amp; Chen, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; L. Li \u0026amp; Chen \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Virchenko et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yasmeen et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis dual pattern in Eastern countries partially aligns with findings from (D. Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in transition economies, emphasizing the importance of absorptive capacity and intern al development. In contrast, the pattern observed in Western countries only a small and significant positive effect from IP. Receipt (H2a) is more consistent with findings from (Leogrande, n.d.; Yang \u0026amp; Cai, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who emphasize the association of IP. Receipt with higher productivity. The insignificance of payments in the West might indicate a mature innovation ecosystem and less reliance on external knowledge for addressing advanced environmental challenges. Therefore, by providing empirical evidence on the dual nature of open innovation, the findings of this study help resolve existing contradictions in the prior literature and demonstrate that the economic-technological context of a region plays a decisive role in shaping the environmental outcomes of open innovation flows.\u003c/p\u003e\u003cp\u003eThis study expands its contributions to theoretical literature in a multitude of ways. First, by providing empirical evidence of the inherent duality of open innovation, this study extends the classical theoretical framework of open innovation, which often emphasizes its integrated benefits. The results demonstrate that merely having open innovation boundaries is insufficient; rather, the direction of knowledge flows (inbound vs. outbound) and, specifically, the economic-institutional context in which these flows occur, lead to vastly different environmental outcomes. This bridges open innovation theory with the literature on environmental economics and regional development, emphasizing the necessity of considering context as a key variable in modeling this relationship. This study shows that a single model cannot explain the relationship between open innovation and Eco-Efficiency across all regions.\u003c/p\u003e\u003cp\u003eThe results of this study state significant implications for policymakers both at the EU level and within national governments. For European Commission and other supranational organizations, these results suggest that adoption of same policies (one-size-fits-all) in innovation and environment may be ineffective or even counterproductive. Instead, innovation strategies should be tailored to each country cluster's level of development, industrial structure, and technological capabilities. For Western EU countries, policies should focus on encouraging the development and export of advanced and high-efficiency green technologies, reinforcing their position as global leaders and, more importantly, the collective interest of the Union\u0026rsquo;s Eco-Efficiency. For Eastern EU countries, there is an urgent need for a dual-pronged smart policy approach: first, guiding intellectual property payments towards clean and green technologies rather than obsolete and polluting ones through tools like tax incentives or preferential financing; and second, strengthening domestic innovation capabilities through targeted support for green R\u0026amp;D, human capital enhancement, and green industrial clusters, aiming to increase the share of high-value-added intellectual property receipts.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study intended to examine the dual effect of open innovation on Eco-Efficiency and the moderating role of regional heterogeneity within the EU. The findings indicated that, at the EU level as a whole, both payments and receipts for intellectual property had a positive and significant effect on Eco-Efficiency. However, analysis at the regional level uncovered a distinct duality: in Eastern countries, payments had a strong negative effect and receipts a strong positive effect, whereas in Western countries, payments were insignificant and only receipts exhibited a small positive impact. These results, which remained robust for various checks, underlined the critical role of considering the economic-technological context in the design of innovation and environmental policies.\u003c/p\u003e\u003cp\u003eDespite its contributions, this paper has several limitations that must be taken into consideration when interpreting results, such as the fact that its relatively short time span-2011-2020-may miss long-term trends. Second, the use of intellectual property payments and receipts as proxies for open innovation, although common, does not capture all dimensions of this complex paradigm. Third, the DEA method has several challenges, including sensitivity to outliers and an inability to conduct classical statistical hypothesis testing. Furthermore, it is noteworthy that the quantitative indicator of the number of environmental regulations does not necessarily reflect the stringency or enforcement effectiveness of these regulations.\u003c/p\u003e\u003cp\u003eThe limitations discussed above and the results of this study indicate promising directions for future research. First, the investigation of a similar relationship within other economic blocs (e.g., ASEAN or NAFTA) could assist in confirming the generalizability of the findings. Secondly, employing alternative or complementary proxies for open innovation, like co-patent data or international research collaboration indices, could highlight different facets of the issue. Third, the examination of the moderating role of significant variable, such as institutional quality, plays in the relationship between open innovation and Eco-Efficiency could provide a greater understanding of the underlying causal mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no relevant financial or non-financial competing interests.\u003c/p\u003e\n\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eThis study did not involve human participants or animals as subjects of research. Therefore, ethical approval from an institutional review board was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs this research is based on regional and aggregated data analysis without any direct involvement of human subjects, individual consent to participate was not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven that the manuscript does not contain any individual personal data, images, or case studies, consent for publication is not applicable.\u003c/p\u003e\n\u003ch2\u003eClinical Trial Registration\u003c/h2\u003e\n\u003cp\u003eThis research is an economic and regional analysis based on secondary data and is not a clinical trial. Therefore, clinical trial registration is not applicable.\u003c/p\u003e\n\u003ch2\u003eMohammad Rahsepar\u003c/h2\u003e\n\u003cp\u003eData curation, Resources, Writing - original draft preparation.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study has not received any sort of funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003e**Amir Hossein Nimjerdi:** Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review \u0026amp;amp; editing, Visualization, Supervision, Project administration.**Mohammad Rahsepar:** Data curation, Resources, Writing - original draft preparation.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData will be made available from corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndersen B, Rossi F. 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PLoS ONE. 2024;19(8):e0293694.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ecological efficiency, Open innovation, Intellectual property, Regional Heterogeneity, Sustainable Development","lastPublishedDoi":"10.21203/rs.3.rs-8074689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8074689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOpen innovation is acknowledged as a dual paradigm that can improve ecological efficiency (Eco-Efficiency) via knowledge dissemination and advanced technologies, while simultaneously escalating environmental pressure through intensified economic activities and resource consumption. This paper analyzes the dual effects of intellectual property payments and receipts as proxies for open innovation on Eco-Efficiency, and examines the heterogeneity of these effects between Eastern and Western European Union (EU) countries from 2011 to 2020. Eco-Efficiency was measured using data envelopment analysis (DEA) under an output-oriented variable returns to scale (BCC) model. To estimate causal relationships, a panel regression model with fixed effects and a feasible generalized least squares (FGLS) estimator was employed to address heteroskedasticity and autocorrelation issues. Results for the entire EU indicated that both payment and receipt variables had a positive and significant impact on Eco-Efficiency. However, regional analysis revealed a clear duality: in Western countries, receipts had a small positive effect while payments were insignificant, whereas in Eastern countries, payments had a strong negative effect and receipts showed a strong positive effect on Eco-Efficiency. These findings, which held robust even when tested with the generalized method of moments (GMM) estimator and an alternative dependent variable, highlight the need for region-specific and intelligent policy approaches in the management of open innovation flows to attain sustainable development goals.\u003c/p\u003e","manuscriptTitle":"Knowledge flows and ecological efficiency: A regional analysis of open innovation in the European Union","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 08:19:28","doi":"10.21203/rs.3.rs-8074689/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-24T11:34:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-05T17:44:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T07:50:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123302966061292215604723886308204184980","date":"2026-02-27T08:24:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237764924561696716971068468762136385275","date":"2026-02-27T07:43:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16260666265298519036004737196389742173","date":"2026-02-24T08:07:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-10T09:17:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-02T06:30:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T11:55:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-20T11:55:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-11-10T08:36:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7301d853-4ceb-4d46-9fbc-f08903cb9d63","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T08:08:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 08:19:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8074689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8074689","identity":"rs-8074689","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}