The globalization and ecological footprint in european countries: Correlation or causation?

Karimli, Turan; Mirzaliyev, Nihal; Guliyev, Hasraddin Article The globalization and ecological footprint in european countries: Correlation or causation? Research in Globalization Provided in Cooperation with: Elsevier Suggested Citation: Karimli, Turan; Mirzaliyev, Nihal; Guliyev, Hasraddin (2024) : The globalization and ecological footprint in european countries: Correlation or causation?, Research in Globalization, ISSN 2590-051X, Elsevier, Amsterdam, Vol. 8, pp. 1-9, https://doi.org/10.1016/j.resglo.2024.100208 This Version is available at: https://hdl.handle.net/10419/331133 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/bync/4.0/). The Globalization and Ecological Footprint in European Countries: Correlation or Causation? Turan Karimli a , b , * , Nihal Mirzaliyev a , b , Hasraddin Guliyev a , c a Azerbaijan State University of Economics, Baku, Azerbaijan b Istanbul University, Department of Economics, Istanbul, Turkey c Western Caspian University, Baku, Azerbaijan ARTICLE INFO JEL Classification: Q57 F64 C23 Keywords: Globalization Ecological footprints Panel granger non-causality test ABSTRACT Exploring the influencing factors of the ecological footprint is a current focus. However, the impacts of globalization on the ecological footprint are inconclusive due to the complexity among variables. The objective of this paper is to investigate the effects of globalization on the ecological footprint in 35 European countries in 1970–2020. While conducting the research, causality, which goes beyond correlation, was emphasized, and the new panel noncausality test is employed. The results indicate that globalization is generally Granger-cause of the ecological footprint in European countries. Additionally, the individual causation test results also show a unidirectional causality from economic globalization to the ecological footprint, bidirectional causality between social globalization and the ecological footprint, and no causality exists between political globalization and the ecological footprint. In conclusion, this paper not only provides valuable insights into the complex dynamics between globalization and ecological footprints but also offers nuanced policy recommendations tailored to European countries. These recommendations are designed to guide them toward achieving long-term sustainability in the face of the intricate relationships between globalization and ecological footprints. 1. Introduction The world is currently grappling with a conflict between economic expansion and sustainable development. Economic expansion, coupled with the advancements in globalization, imposes higher demands on the ecological environment. Consequently, the reduction of environmental degradation and the achievement of sustainable development have become global concerns (Chien et al., 2021; Opuala et al., 2022). The Ecological Footprint (EF), recognized as a comprehensive indicator of sustainable development, encompasses all aspects of environmental data for a given region. This metric not only reflects the overall demand of human activities for natural resources but also measures the biological capacity of the area (Ullah et al., 2021; Yilanci and Gorus, 2020). The ecological footprint serves as a concept quantifying human demand on natural resources by comparing it with nature’s supply. It estimates the biologically productive land and water area needed to produce goods and services consumed by a person or population and to absorb the waste they generate (Gao et al., 2022). Furthermore, the ecological footprint is a crucial indicator of environmental sustainability, signifying the ability to maintain a certain level of resource use and waste generation without compromising the future availability of these resources and the environmental quality (Tabash et al., 2023). In this context, environmental degradation carries serious consequences for human well-being, including increased vulnerability to natural disasters, reduced food security, heightened health risks, and diminished economic opportunities in contemporary times. Therefore, the ecological footprint emerges as a valuable tool for monitoring trends and patterns of environmental degradation. Additionally, it aids in evaluating the effectiveness of actions taken to prevent, mitigate, and adapt to the negative impacts of environmental degradation. Globalization is the process of increasing the integration and interdependence of countries and regions in terms of economic, political, social, and cultural aspects. Globalization has both positive and negative effects on the environment. On one hand, it can promote innovation, cooperation, and efficiency in the use of natural resources. On the other hand, it can increase the demand for energy, materials, and land, and exacerbate the environmental problems such as climate change, biodiversity loss, and pollution (Hill, 2008). One of the main environmental impacts of globalization is the increased transport of goods, which consumes more fuel and produces more greenhouse gas emissions. The * Corresponding author at: Azerbaijan State University of Economics, Baku, Azerbaijan. E-mail addresses: [email protected] (T. Karimli), [email protected] (N. Mirzaliyev), [email protected] (H. Guliyev). Contents lists available at ScienceDirect Research in Globalization journal homepage: www.sciencedirect.com/journal/research-in-globalization https://doi.org/10.1016/j.resglo.2024.100208 Received 27 November 2023; Received in revised form 7 February 2024; Accepted 2 March 2024 Research in Globalization 8 (2024) 100208 2 transport sector accounts for about 20 % of the global CO 2 emissions, and its demand is expected to grow faster than any other sector of the world economy (Transportation Emissions Worldwide, 2023). Another environmental impact of globalization is the change in consumption patterns and lifestyles, which can affect the carbon footprint of individuals and countries. For example, globalization can increase the demand for imported food, which may have a higher carbon footprint than locally produced food, due to the emissions from transportation, processing, and packaging (Lamla, 2009). Moreover, globalization can influence the environmental policies and regulations of different countries, which can have implications for their carbon footprint. For instance, globalization can create pressure for countries to adopt more stringent environmental standards, as consumers and investors become more aware and concerned about the environmental impacts of their choices (Stern, 2004). Alternatively, globalization can also create incentives for countries to lower their environmental standards, as they compete for foreign markets and investments. The relationship between carbon footprint and globalization is complex and dynamic, and it depends on various factors, such as the level and type of globalization, the structure and composition of the economy, the availability and use of renewable energy sources, and the environmental awareness and behavior of the society (Antweiler et al., 2001). Therefore, it is important to adopt a holistic and multidimensional approach to understand and address the environmental challenges posed by globalization, and to seek for solutions that can balance the economic, social, and environmental aspects of development. In this context, the central argument of this paper revolves around the complex relationship between globalization and environmental sustainability, specifically measured through the ecological footprint. The paper acknowledges the global concern regarding the conflict between economic growth and sustainable development, emphasizing that economic expansion, fueled by globalization, places increasing demands on the ecological environment. The main question of this study is: How do the dimensions of globalization, such as economic, social, and political, impact the ecological footprint, and what are the implications for environmental sustainability? Therefore, this study contends that the ecological footprint serves as a comprehensive indicator of sustainable development, capturing the overall demand of human activities on natural resources and the biological capacity of a given region. Furthermore, the unique contribution of this paper to the larger debate lies in its methodological approach and the specific focus on the European context. Unlike previous studies, the research divides globalization into its sub-dimensions such as economic, social, and political and conducts a comprehensive empirical exploration using a large dataset covering the years 1970–2020 for 35 European countries. The paper introduces a nuanced understanding of the relationship between globalization and the ecological footprint by considering the sub-dimensions and employs a robust testing method. Therefore, considering the panel specification, Juodis, Karavias and Sarafidis (2021) developed a new method for testing Granger causality, which is robust test to slope heterogeneity, cross-sectional dependency, and heteroskedasticity, is used in this study. Therefore, this study seeks to advance our understanding by offering not only a more nuanced perspective on the multifaceted relationship between globalization and environmental sustainability but also by presenting a robust empirical analysis grounded in the European context. In summary, the central argument is that the dynamics of globalization, when dissected into economic, social, and political dimensions, significantly influence the ecological footprint, and this impact is critical for understanding and addressing environmental challenges. The paper positions itself as a valuable contribution to the ongoing debate by offering a more nuanced perspective and robust empirical analysis within the European country’s context. The remainder of the manuscript is structured as follows: we review the related studies in Section 2. The methodologies utilized in the analysis are introduced in Section 3. In Section 4, dataset, variables, and empirical results are shown. The discussion and policy implementation are given in Section 5. 2. Literature review The concern among scholars and policymakers over the environmental impact of human activities is due to the recognition that humanity has exceeded the Earth’s ecological carrying capacity threshold. Carbon dioxide (CO 2 ) emissions have been widely used as a measure to assess environmental degradation, which is primarily caused by human activities and poses a significant threat to the natural ecosystem. (Gao et al., 2020). Primarily, their analysis centers on the examination of the association between economic growth and CO 2 emissions, employing the Environmental Kuznets Curve (EKC) hypothesis as a foundational framework. The environmental Kuznets curve (EKC) hypothesis is about how income is related to many environmental factors. Grossman and Krueger (1995) were the first to figure it out, after reading Kuznets, 1955 important article about how economic growth affects income inequality. Kuznets’ theory is based on the fact that the relationship between income inequality and per capita income looks like an upside-down U. In this direction, the EKC hypothesis indicates that in the early phases of economic development, a rise in per capita income contributes favorably to environmental deterioration up to a certain threshold, after which point it has the opposite impact (Ozcan, 2013). In this regard, there are many studies that use CO 2 emissions to measure environmental degradation. (Halicioglu, 2009; Adedoyin et al.,2020; Farooq, 2022). In addition to CO 2 emissions, the following indicators have also been used to measure environmental degradation in different empirical analyses, such as sulfur dioxide emissions per person, nitrogen oxide emissions per person (Sinha, 2017; Wang et al., 2016; Zambrano-Monserrate et al., 2017). Recently, however, several authors have adopted alternative approaches based on the ecological footprint (Hassan et al., 2023; Danish et al., 2019; As¸ıcı & Acar, 2016; Charfeddine & Zouhair, 2017), a concept first introduced by Rees 1992 and subsequently refined by Wackernagel and Rees (1998). The researchers explain multiple reasons for using EF instead of CO 2 to represent environmental degradation (Behjat & Tarazkar, 2021). EF is one of the environmental indicators that provides a comprehensive assessment of environmental degradation. EF considers multiple dimensions of environmental impact, including land use, water consumption, and waste generation. This multidimensional approach provides a more comprehensive view of the relationship between economic growth and environmental impact than CO 2 emissions alone (Costanza, 2000; Jorgenson & Clark, 2012). Furthermore, EF measures the amount of land and water required to sustain human activities, which reflects the actual resource consumption associated with economic growth. In contrast, CO 2 emissions only capture one aspect of environmental impact and do not directly reflect resource consumption (Wackernagel et al., 2002). Due to its lack of coverage of multiple dimensions of the ecological impact of human activity, we regarded the utilization of CO 2 in the study as a weakness in the literature. Consequently, we opted to implement EF as an alternative, which encompasses numerous dimensions of ecological activity. In this direction, our research tries to reveal the relationship between ecological footprint and globalization. Globalization remains a prominent subject of discussion in our modern world, impacting the socio-economic dimensions of people across the globe. While there is no universally agreed-upon definition, according to Jones (2010), globalization can be described as the widening, interconnection, and interdependence of different aspects of society. On the other hand, Rennen and Martens (2003) offer a perspective of globalization as a multifaceted phenomenon encompassing interactions that transcend national borders, involving economics, social dynamics, culture, technology, and the environment. Globalization, driven by foreign direct investment (FDI) and trade, enhances economic openness, energy demand, and financial development. It also serves as a conduit for the dissemination of cultural, social, and T. Karimli et al. Research in Globalization 8 (2024) 100208 3 political values. This global interconnectivity significantly impacts human lives through shifts in capital flows, technological transfers, and environmental consequences. Various indices are employed in scholarly discourse to gauge globalization. One extensively utilized metric is the KOF Globalization Index, introduced by Dreher (2006). This index is a widely accepted tool for evaluating globalization, encompassing three fundamental dimensions: economic, social, and political. In the pursuit of a more comprehensive and adaptable measure of globalization, Gygli et al. (2019), inspired by the work of Martens et al. (2015), conducted a revision of Dreher’s KOF Globalization Index. This present study is recognized as a sophisticated and adaptable evaluation of globalization. Furthermore, beyond the aforementioned indices, the literature introduces additional measures such as de jure and de facto indices to capture nuanced aspects of globalization (Martens et al., 2015). Ecological processes are inseparable from globalization, as it expands individual ecological footprints due to a sharp rise in economic activities. The demands of global integration, coupled with economic disparities, intensify ecological effects, resulting in an unsustainable environmental footprint (Hoekstra & Wiedmann, 2014). Despite its transformative impact on the planet’s health, opinions on globalization’s consequences remain sharply divided. Many studies addressing this issue have been examined in the literature. Regarding the topic of globalization, prior research has explored its influence on Ecological Footprint, yielding divergent findings. Ibrahiem and Hanafy (2020) employed yearly data spanning the period from 1971 to 2014, revealing that globalization played a constructive role in diminishing ecological footprint levels within the context of Egypt. Similarly, Saud et al. (2020) conducted a study examining the relationship between globalization and Ecological Footprint and discovered that globalization had a positive impact on environmental quality within the specific set of countries they analyzed. Destek and Ozsoy (2015) employed the ARDL bound test and asymmetric causality tests to examine the impact of globalization on environmental indicators in Turkey. Their findings established a cointegration between globalization and CO 2 variables. Notably, the asymmetric causality test results indicated a reduction in CO 2 emissions with increased economic globalization. Additional empirical studies echo these findings, indicating a positive relationship between globalization and the environment (Phong, 2019; Bilgili et al., 2020; Shahbaz et al., 2015). On the contrary, research by Sethi et al. (2020) suggests that the intensification of globalization exacerbates the ecological implications of global warming by contributing to increased carbon dioxide emissions in India. In the same direction, in a comprehensive analysis conducted by Figge et al. (2017), which included data from 146 nations, a positive link was found between globalisation and Ecological footprints. Çatık et al. (2024) conducted an investigation into the effects of renewable and nonrenewable energy consumption, income inequality, and globalization on the ecological footprints of 49 countries from 1995 to 2018. The findings suggest that globalization tends to have a negative impact on environmental quality, particularly in the context of lower growth regimes. Several scholarly investigations have concurred with this assertion, corroborating the established findings. (Fell & Maniloff, 2018; Destek & Sarkodie, 2019; Olowu et al., 2018; Shahbaz et al., 2020; Yilanci & Gorus., 2020; Aliyev and Suleymanov, 2023). Furthermore, considering mixed outcomes, a case study on Malaysia by Ahmed et al. (2019) revealed nuanced correlations between Ecological Footprint and globalization, highlighting both detrimental and beneficial implications for the environment. Destek, O˘ guz and Okumus¸ (2023) investigated the impact of trade and financial globalization on environmental quality in 11 transitioning economies from 1995 to 2018, utilizing the CS-ARDL approach. The findings suggest that trade globalization has no significant impact on the environment. However, an increase in both de facto and de jure financial globalization indices is linked to higher carbon emissions, with de jure financial globalization causing more notable environmental damage. In addition, investigations by Haseeb et al. (2018) and Xu et al. (2022) reported an insignificant relationship between globalization and environmental impact, contributing to the nuanced discourse surrounding the multifaceted nature of this association. In the context of limited studies on the relationship between globalization and environmental outcomes in Europe, four notable investigations offer insights into this complex interplay. Leal et al. (2019) explored the connection in 25 EU countries, employing de jure and de facto measures for classification. The results revealed that, overall, globalization increases environmental degradation, with the de jure measure having greater influence on highglobalized countries and the de facto measure having greater influence on low-globalized countries. Vlahini´ c Lenz and Fajdeti´ c (2021) further delved into this relationship, utilizing the Arellano–Bond estimator in a study covering 26 EU countries from 2000 to 2018. Their findings suggested that, during this period, the social and political dimensions of globalization were linked to a reduction in negative climate impacts, hinting at a potential positive aspect of globalization in addressing environmental concerns. Destek et al. (2020) investigated the impact of globalization dimensions on environmental pollution in Central and Eastern European Countries (CEECs) from 1995 to 2015. Employing second-generation panel data methodologies to address cross-sectional dependencies, the study revealed that an increase in overall, economic, and social globalization was associated with rising carbon emissions, while an increase in political globalization corresponded to a reduction in environmental pollution. Sharif et al. (2019) extended this exploration to 15 nations that underwent globalization from 1970 to 2016. Their study, focusing on the correlation between globalization and Ecological footprint, revealed variations in the levels of globalization and its impact on environmental quality among these countries. Notably, Belgium, the Netherlands, and Sweden exhibited a positive influence of globalization on Ecological footprint, while France, Germany, and Hungary displayed a negative impact. Collectively, these studies contribute to our understanding of the nuanced and varied effects of globalization on environmental dynamics in European contexts. In the existing body of literature, the relationship between globalization and environmental degradation remains inconclusive. The absence of definitive conclusions underscores the need for further scholarly investigation, possibly employing refined methodological approaches. From this perspective, the current panel data analysis methodology applied in this research helps to investigate the relationship between globalization and ecological footprint in a more reliable way. However, amidst the extensive body of literature, a notable research gap emerges. While numerous studies have delved into the intricate relationship between globalization and environmental indicators, there remains a paucity of comprehensive investigations within the European context, especially with a specific focus on the nuanced dimensions of economic, social, and political globalization. The need for a more nuanced understanding of how these dimensions interplay with the ecological footprint in the European setting forms the crux of the identified research gap. Therefore, gaining a more nuanced understanding of the direction of this relationship holds the potential to provide additional insights for policymakers in formulating tailored environmental policies within the context of a globalized world. 3. Methodology Like other scientists, economists are interested in “cause and effect” regarding the nature of human behavior concerning economic actions (production, consumption, and trade) and the allocation of resources. We know that sorting out causality from correlation turns out to become a challenge. Sometimes, this would be misleading because most economic variables are time-dependent. A common logical error occurs when you observe that event “A” frequently and consistently happens before event “B”. Simply because of this observation, we should not jump to the conclusion that “A caused B”. Post hoc or ergo propter hoc T. Karimli et al. Research in Globalization 8 (2024) 100208 4 fallacy is a well-known philosophical pitfall. It means “after this (in time), therefore because of this” (Case & Fair, 2007). We call such falsified reasoning spurious causation. This fallacy in human reasoning often occurs simply because A and B’s correlation coefficients become close to one. However, such a high correlation is spurious, too. At first, things appear to be a bit simpler regarding the correlation notion. Calculation of correlation coefficient is a test against whether there is a linear mutual bound between two variables (X and Y), irrespective of the direction of causation. Nevertheless, the replication of observations under laboratory conditions must still be held in such a calculation. Otherwise, one may calculate “spurious” correlations. It is crucial to understand that non-linear causation may push the correlation coefficient away from being perfect (close to −1 or +1) and create a tendency towards zero correlation. That is to say that X and Y may hold a “true” non-linear causation while having no strong correlation. There is no such luxury as a controlled laboratory condition in economics and social sciences. Observations are often recorded only once as time passes (time-series data). There is no chance of replications like the way that natural scientists do. Therefore, keeping certain variables constant (ceteris paribus) is almost impossible while allowing others to change. Thus, economists and statisticians have developed alternative methodologies to overcome this problem. They must implement various statistical tests against collected data to assure that changes in variables are white noise, having a normal distribution with a constant mean and constant standard deviation. Otherwise, the time-dependency of variables may lead to spurious causation/correlation outcomes. Statisticians have been far ahead of economists working with time-series data in scientific vigilance. Identifies seven distinct generational breakthroughs in the evolution of the methodologies used by economists for causation analyses. The biggest leap forward in this breakthrough was made by Nobel Prize winner Clive W.J. Granger. The philosophy of the Granger causality test is understandable and simple. In the social sciences, for X to be the cause of Y, it is necessary for Y to be explained not only by its past but also by the past of X, and we can speak of true causality at this time (Granger, 1969). Granger (1969) developed a statistical concept of causality between two or more time-series variables, according to which a variable x “Granger-causes” a variable y if the variable y can be better predicted using past values of both x and y rather than using solely past values of y. The concept of “Granger causality” has been widely adopted in economics, medicine, chemistry, physics, biology, engineering, and beyond. Granger causality is also useful when the data consist of multiple time series, as in the case of panel data. Methods of testing for Granger causality using panel-data models are very well cited and widely used. Prominent examples include the generalized method of moments (GMM) approach of Holtz-Eakin, Newey and Rosen (1988), which is valid for homogeneous panels with a few time-series observations (T), and the methods of Dumitrescu and Hurlin (2012) and Emirmahmutoglu and Kose (2011), suitable for heterogeneous, large-T panels. Recently, Juodis, Karavias and Sarafidis (2021) developed a new method for testing the null hypothesis of no Granger causality, which is valid in models with homogeneous or heterogeneous coefficients. The novelty of their approach lies in the fact that under the null hypothesis, the Granger-causality parameters equal zero, and thus they are homogeneous. This allows the use of a pooled fixed effects-type estimator for these parameters only, which guarantees a  NT √convergence rate, where N denotes the number of cross-sectional units in the panel and T denotes the number of time-series observations in the panel. To account for the so-called Nickell bias of the pooled estimator, their testing procedure makes use of the half-panel jackknife (HPJ) method of Dhaene and Jochmans (2015). The resulting approach works very well under circumstances that are empirically relevant: many cross-section units, a moderate time dimension, heterogeneous nuisance parameters, and high persistence. The method of Juodis, Karavias and Sarafidis (2021) has several advantages relative to existing approaches. In particular, the GMM approach of Holtz-Eakin, Newey and Rosen (1988) is not appealing when T is (even moderately) large. This is due to the wellknown problem of using too many instruments, which often renders the usual GMM-based inference highly inaccurate; see, for example, Bun and Sarafidis (2015) and remark in Juodis and Sarafidis (2022). Moreover, when feedback based on past own values is heterogeneous (that is, the autoregressive parameters vary across individuals), inferences may not be valid even asymptotically. On the other hand, while the method of Dumitrescu and Hurlin (2012) accommodates heterogeneous slopes under both null and alternative hypotheses, their test statistic is theoretically justified only for sequences where N/T2→0. This implies that when T is sufficiently smaller than N, that is, T≪N, this method can suffer from substantial size distortions. In an extended Monte Carlo experiment, Juodis, Karavias and Sarafidis (2021) show that their method outperforms the method of Dumitrescu and Hurlin (2012) in terms of power. In this study, we use the new version of the Granger noncausality test of Juodis, Karavias and Sarafidis (2021) developed by Xiao et al. (2023). The new version test offers options for both manual and automatic lag-length selection, using a Bayesian information criterion (BIC), allows for cross-sectional dependence and cross-sectional heteroskedasticity in the errors and test for Granger causality in equations with single or multiple relevant variables. 4. Variables, dataset and empirical results 4.1. Variables and dataset This section examines the variables for determining the relationship between globalization and ecological footprint in the European countries. The main variable is the Ecological Footprint of consumption (EFC). The most reported type of Ecological Footprint, it is defined as the area used to support a defined population’s consumption. The consumption Footprint (in global hectares - Gha) includes the area needed to produce the materials consumed and the area needed to absorb the carbon dioxide emissions. The consumption Footprint of a nation is calculated in the National Footprint and Biocapacity Accounts as a nation’s primary production Footprint plus the Footprint of imports minus the Footprint of exports, and is thus, strictly speaking, a Footprint of apparent consumption. Data on ecological footprint was obtained from the Ecological Footprint Network database (Global Footprint Network, 2023). In this research, we use the globalisation index, which we assume causes the Ecological Footprint. Since globalization is a very comprehensive expression, in this research we analysis it by dividing it into subdimensions. We follow Dreher (2006), who, based on Keohane and Nye (2000), distinguishes between three different dimensions of globalization. The Economic Globalisation Index (EGI) characterizes long distance flows of goods, capital, and services as well as information and perceptions that accompany market exchanges. Social Globalisation Index (SGI) expresses the spread of ideas, information, images, and people. The Political Globalisation Index (PGI) characterizes the diffusion of government policies. Data regarding these globalization dimensions is obtained from KOF Globalisation Index (KOF Globalisation Index, 2022). Data pertaining to specified variables were gathered across 35 European countries spanning the years 1970 to 2020. These countries include Albania, Austria, Belgium, Bulgaria, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland, and the United Kingdom. Furthermore, data for the period 1992–2020 were collected for Belarus, Bosnia and Herzegovina, Croatia, Czech Republic, Estonia, Latvia, Lithuania, the Republic of Moldova, the Republic of North Macedonia, the Russian Federation, Slovakia, Slovenia, and Ukraine. T. Karimli et al. Research in Globalization 8 (2024) 100208 5 4.2. Descriptive statistics and correlation analysis The descriptive statistics calculated for the variables used in this paper are summarized in Table 1. The variable, ecological footprint of consumption (EFC), shows that the overall average in European countries is 5.481 Gha, with a minimum of 1.030 Gha (in Albania) and a maximum of 17.280 Gha (in Luxemburg) in 1970–2020. The mean economic globalisation index (EGI) in European countries is 66.492, with a minimum of 18.959 (in Ukraine) and a maximum of 93.033 (in Luxemburg) in 1970–2020. On the other hand, the mean social globalisation index (SGI) in European countries is 69.332, with a minimum of 32.817 (in Poland) and a maximum of 91.838 (in Switzerland) in 1970–2020. In addition, the mean political globalisation index (PGI) in European countries is 78.214, with a minimum of 17.135 (in Bosnia and Herzegovina) and a maximum of 98.144 (in France) in 1970–2020. The correlation matrix for the variables is presented in Table 2. The correlation matrix reveals significant relationships among the variables. Strong positive correlations exist between EGI and SGI (r s = 0.871), and moderate positive correlations exist between EGI and PGI (r s =0.470) as well as SGI and PGI (r s =0.589). Additionally, moderate positive correlations are observed between EFC and EGI (r s =0.482), EFC and SGI (r s =0.415), and EFC and PGI (r s =0.344). As discussed in the methodology section, correlation does not imply causation. In this context, for a correlation to be considered genuine, it needs to be supported by causality. Therefore, in the next section, we first examine the panel data specification then apply a panel causality test. 4.3. Panel specification tests The panel data analysis started by examining the extent of crosssectional dependency. It is commonly assumed that disturbances in panel data models are cross-sectionally independent, especially when the cross-section dimension (N) is large. There is, however, considerable evidence that cross-sectional dependence is often present in panel regression settings. Ignoring cross-sectional dependence in estimation can have serious consequences, with unaccounted for residual dependence resulting in estimator efficiency loss and invalid test statistics (Tato˘ glu, 2012). The panel cross-sectional dependence test is reported in Table 3. We start the panel data analysis by examining cross-section dependence in the panel data by using the most well-known cross-section dependence diagnostic is the Breusch and Pagan (1980) Lagrange Multiplier (LM) test statistic. The asymptotic distribution of LM test is obtained for N fixed as Tij→∞ for all (i,j), and follows from a normality assumption on the errors. It is well known that the standard BreuschPagan LM test statistic is not appropriate for testing in large settings. To address this shortcoming, Pesaran (2004) proposes a standardized version of the LM s statistic, and this test is asymptotically standard normal as first Tij→∞ and then N→∞. Pesaran notes one shortcoming of the LM S which is that E(Tij ρ 2 ij −1)is not centered at zero for finite Tij, so that the statistic is likely to exhibit size distortion for small Tij, and that the distortion will worsen for larger N. To address the size distortion of LM and LMS, Pesaran (2004) proposes an alternative statistic based on the average of the pairwise correlation coefficients for calculate CD statistics which is asymptotically standard normal for Tij→∞ and N→∞ in any order. Further, Pesaran (2004) point provide for a wide array of panel data models, the mean of CD is exactly equal to zero for all Tij >k+1 and all N, so that the CD test is likely to have good properties for both N and Tij small,w and he provides Monte Carlo evidence to support this claim. Table 3 shows the test statistic value of LM, LM S and CD test. In this case, the p-values of the test close to zero, and we strongly reject the null of no correlation at conventional significance levels for all variables. Therefore, it is concluded that there is a cross-sectional dependence for the EFC, EGI, SGI, and PGI variables. 4.4. Panel unit root test The panel unit root tests have been used commonly because they have more power and size specifications compared to traditional unit root tests. The literature on panel unit root tests includes two different groups/generations of tests. The first-generation tests assume that the cross-sectional units are independent of each other. The first-generation tests include the LLC (Levin, Lin, & Chu, 2002), Hadri (2000), IPS (Im et al., 2003), and Fisher-ADF test (Choi, 2001) stationarity panel unit tests (Tato˘ glu, 2017). However, if the panel units (in this case-countries) include cross-sectional dependency, first-generation panel unit root tests have the size and power distortion Banerjee and Wagner (2009). The second-generation tests pay attention to cross-sectional dependence, such as Bai and Ng (2004) PANIC Test and Pesaran’s (2007) crosssectionally augmented IPS (CIPS) test (Yerdelen Tato˘ glu, 2017). In Table 4, the results of the PANIC Test conducted by Bai and Ng (2004) and the CIPS panel unit root test by Pesaran (2007) are presented for each variable, both at the level and in the first difference series. The ADF lag selection, employing AIC, is utilized for the PANIC test, with the maximum factor selection determined through Ahn and Horenstein (2013) method. To enhance factor selection procedures, it is noted that Table 1 Panel Summary Statistics of Variables. Variable Mean Std. dev. Min. Max. Obs. EFC 5.481 2.219 1.030 17.280 1497 EGI 66.492 15.506 18.959 93.033 1497 SGI 69.332 14.127 32.817 91.838 1497 PGI 78.214 17.105 17.135 98.144 1497 Table 2 Spearman correlation matrix. Correlation Matrix EFC EGI SGI PGI EFC 1 EGI 0.482*** 1 SGI 0.415*** 0.871*** 1 PGI 0.344*** 0.470*** 0.589*** 1 Note: ***, **, and *, denotes statistical significance at the 1 %, 5 %, and 10 % level, respectively. Table 3 Cross-sectional dependence test results. Variables Cross-sectional dependence tests Breusch and Pagan (1980) LM Pesaran (2004) scaled LM S Pesaran (2004) CD EFC 4990.490*** 127.419*** 22.711*** EGI 17755.580*** 497.460*** 129.795*** SGI 20911.710*** 588.952*** 143.114*** PGI 16141.991*** 450.685*** 122.911*** Note: *10 % level, **5% level, ***1% level. Table 4 Panel unit root test results. Variables Bai and Ng (2004) PANIC Test Pesaran (2007) CIPS test Level First Difference Level First Difference EFC −1.898 −3.563*** −0.731 −2.346*** EGI −0.483 −3.003** −0.875 −2.159** SGI 3.610 −3.240** −0.648 −2.173** PGI −1.702 3.207** −1.144 −2.643** Note: ***, **, and *, denotes statistical significance at the 1 %, 5 %, and 10 % level, respectively. T. Karimli et al. Research in Globalization 8 (2024) 100208 6 significant improvements can be achieved by demeaning and/or standardizing the time and/or cross-section dimensions. Notably, the Bai and Ng (2004) PANIC Pooled ADF-test outcomes indicate that all variables are identified as integrated of order 1. Similarly, the Pesaran (2007) test, which selects for a lag selection of 1 in the ADF regression, confirms that all variables exhibit integration of order 1. Therefore, we use the first difference panel time series for panel granger causality test. 4.4. Model estimation results In this section, panel regression analysis is performed to support the correlation in Table 2. Fixed effects (FE) and random effects (RE) model results used in panel data are summarized in Table 5. In Table 5 Panel A presents the results of the TWFE (Two-Way Fixed Effects) model estimation with three different specifications: Model EGI, Model SGI, and Model PGI. Fixed effects and time effects are included in all models. Since each variable represents a distinct dimension of globalization, multicollinearity between them is expected. Therefore, we include these variables separately in the model. Each model includes coefficients (β) for the respective economic, social, and political globalization, along with their Driscoll-Kraay (1998) standard errors in brackets. The overall F-statistics and FEs F-statistics denote the significance of the overall models’ and the two-way fixed effects, respectively. The results obtained from the Overall F-test indicate that the TWFE models estimated for economic, social, and political globalization are generally significant, and the F-test for the significance of fixed effects demonstrates that both unit effects and time effects are jointly significant in the model. In Table 5 Panel B, the panel cross-section Heteroskedasticity LR test indicates the groupwise residuals are heteroscedastic in all models because of the null hypothesis residuals is homoscedasticity is rejected. The DW and LBI test statistics show that AR (1) autocorrelation since the test values are away from 2. We further effectuated a model base crosssection independence Pesaran (2015) CD and Xie and Pesaran (2022) CD* test. Although the Pesaran (2004, 2015) CD test is generally used to test cross-sectional dependence in panel data analysis, they show that standard CD test is in fact valid for testing error cross-sectional dependence in panel data models with weak latent factors. However, when the latent factors are semi-strong or strong the use of CD test will result in over-rejection and will no longer be valid. Similarly, they reported that ate for the CD test to be valid, unless the latent factors are weak, namely unless α =max(aj)<0.5. Instead, they recommend using the CD* test. From the outcome of CD* test, we confidently reject the null hypothesis of cross-section independence in all models, which indicates that errors are correlated across panel groups. In consideration of the heteroskedasticity, autocorrelation and cross-section dependence between units within the models, we use Driscoll-Kraay (1998) robust standard errors. In summary, across all three economic SGI, and PGI), the coefficient β is statistically significant, indicating that the economic, social, and political globalization has a statistically significant and positive impact on the ecological footprint at %1 level. As such, a 1 % increase in economic, social, and political globalization approximately increases the ecological footprint by around 21 %, 27 %, and 40 %, respectively. 4.5. Panel causality analysis In this part we use panel causality test developed by Xiao et al. (2023) and Juodis et al. (2021) for panel Granger causality is valid for homogeneous or heterogeneous coefficients and control to crosssectional dependency with bootstrap variance. The results for Granger noncausality are indicated in Table 6. This method involves running N individual regressions to obtain N individual-specific Wald statistics, which are subsequently averaged over the cross-section. For the multivariate functions under study, we test if the selected covariates do not Granger-cause the response variable. The results obtained from the tests are reported in Panel A of Table 6. As we can see from the results, the null hypothesis that EGI, SGI and PGI do not jointly Granger-cause EFC in the European economies is rejected at the 1 % level of significance. Overall, the results in Panel A reveal that globalization does Granger-cause the ecological footprint in the European countries studied. We then consider univariate equations by testing Granger noncausality for each variable separately. The results are given in Panel B of Table 6. The results show a unidirectional causality from EGI to EFC, and bidirectional causality between SGI and EFC. In addition, the results also show that no causality exists between PGI and EFC. 5. Conclusion and policy implications This study examines the causal relationship between globalization and the ecological footprint in 35 European countries from 1970 to 2020. The main variable under investigation is the Ecological Footprint of consumption (EFC), which signifies the area required to support a Table 5 Model Estimation Results. Panel A. TWFE Model Estimation Coefficients Model EGI Model SGI Model PGI β 0.206*** [0.067] 0.265** [0.126] 0.400*** [0.058] constant 0.838*** [0.255] 0.583 [0.495] −0.126 [0.238] R 2 overall 0.287 0.302 0.2733 Overall F-stat 28.27*** 25.35*** 172.14*** FEs F-stat 4.58*** 4.75*** 5.48*** Fixed Effects Yes Yes Yes Time Effects Yes Yes Yes Panel B. Diagnostic Tests Heteroskedasticity test 5229.93*** 5555.98*** 5713.57*** Autocorrelation DW test 0.245 0.240 0.285 Autocorrelation LBI test 0.359 0.346 0.392 CD test −0.12 −0.77 −3.06*** CD* test −3.32*** −4.52*** −4.14*** [] indicates Driscoll - Kraay (1998) robust standard errors. *10 % level, **5% level, ***1% level. Table 6 Panel causality test results. Panel A: Multivariate non-causality test H0:Selected covariates do not Granger-cause EFC Lags HPJ Wald test Decision Conclusion EGI, SGI, PGI ⇒ EFC 3 73.6223 *** H0 is reject Globalization do Granger-cause ECF Panel B: Univariate non-causality test H0:x does not Granger-cause (⇒) y Lags HPJ Wald test Decision Conclusion EGI ⇒ EFC 1 9.008*** H0 is reject Unidirectional EGI ⇒ ECF EFC ⇒ EGI 3 5.462 H0 is not rejected SGI ⇒ EFC 1 4.008** H0 is reject Bidirectional SGI ⇔ ECF EFC ⇒ SGI 3 17.554*** H0 is reject PGI ⇒ EFC 1 0.237 H0 is not rejected No causality EFC ⇒ PGI 3 2.286 H0 is not rejected ***, ** and * denote 1 %, 5 % and 10 % significance levels, respectively; ⇔ denotes bidirectional causality; ⇒ denotes unidirectional causality. T. Karimli et al. Research in Globalization 8 (2024) 100208 7 population’s consumption. The EFC encompasses the areas needed for material production and carbon dioxide absorption. Globalization, considered the assumed to cause changes in EFC, is analyzed through three dimensions: the Economic Globalisation Index (EGI), Social Globalisation Index (SGI), and Political Globalisation Index (PGI). Nowadays, the discussions delve into the limitations of correlation coefficients, emphasizing that a high correlation does not necessarily indicate a causal relationship, as spurious correlations can arise. In Table 2, the correlation matrix indicates significant relationships among the variables, with moderate positive correlations are observed between ecological footprint of consumption and economic globalization, social globalization, and political globalization. However, the correlation does not imply causation, and genuine causality needs to be explored. Therefore, this study employs the updated Granger noncausality test developed by Xiao et al. (2023), based on the work of Juodis, Karavias and Sarafidis (2021). This new version of the test incorporates features such as manual and automatic lag-length selection, utilizing a Bayesian information criterion (BIC). It also accommodates cross-sectional dependence and cross-sectional heteroskedasticity in the errors. The test is designed to assess Granger causality in equations involving either single or multiple relevant variables. This approach enhances the precision and flexibility of the analysis, allowing for a more nuanced exploration of causality relationships in the context of the study. The test results show a unidirectional causality from economic globalization to ecological footprint, and bidirectional causality between social globalization and ecological footprint. In addition, the results also show that no causality exists between political globalization and ecological footprint. Economic globalization is characterized by the flow of goods, capital, services, and information across national borders. This interconnectedness can influence various aspects, including production and trade, resource extraction, supply chain effects, technological innovation, policy interactions, income and consumption patterns, and global environmental governance. The globalized economy may lead to increased extraction of natural resources, changes in supply chain structures, and the transfer of technology, all of which can have effects on ecological footprints in European countries. Empirical studies, such as causality tests, play a crucial role in uncovering the dynamics and nuances of this relationship within specific regions and timeframes. In this context, the advanced panel causality test used in this study shows that economic globalization is the main factor of the ecological footprint in European countries. Reducing the ecological footprint associated with economic globalization in European countries requires a comprehensive strategy encompassing sustainable practices, responsible consumption, and effective policies. Key initiatives include fostering sustainable supply chains with environmentally friendly sourcing and production, encouraging a global transition to renewable energy, and promoting the development and implementation of green technologies. Integrating environmental considerations into trade policies, implementing carbon pricing mechanisms, and enforcing regulations that address environmental concerns are crucial steps. Furthermore, fostering research and development for innovative solutions and balancing economic growth with ecological preservation are integral components of a holistic approach toward decreasing the ecological footprint associated with economic globalization in European countries. The relationship between social globalization and the ecological footprint in European countries involves understanding how interconnected societies, the exchange of ideas, information, and cultural influences across borders may impact the environmental footprint of these nations. Social globalization, encompassing the spread of ideas, images, and people, can influence ecological footprint in several ways. Increased awareness and shared values regarding environmental conservation may lead to changes in individual and collective behaviors, potentially reducing ecological footprints. Cultural exchange facilitated by social globalization may contribute to the adoption of sustainable practices and lifestyles. However, it’s crucial to consider potential challenges, such as increased consumption associated with global cultural influences. In this context, analyzing empirical data can help unveil the dynamics and nuances of the relationship between social globalization and the ecological footprint in European countries. Our findings reveal bidirectional causality between social globalization and the ecological footprint, indicating that they mutually affect each other. In general, social globalization is more strongly linked to the ecological footprint than economic globalization. In other words, increasing social globalization is necessary to reduce the ecological footprint in European countries; on the other hand, reducing the ecological footprint expands social globalization. To increase social globalization in Europe, initiatives should include promoting cultural exchange programs, supporting multilingual education, and organizing cultural festivals to showcase and appreciate the diversity of European cultures. Leveraging digital connectivity through social media and online platforms facilitates crosscultural dialogue and collaboration. Educational partnerships between institutions, policies promoting tolerance and inclusivity, and community-level initiatives further contribute to creating an environment of acceptance and shared understanding. Collaborations in arts, entertainment, and media, as well as fostering youth engagement and cross-border partnerships among businesses and organizations, play pivotal roles in strengthening social ties and promoting a shared European identity. Collectively, these measures contribute to enhancing social globalization, fostering a more interconnected and culturally rich European society, and all of them contribute to increasing awareness of environmental degradation and decreasing ecological footprint. In general, our findings underscore that the influence of economic and social globalization on the ecological footprint is more significant than that of political globalization, thereby making a meaningful scientific contribution towards fostering a cleaner environment. However, like any research endeavor, our study faces certain limitations. Europe’s diversity introduces a layer of complexity to the causality dynamics explored in our study. The interconnectedness of different countries with varying socio-economic and cultural characteristics may result in diverse responses to globalization, leading to varied ecological footprints. To address this, we are commended to provide a more detailed discussion in future research on how the identified causality relationships are influenced by diversity within Europe. Furthermore, we acknowledge the limitations associated with the exclusion of certain European countries from the panel due to data constraints. This exclusion may introduce potential bias into the analysis, particularly if the omitted countries possess unique characteristics that could impact the study’s findings. Therefore, to ensure comprehensive and robust conclusions, future research should endeavor to incorporate a broader range of countries, including those initially excluded due to data limitations. Moreover, applying the same methodological paradigm to diverse country groups is essential to enhance the generalizability and validity of the findings across different geographical contexts and economic conditions. Funding This study was not funded. CRediT authorship contribution statement Turan Karimli: Writing – review & editing, Writing – original draft, Supervision, Formal analysis, Data curation, Conceptualization. Nihal Mirzaliyev: Writing – review & editing, Writing – original draft, Formal analysis, Data curation. 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