Ecological footprint and human well-being nexus: Accounting for broad-based financial development, globalization, and natural resources in the Next-11 countries

Nathaniel, Solomon Prince Article Ecological footprint and human well-being nexus: Accounting for broad-based financial development, globalization, and natural resources in the Next-11 countries Future Business Journal Provided in Cooperation with: Faculty of Commerce and Business Administration, Future University Suggested Citation: Nathaniel, Solomon Prince (2021) : Ecological footprint and human well-being nexus: Accounting for broad-based financial development, globalization, and natural resources in the Next-11 countries, Future Business Journal, ISSN 2314-7210, Springer, Heidelberg, Vol. 7, Iss. 1, pp. 1-18, https://doi.org/10.1186/s43093-021-00071-y This Version is available at: https://hdl.handle.net/10419/246674 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. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Nathaniel Futur Bus J (2021) 7:24 https://doi.org/10.1186/s43093-021-00071-y RESEARCH Ecological footprint andhuman well-being nexus: accounting forbroad-based financial development, globalization, andnatural resources intheNext-11 countries Solomon Prince Nathaniel1,2* Abstract The Next-11 (N11) countries have witnessed great advancements in economic activities in the past few years. However, the simultaneous attainment of environmental sustainability and improved human well-being has remained elusive. This study probes into ecological footprint (EF) and human well-being nexus in N11 countries by applying advanced estimation techniques compatible with heterogeneity, endogeneity, and cross-sectional dependence across country groups. From the findings, human well-being, captured by the human development index, increases the EF, and EF also increases human well-being which suggests a strong trade-off between both indicators. This shows that policies that are channeled toward promoting human well-being are not in consonance with environmental wellness. Financial development and biocapacity increase the EF, while natural resources and globalization reduce it. Human well-being increases the EF in all the countries except in Egypt. This study argues that strong institutions could help mitigate the trade-offs and ease the simultaneous attainment of both environmental preservation and improved human well-being. The limitations of the study, as well as, possible directions for future research are discussed. Keywords: Human well-being, Globalization, Ecological footprint, Financial development, Sustainable development, AMG © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Introduction Environmental matters are now a serious issue and are gaining more popularity by the passing of each day. But, the challenge of maintaining a sustainable environment without inhibiting human well-being is still ubiquitous. Many factors have been found culpable of environmental degradation, but the human factor, is most often than not, ignored. However, human factors are the major drivers of ecological distortions [42, 43, 52, 53]. The link between ecological conditions and human wellbeing cannot be overemphasized. Better environmental management, could, in principle, come with lots of good outcomes for humanity, with a positive synergy between human well-being and environmental conditions [41]. A positive synergy between human well-being and environmental quality is quite ideal and desirable, but it would be erroneous to believe that both are, or should be mutually reinforcing. A trade-off is expected especially in a situation where environmental and growth policies are not well-designed [75]. It is most likely that variables/factors like urbanization, globalization, energy consumption, and natural resources exploration can promote ecological distortions and increase the demands for health, food, education, and wealth which are core human development elements [2, 5, 6, 44, 47, 54, 72]. Open Access Future Business Journal *Correspondence: [email protected] 1 Department of Economics, University of Lagos, Akoka, Nigeria Full list of author information is available at the end of the article Page 2 of 18 Nathaniel Futur Bus J (2021) 7:24 The United Nations rolled out the Sustainable Development Goals (SDGs) in 2015 and listed; Quality Education (Goal 4), No Poverty (Goal 1), Climate Action (Goal 13), Good Health and Well-Being (Goal 3), Zero Hunger (Goal 2), Reduced Inequality (Goal 10), Life on Land (Goal 15), etc., as some of the tenets of the SDGs. The idea behind these goals is to improve human well-being and place humans at a level where they can efficiently contribute to environmental wellness. However, the improvement of human well-being in some of the N11 countries is still in a pitiable state. Also, the simultaneous attainment of Goal 3 and Goal 15 still remains elusive. Though these countries (N11) have witnessed persistent growth in their GDP over the years, there are still some key issues capable of declining human well-being that is yet to be appropriately addressed. As such, the Human Development Index (HDI) of these countries has witnessed little or no improvement. See Table1. Table 1 reveals that all the countries have their EF higher than their BIO, inferring ecological deficit. Korea has the highest HDI, and also the worst environmental records, as the country’s EF is exceedingly higher than its BIO. A closer look at Table1 reveals that each country has witnessed an increase in its HDI between the time periods. The questions begging for answers remain; is there a trade-off between human well-being and environmental quality in the N11 countries? Does the drain in biocapacity, or drench in EF influence human wellbeing in the N11 countries? What is the influence of globalization on human well-being in the N11 countries? This study will assist policymakers in these countries to align their objectives of improving human well-being and the quality of life, with that of preserving the natural ecosystems. Attaining human well-being without deteriorating environmental quality is required in N11 countries. A better understanding of how both are connected is of utmost importance in these countries. This knowledge will inform a desirable human well-being development plan that will enhance sustainable growth and maintain environmental quality. This study is super useful for the N11 countries where factors like globalization, economic growth, urbanization, financial development, and other socio-economic conditions are depleting the biocapacity, causing resource depletion, and increasing the regions ecological footprint (EF). Of little wonder, all the N11 countries are now occupying an ecological deficit territory. Figure1 presents the study framework. Energy consumption, natural resources exploration, and financial development may have consequences on the quality of the environment [16]. Financial development instigates higher loan disbursement to customers at low costs. This access to funding gives the firm the opportunity to create demand for their outputs thereby promoting industrialization [86]. Industrialization stimulates urbanization and energy consumption which gives room for ambient air pollution. Also, air pollution hampers human well-being by creating sickness, infant mortality, and other health defects [51]. At the early stage of financial development, emissions level is expected to rise. However, as financial development persists, emissions decline since the former promotes energy innovations, which might result in energy generation from cleaner sources [86, 87]. This study contributes from the following angles: (1) this is the pioneer study to examine the influence of biocapacity, EF, and financial development on human well-being in the N11 countries. (2) This study employs Table 1 Biocapacity, ecological footprint, and HDI in the N11 countries. Sources Global Footprint Network [25] and UNDP [76] BIO biocapacity Countries 2014 2015 2016 EF BIO HDI EF BIO HDI EF BIO HDI Bangladesh 0.80 0.40 0.57 0.87 0.40 0.58 0.84 0.40 0.59 Egypt 1.96 0.45 0.68 1.91 0.45 0.69 1.81 0.44 0.69 Indonesia 1.68 1.26 0.69 1.63 1.27 0.69 1.68 1.22 0.70 Iran 3.34 0.74 0.78 3.23 0.75 0.78 3.19 0.73 0.79 South Korea 5.73 0.67 0.89 5.85 0.67 0.89 6.00 0.66 0.90 Mexico 2.57 1.20 0.75 2.56 1.16 0.75 2.60 1.17 0.76 Nigeria 1.17 0.70 0.52 1.13 0.70 0.52 1.08 0.68 0.52 Pakistan 0.82 0.38 0.54 0.81 0.36 0.55 0.83 0.36 0.55 Philippines 1.09 0.57 0.69 1.15 0.56 0.70 1.32 0.54 0.70 Turkey 3.25 1.44 0.79 3.34 1.50 0.80 3.35 1.43 0.80 Vietnam 1.78 1.02 0.67 2.01 1.05 0.68 2.12 1.01 0.68 Page 3 of 18 Nathaniel Futur Bus J (2021) 7:24 a positive and comprehensive environmental indicator (EF) that accommodates forest land, built-up land, grazing land, carbon footprint, ocean, and cropland. Furthermore, second-generation econometric techniques have been employed to address heterogeneity, endogeneity, and CD. (3) The augmented mean group (AMG) estimator, Driscoll–Kraay (DK), Panel-corrected standard error (PCSE), and the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model approach are applied to achieve robust estimates which can inform policy formulation that will enhance environmental sustainability and the improvement of human well-being in general, and N11 countries in particular. This study is arranged thus: “Literature review” section presents the literature, and “Methodology, model, and data source” section addresses the methodology and data source. Results are presented and discussed in “Results” section. “Conclusions” section concludes. Literature review Theoretical background/underpinning Gross domestic product (GDP) has been widely used as a measure of economic wellness. The GDP is just the monetary value of goods and services produced within a country, usually for 1year. This metric (GDP) has its limitations as it fails to capture human welfare, health impairment, and education relating to environmental awareness [17]. The intuition here is clear; there is a need for an all-embracing metric that addresses welfare effects, accounts for society’s education, income, and health in the environmental quality-welfare debate. For this reason, the HDI meets our demand. There are studies in the literature that support a positive association between education and life expectancy (both components of HDI) and environmental disasters but failed to establish the same association between the aforementioned variables and GDP [13]. The EF is a relatively new concept, and it appears that only a few studies have used this tool to measure human well-being activities impact the environment. As such, limited research has been completed on human wellbeing as it relates to environmental sustainability. Theoretically, studies in the past have tried to develop an index that addresses social-economic and environmental difficulties (see, for instance, [12]). The two theories that evolve from such integrated sustainable HDI are the win–win and the trade-off approach. The win–win approach believes society/community can achieve double benefits of improved environmental quality and wellbeing, while the trade-off approach argued that both are not self-enforcing that a trade-off is inevitable. This study intends to examine both approaches for the N11 countries with EF as the environmental indicator and HDI as the human well-being indicator. The EF measures mankind’s demand for the regenerative capacity of our planet: Earth’s biocapacity [21]. Apart from being an accounting tool, the EF is an area-based indicator that measures the intensity by which humans use resources and generate waste, relative to that area’s capacity to provide for these activities. It is also referred to as “appropriated carrying Fig. 1 Study framework Page 4 of 18 Nathaniel Futur Bus J (2021) 7:24 capacity” since every person appropriates the productive capacity of nature [15, 23]. It is expected that the outcome of this study will give insight and propose policy directions that will balance these two targets (human well-being and environmental wellness) simultaneously in the N11 countries. HDI, globalization, biocapacity, andecological footprint Most recent studies have tried to link HDI/human development to economic growth and financial development (see, [26, 73]). However, the possibility abounds that HDI can also impact the EF. Environmental pollution and a fragile ecological environment impairs human well-being and health and causes ecological distortions [11, 89]. Environmental degradation affects human wellbeing adversely and inhibits ecological balance. Also, the desire to improve the quality of life exacts pressure on the natural environment which in turn reduces the bio-pro- ductive land. Ahmed etal. [7] examined if human capital reduces the EF in G7 countries. The outcome was in the affirmative. The authors further noted that the level of human development matters for environmental wellness. Zafar etal. [85] explored the impact of human capital on the EF in the USA for the period 1970–2015 through the ARDL approach. Their findings suggest human capital that reduces the EF. Ahmed etal. [4] used the same approach as Zafar etal. [85] and also discovered a similar relationship between human capital and EF in China. Kassouri and Altıntaş [31] discovered that biocapacity and globalization reduce the EF after applying the Interactive Fixed Effects estimator on a panel of 13 MENA countries. There are studies that have tried to link globalization with environmental quality. Shahbaz etal. [67] used the GMM technique to examine the influence of institution, globalization, and trade on the environment in the G7 countries from 1980 to 2014. They discovered that globalization harms the environment. In a similar gesture, Liu etal. [39] explored the effect of renewable energy and globalization on the quality of the environment in G7 countries between 1970 and 2015. From their findings, renewable energy adds to environmental quality, while globalization promotes pollution. Acheampong etal. [1] arrived at a similar result as regards the devastating impact of globalization on the environment in sub-Saharan Africa (SSA). The findings of Shahbaz etal. [67], Liu etal. [39], and Acheampong etal. [1] contradict the recent findings of Saud etal. [66] who discovered that globalization mitigated the EF in one-belt-one-road (OBOR) initiative countries. Hassan etal. [27] used the ARDL approach to investigate the impact of biocapacity, human capital, and growth on the EF in Pakistan. Biocapacity and economic growth influence the environment negatively, while human capital declines the EF. Hassan etal. [28] further probed the influence of natural resource and biocapacity on the EF in Pakistan. The ARDL results revealed that biocapacity and natural resource increase EF. Also, causality flows from both variables to the EF. Pandey etal. [57] investigated the impact of biocapacity and globalization on the environment in Asia from 1971 to 2014. In contrast to the findings of Shahbaz etal. [67] and Acheampong etal. [1], they discovered that globalization promotes environmental quality, while biocapacity exacts a reverse influence on environmental quality. Financial development, urbanization, economic growth, andecological footprint There is an on-going gradual transition from the use of CO2 emissions as an environmental indicator to EF in the cause of measuring the impact of financial development, urbanization, and economic growth on the environment. Zafar etal. [85] and Ahmed etal. [4] used the ARDL technique to investigate the impact of economic growth on the EF in the USA and China, respectively. Both studies agreed that economic growth increases the EF. Ahmed etal. [4] further reported that urbanization contributes to ecological pressure in China. Ahmed etal. [4] argued that urbanization in China is not sustainable and therefore called on policymakers to enact legislation that will enhance urban sustainability considering the population growth rate in China. Nathaniel etal. [49] investigated the interaction between economic growth, urbanization, and EF in CIVETS nations. The AMG estimator was employed. Long-run interaction was found among the variables. Economic growth decreases environmental deterioration, while urbanization increases it in CIVETS countries. Nathaniel etal. [50] studied the effect of urbanization on the EF in the MENA nations. They adopted the AMG algorithm for their study. The result indicated that urbanization and economic growth add to environmental deterioration. Further findings affirmed a one-way directional causality from urbanization and economic growth to the EF in MENA nations. Saud etal. [66] applied the PMG approach to investigate the impact of globalization and financial development on the EF in OBOR countries from 1990 to 2014. Their findings affirmed that financial development increases the EF, while globalization reduces it. Ansari et al. [9] investigated the impact of economic growth on material footprint and EF in 37 Asian countries from 1991 to 2017 using the PMG, GMM, and DOLS techniques. Their findings showed that economic growth increases both indicators (material footprint and EF). Sharif etal. [68] also discovered that economic growth drives the EF in Turkey. Kassouri and Altıntaş [31] Page 5 of 18 Nathaniel Futur Bus J (2021) 7:24 explored the effect of financial development and urbanization on EF in MENA countries. They discovered that both variables increase EF, but exact the opposite influence on human well-being. In conclusion, the literature survey reveals that studies seldom examine the effects of HDI on EF. More so, there is no single study that has investigated the impact of HDI on the EF in N11 countries. The recently introduced financial development index by the IMF has never been used for any analysis that involves the N11 countries. This is the only study that tried to examine the existence of the win–win or trade-off hypothesis in N11 countries. The effect of urbanization, financial development, and globalization on the EF is still murky as consistency in findings remains elusive. Methodology, model anddata source Methods This study adopts econometric procedures that are consistent with the properties of the data used in the study. A wrong procedure will yield bias and inconsistent estimates. To avoid such outcomes, this study proceeds with the CD test. Cross‑sectional dependence As earlier mentioned, results could be biased if CD is ignored. Earlier studies failed to consider the possibility of CD in their empirical analysis; knowing fully well that the world is now a global village where countries are closely knitted. CD is now common due to spillover effect, financial crisis, trade agreements, and international treaties, among others. As such, this study applies the CD tests suggested by Pesaran [60] given as: where ρij = cross sections correlation of error between i and j. T is the time horizon, and N represents cross sections. This study will be more concerned with the first two tests because they are robust in panels where T (time dimension) >N (cross section); which is the exact feature of our dataset. Unit root Apart from the CD tests, the integration properties of the data are also necessary. The presence of CD will determine the choice of the unit root tests. Second-generation tests are preferred in the vicinity of CD. Therefore, this study used the cross-sectionally augmented ADF (CADF) and cross-sectionally augmented IPS (CIPS) introduced by Pesaran [61]. Both tests account for CD and heterogeneity. Pesaran [61] suggested a Dickey–Fuller based tests (CADF) (1) CD = � 2T N(N−1)   N−1 � i=0 N−1 � j=i+1 ρij   N(0, 1 ) that is consistent with structural breaks, heterogeneity and CD. The CADF test equation is given as: ρi is the proxy of the unobservable common factor which Pesaran [61] introduced to eliminate CD emanating from common shocks that might affect all the units. Also, Pesaran [61] suggested a cross-sectional augmented version of the IPS test given as: Both tests (CADF and CIPS) have unique properties. They can address serial correlation and perform better than all the first-generation tests. Cointegration When variables have the same order of integration, let us say, I(1), it becomes necessary to ascertain if they have a long-run relation. The study applies the Westerlund [79] test to investigate the possibility of a cointegrating relationship among the variables. The test controls for CD and nuisance arising from endogeneity. It has a greater explanatory power compared to dynamic cointegration tests. Westerlund [79] constructed four statistics. Two of the four statistics are the group mean statistics, which tests the cointegration of the whole panel, and the panel mean tests, which examines the existence of cointegration in at least one of the units. The test applies bootstrap approach to account for CD and non-strictly exogenous regressors. Parameter estimation The current study adopted the AMG estimator of Bond and Eberhardt [14] because it is consistent with the characteristics of our data. Also, it accounts for heterogeneity and CD which are the two core panel data issues [19]. The AMG is performed in two stages: (2) � yit =�ϕit +βixit−1+ρiT+ n  j = 1 θij�xi,t−j+ε it (3) CIPS = 1 N N  j = 1 CADF t (4) G τ= 1 N N  i=1  αi SE(  α i ) and Gα= 1 N N  i=1 T  αi  αi(1 ) (5) P τ=  α i SE(  αi) and Pα=T  α (6) AMG-Stage1 :�yit =αi+bi�xit +cift+ T  t=2 dt�Dt+e it Page 6 of 18 Nathaniel Futur Bus J (2021) 7:24 xit and yit are the observables. ft represents the unobserved common factor. The country-specific estimates of coefficients, the AMG estimator, and the time dummies are, respectively bt ,  bAMG , and dt . In addition, the DK, CS-ARDL, and the PCSE approach are used to ascertain the robustness of the findings. The DK is robust amidst serial and spatial dependence, heteroscedasticity, and CD. It accommodates balanced and unbalanced panels, both large and small sample sizes, and missing values [10]. The PCSE shares almost the same properties with the DK approach. Besides, the CS-ARDL accommodates non-stationary data, and is capable of addressing CD and endogeneity issues [3]. Causality One limitation of the AMG estimator is that it does not give information about causality. Since the direction of causality aids policy direction, the Dumitrescu and Hurlin [20] test is applied to check the direction of causality. The DH equation is given as: (7) AMG-Stage2 :  bAMG =N−1 N  i=1 b i The intercept and coefficient ςi and πi =  π(1) i,...π(p) i  are fixed. The autoregressive parameter and regression coefficient are, respectively ξ(p) i and π(p) i (Fig.2). Data andmodel The study made use of annual data spanning 1990–2016 for N11 countries; a decision informed by data availability. The variables have been carefully selected with respect to economic theory, data availability, and empirical literature. Two core sustainability indicators (HDI and EF) have been used. The EF has been widely embraced as a comprehensive environmental indicator [45, 46, 49, 50, 52]. The EF is superior to other metrics as it goes further to show how production, investment, and consumption inhabit the regenerative capacity of the Earth [55]. On the other hand, the HDI, designed by the UNDP, is a composite index that englobes three components (adult literacy, per capita gross national income, and life expectancy at birth). Today, the HDI is widely used as an indicator of human well-being [40, 65], though it has its limitation. See the concluding section of this study for the limitations of HDI as an indicator of human well-being. Unlike previous studies that (8) yi,t=ςi+ p  i=1 ξ(p) iyi,t−n+ p  i=1 π(p) ixi,t−n+µi, t Fig. 2 Methodological schema of the study Page 7 of 18 Nathaniel Futur Bus J (2021) 7:24 used the ratio of private credit to GDP, the ratio of current liabilities to GDP, and the ratio of deposits and loans to GDP to measure financial development, the current study used a broad-based financial development index recently introduced by the IMF. This index is way superior to those used by earlier studies in that, it considers the complex multidimensional nature of financial development. It summarizes how developed financial institutions and financial markets are, in terms of access, depth, and efficiency. See Sahay etal. [64] for more information on the index. However, recent studies like Iorember etal. [30] and Kassouri and Altıntaş [31] have used this index. The development of the financial system is associated with access to funds. When people have access to funding, they are more like to consume renewables, hence an improvement in environmental quality. Unsustainable natural resource consumption and exploration reduce the biocapacity, deplete forest, and cause the EF to rise [46]. Despite its contribution to knowledge, innovation, and economic development, urbanization spreads emissions, negatively impacts local food production [80], decreases soil fertility [8], and generates environmental degradation. Globalization opens up the economy and allows for the importation of products, and technologies that could improve human well-being or add to the already existing emissions level [70]. To investigate whether the win–win or trade-off hypothesis is evident in N11 countries, we need to estimate the effect of EF on HDI, and HDI on EF. This study further considered the role of globalization, financial development, urbanization, biocapacity, and natural resource as major determinants of sustainability. The consideration of these variables will give a clearer picture of the applicability of (SDGs 3) and (SDGs 15) simultaneously. The models for this study are: (9) lnef it =τ 0 +τ 1lnhd it +τ 2lnfd it +τ 3ub it +τ 4lnbi it +τ 5lngbit +τ 6nr it +µi 1t (10) lnhd it = ξ0 + ξ1lnef it + ξ2lnfd it + ξ3ub it + ξ4lnbi it + ξ5lngbit + ξ5nr it + µ i 2 t τ0−τ6 and ξ0 − ξ6 are the parameters to be estimated in Model 1 and 2, respectively. t and i are the time dimension and country, respectively. i=1, 2, ..., 11, and as t=1, 2, ...27. µi1t and µi2t are the error terms in model 1 and 2, respectively. Two variables (natural resources rent and urbanization) were not logged because these variables are already in their growth rates. See Table2 for the measurements, symbols, and sources of the variables. Results This section presents the trend of the variables, descriptive statistic and correlation, CD tests, unit root tests, cointegration, and the parameter estimation results. Trend ofthevariables In Fig.3, South Korea and Iran are the two countries with the largest EF. However, the EF appears to be relatively constant only in Pakistan and the Philippines. Figure4 reveals declining biocapacity in almost all the countries, especially in Mexico, Indonesia, Turkey, and Iran. In Fig.5, all the countries have witnessed an increase in their HDI over the years; suggesting improvement in human well-being. Figures6, 7, and 8 present a clearer picture of the trend of each of these variables in the individual N11 countries (see “Appendix”). Table3 presents the summary statistic of the variables in relation to their mean, minimum value, standard deviation, and maximum value. From the results, globalization has the highest average followed by urbanization. These show how urbanized and globalized the N11 countries Table 2 Description of variables S/N Variables Measurement Source Symbols 1 Ecological Footprint global hectares per capita GFN (2019b) ef 2 Biocapacity global hectares per capita GFN [24] bi 3 Natural resource Natural resources rent (% of GDP) WDI [81] nr 4 Urbanization % of total population WDI [81] ub 5 Globalization Overall KOF index KOF [35] gb 6 Financial development Broad‐based index of financial depth access and efficiency IMF [29]fd 7 Human development index Score UNDP [76] hd Page 8 of 18 Nathaniel Futur Bus J (2021) 7:24 are. Urbanization is the most volatile of the variables while financial development is the least volatile of the variables. All the variables, except natural resources, are positively associated with EF. Apart from EF, the remaining variables show a negative correlation with natural resources. HDI, globalization, and biocapacity are positively associated with financial development and urbanization. Table4 confirms the presence of CD. This finding is not strange, as all the N11 countries are signatories to various environmental sustainability treaties, like the Paris Agreement of 2015. Both unit root tests (see Table5) affirmed non-stationarity of the variables at their level form. However, the stationarity of the variables was confirmed after their first difference. With these results, we can comfortably proceed with the cointegration test. Table6 confirms cointegration. Gt and Pt, in both models show probability values that are significant which makes it difficult to reject the null hypothesis of no cointegration. Cointegration is a prerequisite for parameter estimation. Since this condition has been met, we now proceed with the AMG and other estimations. The findings in Table7, Model 1, reveal that natural resources and globalization reduce the EF. A one percent increase in natural resources rent reduce the EF by 0.023%, 0.239%, and 0.412% in the AMG, PCSE, and DK estimators, respectively. The intuition here is that natural resources rent does not harm the environment in the N11 countries. Also, a one percent increase in globalization reduces the EF by 0.049%, 0.087%, and 0.085% in the AMG, PCSE, and DK estimators, respectively. The negative relationship between globalization and EF as shown in the three estimators confirmed that globalization is consistent with environmental sustainability in the N11 countries. Conversely, urbanization, biocapacity, and financial development increase EF. A one percent increase in urbanization is associated with 0.14%, 0.21%, and 0.03% increase in EF in the AMG, PCSE, and DK estimators, respectively. The nexus between financial development and EF is positive and significant across the three estimators; suggesting that financial development increases the EF by 0.02%, 0.01%, and 0.27% in the AMG, Fig. 3 EF in N11 countries Fig. 4 Biocapacity in N11 countries Fig. 5 HDI in N11 countries Page 15 of 18 Nathaniel Futur Bus J (2021) 7:24 Fig. 7 Biocapacity in the individual N11 countries Fig. 8 HDI in the individual N11 countries Page 16 of 18 Nathaniel Futur Bus J (2021) 7:24 Abbreviations N11: Eleven fastest emerging economies; CD: Cross-sectional dependence; EF: Ecological footprint; AMG: Augmented mean group; MEA: Millennium Ecosystem Assessment; SDGs: Sustainable development goals; DH: Dumitrescu and Hurlin; OBOR: One-belt-one-road; FGLS: Feasible generalized least squares; DK: Driscoll and Kraay; PCSE: Panel-corrected standard errors; HDI: Human development index; PMG: Pooled mean group; CIPS: Cross-sectionally augmented IPS; CS-ARDL: Cross-sectionally augmented autoregressive distributed lag; CADF: Cross-sectionally augmented ADF. Acknowledgements The author expresses his gratitude to the anonymous reviewers for their efforts in reviewing the paper and suggesting key modifications that have enhanced the quality of the article. The author also thanks the editor for his cooperation during the review process. Authors’ contributions The study was solely carried out by the corresponding author. Funding No funding was received for this study. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Declaration Competing interests The author declares no competing interest. Author details 1 Department of Economics, University of Lagos, Akoka, Nigeria. 2 S chool of Foundation, Lagos State University, Badagry, Nigeria. Received: 3 November 2020 Accepted: 26 April 2021 References 1. Acheampong AO, Adams S, Boateng E (2019) Do globalization and renewable energy contribute to carbon emissions mitigation in Sub- Saharan Africa? Sci Total Environ 677:436–446 2. Adedoyin FF, Nathaniel S, Adeleye N (2021) An investigation into the anthropogenic nexus among consumption of energy, tourism, and economic growth: do economic policy uncertainties matter? Environ Sci Pollut Res 28(3):2835–2847 3. Ahmad M, Jiang P, Murshed M, Shehzad K, Akram R, Cui L, Khan Z (2021) Modelling the dynamic linkages between eco-innovation, urbanization, economic growth and ecological footprints for G7 countries: does financial globalization matter? Sustain Cities Soc 70:102881 4. Ahmed Z, Asghar MM, Malik MN, Nawaz K (2020) Moving towards a sustainable environment: the dynamic linkage between natural resources, human capital, urbanization, economic growth, and ecological footprint in China. Resour Policy 67:101677 5. Ahmed Z, Cary M, Le HP (2021) Accounting asymmetries in the long-run nexus between globalization and environmental sustainability in the United States: an aggregated and disaggregated investigation. Environ Impact Assess Rev 86:106511 6. Ahmed Z, Nathaniel SP, Shahbaz M (2021) The criticality of information and communication technology and human capital in environmental sustainability: evidence from Latin American and Caribbean countries. J Clean Prod 286:125529 7. Ahmed Z, Zafar MW, Ali S (2020) Linking urbanization, human capital, and the ecological footprint in G7 countries: an empirical analysis. Sustain Urban Areas 55:102064 8. Ali R, Bakhsh K, Yasin MA (2019) Impact of urbanization on CO2 emissions in emerging economy: evidence from Pakistan. Sustain Urban Areas 48:101553 9. Ansari MA, Haider S, Khan NA (2020) Environmental Kuznets curve revisited: an analysis using ecological and material footprint. Ecol Ind 115:106416 10. Baloch MA, Zhang J, Iqbal K, Iqbal Z (2019) The effect of financial development on ecological footprint in BRI countries: evidence from panel data estimation. Environ Sci Pollut Res 26(6):6199–6208 11. Banerjee S, Murshed M (2020) Do emissions implied in net export validate the pollution haven conjecture? Analysis of G7 and BRICS countries. Int J Sustain Econ 12(3):297–319 12. Biggeri M, Mauro V (2018) Towards a more ‘sustainable’ human development index: integrating the environment and freedom. Ecol Ind 91:220–231 13. Biggeri M, Clark DA, Ferrannini A, Mauro V (2019) Tracking the SDGs in an ‘integrated’ manner: a proposal for a new index to capture synergies and trade-offs between and within goals. World Dev 122:628–647 14. Bond S, Eberhardt M (2013) Accounting for unobserved heterogeneity in panel time series models. University of Oxford, Oxford 15. Cerutti AK, Beccaro GL, Bruun S, Donno D, Bonvegna L, Bounous G (2016) Assessment methods for sustainable tourism declarations: the case of holiday farms. J Clean Prod 111:511–519 16. Cheng Z, Li L, Liu J (2020) Natural resource abundance, resource industry dependence and economic green growth in China. Resour Policy 68:101734 17. Costanza R, Kubiszewski I, Giovannini E, Lovins H, McGlade J, Pickett KE, Ragnarsdóttir KV, Roberts D, De Vogli R, Wilkinson R (2014) Development: time to leave GDP behind. Nat News 505(7483):283 18. Danish, Ulucak R, Khan SU (2020) Determinants of the ecological footprint: role of renewable energy, natural resources, and urbanization. Sustain Cities Soc 54:101996 19. Dogan E, Ulucak R, Kocak E, Isik C (2020) The use of ecological footprint in estimating the Environmental Kuznets Curve hypothesis for BRICST by considering cross-section dependence and heterogeneity. Sci Total Environ 723:138063–138063 20. Dumitrescu EI, Hurlin C (2012) Testing for Granger non-causality in heterogeneous panels. Econ Model 29(4):1450–1460 21. El-Deeb MS, Halim YT (2020) Using Ecological Footprint Accounting model as a tool for sustainable development in the hospitality industry: evidence from Egypt. Account J 24(2):564–602. https:// doi. org/ 10. 21608/ ATASU. 2020 22. Erdoğan S, Çakar ND, Ulucak R, Kassouri Y (2021) The role of natural resources abundance and dependence in achieving environmental sustainability: evidence from resource-based economies. Sustain Dev 29(1):143–154 23. Fernández M, Alférez A, Vidal S, Fernández MY, Albareda S (2016) Methodological approaches to change consumption habits of future teachers in Barcelona, Spain: reducing their personal Ecological Footprint. J Clean Prod 122:154–163 24. GFN (2019) Ecological Footprint per Person of Country’s Population (in global hectares). https:// data. footp rintn etwork. org/#/ Accessed 1st May 2019 25. Global Footprint Network (2019) Advancing the science of sustainability. http:// data. footp rintn etwork. org/?_ ga=2. 12866 2604. 13934 81519. 15895 55821- 40137 3056. 15472 19594#/ count ryTre nds? type= BCtot ,EFCto t& cn= 351. Accessed 1 June 2019 26. Guan J, Kirikkaleli D, Bibi A, Zhang W (2020) Natural resources rents nexus with financial development in the presence of globalization: is the “resource curse” exist or myth? Resour Policy 66:101641 27. Hassan ST, Baloch MA, Mahmood N, Zhang J (2019) Linking economic growth and ecological footprint through human capital and biocapacity. Sustain Urban Areas 47:101516 28. Hassan ST, Xia E, Khan NH, Shah SMA (2019) Economic growth, natural resources, and ecological footprints: evidence from Pakistan. Environ Sci Pollut Res 26(3):2929–2938 29. International Monetary Fund (IMF) (2016) Introducing a new broad‐based index of financial development, IMF Working Paper WP/16/5. http:// data. imf. org/? sk¼F8032 E80- B36C- 43B1- AC26- 493C5 B1CD3 3B. Accessed 1 Aug 2019 30. Iorember PT, Goshit GG, Dabwor DT (2020) Testing the nexus between renewable energy consumption and environmental quality in Nigeria: the role of broad-based financial development. Afr Dev Rev 32(2):163–175 Page 17 of 18 Nathaniel Futur Bus J (2021) 7:24 31. Kassouri Y, Altıntaş H (2020) Human well-being versus ecological footprint in MENA countries: a trade-off? J Environ Manag 263:110405 32. Khalid K, Usman M, Mehdi MA (2021) The determinants of environmental quality in the SAARC region: a spatial heterogeneous panel data approach. Environ Sci Pollut Res 28(6):6422–6436 33. Khan Z, Hussain M, Shahbaz M, Yang S, Jiao Z (2020) Natural resource abundance, technological innovation, and human capital nexus with financial development: a case study of China. Resour Policy 65:101585 34. Kirikkaleli D, Adebayo TS, Khan Z, Ali S (2021) Does globalization matter for ecological footprint in Turkey? Evidence from dual adjustment approach. Environ Sci Pollut Res 28(11):14009–14017 35. KOF (2017) KOF index of globalization [WWW Document]. https:// www. kof. ethz. ch/ en/ forec asts- and- indic ators/ indic ators/ kofgloba lisat ionindex. html. Accessed: 5 July 2020 36. Kongbuamai N, Bui Q, Yousaf HMAU, Liu Y (2020) The impact of tourism and natural resources on the ecological footprint: a case study of ASEAN countries. Environ Sci Pollut Res 27(16):19251–19264 37. Kurniawan R, Managi S (2018) Coal consumption, urbanization, and trade openness linkage in Indonesia. Energy Policy 121:576–583 38. Lashitew AA, Werker E (2020) Do natural resources help or hinder development? Resource abundance, dependence, and the role of institutions. Resour Energy Econ 61:101183 39. Liu M, Ren X, Cheng C, Wang Z (2020) The role of globalization in CO2 emissions: a semi-parametric panel data analysis for G7. Sci Total Environ 718:137379 40. Long X, Yu H, Sun M, Wang XC, Klemeš JJ, Xie W, Wang C, Li W, Wang Y (2020) Sustainability evaluation based on the three-dimensional Ecological Footprint and Human Development Index: a case study on the four island regions in China. J Environ Manag 265:110509 41. Millennium Ecosystem Assessment (2005) Ecosystems and human wellbeing. https:// www. mille nnium asses sment. org/ docum ents/ docum ent. 358. aspx. pdf. Accessed 10 Dec 2019 42. Murshed M, Alam R, Ansarin A (2021) The environmental Kuznets curve hypothesis for Bangladesh: the importance of natural gas, liquefied petroleum gas, and hydropower consumption. Environ Sci Pollut Res 28(14):17208–17227 43. Murshed M, Ali S, Haseeb M, Nathaniel SP (2021) Modelling the public moral hazard problem of international remittance inflows in Bangladesh. Int J Sustain Econ 13(2):1 44. Murshed M, Haseeb M, Alam MS (2021) The Environmental Kuznets Curve hypothesis for carbon and ecological footprints in South Asia: the role of renewable energy. GeoJournal 1–28 45. Nathaniel SP (2020) Ecological footprint, energy use, trade, and urbanization linkage in Indonesia. GeoJournal 1–14 46. Nathaniel SP (2021) Environmental degradation in ASEAN: assessing the criticality of natural resources abundance, economic growth and human capital. Environ Sci Pollut Res 1–13 47. Nathaniel SP, Adeleye N (2021) Environmental preservation amidst carbon emissions, energy consumption, and urbanization in selected African countries: implication for sustainability. J Clean Prod 285:125409 48. Nathaniel SP, Bekun F, Faizulayev A (2021) Modelling the impact of energy consumption, natural resources, and urbanization on ecological footprint in South Africa: assessing the moderating role of human capital. Int J Energy Econ Policy 11(3):130–139 49. Nathaniel SP, Nwulu N, Bekun F (2021) Natural resource, globalization, urbanization, human capital, and environmental degradation in Latin American and Caribbean countries. Environ Sci Pollut Res 28(5):6207–6221 50. Nathaniel SP, Yalçiner K, Bekun FV (2020) Assessing the environmental sustainability corridor: linking natural resources, renewable energy, human capital, and ecological footprint in BRICS. Resour Policy 70:101924 51. Nathaniel S, Khan S (2020) Public health financing, environmental quality, and the quality of life in Nigeria. J Public Aff 20:e2103 52. Nathaniel S, Anyanwu O, Shah M (2020) Renewable energy, urbanization, and ecological footprint in the Middle East and North Africa region. Environ Sci Pollut Res 1–13 53. Nathaniel S, Nwodo O, Sharma G, Shah M (2020) Renewable energy, urbanization, and ecological footprint linkage in CIVETS. Environ Sci Pollut Res 27(16):19616–19629 54. Nathaniel SP, Barua S, Ahmed Z (2021) What drives ecological footprint in top ten tourist destinations? Evidence from advanced panel techniques. Environ Sci Pollut Res. https:// doi. org/ 10. 1007/ s11356- 021- 13389-5 55. Omojolaibi J, Nathaniel SP (2020) Assessing the potency of environmental regulation in maintaining environmental sustainability in MENA countries: an advanced panel data estimation. J Public Affairs e2526 56. Panayotou T (1993) Empirical tests and policy analysis of environmental degradation at different stages of economic development (No. 992927783402676). International Labour Organization 57. Pandey S, Dogan E, Taskin D (2020) Production-based and consumptionbased approaches for the energy-growth-environment nexus: evidence from Asian countries. Sustain Prod Consump 23:274–281 58. Pata UK (2021) Renewable and non-renewable energy consumption, economic complexity, CO 2 emissions, and ecological footprint in the USA: testing the EKC hypothesis with a structural break. Environ Sci Pollut Res 28(1):846–861 59. Pata UK, Caglar AE (2021) Investigating the EKC hypothesis with renewable energy consumption, human capital, globalization and trade openness for China: evidence from augmented ARDL approach with a structural break. Energy 216:119220 60. Pesaran HM (2004) General diagnostic tests for cross-section dependence in panels. Fac Econ. https:// doi. org/ 10. 17863/ CAM. 5113 61. Pesaran MH (2007) A simple panel unit root test in the presence of crosssection dependence. J Appl Econ 22:265–312 62. Rongwei X, Xiaoying Z (2020) Is financial development hampering or improving the resource curse? New evidence from China. Resour Policy 67:101676 63. Sagar AD, Najam A (1998) The human development index: a critical review. Ecol Econ 25(3):249–264 64. Sahay R, Čihák M, N’diaye P, Barajas A, Bi R, Ayala D, Gao Y, Kyobe A, Nguyen L, Svirydzenka K (2015) Rethinking financial deepening: stability and growth in emerging markets. IMF Staff Discussion Note. International Monetary Fund 65. Sarkodie SA, Adams S (2020) Electricity access, human development index, governance and income inequality in Sub-Saharan Africa. Energy Rep 6:455–466 66. Saud S, Chen S, Haseeb A (2020) The role of financial development and globalization in the environment: accounting ecological footprint indicators for selected one-belt-one-road initiative countries. J Clean Prod 250:119518 67. Shahbaz M, Balsalobre D, Shahzad SJH (2019) The influencing factors of CO2 emissions and the role of biomass energy consumption: statistical experience from G-7 countries. Environ Model Assess 24(2):143–161 68. Sharif A, Baris-Tuzemen O, Uzuner G, Ozturk I, Sinha A (2020) Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: evidence from Quantile ARDL approach. Sustain Urban Areas 57:102138 69. Sharma R, Sinha A, Kautish P (2021) Does financial development reinforce environmental footprints? Evidence from emerging Asian countries. Environ Sci Pollut Res 28(8):9067–9083 70. Sinha A, Sengupta T (2019) Impact of natural resource rents on human development: what is the role of globalization in Asia Pacific countries? Resour Policy 63:101413 71. Sinha A, Sengupta T, Alvarado R (2020) Interplay between technological innovation and environmental quality: formulating the SDG policies for next 11 economies. J Clean Prod 242:118549 72. Solarin SA, Nathaniel SP, Bekun FV, Okunola AM, Alhassan A (2021) Towards achieving environmental sustainability: environmental quality versus economic growth in a developing economy on ecological footprint via dynamic simulations of ARDL. Environ Sci Pollut Res 28(14):17942–17959 73. Sun Y, Ak A, Serener B, Xiong D (2020) Natural resource abundance and financial development: a case study of emerging seven (E− 7) economies. Resour Policy 67:101660 74. Ulucak R, Bilgili F (2018) A reinvestigation of EKC model by ecological footprint measurement for high, middle and low income countries. J Clean Prod 188:144–157 75. UNDP (2011) Sustainability and equity: a better future for all, Human Development Report 2011. https:// doi. org/ 10. 2307/ 21377 95 76. UNDP (2017) Human development report 2017. United Nations Dev. Program, 12 Jan 2019. http:// hdr. undp. org/ en/ data Page 18 of 18 Nathaniel Futur Bus J (2021) 7:24 77. Usman M, Khalid K, Mehdi MA (2021) What determines environmental deficit in Asia? Embossing the role of renewable and non-renewable energy utilization. Renew Energy 168:1165–1176 78. Wang Z, Bui Q, Zhang B, Pham TLH (2020) Biomass energy production and its impacts on the ecological footprint: an investigation of the G7 countries. Sci Total Environ 743:140741 79. Westerlund J (2007) Error correction based panel cointegration tests. Oxf Bull Econ Stat 69:709–748 80. Winoto J, Schultink G (1996) Impacts of urbanization on agricultural sustainability and rural life in West Java, Indonesia. Research report from the Michigan State University Agricultural Experiment Station, East Lansing 81. World Development Indicator (WDI) (2019). World Bank Development Indicators database (online) available at https:// data. world bank. org/. Accessed 24 Oct 2019 82. Yang B, Jahanger A, Ali M (2021b) Remittance inflows affect the ecological footprint in BICS countries: do technological innovation and financial development matter? Environ Sci Pollut Res 1–19 83. Yang B, Jahanger A, Usman M, Khan MA (2021) The dynamic linkage between globalization, financial development, energy utilization, and environmental sustainability in GCC countries. Environ Sci Pollut Res 147(2):621–649 84. Yasin I, Ahmad N, Chaudhary MA (2020) Catechizing the environmentalimpression of urbanization, financial development, and political institutions: a circumstance of ecological footprints in 110 developed and less-developed countries. Soc Indic Res 147(2):621–649 85. Zafar MW, Zaidi SAH, Khan NR, Mirza FM, Hou F, Kirmani SAA (2019) The impact of natural resources, human capital, and foreign direct investment on the ecological footprint: the case of the United States. Resour Policy 63:101428 86. Zafar MW, Zaidi SAH, Sinha A, Gedikli A, Hou F (2019) The role of stock market and banking sector development, and renewable energy consumption in carbon emissions: insights from G-7 and N-11 countries. Resour Policy 62:427–436 87. Zaidi SAH, Zafar MW, Shahbaz M, Hou F (2019) Dynamic linkages between globalization, financial development and carbon emissions: evidence from Asia Pacific Economic Cooperation countries. J Clean Prod 228:533–543 88. Zhang L, Godil DI, Bibi M, Khan MK, Sarwat S, Anser MK (2021) Caring for the environment: how human capital, natural resources, and economic growth interact with environmental degradation in Pakistan? A dynamic ARDL approach. Sci Total Environ 774:145553 89. Zhou Y, Li Y, Liu Y (2020) The nexus between regional eco-environmental degradation and rural impoverishment in China. Habitat Int 96:102086 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.