Frontier Technology Readiness as a driver of environmental quality: ARDL evidence from SCO Member States

Frontier Technology Readiness as a driver of environmental quality: ARDL evidence from SCO Member States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Frontier Technology Readiness as a driver of environmental quality: ARDL evidence from SCO Member States Mduduzi Biyase, Ashutosh Rai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7958890/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the complex, dynamic relationship between Frontier Technology Readiness (FTR) and long-term environmental degradation, proxied by the Ecological Footprint (EF) per person, across seven highly interconnected Shanghai Cooperation Organisation (SCO) economies. Recognizing that these large, energy-intensive economies face legacies of carbon- and resource-intensive growth while pursuing ambitious digital and green transition goals, the research addresses a critical gap in understanding how advanced technology capacity impacts sustainability in this vital geopolitical bloc. We enhance methodological rigor by utilizing the UNCTAD Frontier Technology Readiness Index (FTRI), a conceptually based and calibrated measure of technological capacity, moving beyond generic proxies commonly used in literature. We employ the Panel Autoregressive Distributed Lag (ARDL) model, incorporating second-generation estimators to explicitly address the strong cross-sectional dependence prevalent among SCO members due to shared economic and environmental linkages. The core empirical finding is that Frontier Technology Readiness significantly reduces environmental degradation in the long run. The long-run coefficient for FTR is negative and statistically significant at the 1 percent level, establishing technology as a structural driver of improved environmental quality. This conclusion is proven highly robust, confirmed by alternative long-run estimators Panel FMOLS and Panel DOLS, and crucially, maintained statistical significance when verified using a sub-sample that excluded the large economies of China and India. Crucially, analysis of the short-run dynamics reveals that the immediate change in FTR is statistically insignificant, indicating that environmental benefits are realized as a sustained, long-term effect rather than an immediate one. We also confirm that economic growth and mineral dependency promote the Ecological Footprint in the long term, while financial development and high population density help reduce it. Despite the established long-run equilibrium, causality analysis reveals no direct Granger-causal link between FTR and EF, highlighting the complexity of interaction effects. These findings attest to the necessity of fostering the innovation and sustained dissemination of frontier technologies at a global scale to achieve long-term reduction targets in carbon emissions. JEL classification : O3, O13 Panel DOLS Panel FMOLS Causality Tests SCO Ecological Footprint Frontier Technology Readiness Index Figures Figure 1 Figure 2 Introduction Technological change is widely acknowledged as the decisive lever for reconciling sustained global economic development with environmental stewardship, yet the precise pathways and requisite conditions through which large, heterogeneous geopolitical blocks convert nascent "frontier" technologies into measurable, sustained gains in environmental quality (EQ) remain empirically ambiguous and poorly understood. This study addresses this critical research blocks convert nascent "frontier" technologies into measurable, sustained gains in environmental quality (EQ) remain empirically ambiguous and poorly understood. This study addresses this critical research lacuna by rigorously investigating the dynamic relationship between Frontier Technology Readiness (FTR) and environmental pressure, quantified comprehensively by the Ecological Footprint (EF) per person, across a panel of seven Shanghai Cooperation Organisation (SCO) economies. The SCO region—comprising large, energy-intensive nations like China, India, and Russia—presents an essential natural laboratory for this analysis, as member states are concurrently burdened by legacies of carbon- and resource-intensive growth while pursuing ambitious imperatives for digital transformation and global climate commitments. The theoretical relationship between FTR and EQ is inherently ambiguous and often conflicting. While enhanced readiness is expected to foster efficiency, smart-grid systems, and the diffusion of clean technologies, the empirical record frequently highlights counteracting forces. Specifically, early stages of digital infrastructure expansion often generate rebound effects through higher electricity demand and data center loads, particularly in fossil-dependent economies, potentially increasing environmental pressure before efficiency gains materialize. Furthermore, observed trends among SCO members reveal divergence: for instance, highly ready economies like Russia and China show steadily increasing FTR scores, yet simultaneously exhibit high or slowly rising Ecological Footprints, suggesting that technological readiness does not immediately translate into environmental abatement. This complexity underscores the need to disentangle the relationship between these variables. Our work provides several contributions to the existing literature, overcoming identified methodological and measurement deficiencies. First, we establish a novel geopolitical focus by systematically analyzing the SCO bloc, recognizing the strong cross-sectional dependence prevalent due to shared economic and environmental linkages. Second, we advance measurement integrity by employing the Frontier Technology Readiness Index (FTRI) developed by UNCTAD, a conceptually based and calibrated measure that captures a country's sophisticated capacity across five pillars, moving beyond generic proxies. Third, we adopt a methodologically robust dynamic approach tailored to the data's properties, utilizing the Panel Autoregressive Distributed Lag (ARDL) model to accommodate the confirmed plethoric mix of integration orders (I(0) and I(1)) and allowing us to precisely estimate both short-run adjustments and the existence of a stable long-run equilibrium, which the Pedroni cointegration tests overwhelmingly confirm. This rigorous methodology leads to the powerful and empirically validated finding that Frontier Technology Readiness significantly reduces environmental degradation in the long run. This core result is established as highly robust, confirmed by verification using alternative long-run estimators (Panel FMOLS and Panel DOLS) and, most assertively, by re-estimation on a sub-sample that excluded the dominant economies of China and India. Conversely, the short-run effect of FTR is statistically insignificant, demonstrating that environmental benefits accrue as a sustained, long-term impact. Moreover, while a long-run relationship exists, causality analysis reveals no direct Granger-causal link between FTR and EF, highlighting the complexity and mediated nature of this interaction. The remainder of this study is structured as follows: Section 2 reviews the relevant literature concerning the FTR-EQ nexus and the ambiguity of its effects. Section 3 details the methodology, including the data description for the Ecological Footprint and the Frontier Technology Readiness Index, and outlines the application of the CIPS unit root, Pedroni cointegration, and Panel ARDL models. Section 4 presents the core empirical findings, including the long-run and short-run ARDL estimations, the robustness checks (sub-sample, FMOLS, and DOLS), and the Granger causality analysis. Finally, Section 5 discusses the policy implications of these results and concludes the study Literature review The nexus between technological advancement and environmental sustainability has received significant scholarly attention in recent decades, particularly in the context of rapid digitalization and the diffusion of frontier technologies. UNCTAD (2021) introduced the Frontier Technology Readiness Index (FTRI) to capture countries’ capacities to adopt and scale advanced technologies such as artificial intelligence, big data, robotics, blockchain, and renewable energy systems. The index is built on five pillars—information and communication technology (ICT) deployment, skills, research and development (R&D) activity, industry activity, and access to finance—each of which plays a critical role in determining whether technological readiness translates into productivity gains and environmental improvements. The literature on environmental quality (EQ), however, presents an inherently complex and multi-dimensional picture. While carbon dioxide (CO₂) emissions remain the most widely used proxy of environmental degradation (Shahbaz, Nasir, & Roubaud, 2019), broader indicators such as ecological footprint (Wackernagel & Rees, 1996) and composite metrics such as the Environmental Performance Index (Wendling, Emerson, de Sherbinin, & Esty, 2024) provide complementary insights by integrating ecosystem vitality, resource efficiency, and environmental health. The theoretical relationship between FTR and EQ is ambiguous. On the positive side, enhanced readiness fosters diffusion of clean technologies, smart-grid systems, digital monitoring, and process innovation, which can reduce energy intensity and emissions (Karakaya & Sriwannawit, 2015). Conversely, early stages of digital infrastructure expansion often generate rebound effects through higher electricity demand, data center loads, and increased device manufacturing, particularly in fossil-dependent economies (Zhang, Li, & Huang, 2022). Empirical studies frequently identify non-linearities, such as inverted-U relationships between digitalization and emissions, suggesting that only after surpassing a technological threshold do efficiency and dematerialization gains outweigh the scale effects (Shahbaz, Sinha, & Vo, 2022; Li & Wang, 2023). In the context of the Shanghai Cooperation Organisation (SCO)—which includes China, India, Russia, Pakistan, Central Asian states, Iran, and Belarus—heterogeneity in technological readiness and energy structure is especially salient. China and India have rapidly expanded renewable deployment and digital ecosystems, while Russia, Iran, and Kazakhstan remain fossil-fuel intensive (Duarte & Pinho, 2023). Legacy infrastructure inefficiencies in Central Asia, combined with underinvestment in clean technologies, present challenges but also opportunities for substantial abatement through frontier digital upgrades in energy and water systems (OECD, 2022). Thus, SCO economies represent a natural laboratory for analyzing the FTR–EQ nexus. Empirical evidence supports the potential of green technological innovation to mitigate emissions. Bilgili, Koçak, and Bulut (2016) demonstrated that renewable energy innovation in OECD economies reduced CO₂ emissions over the long run. Chen, Pao, and Wang (2019) applied the ARDL approach to China and confirmed that R&D investment in renewable technologies was a key factor in long-term emission reduction. At the same time, research suggests that generic technological innovation and financial development can exacerbate environmental degradation if not explicitly directed toward clean energy or efficiency-enhancing applications (Zhang & Zhou, 2021). Similarly, in emerging economies, Destek and Sinha (2020) found that technological innovation reduced ecological footprint in the long run, although resource rents and trade openness counterbalanced these improvements. Digitalization, a major component of frontier readiness, has shown complex and often transitional effects. Salahuddin and Alam (2016) reported that ICT expansion initially raised energy consumption in OECD countries, while Shahbaz et al. (2022) highlighted that, in the longer term, digital transformation contributed to environmental improvements through smart energy systems and innovation spillovers. Recent cross-country evidence confirms an inverted-U dynamic, wherein early digitalization increases emissions but later phases deliver efficiency-driven abatement (Li & Wang, 2023). This is particularly relevant for SCO economies with rapidly expanding digital infrastructure but still fossil-dominant power mixes. Methodologically, the Autoregressive Distributed Lag (ARDL) framework (Pesaran, Shin, & Smith, 2001) is widely recognized as appropriate for examining technology–environment linkages, especially in small samples with mixed orders of integration. The ARDL bounds-testing approach allows for simultaneous estimation of short-run elasticities and long-run cointegrating relationships, with the error-correction mechanism capturing adjustment back to equilibrium. Its extensions, including Nonlinear ARDL (NARDL) to capture asymmetries (Shin, Yu, & Greenwood-Nimmo, 2014) and Pooled Mean Group ARDL (Pesaran, Shin, & Smith, 1999) for panel analysis, have been employed in studies of green innovation, digitalization, and ecological footprint. Zhang et al. (2022), for example, identified threshold effects in the digital economy–emissions relationship, while Chen et al. (2019) confirmed long-run abatement impacts of renewable R&D. Given the diversity and limited annual data availability for SCO members, ARDL represents a robust and flexible methodological strategy. Synthesizing the literature yields several stylized findings: (i) frontier readiness, when aligned with green innovation and renewable energy, improves environmental quality in the long run (Bilgili et al., 2016; Chen et al., 2019); (ii) ICT expansion can worsen environmental outcomes in the short run but tends to deliver gains after reaching a readiness threshold (Shahbaz et al., 2022); (iii) institutional quality and financial structures moderate the effectiveness of FTR, with green finance playing a pivotal role (Zhang & Zhou, 2021); and (iv) resource dependence weakens the positive impact of technological readiness unless accompanied by energy system reform (Destek & Sinha, 2020). Despite this, research gaps remain. Few studies explicitly operationalize UNCTAD’s FTR index, with most using proxies such as broadband penetration or patent intensity, and none to date have systematically applied ARDL analysis to the SCO bloc as a whole. Addressing these gaps would enhance our understanding of how frontier technology readiness interacts with diverse economic structures to influence environmental outcomes. Overall, the balance of evidence suggests that frontier technology readiness can be a structural driver of improved environmental quality, though its impact is mediated by energy mix, institutional frameworks, and financial alignment. For SCO economies, frontier technology adoption offers both risks and opportunities: without clean energy expansion, digitalization may exacerbate emissions, but with appropriate policy support, readiness in ICT, R&D, and green finance can accelerate environmental sustainability. Empirical analysis using ARDL can provide nuanced insights into these dynamics by disentangling short-run pressures from long-run equilibria and accounting for country-specific heterogeneity within the SCO framework. Methodology Data The article utilizes data panel for the SCO economies of China, Kyrgyzstan, Russia, Tajikistan, Uzbekistan, India, and Pakistan. The main aim is to analyze the effects of technology readiness, economic growth, mineral resource reliance, population density, and financial development on ecological footprint. Table 1 defines the variables used in the paper, mentioning their role, measurement units, and data sources. The focus is on Ecological Footprint (EF) which is the dependent variable represented by ecological footprint per person, with data being sourced from the Global Footprint Network. The main explanatory variable is Frontier Technology Readiness Index (FTR), an index from 0 to 1, with data being sourced from the United Nations Conference on Trade and Development (UNCTAD). The table also contains a number of control variables used to adjust the analysis: Economic Growth (GDPPC), measuring GDP per capita in constant 2015 US dollars; Mineral Resources, measuring mineral rents as a percent of GDP; Population Density, measuring persons per square kilometre; and Financial Development (FD), measuring domestic credit to the private sector as a percent of GDP. Data for all control variables are sourced from the World Bank. Table 1 : Variables Status Measurement Units Sources Variables Status Measurement units Sources Ecological footprint (EF) Dependent variable Ecological footprint per person Global Footprint Network Technology Readiness Index (FTR) Explanatory Index from 0 to 1 United Nations Conference on trade and development (UNCTAD) Economic growth (GDPPC) Control GDP per capita (constant 2015 US$) World Bank (WDI) Mineral resources Control Mineral rents (% of GDP) World Bank (WDI) Population density Control Population density (people per sq. km of land area) World Bank (WDI) Financial development (FD) Control Domestic credit to private sector by banks (% of GDP) World Bank (WDI) Econometric techniques As noted above the present study investigates effect of the Technology Readiness Index (FTR) on ecological footprint (EF), measured as ecological footprint per person, which serves as a comprehensive indicator of environmental pressure. In line with recent approaches in environmental economics and sustainability (Han & Sun, 2024), the empirical model is specified in a linear functional form as: where technological readiness is captured by the FTR variable, GDP per capita represents economic growth measured in constant 2015 US$, MR shows mineral rent as a percentage of GDP, population density presents the number of people per square kilometer of land area, and FD pertains to the financial development measuring domestic credit to the private sector by banks as a percentage of GDP. The expected signs of the explanatory variables are backed by previous theoretical and empirical evidence. Technological readiness (FTR) can reduce ecological pressure as it aids in cleaning production, resource efficiency, and the distribution of cleaner technologies. Ecological pressure may rise due to industrialization, predominantly at lower income levels, whereas at higher income levels, people may be more aware and capable of pursuing sustainability efforts (confirming the Environmental Kuznets Curve hypothesis). Mineral resource dependency (MR) is believed to increase the ecological footprint as extractive activities tend to increase environmental degradation. Meanwhile, population density (PD) is double-edged: the higher it gets, the more ecological pressure may operate through resource use; however, it is also possible for such higher density to bring about efficiency gains and greening of infrastructure on the urban side. Financial development (FD), on the other hand, is expected to reduce the ecological footprint when credits are channeled to green investment. However, in some contexts, it may increase resource-intensive economic activities, leaving its effect ambiguous. To establish whether ecological footprint and its determinants are interlinked in the SCO economies, cross-sectional dependence tests such as Breusch–Pagan LM, Pesaran scaled LM, bias-corrected scaled LM, and Pesaran CD are performed. The null hypothesis in each test upholds the absence of cross-sectional dependence, while the alternative assumes the presence of dependence among countries. Testing for the presence of cross-sectional dependence matters especially in this study, given that the SCO members are tied in strong economic, trade, and environmental linkages. Hence, we have an instance where shocks in energy consumption, financial channels, or technological readiness in one member state spill over to other countries through mechanisms such as regional cooperation agreements, shared infrastructure, or cross-border investment. Failure to acknowledge such interdependencies may yield biased estimates and erroneous policy recommendations. After proving the presence of cross-sectional dependence, the study moves on to investigate the order of integration of the variables using the Cross-sectionally Augmented Im, Pesaran, and Shin (CIPS) test proposed by Pesaran (2007). The CIPS test extends the traditional IPS panel unit root framework by including cross-sectional averages of lagged levels and first differences into the regression equation, thereby controlling for unobserved common factors inducing dependence across countries. The null hypothesis of the CIPS test is that all series contain a unit root, whereas the alternative is that at least some of them are stationary. The use of the CIPS test gains the highest relevance in the SCO setting, as the economies are highly interconnected through trade, through flows of funds, and through environmental spillovers. The usual panel unit root tests of the first generation would -- because of these cross-country correlations -- fail and make incorrect inferences about the time-series properties of the data. By using the CIPS test, the study ensures that the integration properties of ecological footprint, technology readiness, economic growth, mineral resources, population density, and financial development are correctly identified and sets the stage for credible panel estimation. After the integration order of the variables is determined, the study proceeds with employing the Pedroni residual cointegration test to look for evidence of a long-run equilibrium relationship between the ecological footprint and its major determinants. Pedroni's methodology is particularly suitable for this case because it allows for heterogeneity across countries by permitting individual-specific short-run dynamics and fixed effects, while testing in common for long-run cointegration. Under the null hypothesis of the Pedroni tests, the variables are not cointegrated; under the alternative hypothesis, the variables are cointegrated. The bootstrapped panel and group test procedures proposed by the test provide seven test statistics, four of which are within dimension (panel tests) and three between dimension (group tests), for better robustness and reliability. The application of this test is especially relevant within the SCO context, where member economies are diverse in their degrees of development, energy structures, and environmental pressures but yet interlinked through trade, investment, and regional policies. The indication of cointegration assures that, despite their short-run deviation from each other, ecological footprint, technological readiness, economic growth, dependence on mineral resources, population density, and financial development move together over the long term. Panel autoregressive distributed lag (PARDL) model After specifying the functional relationships between ecological footprint and its determinants, the study estimates both short-run and long-run dynamics within an econometric framework that allows for mixed orders of integration. The variables such as FTR, GDPpc, MR, PD, and FD could be either stationary at level (I(0)) or first difference stationary (I(1)). The usual cointegration methods such as Engle–Granger or Johansen require that all variables be integrated of the same order, an assumption which may not hold here. Therefore, the ARDL bounds testing approach was utilized to break this limitation. The ARDL framework has several useful practical advantages. First, it allows estimation of relationships regardless of whether the variables are I(0) or I(1), which is usually considered a single most important cause of pre-test bias in cointegration analysis. Second, it simultaneously models the short-term dynamics and long-run relationships in a single-equation specification, hence giving insights into the adjustment speed of deviations from equilibrium. Third, it allows relatively small samples to be used, making it relevant for panels with limited temporal observations. And last, the error-correction form clearly shows the speed of adjustment from short-run shocks back into long-run equilibrium, which is a very important aspect to consider in environmental and sustainability studies. The first-difference form of the model is presented as follows: Empirical analysis The trends for the Ecological Footprint (environment) and the Frontier Technology Readiness are presented by Figures 1 and 2 for a selected group of seven countries across the years 2010-2022. Figure 1 represents the per capita Ecological Footprint for China, India, Kyrgyz Republic, Pakistan, Russia, Tajikistan, and Uzbekistan. The graphs show diverse trends in these countries. For instance, Russia almost always has the highest, starting at about 5.6 and moving to beyond 6 by 2022. Pakistan and Tajikistan show low ecological footprints relative to Russia and are generally steady, being below 1.0 and 1.3, respectively. China shows a steady but slow increase from just above 3.1 to nearly 3.7. Figure 2 details the trends in the Frontier Technology Readiness (FTR) index for the same countries over a similar time horizon and frequency. The index takes values from 0 to 1, with higher values indicating higher readiness of frontier technologies. The trends here, however, vary by open country category. Both Russia and China reach the highest technological readiness levels showing a clear trend of increase with Russia going from 0.74 to above 0.80, and China ascending from just about 0.76 to almost 0.90. At the bottom tier in terms of FTR scores are Pakistan and Tajikistan, with Pakistan never rising above 0.40 and Tajikistan going lower than 0.36. Most countries including India and Uzbekistan show an overall trend of improvement in their FTR index over this 12-year period, albeit in some cases very modest. Table 2 reports descriptive statistics of the variables used in this study. The average EF is 2.17, and the standard deviation is 1.51; this gives an impression of the extent to which the countries in the sample have experienced disparate environmental impacts in the given time setting. The FTR in question has a mean value of 0.52, ranging from 0.17 at minimum to 0.91 at maximum; in other words, this shows that countries under consideration fall along a broad spectrum of technological readiness. Similarly, we observe variations in economic development (GDP-pc), mineral resources (MINERAL), and population density (POP-d), as well as financial development (FD), given the high values of their respective standard deviations compared to their means. The skewness values, especially for EF, MINERAL, and FD, are above 1, which implies that the right tail of these variable distributions are more stretched-out than the left, with higher concentrations of observations along the lower end of their respective ranges. The kurtosis values for MINERAL and FD here are above 3 also, an indicator for leptokurtic distributions-the ones having a heavier-tailed density with a bigger chance of extreme values relative to a normal distribution). Table 2 : descriptive stats EF FTR GDP-pc MINERAL POP-d FD Mean 2.173491 0.521351 3754.981 2.346834 153.7814 46.21619 Median 1.665096 0.421400 1583.998 1.088412 72.21980 23.97447 Maximum 5.734359 0.911100 11469.57 11.15020 475.6520 179.1041 Minimum 0.747272 0.169400 770.6041 0.014642 8.722636 9.327727 Std. Dev. 1.510180 0.224625 3546.233 2.765861 151.7946 45.80849 Skewness 1.051581 0.191005 0.937967 1.398599 0.958056 1.613200 Kurtosis 2.791367 1.443594 2.121904 4.005334 2.517590 4.432518 Table 3 contains the test results for cross-sectional dependence for the variables in the model. The values for the Breusch-Pagan LM, Pesaran scaled LM, bias-corrected scaled LM, and Pesaran CD statistics are quite high for most variables, and each statistic goes beyond its critical value, consistently suggesting the presence of cross-sectional dependence on most variables, namely economic freedom (EF), financial trade reform (FTR), GDP per capita (GDP-pc), mineral rents (MINERAL), and population density. This would mean that a shock or policy implemented in one country can affect others within the panel, denoting a very strong linkage across economies. Nonetheless, the Pesaran CD statistic for the financial development (FD) variable is relatively rather low, estimating at 1.02, to suggest weaker cross-sectional dependence vis-a-vis others. Overall, with the results, the analysis infers the existence of strong cross-sectional dependence in the data, warranting the employment of second-generation panel estimators suited for such interdependencies. Table 3 : Cross-sectional dependence results. Breusch-Pagan LM Pesaran scaled LM Bias-corrected scaled LM Pesaran CD EF 58.08887 5.722937 5.453706 2.811904 FTR 64.87375 6.769866 6.500636 4.130184 GDP-pc 254.2084 35.98484 35.71561 15.90717 MINERAL 80.39488 9.164828 8.846647 7.652804 POP-d 239.2378 33.67482 33.38315 15.3823 FD 68.9711 7.402102 7.132871 1.020309 The CIPS (Cross-sectionally Implemented Panel Unit Root) test results in Table 4 are meant to assess whether the variables are stationary or non-stationary in their levels or first differences. Tests for stationarity are a necessary precondition for most time-series and panel data analyses to avoid spurious results. Test statistics at the level of each variable are denoted as I(0) and at first difference I(1). Thus, the results indicate that two variables, Mineral Resources (MINERAL) and Financial Development (FD), are stationary in their levels (I(0)), because their test statistics (-3.56414 for MINERAL; -4.22611 for FD) are significant at the 1% level or beyond (denoted by '***'). Hence, at their original levels, the two series do not have unit roots. Conversely, Ecological Footprint (EF), Frontier Technology Readiness (FTR), Economic Growth (GDP-pc), and Population Density (POP-d) are variables that are non-stationary in their levels (I(0)) but become stationary upon first differencing (I(1)); this has been proved by the insignificance of their test statistics at I(0) and the significance of their statistics at I(1). For instance, EF's statistic of -2.57509 is significant at the 5% level (''), and GDP-pc's statistic of -3.23059 is significant at the 1% level ('*') after first differencing. Therefore, the CIPS test reveals a plethoric mix of integration orders among the studied variables-one set is stationary at levels I(0), while the other is integrated into the first order of I(1). Therefore, this result allow for the possibility of applying panel data frameworks like the Panel Autoregressive Distributed Lag (ARDL) model reported in later tables, which can deal with variables of different integration orders. Table 4 : CIPS unit root test. Variables I(0) I(1) EF -1.47001 -2.57509** FTR -1.25514 -2.88636** GDP-pc -1.35965 -3.23059*** MINERAL -3.56414*** - POP-d -1.72405 -2.15186** FD -4.22611*** - Table 5 contains the result of the Pedroni Extensions Residual Cointegration Test, which tests the presence of a long-run equilibrium relationship among the variables under investigation. The validity of this test is imperative as it justifies the application of some econometric model for instance, time-series data in which variables are not necessarily stationary on their own. This table presents seven different statistics divided into two groups depending on the assumption made on the autoregressive coefficients of the panel data. The first set of statistics, called "within-dimension," assumes commonity of the autoregressive coefficients across all countries in the sample. Here these two statistics are highly significant: the Panel PP-Statistic with a p-value of 0.0000, and the Panel ADF-Statistic with a p-value of 0.0017. Thus, these tests reject with great confidence the null hypothesis of no cointegration existing amongst the variables considered here. The second category, between-dimension, is regarded to be more robust, for it accommodates the possibility of the autoregressive coefficients varying for each individual country. The statistics accompanying this category, especially the Group PP-Statistic and the Group ADF-Statistic, are also found to be statistically significant at the one percent level, showing an definite p-value of 0.0000. Thus the ample and overwhelming statistical significance of various tests in either category testifies to a presumption of failing to reject the presence of cointegration. In other words, the presence of an Ecological Footprint, Frontier Technology Readiness, and other control variables is tied by a stable long-run relationship, which further justifies the use of the Panel Autoregressive Distributed Lag (ARDL) model concerned with the mainstream analysis found in the tables that follow. Our empirical results logically follow one another, starting with the main result and then carrying out stringent tests of its validity. First, we present the main results of the Panel ARDL model in Table 6, thus basing the long-run reduction of the ecological footprint by the frontier technology readiness on our core finding. We then ensure this finding is robust by removing China and India from the sample in Table 7 and by the estimation methods FMOLS and DOLS in Tables A5 and A6. Lastly, we investigate the causal pathways among all variables in Table 8, reflecting the complex web of interactions at play between environmental outcomes. The main estimation results unfolding from the Panel Autoregressive Distributed Lag (ARDL) model are displayed, to analyze the long-run and short-run joins between frontier technology readiness and the ecological footprint for the entire set of countries. The table is divided into two half to illustrate these temporal effects separately. The upper part of the table presents the long-run coefficients showing the sustained, equilibrium impacts exerted by the explanatory variables over the Ecological Footprint. The main result here relates to the Frontier Technology Readiness Index (LFTR). The coefficient for LFTR is -0.142083, negative and significant at 1 percent, thereby indicating a strong inverse relationship between technological readiness and environmental impact over long time horizons. In other words, a percent increase in the FTR index results in a decrease in the Ecological Footprint of the order of 0.142 percent, ceteris paribus. This is in line with an increasing number of studies that have empirically proven that investments in technological innovation, especially green and energy-efficient technologies, serve to curb environmental degradation over time (Han and Sun, 2024 and Shahbaz et al., 2022). The analysis further shows a negative significant coefficient of -0.099 for financial development. This is consistent with empirical evidence showing that a more developed financial sector can contribute to diminishing environmental degradation, where studies have argued that efficient financial systems channel investments into clean energy projects and environmentally friendly technologies to improve environmental quality. The coefficient for GDP per capita shows that a rise in economic growth by 1% increases in the long term the Ecological Footprint by about 0.48%, this being positive with significance (0.477). This result is according with numerous empirical researches on the environmental Kuznets curve (EKC) hypothesis, most of which find that in the developing or emerging stages of growth, a more or less direct, positive relationship exists between higher incomes and environmental pressure( Hassan et al, 2023, Biyase et al, 2024). The study also found a positive coefficient of 0.067 for mineral resource rents, suggesting that greater dependence on extractive industries is associated with long-term environmental degradation. This result confirms many cross-country empirical studies which find that resource-abundant economies typically suffer from higher levels of pollution and ecological damage because of the environmentally intensive nature of extraction activities (Hassan et al, 2023). In addition, the results show a strong negative coefficient for population density (-0.790), in line with several empirical studies on urbanization and its influence on the environment. Such studies often conclude that denser urban living can facilitate efficiencies in resource use, e.g. through shared infrastructure and public transport, hence lowering the ecological footprint on a per capita basis when compared to less dense areas (Hassan et al, 2023). The bottom part of the table presents short-run dynamics specifying year-to-year adjustments. An important item in this is the error correction term (COINTEQ), which yields a coefficient of -0.792827 and is statistically significant. This demonstrates that a long-run stable relationship does exist, with such rapid speed of adjustment that approximately 79% of any deviation from equilibrium is corrected in one year. In its short-run effects, however, the Frontier Technology Readiness (D(LFTR)) does lack statistical significance. This suggests that environmental benefits associated with technological improvements are perhaps substantially measurable over time but have no discernible impact in the short run, regarding the data under present consideration. Of the rest, in the short run, only changes in population density (D(LPOP-d)) prove statistically significant. This coefficient value is positive, indicating that strong increases in density immediately exert pressure on the environment. Table 7 shows the result of an important robustness check. It was done by re-estimating the Panel Autoregressive Distributed Lag Model with a sub-sample of the data. China and India were removed for this exercise to verify that our findings were not unduly affected by these two large economies. The evidence put forward in this table confirms the stability and reliability of my core findings that are strikingly consistent with the full sample estimates. In the long term, however, the sub-sample analysis maintains a significant negative relationship between technological advancement and environmental impact. The coefficient of the Frontier Technology Readiness Index (LFTR) stands at -0.145 and remains statistically significant at the 1% level, almost equal to the full-sample regression. This gives a strong exclusive support to our main conclusion: that a readiness level for technology that is higher translates into an Ecological Footprint that is lower in the long run. The long-term impacts of the remaining independent variables are also same: economic growth and mineral resources remain positively significant while financial development and population density have a statistically significant negative impact on the ecological footprint. Shifting to the short-run dynamics reported in the lower part of the table, these again are symmetrical with initial results. The error correction term (COINTEQ) is negative, statistically significant; this confirms the presence of a stable long-run relationship in this smaller country group and implies a rapid speed of adjustment back to equilibrium. As with the full sample model, the immediate year-on-year change in the Frontier Technology Readiness Index is statistically insignificant, further reinforcing the view that its environmental benefits are a long-run, and not an immediate, aspect. Hence, the findings in Table 6 offer very strong evidence that my seminal findings stand good and are not merely an outcome of the peculiar composition of the full sample. Table 7 : sub-sample ARDL estimates of the effect of FTR on environmental quality (ecological footprint) To provide an assertive power to primary findings, the next robustness checks were performed, considering two alternative econometric methods: FMOLS and Panel DOLS. The results presented in Tables A5 and A6 in the appendix are crucial to confirm that the key findings are not dependent on the ARDL model selection for the main analysis. These methods are designed to provide consistent long-run estimates when cointegrated panel data are present. The results obtained from both models strongly support the key findings of our study. The Panel FMOLS estimates from Table A5 indicate that the Frontier Technology Readiness Index (LFTR) is associated with a coefficient of -0.165. The coefficient is negative and statistically significant, confirming yet again that an increase in technological readiness leads to a reduction in the Ecological Footprint in the long-term. Meanwhile, in this model, the control variable for economic growth has been an important driver of environmental degradation, whereas other controls such as financial development, mineral resources, and population density have shown no statistically significant impact. Likewise, the Panel DOLS results from Table A6 further back our conclusion. The coefficient in the long run for Frontier Technology Readiness Index is estimated here as -0.148, which is again negative and statistically significant. This value, in fact, stands so close to the coefficient from our primary ARDL model (-0.142) that it lends greater weight to the argument for an established, dependable relationship. As for the FMOLS model, only economic growth proved significant amongst the control variables, yet the essential negative relation between technology and the ecological footprint remained robust. After establishing the long-run relation and confirming robustness, the last analytical step involved exploring the causal dynamics existing among the variables using the Dumitrescu-Hurlin panel causality tests, with results reported in Table 8. This test is necessary to grasp the directional influences between variables, whereas before, we had only correlated them. The causality analysis gave us quite a few important insights. They most importantly demonstrated no direct causal relations from one direction to the other between our key variable Frontier Technology Readiness (LFTR) and the Ecological Footprint (LEF). This means that even though these two variables share a long-run common equilibrium, not one directly Granger-causes the other. But the test does identify other complex causal relations that jointly dictate environmental outcomes. There is significant bidirectional causality between the Ecological Footprint and both financial development (LFD) and mineral resources (LMINERAL). This means that the financial sector changes the environment, and environment states influence the financial sector; the same two-way causal relations exist between mineral extraction and the environment. Moreover, we found significant unidirectional causality running from the Ecological Footprint to population density (LEF does not homogeneously cause LPOPD). Conclusion This study examined the dynamic relationship between Frontier Technology Readiness and Environmental Quality, using the Ecological Footprint (EF) per person as the proxy for environmental pressure, within seven Shanghai Cooperation Organisation (SCO) economies: China, India, Kyrgyzstan, Russia, Tajikistan, Uzbekistan, and Pakistan. The study utilizes the Panel Autoregressive Distributed Lag (ARDL) model, a robust econometric technique chosen because the variables exhibit a plethoric mix of integration orders (I(0) and I(1)), an assumption confirmed by the CIPS unit root test. The presence of cross-sectional dependence among these interconnected SCO members was also confirmed, which justified the use of second-generation panel estimators. Crucially, Pedroni cointegration tests overwhelmingly confirm the existence of a stable long-run equilibrium relationship among the Ecological Footprint and its determinants. The primary empirical finding is that Frontier Technology Readiness significantly reduces environmental degradation in the long run. The long-run coefficient for the FTR Index is negative and statistically significant at the 1 percent level, indicating that a higher level of technological readiness translates to a lower Ecological Footprint. This conclusion is robustly supported across multiple checks: re-estimation using a sub-sample that excludes the large economies of China and India maintained a significant negative FTR coefficient, confirming the finding's stability. Furthermore, verification using alternative long-run estimators, Panel FMOLS and Panel DOLS, yielded consistent negative and significant coefficients for FTR, lending greater credibility and validity to the established relationship. The source literature supports this finding, suggesting that investments in technological innovation, particularly green and energy-efficient technologies, effectively curb environmental degradation over time. Regarding control variables, the study confirms that traditional economic drivers increase long-run environmental pressure. Economic growth (GDP per capita) positively and significantly increases the Ecological Footprint, a result aligning with the Environmental Kuznets Curve (EKC) hypothesis often observed during emerging stages of growth. Similarly, reliance on mineral resource rents increases the EF, suggesting that dependence on extractive industries leads to long-term ecological damage. Conversely, financial development and high population density are associated with statistically significant long-term reductions in the Ecological Footprint, possibly because financial systems channel credit toward green investments and denser urban environments facilitate efficiencies through shared infrastructure. An analysis of the short-run dynamics reveals that the immediate, year-to-year change in FTR is statistically insignificant in affecting the Ecological Footprint. This crucial finding suggests that the environmental benefits of technological advancements, such as the use of Artificial Intelligence (AI) in optimization and efficiency, manifest as a sustained, long-term effect rather than an immediate one. Although a long-run equilibrium is confirmed, causality analysis using the Dumitrescu-Hurlin panel causality tests indicated no direct Granger-causal relations running between FTR and EF. However, significant bidirectional causality was identified between the Ecological Footprint and both financial development and mineral resources, emphasizing the complex feedback loops dictating environmental outcomes. These findings underscore the critical policy imperative that promoting the innovation and diffusion of frontier technologies, like AI, on a global scale is essential for achieving long-term carbon reduction targets and helping nations, such as India, leapfrog resource-intensive development models toward a greener future. Declarations Funding: The authors received no funding for this research. Declarations Ethics approval and consent to participate Not applicable. Consent for Publication Not applicable. Competing interests The authors declare no competing interests. Corresponding author : Mduduzi Biyase Email Address: [email protected] Data availability Data are available from the authors upon request References Bilgili, F., Koçak, E., & Bulut, Ü. (2016). The dynamic impact of renewable energy consumption on CO₂ emissions: A revisited Environmental Kuznets Curve approach. Renewable and Sustainable Energy Reviews, 54, 838–845. Biyase,M, F. Kirsten, S. Mbatha, B. Ataro, (2024) Remittance and carbon dioxide emissions in the Southern African Customs Union region: is there a modified environmental Kuznets curve? Sustain. Futures 8 100315. Chen, S., Pao, H., & Wang, H. (2019). The causal relationships among economic growth, renewable energy, R&D, and CO₂ emissions: Evidence from China. Energy Policy, 123, 362–372. Destek, M. A., & Sinha, A. (2020). Renewable, non-renewable energy consumption, economic growth, trade openness and ecological footprint: Evidence from OECD countries. Journal of Cleaner Production, 242, 118537. Duarte, P., & Pinho, C. (2023). Energy transitions in Central Asia: Challenges and opportunities. Energy Policy, 174, 113399. Karakaya, E., & Sriwannawit, P. (2015). Barriers to the adoption of photovoltaic systems: The state of the art. Renewable and Sustainable Energy Reviews, 49, 60–66. Li, Y., & Wang, C. (2023). Digital economy and environmental sustainability: Evidence from G20 economies. Technological Forecasting and Social Change, 190, 122337. OECD. (2022). Green transition in Central Asia: Investment and policy challenges. Paris: OECD Publishing. Pesaran, M. H., Shin, Y., & Smith, R. J. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94(446), 621–634. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. Salahuddin, M., & Alam, K. (2016). Information and communication technology, electricity consumption and economic growth in OECD countries: A panel data analysis. International Journal of Electrical Power & Energy Systems, 76, 185–193. Shahbaz, M., Nasir, M. A., & Roubaud, D. (2019). Environmental degradation in France: The effects of FDI, financial development, and energy innovations. Energy Economics, 74, 843–857. Shahbaz, M., Sinha, A., & Vo, X. V. (2022). Digitalization and the environment: A critical review and research agenda. Technological Forecasting and Social Change, 176, 121485. Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer. UNCTAD. (2021). Technology and Innovation Report 2021: Catching technological waves – Innovation with equity. Geneva: United Nations Conference on Trade and Development. Wackernagel, M., & Rees, W. (1996). Our ecological footprint: Reducing human impact on the earth. New Society Publishers. Wendling, Z. A., Emerson, J. W., de Sherbinin, A., & Esty, D. C. (2024). 2024 Environmental Performance Index. Yale Center for Environmental Law & Policy. Zhang, X., & Zhou, Y. (2021). Financial development, technological innovation and CO₂ emissions: Evidence from China. Environmental Science and Pollution Research, 28, 56946–56960. Zhang, Y., Li, Y., & Huang, J. (2022). Digital economy and carbon emissions: Evidence from panel threshold analysis. Energy Economics, 105, 105751. Hassan, A et al., (2023) Green growth as a determinant of ecological footprint: do ICT diffusion, environmental innovation, and natural resources matter? PLoS One 18 (9) e0287 Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":29653,"visible":true,"origin":"","legend":"\u003cp\u003etrends in environmental quality (ecological footprint)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7958890/v1/6d1eba5a1e9def92eaca172f.png"},{"id":95708939,"identity":"1fb9c8bc-fc1d-4fc9-a773-923727a9bd30","added_by":"auto","created_at":"2025-11-12 07:31:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29108,"visible":true,"origin":"","legend":"\u003cp\u003etrends in environmental quality (ecological footprint)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7958890/v1/5a90e4c66dc52bc404ec3e14.png"},{"id":102413681,"identity":"5bb81d23-7d4b-4c92-9320-555eb54a6b40","added_by":"auto","created_at":"2026-02-11 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acknowledged as the decisive lever for reconciling sustained global economic development with environmental stewardship, yet the precise pathways and requisite conditions through which large, heterogeneous geopolitical blocks convert nascent \"frontier\" technologies into measurable, sustained gains in environmental quality (EQ) remain empirically ambiguous and poorly understood. This study addresses this critical research blocks convert nascent \"frontier\" technologies into measurable, sustained gains in environmental quality (EQ) remain empirically ambiguous and poorly understood. This study addresses this critical research lacuna by rigorously investigating the dynamic relationship between Frontier Technology Readiness (FTR) and environmental pressure, quantified comprehensively by the Ecological Footprint (EF) per person, across a panel of seven Shanghai Cooperation Organisation (SCO) economies. The SCO region\u0026mdash;comprising large, energy-intensive nations like China, India, and Russia\u0026mdash;presents an essential natural laboratory for this analysis, as member states are concurrently burdened by legacies of carbon- and resource-intensive growth while pursuing ambitious imperatives for digital transformation and global climate commitments.\u003c/p\u003e\u003cp\u003eThe theoretical relationship between FTR and EQ is inherently ambiguous and often conflicting. While enhanced readiness is expected to foster efficiency, smart-grid systems, and the diffusion of clean technologies, the empirical record frequently highlights counteracting forces. Specifically, early stages of digital infrastructure expansion often generate rebound effects through higher electricity demand and data center loads, particularly in fossil-dependent economies, potentially increasing environmental pressure before efficiency gains materialize. Furthermore, observed trends among SCO members reveal divergence: for instance, highly ready economies like Russia and China show steadily increasing FTR scores, yet simultaneously exhibit high or slowly rising Ecological Footprints, suggesting that technological readiness does not immediately translate into environmental abatement. This complexity underscores the need to disentangle the relationship between these variables.\u003c/p\u003e\u003cp\u003eOur work provides several contributions to the existing literature, overcoming identified methodological and measurement deficiencies. First, we establish a novel geopolitical focus by systematically analyzing the SCO bloc, recognizing the strong cross-sectional dependence prevalent due to shared economic and environmental linkages. Second, we advance measurement integrity by employing the Frontier Technology Readiness Index (FTRI) developed by UNCTAD, a conceptually based and calibrated measure that captures a country's sophisticated capacity across five pillars, moving beyond generic proxies. Third, we adopt a methodologically robust dynamic approach tailored to the data's properties, utilizing the Panel Autoregressive Distributed Lag (ARDL) model to accommodate the confirmed plethoric mix of integration orders (I(0) and I(1)) and allowing us to precisely estimate both short-run adjustments and the existence of a stable long-run equilibrium, which the Pedroni cointegration tests overwhelmingly confirm. This rigorous methodology leads to the powerful and empirically validated finding that Frontier Technology Readiness significantly reduces environmental degradation in the long run. This core result is established as highly robust, confirmed by verification using alternative long-run estimators (Panel FMOLS and Panel DOLS) and, most assertively, by re-estimation on a sub-sample that excluded the dominant economies of China and India. Conversely, the short-run effect of FTR is statistically insignificant, demonstrating that environmental benefits accrue as a sustained, long-term impact. Moreover, while a long-run relationship exists, causality analysis reveals no direct Granger-causal link between FTR and EF, highlighting the complexity and mediated nature of this interaction.\u003c/p\u003e\u003cp\u003eThe remainder of this study is structured as follows: Section 2 reviews the relevant literature concerning the FTR-EQ nexus and the ambiguity of its effects. Section 3 details the methodology, including the data description for the Ecological Footprint and the Frontier Technology Readiness Index, and outlines the application of the CIPS unit root, Pedroni cointegration, and Panel ARDL models. Section 4 presents the core empirical findings, including the long-run and short-run ARDL estimations, the robustness checks (sub-sample, FMOLS, and DOLS), and the Granger causality analysis. Finally, Section 5 discusses the policy implications of these results and concludes the study\u003c/p\u003e"},{"header":"Literature review","content":"\u003cp\u003eThe nexus between technological advancement and environmental sustainability has received significant scholarly attention in recent decades, particularly in the context of rapid digitalization and the diffusion of frontier technologies. UNCTAD (2021) introduced the Frontier Technology Readiness Index (FTRI) to capture countries\u0026rsquo; capacities to adopt and scale advanced technologies such as artificial intelligence, big data, robotics, blockchain, and renewable energy systems. The index is built on five pillars\u0026mdash;information and communication technology (ICT) deployment, skills, research and development (R\u0026amp;D) activity, industry activity, and access to finance\u0026mdash;each of which plays a critical role in determining whether technological readiness translates into productivity gains and environmental improvements. The literature on environmental quality (EQ), however, presents an inherently complex and multi-dimensional picture. While carbon dioxide (CO₂) emissions remain the most widely used proxy of environmental degradation (Shahbaz, Nasir, \u0026amp; Roubaud, 2019), broader indicators such as ecological footprint (Wackernagel \u0026amp; Rees, 1996) and composite metrics such as the Environmental Performance Index (Wendling, Emerson, de Sherbinin, \u0026amp; Esty, 2024) provide complementary insights by integrating ecosystem vitality, resource efficiency, and environmental health.\u003c/p\u003e\n\u003cp\u003eThe theoretical relationship between FTR and EQ is ambiguous. On the positive side, enhanced readiness fosters diffusion of clean technologies, smart-grid systems, digital monitoring, and process innovation, which can reduce energy intensity and emissions (Karakaya \u0026amp; Sriwannawit, 2015). Conversely, early stages of digital infrastructure expansion often generate rebound effects through higher electricity demand, data center loads, and increased device manufacturing, particularly in fossil-dependent economies (Zhang, Li, \u0026amp; Huang, 2022). Empirical studies frequently identify non-linearities, such as inverted-U relationships between digitalization and emissions, suggesting that only after surpassing a technological threshold do efficiency and dematerialization gains outweigh the scale effects (Shahbaz, Sinha, \u0026amp; Vo, 2022; Li \u0026amp; Wang, 2023).\u003c/p\u003e\n\u003cp\u003eIn the context of the Shanghai Cooperation Organisation (SCO)\u0026mdash;which includes China, India, Russia, Pakistan, Central Asian states, Iran, and Belarus\u0026mdash;heterogeneity in technological readiness and energy structure is especially salient. China and India have rapidly expanded renewable deployment and digital ecosystems, while Russia, Iran, and Kazakhstan remain fossil-fuel intensive (Duarte \u0026amp; Pinho, 2023). Legacy infrastructure inefficiencies in Central Asia, combined with underinvestment in clean technologies, present challenges but also opportunities for substantial abatement through frontier digital upgrades in energy and water systems (OECD, 2022). Thus, SCO economies represent a natural laboratory for analyzing the FTR\u0026ndash;EQ nexus.\u003c/p\u003e\n\u003cp\u003eEmpirical evidence supports the potential of green technological innovation to mitigate emissions. Bilgili, Ko\u0026ccedil;ak, and Bulut (2016) demonstrated that renewable energy innovation in OECD economies reduced CO₂ emissions over the long run. Chen, Pao, and Wang (2019) applied the ARDL approach to China and confirmed that R\u0026amp;D investment in renewable technologies was a key factor in long-term emission reduction. At the same time, research suggests that generic technological innovation and financial development can exacerbate environmental degradation if not explicitly directed toward clean energy or efficiency-enhancing applications (Zhang \u0026amp; Zhou, 2021). Similarly, in emerging economies, Destek and Sinha (2020) found that technological innovation reduced ecological footprint in the long run, although resource rents and trade openness counterbalanced these improvements.\u003c/p\u003e\n\u003cp\u003eDigitalization, a major component of frontier readiness, has shown complex and often transitional effects. Salahuddin and Alam (2016) reported that ICT expansion initially raised energy consumption in OECD countries, while Shahbaz et al. (2022) highlighted that, in the longer term, digital transformation contributed to environmental improvements through smart energy systems and innovation spillovers. Recent cross-country evidence confirms an inverted-U dynamic, wherein early digitalization increases emissions but later phases deliver efficiency-driven abatement (Li \u0026amp; Wang, 2023). This is particularly relevant for SCO economies with rapidly expanding digital infrastructure but still fossil-dominant power mixes.\u003c/p\u003e\n\u003cp\u003eMethodologically, the Autoregressive Distributed Lag (ARDL) framework (Pesaran, Shin, \u0026amp; Smith, 2001) is widely recognized as appropriate for examining technology\u0026ndash;environment linkages, especially in small samples with mixed orders of integration. The ARDL bounds-testing approach allows for simultaneous estimation of short-run elasticities and long-run cointegrating relationships, with the error-correction mechanism capturing adjustment back to equilibrium. Its extensions, including Nonlinear ARDL (NARDL) to capture asymmetries (Shin, Yu, \u0026amp; Greenwood-Nimmo, 2014) and Pooled Mean Group ARDL (Pesaran, Shin, \u0026amp; Smith, 1999) for panel analysis, have been employed in studies of green innovation, digitalization, and ecological footprint. Zhang et al. (2022), for example, identified threshold effects in the digital economy\u0026ndash;emissions relationship, while Chen et al. (2019) confirmed long-run abatement impacts of renewable R\u0026amp;D. Given the diversity and limited annual data availability for SCO members, ARDL represents a robust and flexible methodological strategy.\u003c/p\u003e\n\u003cp\u003eSynthesizing the literature yields several stylized findings: (i) frontier readiness, when aligned with green innovation and renewable energy, improves environmental quality in the long run (Bilgili et al., 2016; Chen et al., 2019); (ii) ICT expansion can worsen environmental outcomes in the short run but tends to deliver gains after reaching a readiness threshold (Shahbaz et al., 2022); (iii) institutional quality and financial structures moderate the effectiveness of FTR, with green finance playing a pivotal role (Zhang \u0026amp; Zhou, 2021); and (iv) resource dependence weakens the positive impact of technological readiness unless accompanied by energy system reform (Destek \u0026amp; Sinha, 2020). Despite this, research gaps remain. Few studies explicitly operationalize UNCTAD\u0026rsquo;s FTR index, with most using proxies such as broadband penetration or patent intensity, and none to date have systematically applied ARDL analysis to the SCO bloc as a whole. Addressing these gaps would enhance our understanding of how frontier technology readiness interacts with diverse economic structures to influence environmental outcomes.\u003c/p\u003e\n\u003cp\u003eOverall, the balance of evidence suggests that frontier technology readiness can be a structural driver of improved environmental quality, though its impact is mediated by energy mix, institutional frameworks, and financial alignment. For SCO economies, frontier technology adoption offers both risks and opportunities: without clean energy expansion, digitalization may exacerbate emissions, but with appropriate policy support, readiness in ICT, R\u0026amp;D, and green finance can accelerate environmental sustainability. Empirical analysis using ARDL can provide nuanced insights into these dynamics by disentangling short-run pressures from long-run equilibria and accounting for country-specific heterogeneity within the SCO framework.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eData\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe article utilizes data panel for the SCO economies of China, Kyrgyzstan, Russia, Tajikistan, Uzbekistan, India, and Pakistan. The main aim is to analyze the effects of technology readiness, economic growth, mineral resource reliance, population density, and financial development on ecological footprint. Table 1 defines the variables used in the paper, mentioning their role, measurement units, and data sources. The focus is on Ecological Footprint (EF) which is the dependent variable represented by ecological footprint per person, with data being sourced from the Global Footprint Network. The main explanatory variable is Frontier Technology Readiness Index (FTR), an index from 0 to 1, with data being sourced from the United Nations Conference on Trade and Development (UNCTAD). The table also contains a number of control variables used to adjust the analysis: Economic Growth (GDPPC), measuring GDP per capita in constant 2015 US dollars; Mineral Resources, measuring mineral rents as a percent of GDP; Population Density, measuring persons per square kilometre; and Financial Development (FD), measuring domestic credit to the private sector as a percent of GDP. Data for all control variables are sourced from the World Bank.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Variables Status Measurement Units Sources\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eStatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eMeasurement units\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eSources\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eEcological footprint (EF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eDependent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eEcological footprint per person\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eGlobal Footprint Network\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eTechnology Readiness Index (FTR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eExplanatory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eIndex from 0 to 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eUnited Nations Conference on trade and development (UNCTAD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eEconomic growth (GDPPC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eControl\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eGDP per capita (constant 2015 US$)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eWorld Bank\u003cbr\u003e\u0026nbsp;(WDI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMineral resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eMineral rents (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eWorld Bank (WDI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003ePopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003ePopulation density (people per sq. km of land area)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eWorld Bank (WDI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eFinancial development (FD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eDomestic credit to private sector by banks (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003eWorld Bank (WDI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eEconometric techniques\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs noted above the present study investigates effect of the Technology Readiness Index (FTR) on ecological footprint (EF), measured as ecological footprint per person, which serves as a comprehensive indicator of environmental pressure. In line with recent approaches in environmental economics and sustainability (Han \u0026amp; Sun, 2024), the empirical model is specified in a linear functional form as:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere technological readiness is captured by the FTR variable, GDP per capita represents economic growth measured in constant 2015 US$, MR shows mineral rent as a percentage of GDP, population density presents the number of people per square kilometer of land area, and FD pertains to the financial development measuring domestic credit to the private sector by banks as a percentage of GDP. The expected signs of the explanatory variables are backed by previous theoretical and empirical evidence. Technological readiness (FTR) can reduce ecological pressure as it aids in cleaning production, resource efficiency, and the distribution of cleaner technologies. Ecological pressure may rise due to industrialization, predominantly at lower income levels, whereas at higher income levels, people may be more aware and capable of pursuing sustainability efforts (confirming the Environmental Kuznets Curve hypothesis). Mineral resource dependency (MR) is believed to increase the ecological footprint as extractive activities tend to increase environmental degradation. Meanwhile, population density (PD) is double-edged: the higher it gets, the more ecological pressure may operate through resource use; however, it is also possible for such higher density to bring about efficiency gains and greening of infrastructure on the urban side. Financial development (FD), on the other hand, is expected to reduce the ecological footprint when credits are channeled to green investment. However, in some contexts, it may increase resource-intensive economic activities, leaving its effect ambiguous.\u003c/p\u003e\n\u003cp\u003eTo establish whether ecological footprint and its determinants are interlinked in the SCO economies, cross-sectional dependence tests such as Breusch\u0026ndash;Pagan LM, Pesaran scaled LM, bias-corrected scaled LM, and Pesaran CD are performed. The null hypothesis in each test upholds the absence of cross-sectional dependence, while the alternative assumes the presence of dependence among countries. Testing for the presence of cross-sectional dependence matters especially in this study, given that the SCO members are tied in strong economic, trade, and environmental linkages. Hence, we have an instance where shocks in energy consumption, financial channels, or technological readiness in one member state spill over to other countries through mechanisms such as regional cooperation agreements, shared infrastructure, or cross-border investment. Failure to acknowledge such interdependencies may yield biased estimates and erroneous policy recommendations.\u003c/p\u003e\n\u003cp\u003eAfter proving the presence of cross-sectional dependence, the study moves on to investigate the order of integration of the variables using the Cross-sectionally Augmented Im, Pesaran, and Shin (CIPS) test proposed by Pesaran (2007). The CIPS test extends the traditional IPS panel unit root framework by including cross-sectional averages of lagged levels and first differences into the regression equation, thereby controlling for unobserved common factors inducing dependence across countries. The null hypothesis of the CIPS test is that all series contain a unit root, whereas the alternative is that at least some of them are stationary. The use of the CIPS test gains the highest relevance in the SCO setting, as the economies are highly interconnected through trade, through flows of funds, and through environmental spillovers. The usual panel unit root tests of the first generation would -- because of these cross-country correlations -- fail and make incorrect inferences about the time-series properties of the data. By using the CIPS test, the study ensures that the integration properties of ecological footprint, technology readiness, economic growth, mineral resources, population density, and financial development are correctly identified and sets the stage for credible panel estimation.\u003c/p\u003e\n\u003cp\u003eAfter the integration order of the variables is determined, the study proceeds with employing the Pedroni residual cointegration test to look for evidence of a long-run equilibrium relationship between the ecological footprint and its major determinants. Pedroni\u0026apos;s methodology is particularly suitable for this case because it allows for heterogeneity across countries by permitting individual-specific short-run dynamics and fixed effects, while testing in common for long-run cointegration. Under the null hypothesis of the Pedroni tests, the variables are not cointegrated; under the alternative hypothesis, the variables are cointegrated. The bootstrapped panel and group test procedures proposed by the test provide seven test statistics, four of which are within dimension (panel tests) and three between dimension (group tests), for better robustness and reliability. The application of this test is especially relevant within the SCO context, where member economies are diverse in their degrees of development, energy structures, and environmental pressures but yet interlinked through trade, investment, and regional policies. The indication of cointegration assures that, despite their short-run deviation from each other, ecological footprint, technological readiness, economic growth, dependence on mineral resources, population density, and financial development move together over the long term.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel autoregressive distributed lag (PARDL) model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter specifying the functional relationships between ecological footprint and its determinants, the study estimates both short-run and long-run dynamics within an econometric framework that allows for mixed orders of integration. The variables such as FTR, GDPpc, MR, PD, and FD could be either stationary at level (I(0)) or first difference stationary (I(1)). The usual cointegration methods such as Engle\u0026ndash;Granger or Johansen require that all variables be integrated of the same order, an assumption which may not hold here. Therefore, the ARDL bounds testing approach was utilized to break this limitation. The ARDL framework has several useful practical advantages. First, it allows estimation of relationships regardless of whether the variables are I(0) or I(1), which is usually considered a single most important cause of pre-test bias in cointegration analysis. Second, it simultaneously models the short-term dynamics and long-run relationships in a single-equation specification, hence giving insights into the adjustment speed of deviations from equilibrium. Third, it allows relatively small samples to be used, making it relevant for panels with limited temporal observations. And last, the error-correction form clearly shows the speed of adjustment from short-run shocks back into long-run equilibrium, which is a very important aspect to consider in environmental and sustainability studies.\u003c/p\u003e\n\u003cp\u003eThe first-difference form of the model is presented as follows: \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmpirical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe trends for the Ecological Footprint (environment) and the Frontier Technology Readiness are presented by Figures 1 and 2 for a selected group of seven countries across the years 2010-2022. Figure 1 represents the per capita Ecological Footprint for China, India, Kyrgyz Republic, Pakistan, Russia, Tajikistan, and Uzbekistan. The graphs show diverse trends in these countries. For instance, Russia almost always has the highest, starting at about 5.6 and moving to beyond 6 by 2022. Pakistan and Tajikistan show low ecological footprints relative to Russia and are generally steady, being below 1.0 and 1.3, respectively. China shows a steady but slow increase from just above 3.1 to nearly 3.7. Figure 2 details the trends in the Frontier Technology Readiness (FTR) index for the same countries over a similar time horizon and frequency. The index takes values from 0 to 1, with higher values indicating higher readiness of frontier technologies. The trends here, however, vary by open country category. Both Russia and China reach the highest technological readiness levels showing a clear trend of increase with Russia going from 0.74 to above 0.80, and China ascending from just about 0.76 to almost 0.90. At the bottom tier in terms of FTR scores are Pakistan and Tajikistan, with Pakistan never rising above 0.40 and Tajikistan going lower than 0.36. Most countries including India and Uzbekistan show an overall trend of improvement in their FTR index over this 12-year period, albeit in some cases very modest.\u003c/p\u003e\n\u003cp\u003eTable 2 reports descriptive statistics of the variables used in this study. The average EF is 2.17, and the standard deviation is 1.51; this gives an impression of the extent to which the countries in the sample have experienced disparate environmental impacts in the given time setting. The FTR in question has a mean value of 0.52, ranging from 0.17 at minimum to 0.91 at maximum; in other words, this shows that countries under consideration fall along a broad spectrum of technological readiness. Similarly, we observe variations in economic development (GDP-pc), mineral resources (MINERAL), and population density (POP-d), as well as financial development (FD), given the high values of their respective standard deviations compared to their means. The skewness values, especially for EF, MINERAL, and FD, are above 1, which implies that the right tail of these variable distributions are more stretched-out than the left, with higher concentrations of observations along the lower end of their respective ranges. The kurtosis values for MINERAL and FD here are above 3 also, an indicator for leptokurtic distributions-the ones having a heavier-tailed density with a bigger chance of extreme values relative to a normal distribution).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 595px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: descriptive stats\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003eFTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003eGDP-pc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003eMINERAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003ePOP-d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003eFD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;2.173491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.521351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;3754.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;2.346834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;153.7814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;46.21619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;Median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.665096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.421400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;1583.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.088412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;72.21980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;23.97447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;Maximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;5.734359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.911100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;11469.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;11.15020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;475.6520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;179.1041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;Minimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.747272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.169400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;770.6041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.014642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;8.722636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;9.327727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;Std. Dev.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.510180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.224625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;3546.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;2.765861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;151.7946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;45.80849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;Skewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.051581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.191005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.937967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.398599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.958056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.613200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;Kurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;2.791367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.443594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;2.121904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;4.005334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;2.517590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;4.432518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 contains the test results for cross-sectional dependence for the variables in the model. The values for the Breusch-Pagan LM, Pesaran scaled LM, bias-corrected scaled LM, and Pesaran CD statistics are quite high for most variables, and each statistic goes beyond its critical value, consistently suggesting the presence of cross-sectional dependence on most variables, namely economic freedom (EF), financial trade reform (FTR), GDP per capita (GDP-pc), mineral rents (MINERAL), and population density. This would mean that a shock or policy implemented in one country can affect others within the panel, denoting a very strong linkage across economies. Nonetheless, the Pesaran CD statistic for the financial development (FD) variable is relatively rather low, estimating at 1.02, to suggest weaker cross-sectional dependence vis-a-vis others. Overall, with the results, the analysis infers the existence of strong cross-sectional dependence in the data, warranting the employment of second-generation panel estimators suited for such interdependencies.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e: Cross-sectional dependence results.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003eBreusch-Pagan LM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003ePesaran scaled LM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003eBias-corrected scaled LM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003ePesaran CD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e58.08887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5.722937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e5.453706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e2.811904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eFTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e64.87375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6.769866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e6.500636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e4.130184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eGDP-pc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e254.2084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e35.98484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e35.71561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e15.90717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eMINERAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e80.39488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e9.164828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e8.846647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e7.652804\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003ePOP-d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e239.2378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e33.67482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e33.38315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e15.3823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eFD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e68.9711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 19px;\"\u003e\n \u003cp\u003e7.402102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e7.132871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e1.020309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;The CIPS (Cross-sectionally Implemented Panel Unit Root) test results in Table 4 are meant to assess whether the variables are stationary or non-stationary in their levels or first differences. Tests for stationarity are a necessary precondition for most time-series and panel data analyses to avoid spurious results. Test statistics at the level of each variable are denoted as I(0) and at first difference I(1). Thus, the results indicate that two variables, Mineral Resources (MINERAL) and Financial Development (FD), are stationary in their levels (I(0)), because their test statistics (-3.56414 for MINERAL; -4.22611 for FD) are significant at the 1% level or beyond (denoted by \u0026apos;***\u0026apos;). Hence, at their original levels, the two series do not have unit roots. Conversely, Ecological Footprint (EF), Frontier Technology Readiness (FTR), Economic Growth (GDP-pc), and Population Density (POP-d) are variables that are non-stationary in their levels (I(0)) but become stationary upon first differencing (I(1)); this has been proved by the insignificance of their test statistics at I(0) and the significance of their statistics at I(1). For instance, EF\u0026apos;s statistic of -2.57509 is significant at the 5% level (\u0026apos;\u0026apos;), and GDP-pc\u0026apos;s statistic of -3.23059 is significant at the 1% level (\u0026apos;*\u0026apos;) after first differencing. Therefore, the CIPS test reveals a plethoric mix of integration orders among the studied variables-one set is stationary at levels I(0), while the other is integrated into the first order of I(1). Therefore, this result allow for the possibility of applying panel data frameworks like the Panel Autoregressive Distributed Lag (ARDL) model reported in later tables, which can deal with variables of different integration orders.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"53%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e: CIPS unit root test.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003eI(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003eI(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-1.47001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-2.57509**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eFTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-1.25514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-2.88636**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eGDP-pc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-1.35965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-3.23059***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eMINERAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-3.56414***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003ePOP-d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-1.72405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-2.15186**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eFD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-4.22611***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5 contains the result of the Pedroni Extensions Residual Cointegration Test, which tests the presence of a long-run equilibrium relationship among the variables under investigation. The validity of this test is imperative as it justifies the application of some econometric model for instance, time-series data in which variables are not necessarily stationary on their own. This table presents seven different statistics divided into two groups depending on the assumption made on the autoregressive coefficients of the panel data. The first set of statistics, called \u0026quot;within-dimension,\u0026quot; assumes commonity of the autoregressive coefficients across all countries in the sample. Here these two statistics are highly significant: the Panel PP-Statistic with a p-value of 0.0000, and the Panel ADF-Statistic with a p-value of 0.0017. Thus, these tests reject with great confidence the null hypothesis of no cointegration existing amongst the variables considered here. The second category, between-dimension, is regarded to be more robust, for it accommodates the possibility of the autoregressive coefficients varying for each individual country. The statistics accompanying this category, especially the Group PP-Statistic and the Group ADF-Statistic, are also found to be statistically significant at the one percent level, showing an definite p-value of 0.0000. Thus the ample and overwhelming statistical significance of various tests in either category testifies to a presumption of failing to reject the presence of cointegration. In other words, the presence of an Ecological Footprint, Frontier Technology Readiness, and other control variables is tied by a stable long-run relationship, which further justifies the use of the Panel Autoregressive Distributed Lag (ARDL) model concerned with the mainstream analysis found in the tables that follow.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eOur empirical results logically follow one another, starting with the main result and then carrying out stringent tests of its validity. First, we present the main results of the Panel ARDL model in Table 6, thus basing the long-run reduction of the ecological footprint by the frontier technology readiness on our core finding. We then ensure this finding is robust by removing China and India from the sample in Table 7 and by the estimation methods FMOLS and DOLS in Tables A5 and A6. Lastly, we investigate the causal pathways among all variables in Table 8, reflecting the complex web of interactions at play between environmental outcomes.\u003c/p\u003e\n\u003cp\u003eThe main estimation results unfolding from the Panel Autoregressive Distributed Lag (ARDL) model are displayed, to analyze the long-run and short-run joins between frontier technology readiness and the ecological footprint for the entire set of countries. The table is divided into two half to illustrate these temporal effects separately.\u003c/p\u003e\n\u003cp\u003eThe upper part of the table presents the long-run coefficients showing the sustained, equilibrium impacts exerted by the explanatory variables over the Ecological Footprint. The main result here relates to the Frontier Technology Readiness Index (LFTR). The coefficient for LFTR is -0.142083, negative and significant at 1 percent, thereby indicating a strong inverse relationship between technological readiness and environmental impact over long time horizons. In other words, a percent increase in the FTR index results in a decrease in the Ecological Footprint of the order of 0.142 percent, ceteris paribus. This is in line with an increasing number of studies that have empirically proven that investments in technological innovation, especially green and energy-efficient technologies, serve to curb environmental degradation over time (Han and Sun, 2024 and Shahbaz et al., 2022). The analysis further shows a negative significant coefficient of -0.099 for financial development. This is consistent with empirical evidence showing that a more developed financial sector can contribute to diminishing environmental degradation, where studies have argued that efficient financial systems channel investments into clean energy projects and environmentally friendly technologies to improve environmental quality.\u003c/p\u003e\n\u003cp\u003eThe coefficient for GDP per capita shows that a rise in economic growth by 1% increases in the long term the Ecological Footprint by about 0.48%, this being positive with significance (0.477). This result is according with numerous empirical researches on the environmental Kuznets curve (EKC) hypothesis, most of which find that in the developing or emerging stages of growth, a more or less direct, positive relationship exists between higher incomes and environmental pressure( Hassan et al, 2023, Biyase et al, 2024). The study also found a positive coefficient of 0.067 for mineral resource rents, suggesting that greater dependence on extractive industries is associated with long-term environmental degradation. This result confirms many cross-country empirical studies which find that resource-abundant economies typically suffer from higher levels of pollution and ecological damage because of the environmentally intensive nature of extraction activities (Hassan et al, 2023). In addition, the results show a strong negative coefficient for population density (-0.790), in line with several empirical studies on urbanization and its influence on the environment. Such studies often conclude that denser urban living can facilitate efficiencies in resource use, e.g. through shared infrastructure and public transport, hence lowering the ecological footprint on a per capita basis when compared to less dense areas (Hassan et al, 2023).\u003c/p\u003e\n\u003cp\u003eThe bottom part of the table presents short-run dynamics specifying year-to-year adjustments. An important item in this is the error correction term (COINTEQ), which yields a coefficient of -0.792827 and is statistically significant. This demonstrates that a long-run stable relationship does exist, with such rapid speed of adjustment that approximately 79% of any deviation from equilibrium is corrected in one year. In its short-run effects, however, the Frontier Technology Readiness (D(LFTR)) does lack statistical significance. This suggests that environmental benefits associated with technological improvements are perhaps substantially measurable over time but have no discernible impact in the short run, regarding the data under present consideration. Of the rest, in the short run, only changes in population density (D(LPOP-d)) prove statistically significant. This coefficient value is positive, indicating that strong increases in density immediately exert pressure on the environment.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eTable 7 shows the result of an important robustness check. It was done by re-estimating the Panel Autoregressive Distributed Lag Model with a sub-sample of the data. China and India were removed for this exercise to verify that our findings were not unduly affected by these two large economies. The evidence put forward in this table confirms the stability and reliability of my core findings that are strikingly consistent with the full sample estimates. In the long term, however, the sub-sample analysis maintains a significant negative relationship between technological advancement and environmental impact. The coefficient of the Frontier Technology Readiness Index (LFTR) stands at -0.145 and remains statistically significant at the 1% level, almost equal to the full-sample regression. This gives a strong exclusive support to our main conclusion: that a readiness level for technology that is higher translates into an Ecological Footprint that is lower in the long run. The long-term impacts of the remaining independent variables are also same: economic growth and mineral resources remain positively significant while financial development and population density have a statistically significant negative impact on the ecological footprint. Shifting to the short-run dynamics reported in the lower part of the table, these again are symmetrical with initial results. The error correction term (COINTEQ) is negative, statistically significant; this confirms the presence of a stable long-run relationship in this smaller country group and implies a rapid speed of adjustment back to equilibrium. As with the full sample model, the immediate year-on-year change in the Frontier Technology Readiness Index is statistically insignificant, further reinforcing the view that its environmental benefits are a long-run, and not an immediate, aspect. Hence, the findings in Table 6 offer very strong evidence that my seminal findings stand good and are not merely an outcome of the peculiar composition of the full sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7\u003c/strong\u003e: sub-sample ARDL estimates of the effect of FTR on environmental quality (ecological footprint)\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eTo provide an assertive power to primary findings, the next robustness checks were performed, considering two alternative econometric methods: FMOLS and Panel DOLS. The results presented in Tables A5 and A6 in the appendix are crucial to confirm that the key findings are not dependent on the ARDL model selection for the main analysis. These methods are designed to provide consistent long-run estimates when cointegrated panel data are present. The results obtained from both models strongly support the key findings of our study. The Panel FMOLS estimates from Table A5 indicate that the Frontier Technology Readiness Index (LFTR) is associated with a coefficient of -0.165. The coefficient is negative and statistically significant, confirming yet again that an increase in technological readiness leads to a reduction in the Ecological Footprint in the long-term. Meanwhile, in this model, the control variable for economic growth has been an important driver of environmental degradation, whereas other controls such as financial development, mineral resources, and population density have shown no statistically significant impact.\u003c/p\u003e\n\u003cp\u003eLikewise, the Panel DOLS results from Table A6 further back our conclusion. The coefficient in the long run for Frontier Technology Readiness Index is estimated here as -0.148, which is again negative and statistically significant. This value, in fact, stands so close to the coefficient from our primary ARDL model (-0.142) that it lends greater weight to the argument for an established, dependable relationship. As for the FMOLS model, only economic growth proved significant amongst the control variables, yet the essential negative relation between technology and the ecological footprint remained robust.\u003c/p\u003e\n\u003cp\u003eAfter establishing the long-run relation and confirming robustness, the last analytical step involved exploring the causal dynamics existing among the variables using the Dumitrescu-Hurlin panel causality tests, with results reported in Table 8. This test is necessary to grasp the directional influences between variables, whereas before, we had only correlated them. The causality analysis gave us quite a few important insights. They most importantly demonstrated no direct causal relations from one direction to the other between our key variable Frontier Technology Readiness (LFTR) and the Ecological Footprint (LEF). This means that even though these two variables share a long-run common equilibrium, not one directly Granger-causes the other. But the test does identify other complex causal relations that jointly dictate environmental outcomes. There is significant bidirectional causality between the Ecological Footprint and both financial development (LFD) and mineral resources (LMINERAL). This means that the financial sector changes the environment, and environment states influence the financial sector; the same two-way causal relations exist between mineral extraction and the environment. Moreover, we found significant unidirectional causality running from the Ecological Footprint to population density (LEF does not homogeneously cause LPOPD).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the dynamic relationship between Frontier Technology Readiness and Environmental Quality, using the Ecological Footprint (EF) per person as the proxy for environmental pressure, within seven Shanghai Cooperation Organisation (SCO) economies: China, India, Kyrgyzstan, Russia, Tajikistan, Uzbekistan, and Pakistan. The study utilizes the Panel Autoregressive Distributed Lag (ARDL) model, a robust econometric technique chosen because the variables exhibit a plethoric mix of integration orders (I(0) and I(1)), an assumption confirmed by the CIPS unit root test. The presence of cross-sectional dependence among these interconnected SCO members was also confirmed, which justified the use of second-generation panel estimators. Crucially, Pedroni cointegration tests overwhelmingly confirm the existence of a stable long-run equilibrium relationship among the Ecological Footprint and its determinants. The primary empirical finding is that Frontier Technology Readiness significantly reduces environmental degradation in the long run. The long-run coefficient for the FTR Index is negative and statistically significant at the 1 percent level, indicating that a higher level of technological readiness translates to a lower Ecological Footprint. This conclusion is robustly supported across multiple checks: re-estimation using a sub-sample that excludes the large economies of China and India maintained a significant negative FTR coefficient, confirming the finding's stability. Furthermore, verification using alternative long-run estimators, Panel FMOLS and Panel DOLS, yielded consistent negative and significant coefficients for FTR, lending greater credibility and validity to the established relationship. The source literature supports this finding, suggesting that investments in technological innovation, particularly green and energy-efficient technologies, effectively curb environmental degradation over time.\u003c/p\u003e\u003cp\u003eRegarding control variables, the study confirms that traditional economic drivers increase long-run environmental pressure. Economic growth (GDP per capita) positively and significantly increases the Ecological Footprint, a result aligning with the Environmental Kuznets Curve (EKC) hypothesis often observed during emerging stages of growth. Similarly, reliance on mineral resource rents increases the EF, suggesting that dependence on extractive industries leads to long-term ecological damage. Conversely, financial development and high population density are associated with statistically significant long-term reductions in the Ecological Footprint, possibly because financial systems channel credit toward green investments and denser urban environments facilitate efficiencies through shared infrastructure. An analysis of the short-run dynamics reveals that the immediate, year-to-year change in FTR is statistically insignificant in affecting the Ecological Footprint. This crucial finding suggests that the environmental benefits of technological advancements, such as the use of Artificial Intelligence (AI) in optimization and efficiency, manifest as a sustained, long-term effect rather than an immediate one. Although a long-run equilibrium is confirmed, causality analysis using the Dumitrescu-Hurlin panel causality tests indicated no direct Granger-causal relations running between FTR and EF. However, significant bidirectional causality was identified between the Ecological Footprint and both financial development and mineral resources, emphasizing the complex feedback loops dictating environmental outcomes. These findings underscore the critical policy imperative that promoting the innovation and diffusion of frontier technologies, like AI, on a global scale is essential for achieving long-term carbon reduction targets and helping nations, such as India, leapfrog resource-intensive development models toward a greener future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding: The authors received no funding for this research.\u003c/p\u003e\n\u003cp\u003eDeclarations\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate \u003cstrong\u003eNot applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for Publication \u003cstrong\u003eNot applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompeting interests The authors declare \u003cstrong\u003eno competing interests.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorresponding author\u003cstrong\u003e: Mduduzi Biyase\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmail Address:\u003cstrong\[email protected]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eData are available from the authors upon request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBilgili, F., Ko\u0026ccedil;ak, E., \u0026amp; Bulut, \u0026Uuml;. 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Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In Festschrift in Honor of Peter Schmidt (pp. 281\u0026ndash;314). Springer.\u003c/li\u003e\n \u003cli\u003eUNCTAD. (2021). Technology and Innovation Report 2021: Catching technological waves \u0026ndash; Innovation with equity. Geneva: United Nations Conference on Trade and Development.\u003c/li\u003e\n \u003cli\u003eWackernagel, M., \u0026amp; Rees, W. (1996). Our ecological footprint: Reducing human impact on the earth. New Society Publishers.\u003c/li\u003e\n \u003cli\u003eWendling, Z. A., Emerson, J. W., de Sherbinin, A., \u0026amp; Esty, D. C. (2024). 2024 Environmental Performance Index. Yale Center for Environmental Law \u0026amp; Policy.\u003c/li\u003e\n \u003cli\u003eZhang, X., \u0026amp; Zhou, Y. (2021). Financial development, technological innovation and CO₂ emissions: Evidence from China. Environmental Science and Pollution Research, 28, 56946\u0026ndash;56960.\u003c/li\u003e\n \u003cli\u003eZhang, Y., Li, Y., \u0026amp; Huang, J. (2022). Digital economy and carbon emissions: Evidence from panel threshold analysis. Energy Economics, 105, 105751.\u003c/li\u003e\n \u003cli\u003eHassan, A et al., (2023) Green growth as a determinant of ecological footprint: do ICT diffusion, environmental innovation, and natural resources matter? PLoS One 18 (9) e0287\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Panel DOLS, Panel FMOLS, Causality Tests, SCO, Ecological Footprint, Frontier Technology Readiness Index","lastPublishedDoi":"10.21203/rs.3.rs-7958890/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7958890/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the complex, dynamic relationship between Frontier Technology Readiness (FTR) and long-term environmental degradation, proxied by the Ecological Footprint (EF) per person, across seven highly interconnected Shanghai Cooperation Organisation (SCO) economies. Recognizing that these large, energy-intensive economies face legacies of carbon- and resource-intensive growth while pursuing ambitious digital and green transition goals, the research addresses a critical gap in understanding how advanced technology capacity impacts sustainability in this vital geopolitical bloc. We enhance methodological rigor by utilizing the UNCTAD Frontier Technology Readiness Index (FTRI), a conceptually based and calibrated measure of technological capacity, moving beyond generic proxies commonly used in literature. We employ the Panel Autoregressive Distributed Lag (ARDL) model, incorporating second-generation estimators to explicitly address the strong cross-sectional dependence prevalent among SCO members due to shared economic and environmental linkages. The core empirical finding is that Frontier Technology Readiness significantly reduces environmental degradation in the long run. The long-run coefficient for FTR is negative and statistically significant at the 1 percent level, establishing technology as a structural driver of improved environmental quality. This conclusion is proven highly robust, confirmed by alternative long-run estimators Panel FMOLS and Panel DOLS, and crucially, maintained statistical significance when verified using a sub-sample that excluded the large economies of China and India. Crucially, analysis of the short-run dynamics reveals that the immediate change in FTR is statistically insignificant, indicating that environmental benefits are realized as a sustained, long-term effect rather than an immediate one. We also confirm that economic growth and mineral dependency promote the Ecological Footprint in the long term, while financial development and high population density help reduce it. Despite the established long-run equilibrium, causality analysis reveals no direct Granger-causal link between FTR and EF, highlighting the complexity of interaction effects. These findings attest to the necessity of fostering the innovation and sustained dissemination of frontier technologies at a global scale to achieve long-term reduction targets in carbon emissions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL classification\u003c/strong\u003e: O3, O13\u003c/p\u003e","manuscriptTitle":"Frontier Technology Readiness as a driver of environmental quality: ARDL evidence from SCO Member States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 07:31:34","doi":"10.21203/rs.3.rs-7958890/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2b0e80b0-68d8-4d5f-a523-0f1e7c4ace98","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T12:27:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-12 07:31:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7958890","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7958890","identity":"rs-7958890","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}