The Impact of Renewable Energy Consumption on Environmental Degradation: Evidence from OIC Countries

The Impact of Renewable Energy Consumption on Environmental Degradation: Evidence from OIC Countries | 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 The Impact of Renewable Energy Consumption on Environmental Degradation: Evidence from OIC Countries Maram Issam Khateeb, Fathin Faizah Said, Norlida hanim, Zulkefly Abdul Karim, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9246399/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract This study investigates the impact of renewable energy consumption (REC) and economic structure on environmental degradation, measured by the ecological footprint (ECF), across 35 Organization of Islamic Cooperation (OIC) countries over 5 years. Employing the two-step system GMM estimation framework to address endogeneity, the study analyses the dynamic relationships between ecological footprint, renewable energy consumption, GDP, the square of GDP, per capita industrial, agricultural, and services value-added, the square of per capita industrial, agricultural, and services value-added, financial development, trade openness, and green investment. The results demonstrate various key findings: (1) the results confirm the highly persistent nature of ecological footprint, with lagged ECF coefficients ranging from 0.722 to 0.896 across all specifications. (2) Renewable energy consumption demonstrates a consistently negative and significant relationship with ecological footprint, confirming that greater clean energy adoption reduces environmental pressure. (3) The analysis finds no evidence for the Environmental Kuznets Curve (EKC) hypothesis. Instead of an inverted U-shaped relationship, the results indicate a U-shaped curve is observed for GDP at the aggregate level and is further validated through a sectoral lens, with services value-added also demonstrating significant U-curve relationships with the ecological footprint. (4) Trade openness (TO) is also a persistent positive driver, supporting the 'pollution haven' hypothesis. (5) Finally, the green investment policy (GI) exhibits a consistently negative and highly significant coefficient across all four models, proving to be the most robust factor in reducing the ecological footprint. The study recommends that OIC countries move beyond isolated measures like promoting renewables or efficiency. Instead, it calls for a comprehensive policy package featuring strict green investment mandates, financial sector reforms for sustainability, consumption-based environmental accounting, and implementing deliberate greening of service sectors to genuinely decouple growth from degradation. Renewable Energy Consumption Ecological Footprint Environmental Kuznets Curve Green Investment Trade Openness Financial Development OIC Countries 1. Introduction The phenomenon of climate change is one of the phenomena that began several centuries ago, and despite the enormous interest in this phenomenon since its appearance, its pace and intensity have increased dramatically in recent times. Human activities related to industrial and agricultural production, activities related to transportation, and many others have led to rapid and dramatic changes in climatic and environmental changes such as high temperatures, rainfall, snow melting, etc., which have severe negative effects on humans because they directly affect the availability of necessities such as food and water and contribute to the deterioration of sanitary conditions. If the light is shed on the member countries of the Organization of Islamic Cooperation (OIC), they are countries that are considered among the countries most vulnerable to environmental changes resulting from increasing human activities, especially in low-income countries, most of whose population suffers from poverty, In addition to the least developed countries, which are characterized by weak capabilities at the level of mitigating changes and adapting to them, in addition to the possibility of their severe vulnerability to environmental degradation, here the matter is of concern, and great attention must be focused on it (IPCC, 2022 ). The Organization of Islamic Cooperation is the second largest intergovernmental organization after the United Nations, with a membership of fifty-seven (57) member states spread over four continents. The Organization of Islamic Cooperation has paid attention to environmental and sustainability issues, as it considered it a major part of the Organization of Islamic Cooperation’s Program of Action for the year 2025. The aim of this program is to “provide directives and guidance to Member States to protect and preserve the environment, encourage sustainable production and consumption patterns, and enhance capabilities to reduce disaster risks and climate change mitigation and adaptation” (OIC environment report, 2021 ). According to the OIC Environment Report 2021 , this report, which was issued in 2021, provided a comprehensive analysis of the current environmental situation in the OIC member countries and the challenges they face, based on the latest available statistics in this field. This publication also evaluated the progress made by the OIC member countries towards achieving the goals included in the sustainable development goals related to the environment and the extent to which they fulfil the commitments of the Paris Agreement. Furthermore, the report considered that environmental capital is one of the basic components that form wealth in the OIC countries, as it is affected by more than a third of the total wealth. The report indicated that natural resource revenues represent 13.8% of its gross domestic product. Although OIC countries depend a lot on natural resources, they still record rates below their counterparts in developing and developed countries in terms of environmental performance and sustainability. The report showed that despite the social and economic developments in the countries of the OIC member states, they were accompanied by environmental deterioration and changes that negatively affected the climate. For example, the annual rate of deforestation in OIC countries increased from 0.27% during the period 2000–2010 to 0.44% in the period 2010–2020, despite the decline in the rate of deforestation at the global level during the same period. In addition, air pollution, which is considered one of the major problems that poses a threat to the health and well-being of societies in many OIC member countries, as a result of this pollution, 1.6 million premature deaths were recorded in 2019. Finally, the depletion of water resources, which is also considered a problem. Which works to make Member States more vulnerable, for example, there are 29 countries experiencing water stress, and 18 of them are at critical levels (OIC environment report, 2021 ). As for direct environmental pollution and the emission of toxic gases, although the average per capita emissions of greenhouse gases in the OIC countries are less than the global average, the rate of increase is sharp and rapid. Where the report revealed that during the years 1990 and 2017, the rate of emissions increased by 77%, reaching 9 gigatonnes of carbon dioxide equivalent; in contrast, the increase at the global level was only 43% (OIC environment report, 2021 ). Furthermore, the total ecological footprint in the OIC countries in 2018, at the country level, seven OIC countries have the highest total ecological footprint among the countries as a whole. Moreover, there are 24 OIC countries with the highest total ecological footprint in the world, which amounted to about 2.68 per capita, with these countries recording a total above 2.68 per capita, and the highest total of these countries was in Qatar, where Qatar recorded a total ecological footprint of about 14.1 per capita. Finally, the top five ecological footprint-intensive economies in OIC countries were as follows: Qatar (14.1), Saudi Arabia and Kazakhstan (4.9), Turkmenistan (4.9), Malaysia (4.2), Guyana and Libya (3.4) (Global Footprint Network database, 2018). As mention, Climate change has accelerated environmental degradation across many member states of the Organization of Islamic Cooperation (OIC), with severe consequences for ecosystems, economies, and public health. Deforestation, air pollution, and water scarcity are escalating, particularly in low-income OIC nations. For instance, Pakistan loses 27,000 hectares of forest annually due to unsustainable land use (FAO, 2020), while Bangladesh ranks among the top countries for air pollution-related deaths, with over 200,000 fatalities per year (WHO, 2021). Water stress is equally critical; 14 of the 17 most water-stressed countries globally are OIC members, including Saudi Arabia, Iran, and Egypt (WRI, 2023). Despite these challenges, OIC countries are making strides in renewable energy adoption. for example, Turkey leads with 54% of its electricity generated from renewables (IRENA, 2023 ), while Morocco’s solar complex is the world’s largest concentrated solar power plant, reducing CO₂ emissions by 760,000 tons annually (World Bank, 2022). However, fossil fuels still dominate energy portfolios in many OIC states. For instance: Saudi Arabia relies on oil and gas for 99% of its energy (IEA, 2023 ). and Iran, despite having 15 GW of untapped wind potential, generates less than 1% of its energy from renewables (IRENA, 2022 ). The main purpose of this paper is to examine the relationship between renewable energy consumption and environmental degradation in OIC Countries. The subject of renewable energy usage and its relation to environmental degradation have received limited attention from studies focused on OIC countries. Consequently, by employing the panel VAR model with a two-step system GMM approach, the present research assists policymakers and decision-makers in these nations by addressing various aspects of this issue and filling gaps in the existing literature. This study offers crucial insights that enable policymakers to tackle the significant pollution levels in OIC countries and to pursue effective mitigation strategies. It encourages the promotion and support of diverse activities that utilize clean energy sources across the region. 2. Data and methodology 2.1. Sample selection This study utilizes a balanced panel dataset of 35 OIC countries observed over five non-overlapping five-year periods from 2000 to 2024 (2000–2004, 2005–2009, 2010–2014, 2015–2019, and 2020–2024). The five-year averaging transformation mitigates short-term cyclical fluctuations and yields a panel structure with 35 cross-sectional units and five time periods, comprising 175 total observations. This "small T, large N" configuration is ideally suited for the System GMM estimator employed in the analysis (Roodman, 2009 ). Moreover, the selection of these nations is particularly relevant to this research, as many are heavily dependent on fossil fuels and other environmentally damaging energy sources to drive their economic expansion. This reliance makes them highly vulnerable to the impacts of environmental degradation. Data for this analysis was systematically compiled from reputable international sources, including the World Bank's Open Data, the International Monetary Fund (IMF), and the Global Footprint Network (GFN). 2.2. Definition of variables 2.2.1. Dependent variables Referring to Al-kubati et al. ( 2022 ); Dogan et al. ( 2019 ); and Chu (2022), this study utilizes the Ecological Footprint of Consumption (ECF), which is the sum of various land types (built-up, carbon, cropland, fishing grounds, forest products, and grazing land) and is sourced from the Global Footprint Network (GFN). 2.2.2. Independent variables Referring to Gupta et al. (2022); Zambrano-Monserrate et al. ( 2020 ); Dogan et al. ( 2020 ); Dogan and Shah ( 2022 ); and Al-kubati et al. ( 2022 ), the independent variables were selected based on their relevance to the study's objectives and their role in explaining the dependent variables. These variables and their sources are as follows: Gross Domestic Product per capita (GDP) and its squared term (SGDP) are used to test the environmental Kuznets curve hypothesis. GDP is sourced from the World Bank (WB), and the squared term is derived from this data. The Financial Development Index (FD) is a comprehensive index compiled from the International Monetary Fund (IMF), which summarizes the development of financial institutions and markets in terms of depth, accessibility, and efficiency. Energy Intensity (EE) is a proxy for technology, representing GDP per unit of energy use, sourced from the World Bank (WB). Trade Openness (TO) is measured as the ratio of exports and imports of goods and services as a percentage of GDP, sourced from the World Bank (WB). Per capita industrial value-added (IV), agricultural value-added (AV), and services value-added (SV) and their squared values are all sourced from the World Bank (WB) and measure the value-added per worker in each respective economic sector. A binary dummy variable, D, is included to account for a country's adoption of green investment and renewable energy policies. It takes a value of 1 if a country has implemented such a policy in a given year and 0 otherwise. 2.3. Model specification and estimation method The current study examines the impact of renewable energy consumption on environmental degradation in OIC countries for the period from 2000 to 2024. To achieve this goal, the study used the two-step system GMM estimation. In order to estimate a model with large N, for example, a dynamic panel data model with N larger than T, then the GMM (Generalized Method of Moments) estimation by Arellano-Bond is a two-step estimator commonly used for this target. This method addresses endogeneity by using lagged dependent variables as instruments. Furthermore, it’s a popular method for estimating models with unobserved country effects and serial correlation in the errors. Moreover, GMM is usually used to calculate the consistent estimates of the below equation, especially in small T and large N (Arellano and Bond ( 1991 ); Blundell and Bond ( 1998 )). Finally, the estimation strategy of the two-step System GMM estimator, following Arellano and Bover (1995) and Blundell and Bond ( 1998 ), combines equations in both first-differences and levels. In the first step, the model is estimated under the assumption of homoscedastic errors to obtain consistent residuals. In the second step, these residuals are used to construct the optimal weighting matrix, and the model is re-estimated to produce more efficient two-step GMM estimates that are robust to heteroskedasticity and autocorrelation. This approach addresses endogeneity by using lagged levels as instruments for the differenced equation and lagged differences as instruments for the level’s equation, while also accounting for unobserved country-specific fixed effects (Roodman, 2009 ). The equation captures the dynamic nature of panel data, where the current value of the dependent variable is regressed on its lagged value, along with other independent variables and country-specific effects. The equation of general dynamic panel data of the two-step system GMM model as established by Blundell and Bond ( 1998 ) is specified as follows: $$\:{\text{E}\text{C}\text{F}}_{it}={{\gamma\:}\text{E}\text{C}\text{F}}_{it-1}+{X}_{it}\beta\:+{{\mu\:}}_{i}+{{\epsilon\:}}_{it}$$ where i = 1, 2, …., N, t = 1, 2, …., T, ECF 𝒊𝒕 is the Ecological Footprint of Consumption for country i in period t, \(\:{\text{E}\text{C}\text{F}}_{it-1}\:\) is the lagged dependent variable, capturing the persistence and dynamic nature of environmental degradation, \(\:{X}_{it}\) is a vector of independent variables, \(\:{{\mu\:}}_{i}\) is represents the unobserved time-invariant country-specific effects (unobserved heterogeneity), \(\:{{\epsilon\:}}_{it}\) the idiosyncratic error term, assumed to be white noise, and \(\:{\gamma\:}\) , \(\:\beta\:\) are the parameters to be estimated. By incorporating all selected independent variables, the current study will estimate four models; the full reduced-form for five models for the two-step System GMM estimation is explicitly given as: $$\:{ECF}_{i,t}={{\gamma\:}\text{E}\text{C}\text{F}}_{it-1}+{{\beta\:}}_{1}{REC}_{i,t}+{{\beta\:}}_{2}{GDPC}_{i,t}+{{\beta\:}}_{3}{SGDPC}_{i,t}+{{\beta\:}}_{4}{FD}_{i,t}+{{\beta\:}}_{5}{TO}_{i,t}+{{{\beta\:}}_{6}EE}_{i,t}+{{\beta\:}}_{7}{GI}_{i,t}+{{\mu\:}}_{i}+{{\epsilon\:}}_{it}$$ $$\:{ECF}_{i,t}={{\gamma\:}\text{E}\text{C}\text{F}}_{it-1}+{{\beta\:}}_{1}{REC}_{i,t}+{{{\beta\:}}_{2}VA}_{i,t}+{{\beta\:}}_{3}SVA+{{\beta\:}}_{4}{FD}_{i,t}+{{\beta\:}}_{5}{TO}_{i,t}+{{\beta\:}}_{6}{EE}_{i,t}++{{\beta\:}}_{7}{GI}_{i,t}+{{\mu\:}}_{i}+{{\epsilon\:}}_{it}$$ $$\:{ECF}_{i,t}={{\gamma\:}\text{E}\text{C}\text{F}}_{it-1}+{{\beta\:}}_{1}{REC}_{i,t}+{{{\beta\:}}_{2}VI}_{i,t}+{{\beta\:}}_{3}SVI+{{\beta\:}}_{4}{FD}_{i,t}+{{\beta\:}}_{5}{TO}_{i,t}+{{\beta\:}}_{6}{EE}_{i,t}+{{\beta\:}}_{7}{GI}_{i,t}+{{\mu\:}}_{i}+{{\epsilon\:}}_{it}$$ $$\:{ECF}_{i,t}={{\gamma\:}\text{E}\text{C}\text{F}}_{it-1}+{{\beta\:}}_{1}{REC}_{i,t}+{{{\beta\:}}_{2}VS}_{i,t}+{{\beta\:}}_{3}SVS+{{\beta\:}}_{4}{FD}_{i,t}+{{\beta\:}}_{5}{TO}_{i,t}{+{\beta\:}}_{6}{EE}_{i,t}+{{\beta\:}}_{7}{GI}_{i,t}+{{\mu\:}}_{i}+{{\epsilon\:}}_{it}$$ Where ECF is the dependent variable and refers to the ecological footprint of consumption, REC is renewable energy consumption, GDP and SGDP per capita refer to GDP and the square of GDP per capita, respectively, FD refers to financial development, T is trade openness, EE is energy efficiency, VI and SVI are industrial value-added and its square, VS and SVS refer to service value-added and its square, VA and SVA are agriculture value-added and its square, and GI refers to green investment. Table 1 Descriptive statistics of variables. Variable Obs. Mean SD Min Max ECF 175 1.971109 1.267045 0.4567642 7.080758 REC 175 41.68404 32.87703 0.00768 95.46 GDP 175 1.37e + 11 2.22e + 11 198.5914 1.16e + 12 FD 175 0.2213087 0.1664733 0.0373484 0.7515666 EE 175 -3.059028 121.5464 -1600.946 32.51976 TO 175 65.28052 35.69699 5.76221 205.5394 VA 175 6252.719 8278.519 198.5914 47487.93 VI 175 17782.17 27040.46 198.5914 204648.3 VS 175 9841.595 8434.022 198.5914 38094.03 GI 175 0.4342857 0.4970851 0 1 3. Results 3.1. Descriptive statistics Table 1 provides descriptive statistics for the dataset, which includes 175 country-year observations across 35 of the Organisation of Islamic Cooperation (OIC). A key finding is the significant role of ecological footprint (ECF), which is the main variable in the current study. The data reveals that the average ECF for the OIC region is 1.971, with values ranging from a minimum of 0.456 to a maximum of 7.080 across 175 observations. This substantial variance highlights the diverse levels of environmental impact and biocapacity existing among the various countries in the sample. Furthermore, Renewable Energy Consumption (REC) shows a significant presence with a mean of 41.68%, though it fluctuates widely from near zero to 95.46%, indicating a heterogeneous transition toward sustainable energy sources within the group. 3.2. Model Selection and Estimation Strategy Our analysis employs a fixed-effects panel regression to examine the dynamics of the ecological footprint (ECF) over time. The results, as presented in Table 2 , reveal a significant and highly persistent effect of the lagged ecological footprint (L.ECF), with a coefficient of 0.741. This strong autocorrelation suggests that ECF is a near-unit-root process, a finding that critically informs our choice of estimation strategy. Given this high persistence, traditional methods like difference GMM would likely suffer from a weak instruments problem (Blundell and Bond, 1998 ). Therefore, we utilize the system GMM estimator, which is better suited for dynamic panel models with highly persistent dependent variables and is robust to potential endogeneity (Arellano and Bover, 1995; Blundell and Bond, 1998 ). Furthermore, the F-test for the fixed effects is highly significant, F (34,104) = 3.37, decisively rejecting the null hypothesis that all panel-specific effects are zero. The rho value of 0.571 indicates that 57.1% of the total variance is attributable to unobserved, time-invariant, country-specific factors. This confirms the presence of significant unobserved heterogeneity, further justifying the use of a dynamic panel model that accounts for these fixed effects. Given that our number of cross-sections is small (N < 100), a one-step system GMM is a robust and efficient choice for our primary estimation, though a two-step estimator could also be employed (Arellano and Bond, 1991 ; Roodman, 2009 ). Moreover, the limited time dimension of the panel, with only five observations per country, provides strong justification for employing System GMM over Difference GMM, as Difference GMM estimators are known to suffer from significant finite sample bias and weak instrument problems when the number of time periods is small, whereas System GMM was specifically developed for (small T, large N) dynamic panels (Roodman, 2009 ), precisely the structure observed in this study, with 35 countries observed over four periods, making it the more appropriate and robust choice for obtaining consistent and efficient parameter estimates under these conditions. Table 2 Fixed-effects (within) regression estimation results. Variables Coefficient Std. Err. p-value L.ECF 0.741 0.061 0.0000 (***) Cons 0.518 0.126 0.0000 (***) rho 0.571 ------------ ------------ F test that all u_i = 0 3.37 ------------ 0.0000 (***) Notes: Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1. 3.3. Main regression results This section presents and interprets the findings from the system generalized method of moments (Sys-GMM) estimations conducted to analyse the impact of renewable energy consumption, beside other variables, on the ecological footprint of consumption (ECF) in 35 OIC countries for five years. The study estimated four distinct models to test the core hypotheses, including the environmental Kuznets curve (EKC), and to explore the nuanced effects of economic structure. To address potential endogeneity and unobserved country-specific effects, this study employs the two-step system generalized method of moments (GMM) estimator. The results, presented in Table 3 , are robust across four model specifications, each exploring different dimensions of economic structure. The consistency of the findings is supported by the stable number of observations (175) and a number of instruments, which mitigates the risk of instrument proliferation and ensures the reliability of the specification tests (Roodman, 2009 ). Firstly, the lagged dependent variable ECF t−1 exhibits a strong positive and statistically significant coefficient across all four models. This finding confirms the highly persistent nature of ecological footprint in OIC member states, indicating that current environmental degradation levels are substantially conditioned by historical patterns. The coefficient magnitudes, consistently above 0.7, suggest a near-unit-root process characteristic of environmental indicators with strong inertial properties (Blundell & Bond, 1998 ). This persistence implies that without aggressive policy interventions, OIC countries are likely to remain locked into existing environmental degradation trajectories. The results align with previous dynamic panel studies of environmental sustainability that document strong temporal dependence in ecological footprint measures (Salahuddin et al., 2019 ). From a policy perspective, this path dependence underscores the urgency of early and substantive interventions, as delaying action makes subsequent environmental improvements increasingly difficult to achieve. Secondly, Renewable energy consumption demonstrates a consistently negative relationship with ecological footprint across all specifications, with coefficients ranging from − 0.002 to -0.004. The effect is statistically significant at 10% and 5%, respectively, in Models (1), (2), and (3), though it is insignificant in Model (4). The negative coefficient confirms that greater adoption of renewable energy sources contributes to reducing environmental pressure, consistent with the theoretical expectation that cleaner energy portfolios mitigate ecological degradation (Bhattacharya et al., 2017 ; Dogan et al., 2020 ; and Dogan and Shah, 2022 ). However, the modest magnitude of the coefficient suggests that while renewable energy adoption is beneficial, its current impact within OIC countries remains limited. This may reflect the relatively low share of renewables in the overall energy mix across many OIC member states or inefficiencies in renewable energy deployment. The finding corroborates previous research demonstrating that the environmental benefits of renewable energy are contingent upon scale, technological efficiency, and the displacement of fossil fuel consumption (Sharif et al., 2019 ). For OIC policymakers, these results suggest that accelerating renewable energy transition and improving conversion efficiencies should remain policy priorities. Thirdly, the relationship between economic output and ecological footprint is examined through both linear (GDP) and quadratic (GDPS) specifications in Model (1). GDP exhibits a positive and statistically significant coefficient of 0.054, indicating that higher economic output is associated with greater environmental pressure. This finding is consistent with the scale effect literature, which posits that economic expansion typically increases resource throughput and waste generation unless accompanied by sufficient technological or compositional changes (Grossman and Krueger, 1995 ; Kongbuamai et al., 2020 ; and Khan et al., 2021 ). The squared GDP term (GDPS) is a positive and statistically significant coefficient of 0.036. This positive quadratic term indicates an upward slope, as it suggests a U-shaped relationship rather than the inverted U-shape predicted by the Environmental Kuznets Curve (EKC) hypothesis. This finding implies that within the OIC sample, environmental degradation does not automatically decline after surpassing an income threshold; instead, it continues to rise with economic development. This result aligns with recent critical assessments of the EKC hypothesis that question its universal applicability, particularly in developing and emerging economy contexts (Stern, 2017 ). The absence of automatic environmental decoupling suggests that OIC countries cannot rely on economic growth alone to resolve environmental challenges and must pursue deliberate green growth strategies. Fourthly, models from (2) to (4) delve deeper by disaggregating the economy into its core sectors, agriculture (AV), industry (IV), and services (SV), and their squared terms to test for non-linear effects. These sectoral composition variables reveal nuanced patterns regarding the environmental intensity of different economic activities as suggested by the Environmental Kuznets Curve hypothesis at the sectoral level (Grossman and Krueger, 1995 ). For example, agricultural value-added (AV) and its squared term (AVS) in Model (2) fail to achieve statistical significance, suggesting that the agricultural sector's contribution to ecological footprint in OIC countries is not clearly delineated through a simple linear or quadratic specification. This may reflect the heterogeneity within the agricultural sector across OIC member states, ranging from traditional subsistence farming to modern, input-intensive agricultural systems with vastly different environmental footprints (Ali et al., 2025 ). Furthermore, industrial value-added (IV) in Model (3) is positive and insignificant, while its squared term (IVS) is also insignificant. This finding may indicate that the relationship between industrial activity and ecological footprint is mediated through other variables already included in the model, or that industrial composition effects are better captured through trade and GDP variables (Akyol and Soyyigit, 2025 ; Khan et al., 2021 ). It may also reflect that within the OIC sample, countries at different stages of industrial development exhibit varying environmental profiles that aggregate to insignificant net effects (Khan et al., 2021 ). In addition, services sector value-added (SV) in Model (4) demonstrates a positive and statistically significant coefficient of 0.271 at 1%, while its squared term (SVS) is also positive and significant at 1%. The results suggest that in the OIC context, services sector expansion is associated with an increased ecological footprint. This may reflect the energy and resource demands of modern service sectors, including transportation, logistics, tourism, and data centres, all of which have substantial environmental footprints (Ehigiamusoe et al., 2025 ; Murshed et al., 2021 ). For instance, the transport and building subsectors within services are major energy consumers, and without a transition to clean energy, their expansion leads to higher emissions (Ehigiamusoe et al., 2025 ). The positive quadratic term indicates that this relationship strengthens as the services sector expands. which underscores the need for robust climate policies and increased investment in clean energy specifically targeting the service sector (Murshed et al., 2021 ; Ehigiamusoe et al., 2025 ). Moreover, these results agreed with Al-Kubati et al. ( 2022 ), who found a U-shaped relationship in the service sector in lower-middle-income and lower-income countries. For OIC countries pursuing services-led development strategies, these results suggest that such transitions should be accompanied by deliberate greening of services sectors. Moreover, the energy efficiency variable, proxied by GDP per unit of energy use, fails to achieve statistical significance in any model specification. The absence of a significant relationship may reflect several underlying dynamics. First, it may indicate the presence of rebound effects (or Jevons' Paradox), where improved efficiency lowers the effective cost of energy services, spurring greater consumption and overall resource use that partially or fully offsets the initial efficiency savings (Sorrell, 2007 ; Polimeni et al., 2008 ). Empirical research has documented rebound effects ranging from modest to substantial across different sectors and regions, with developing economies often exhibiting larger rebounds due to suppressed energy demand (Greening et al., 2000 ). Second, it may suggest that energy efficiency improvements in the OIC region have not been sufficiently large or widespread to translate into measurable reductions in aggregate ecological footprint. This interpretation aligns with research demonstrating that technological efficiency gains often fail to deliver absolute environmental impact reductions without complementary policy measures such as carbon pricing, regulatory standards, or behavioural interventions (York and McGee, 2016 ). For OIC policymakers, this result implies that energy efficiency policies should be designed and implemented alongside measures that address potential rebound effects to ensure that efficiency gains translate into genuine environmental improvements. Trade openness exhibits a positive and statistically significant coefficient across all four model specifications at the 1% level. This robust finding indicates that greater integration into global trade markets is associated with higher ecological footprint in OIC member countries. The result provides empirical support for the pollution haven hypothesis, which posits that trade liberalization can lead to the spatial relocation of environmentally intensive production from countries with stringent environmental regulations to those with weaker standards (Copeland and Taylor, 2004 ). This result agreed with Zambrano-Monserrate et al. 2020 , Kongbuamai et al. 2020 , and Ali et al. 2021 ). For many OIC countries, comparative advantage remains concentrated in resource-intensive and primary commodity sectors, and trade expansion has often intensified specialization in these environmentally demanding activities. The finding is consistent with research documenting the environmental costs of trade liberalization in developing and emerging economies (Shahbaz et al., 2017 ). From a policy perspective, this result underscores the importance of complementary environmental provisions within trade agreements and the need for OIC countries to upgrade their productive structures toward cleaner segments of global value chains. The financial development index demonstrates a consistently negative relationship with ecological footprint across all four models. However, none of these coefficients are statistical significance at conventional levels. This finding contributes to the mixed literature on finance-environment linkages. The non-significant result in the OIC context may reflect the offsetting nature of these competing channels. It may also indicate that the financial sectors in many OIC member states have not yet reached the level of sophistication required to effectively channel capital toward environmentally beneficial activities. Alternatively, the relatively low mean financial development index score of 0.221 reported in the descriptive statistics suggests that the overall level of financial sector development across the sample remains modest, potentially limiting its environmental impact. Finally, the green investment demonstrates a negative and statistically significant effect on ecological footprint across all four model specifications at 1% and 5%. This finding represents one of the most policy-relevant results of the analysis. The negative coefficient indicates that periods during which specific renewable energy policies were active are associated with approximately 7.5–8.1% lower ecological footprint compared to periods without such policies. This result provides empirical validation for the effectiveness of targeted green investment policies in the OIC context. Moreover, it robustly demonstrates that deliberate government policy for green investment and renewables is a potent tool for mitigating environmental impact, regardless of the underlying economic structure. The finding aligns with a growing body of evidence demonstrating that well-designed policy interventions can successfully accelerate environmental improvements (Popp et al., 2010 ; Abdou et al., 2022 ). Importantly, the mean value of the green investment dummy (0.434) indicates that such policies were active during only 43.4% of the period studied, suggesting considerable scope for policy expansion and intensification. For OIC policymakers, this result offers clear evidence that renewable energy support mechanisms, feed-in tariffs, clean technology subsidies, and related policy instruments represent effective tools for reducing environmental pressure. Table 3 Two-Step System GMM estimation results. Variables Model (1) Model (2) Model (3) Model (4) Dependent Variable: Ecological Footprint Consumption (ECF) ECF t-1 0.809*** 0.896*** 0.833*** 0.722*** REC -0.003* -0.003** -0.004** -0.0018 GDP 0.054* GDPS 0.036*** EE 0.075 0.074 0.099 0.081 FD -0.464 -0.329 -0.202 -0.397 TO 0.005*** 0.003*** 0.003*** 0.004*** GI -0.081*** -0.081*** -0.077*** -0.075** AV 0.020 AVS 0.012 IV 0.084 IVS 0.022 SV 0.271*** SVS 0.221*** Cons. 0.055 0.169 0.217 0.235 Obs. 140 140 140 140 Groups 35 35 35 35 Instruments 11 11 11 11 Notes: Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1. Table 4 Diagnostic Tests and Model Validity results. Variables Model (1) Model (2) Model (3) Model (4) Arellano-Bond test for AR (1) -2.07 (0.038) -1.81 (0.070) -2.31 (0.021) -1.94 (0.053) Arellano-Bond test for AR (2) -1.54 (0.124) -1.55 (0.120) -1.63 (0.103) -1.72 (0.085) Sargan test 1.47 (0.480) 2.32 (0.313) 1.37 (0.503) 0.64 (0.726) Hansen test 0.50 (0.777) 0.69 (0.707) 0.54 (0.765) 0.66 (0.720) Notes: Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p Chi2 Model (1) 930.29 0.0000*** Model (2) 1586.29 0.0000*** Model (3) 1071.93 0.0000*** Model (4) 1017.57 0.0000*** Notes: Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1. 3.4. Diagnostic Tests and Model Validity The diagnostic tests and model validity reported in Table 4 provide strong evidence that our models are well-specified and that the employed instruments are valid. First, the Arellano-Bond tests for autocorrelation are fundamental to the consistency of the GMM estimator. The test for AR (1) in first differences consistently rejects the null hypothesis of no autocorrelation across all four models, with p-values ranging from 0.021 to 0.070, as the first-differencing process naturally introduces first-order serial correlation (Roodman, 2009 ). More critically, the test for AR (2) fails to reject the null hypothesis in all specifications, with p-values ranging from 0.085 to 0.124. The absence of second-order serial correlation in the error terms validates the assumption that the moment conditions are correctly specified, confirming that the initial conditions of the dynamic process are exogenous and that the study instrument set is internally consistent (Blundell and Bond, 1998 ). Second, the Sargan and Hansen tests of over-identifying restrictions assess the joint validity of the study instruments. The null hypothesis for both tests is that the instruments are valid, i.e., uncorrelated with the error term. As shown in Table 4 , the p-values for the Hansen test are consistently high across all models, ranging from 0.707 to 0.777, providing robust evidence that we cannot reject the null hypothesis. This indicates that the full instrument set is exogenous and appropriate for estimation. While the Sargan test is also insignificant across all models, with p-values ranging from 0.313 to 0.726, it is sensitive to heteroskedasticity. Therefore, we place greater reliance on the Hansen test, which is robust in the presence of heteroskedasticity, further strengthening our confidence in the results (Roodman, 2009 ). The consistently non-significant results from both tests suggest that our models do not suffer from over-identification, and the dynamic panel bias has been effectively controlled for. Finally, the Wald chi-square statistics for all four models, as presented in Table 5 , are highly significant at 1%. This indicates that the independent variables included in each model are jointly significant and that the models, as a whole, have strong explanatory power in predicting the ecological footprint. The Wald statistics range from 930.29 in Model (1) to 1,586.29 in Model (2), demonstrating substantial explanatory power across all specifications. Moreover, the consistently significant Wald statistics across all models provide strong confidence that the reported relationships are not due to random chance and that the model estimations are sound. In summary, the diagnostic tests collectively affirm that the study's empirical strategy is sound. The presence of AR (1) but not AR (2) and the failure to reject the null in the Hansen test provide a solid foundation for the interpretation of the substantive results that follows. Thus, the models are well-specified, the instruments are valid, and the results are reliable for inference. 3.5. Robustness Test of Two-Step System GMM Model To assess the robustness of the study's primary two-step System GMM findings, we re-estimated all four models using the one-step System GMM estimator with a restricted lag structure of (2 2), meaning the second lag of the dependent variable and predetermined variables were used as instruments. This robustness check serves multiple purposes: it addresses concerns about instrument proliferation in two-step GMM, evaluates the sensitivity of the study coefficient estimates to alternative estimator choices, and ensures that the study findings are not artifacts of the specific lag structure employed (Roodman, 2009 ). The one-step results presented in Table 6 demonstrate remarkable consistency with the two-step estimates reported in Table 3 , reinforcing confidence in the stability of our findings. The lagged ecological footprint (ECFₜ₋₁) remains highly significant across all models with coefficients ranging from 0.758 to 0.816, confirming the persistent nature of environmental degradation in OIC member states (Blundell & Bond, 1998 ). Renewable energy consumption (REC) maintains its negative and significant relationship with ecological footprint in all four models, with coefficients of -0.003 to -0.004, substantiating the environmental benefits of clean energy adoption (Bhattacharya et al., 2017 ). The GDP variables in Model (1) retain their significance, with a GDP coefficient of 0.055 (p < 0.1) and a GDPS coefficient of 0.035 (p < 0.01), reinforcing the U-shaped relationship between economic development and environmental pressure observed in the two-step results. Trade openness (TO) continues to exhibit a positive and statistically significant effect across all specifications at the 1% level, consistently supporting the pollution haven hypothesis (Copeland & Taylor, 2004 ). The green investment (GI) remains negative in all models, though it achieves statistical significance only in Model (1) at the 5% level. This slight reduction in significance compared to the two-step results is expected, as one-step estimators are generally less efficient than their two-step counterparts (Arellano & Bond, 1991 ). However, the consistent sign and similar magnitude across specifications suggest that the substantive conclusion that green investment policies reduce ecological footprint remains valid. Finally, the sectoral variables exhibit strong consistency with the two-step findings. Industrial value-added (IV) in Model (3) now achieves marginal significance at the 10% level with a coefficient of 0.112, while its squared term (IVS) remains insignificant. Most importantly, the services sector variables (SV and SVS) in Model (4) remain positive and highly significant at the 1% level, with coefficients of 0.267 and 0.209, respectively, strongly confirming that services sector expansion in OIC countries is associated with increased environmental pressure (Lenzen et al., 2018 ). Table 6 One-Step System GMM estimation results. Variables Model (1) Model (2) Model (3) Model (4) Dependent Variable: Ecological Footprint Consumption (ECF) ECF t−1 0.758*** 0.816*** 0.776*** 0.776*** REC -0.003** -0.004** -0.004*** -0.004*** GDP 0.055* GDPS 0.035*** EE 0.111 0.124 0.136 0.136 FD -0.290 -0.167 -0.101 -0.101 TO 0.006*** 0.003*** 0.003*** 0.003*** GI -0.075** -0.069 -0.071 -0.071 AV 0.034 AVS 0.014 IV 0.112* IVS 0.0315 SV 0.267** SVS 0.209*** Cons. 0.040 0.172 0.226 0.226 Obs. 140 140 140 140 Groups 35 35 35 35 instruments 10 10 11 11 Notes: Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1. Table 7 Diagnostic Tests and Model Validity results. Variables Model (1) Model (2) Model (3) Model (4) Arellano-Bond test for AR (1) -2.01 (0.044) -1.83 (0.067) -1.82 (0.069) -1.82 (0.069) Arellano-Bond test for AR (2) -1.57 (0.115) -1.61 (0.108) -1.58 (0.115) -1.58 (0.115) Sargan test 0.86 (0.353) 1.42 (0.233) 1.37 (0.503) 1.37 (0.503) Hansen test 0.25 (0.617) 0.28 (0.594) 0.54 (0.765) 0.54 (0.765) Notes: Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p Chi2 Model (1) 798.52 0.0000*** Model (2) 1933.99 0.0000*** Model (3) 939.67 0.0000*** Model (4) 939.67 0.0000*** Notes: Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1. The diagnostic tests in Table 7 provide strong support for the validity of the one-step specifications. The Arellano-Bond test for AR (1) appropriately rejects the null hypothesis in all models. Critically, the AR (2) tests fail to reject the null across all specifications, validating the moment conditions and confirming the absence of second-order autocorrelation (Arellano & Bond, 1991 ). The Hansen test statistics are insignificant across the four models. This robustly confirms that the instruments are exogenous and uncorrelated with the error term. The Sargan test similarly fails to reject the null in all specifications. Importantly, with instruments restricted to lags 2 and 2 only, the instrument count remains appropriately low (10–11 instruments for 35 groups), satisfying the rule of thumb that instruments should not exceed the number of groups and mitigating concerns about instrument proliferation (Roodman, 2009 ). The Wald chi-square statistics presented in Table 8 are highly significant across all models (p < 0.000). These results confirm that the independent variables are jointly significant and that the models possess strong explanatory power. Thus, the one-step System GMM estimation with restricted lag structure (2 2) provides compelling evidence that our primary findings are robust to alternative estimator choices and instrument specifications. The coefficient estimates maintain consistent signs, magnitudes, and significance patterns across both estimation approaches. The diagnostic tests confirm that the restricted instrument set remains valid and that the models are free from second-order autocorrelation. 4. Conclusion This study utilizes a balanced panel dataset of 35 OIC countries observed over five non-overlapping five-year periods from 2000 to 2024 (2000–2004, 2005–2009, 2010–2014, 2015–2019, and 2020–2024), comprising 175 total observations. is ideally suited for the System GMM estimator employed in the analysis. Employing a robust two-step System GMM estimator within a dynamic panel framework to address endogeneity, unobserved heterogeneity, and the persistent nature of ecological footprint, our findings challenge several conventional assumptions and offer pointed policy insights. The results confirm the highly persistent nature of ecological footprint across all specifications, indicating that current environmental degradation is substantially conditioned by historical patterns. Renewable energy consumption demonstrates a consistently negative and significant relationship with ecological footprint in three of four models, confirming that greater clean energy adoption contributes to reducing environmental pressure. The analysis results show that the Environmental Kuznets Curve (EKC) hypothesis is not valid for this context; instead, it reveals a U-shaped relationship between GDP and the ECF, with both linear GDP and squared GDP terms positive and significant. This indicates that economic growth continues to exacerbate environmental pressures without evidence of an automatic turning point. This trend is mirrored at the sectoral level, where services sector value-added demonstrates a positive and significant coefficient with a positive quadratic term, challenging the conventional assumption of a clean services sector and revealing that modern service activities carry substantial environmental footprints that intensify as the sector expands. Contrary to expectations, energy efficiency gains fail to achieve statistical significance in any model specification, providing empirical support for the Jevons Paradox and rebound effects. This implies that efficiency improvements, while beneficial for economic reasons, may even spur greater resource consumption through rebound effects if implemented without complementary policies. Furthermore, trade openness emerges as a persistent positive driver of environmental degradation across all models, reinforcing the 'pollution haven' dynamic. Financial development, while consistently negative in sign, fails to achieve statistical significance, reflecting the modest level of financial sector development across the sample and the offsetting nature of competing channels through which finance affects environmental outcomes. A green investment policy is one powerful and robust factor for mitigating environmental impact. The green investment exhibits a negative and highly significant coefficient across all four models, underscoring that periods with active renewable energy policies are associated with approximately 7.5–8.1% lower ecological footprint compared to periods without such policies. This provides robust empirical validation that targeted, policy-driven investments are the most effective tool for directly reducing environmental pressure. Consequently, the path to genuine sustainability requires a comprehensive and integrated policy package. This should include (1) the implementation of strict green investment mandates and renewable energy targets to channel capital towards sustainable infrastructure and technologies; (2) financial sector reforms that explicitly integrate sustainability criteria to enable effective channelling of resources toward green projects; (3) the deliberate greening of service sectors through green building codes, sustainable transportation, and tourism certification programs; (4) complementary environmental provisions within trade agreements to address pollution haven dynamics; and (5) the adoption of consumption-based environmental accounting to fully capture the global environmental cost of domestic economic activities and trade. Finally, only through such a concerted strategy can OIC countries stop sacrificing the environment on account of economic expansion and navigate toward a sustainable future. Declarations Acknowledgements The authors would like to express their gratitude to the Faculty of Economics and Management and the Center for Sustainable and Inclusive Development Studies (SID) at Universiti Kebangsaan Malaysia (UKM) for providing the necessary facilities and research environment. Special thanks to samer Al-khatib for their valuable feedback and assistance with language proofreading and collecting data. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ Contributions Maram Issam Khateeb: Writing original draft, software, formal analysis, data curation, investigation, methodology, and conceptualization. Fathin Faizah Said: Writing, review & editing, supervision, methodology, and validation. Norlida Hanim Mohd. Salleh: writing, review and editing, and supervision. Zulkefly Abdul Karim: Writing, review and editing, and supervision. Samer Al-Khatib: Writing, review, and editing. Ethical Approval This is not applicable. Consent to Participate This is not applicable. Consent to Publish This is not applicable. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Availability Statement Data will be made available from the corresponding author upon reasonable request. Clinical trial number This is not applicable. References Abdou, A. H., Hassan, T. H., Salem, A. E., Elsaied, M. A., Elsaed, A. A. 2022. Determinants and Consequences of Green Investment in the Saudi Arabian Hotel Industry. Sustainability, 14, 16905. Akyol, M., & Soyyigit, S. 2025. 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Global ecological footprint and spatial dependence between countries. Journal of Environmental Management, 272, 111069. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Apr, 2026 Reviews received at journal 25 Apr, 2026 Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 12 Apr, 2026 Editor invited by journal 10 Apr, 2026 Editor assigned by journal 28 Mar, 2026 Submission checks completed at journal 28 Mar, 2026 First submitted to journal 27 Mar, 2026 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. 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Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe phenomenon of climate change is one of the phenomena that began several centuries ago, and despite the enormous interest in this phenomenon since its appearance, its pace and intensity have increased dramatically in recent times. Human activities related to industrial and agricultural production, activities related to transportation, and many others have led to rapid and dramatic changes in climatic and environmental changes such as high temperatures, rainfall, snow melting, etc., which have severe negative effects on humans because they directly affect the availability of necessities such as food and water and contribute to the deterioration of sanitary conditions. If the light is shed on the member countries of the Organization of Islamic Cooperation (OIC), they are countries that are considered among the countries most vulnerable to environmental changes resulting from increasing human activities, especially in low-income countries, most of whose population suffers from poverty, In addition to the least developed countries, which are characterized by weak capabilities at the level of mitigating changes and adapting to them, in addition to the possibility of their severe vulnerability to environmental degradation, here the matter is of concern, and great attention must be focused on it (IPCC, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Organization of Islamic Cooperation is the second largest intergovernmental organization after the United Nations, with a membership of fifty-seven (57) member states spread over four continents. The Organization of Islamic Cooperation has paid attention to environmental and sustainability issues, as it considered it a major part of the Organization of Islamic Cooperation\u0026rsquo;s Program of Action for the year 2025. The aim of this program is to \u0026ldquo;provide directives and guidance to Member States to protect and preserve the environment, encourage sustainable production and consumption patterns, and enhance capabilities to reduce disaster risks and climate change mitigation and adaptation\u0026rdquo; (OIC environment report, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the OIC Environment Report \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, this report, which was issued in 2021, provided a comprehensive analysis of the current environmental situation in the OIC member countries and the challenges they face, based on the latest available statistics in this field. This publication also evaluated the progress made by the OIC member countries towards achieving the goals included in the sustainable development goals related to the environment and the extent to which they fulfil the commitments of the Paris Agreement. Furthermore, the report considered that environmental capital is one of the basic components that form wealth in the OIC countries, as it is affected by more than a third of the total wealth. The report indicated that natural resource revenues represent 13.8% of its gross domestic product. Although OIC countries depend a lot on natural resources, they still record rates below their counterparts in developing and developed countries in terms of environmental performance and sustainability. The report showed that despite the social and economic developments in the countries of the OIC member states, they were accompanied by environmental deterioration and changes that negatively affected the climate. For example, the annual rate of deforestation in OIC countries increased from 0.27% during the period 2000\u0026ndash;2010 to 0.44% in the period 2010\u0026ndash;2020, despite the decline in the rate of deforestation at the global level during the same period. In addition, air pollution, which is considered one of the major problems that poses a threat to the health and well-being of societies in many OIC member countries, as a result of this pollution, 1.6\u0026nbsp;million premature deaths were recorded in 2019. Finally, the depletion of water resources, which is also considered a problem. Which works to make Member States more vulnerable, for example, there are 29 countries experiencing water stress, and 18 of them are at critical levels (OIC environment report, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs for direct environmental pollution and the emission of toxic gases, although the average per capita emissions of greenhouse gases in the OIC countries are less than the global average, the rate of increase is sharp and rapid. Where the report revealed that during the years 1990 and 2017, the rate of emissions increased by 77%, reaching 9 gigatonnes of carbon dioxide equivalent; in contrast, the increase at the global level was only 43% (OIC environment report, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the total ecological footprint in the OIC countries in 2018, at the country level, seven OIC countries have the highest total ecological footprint among the countries as a whole. Moreover, there are 24 OIC countries with the highest total ecological footprint in the world, which amounted to about 2.68 per capita, with these countries recording a total above 2.68 per capita, and the highest total of these countries was in Qatar, where Qatar recorded a total ecological footprint of about 14.1 per capita. Finally, the top five ecological footprint-intensive economies in OIC countries were as follows: Qatar (14.1), Saudi Arabia and Kazakhstan (4.9), Turkmenistan (4.9), Malaysia (4.2), Guyana and Libya (3.4) (Global Footprint Network database, 2018).\u003c/p\u003e \u003cp\u003eAs mention, Climate change has accelerated environmental degradation across many member states of the Organization of Islamic Cooperation (OIC), with severe consequences for ecosystems, economies, and public health. Deforestation, air pollution, and water scarcity are escalating, particularly in low-income OIC nations. For instance, Pakistan loses 27,000 hectares of forest annually due to unsustainable land use (FAO, 2020), while Bangladesh ranks among the top countries for air pollution-related deaths, with over 200,000 fatalities per year (WHO, 2021). Water stress is equally critical; 14 of the 17 most water-stressed countries globally are OIC members, including Saudi Arabia, Iran, and Egypt (WRI, 2023).\u003c/p\u003e \u003cp\u003eDespite these challenges, OIC countries are making strides in renewable energy adoption. for example, Turkey leads with 54% of its electricity generated from renewables (IRENA, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while Morocco\u0026rsquo;s solar complex is the world\u0026rsquo;s largest concentrated solar power plant, reducing CO₂ emissions by 760,000 tons annually (World Bank, 2022). However, fossil fuels still dominate energy portfolios in many OIC states. For instance: Saudi Arabia relies on oil and gas for 99% of its energy (IEA, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). and Iran, despite having 15 GW of untapped wind potential, generates less than 1% of its energy from renewables (IRENA, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe main purpose of this paper is to examine the relationship between renewable energy consumption and environmental degradation in OIC Countries. The subject of renewable energy usage and its relation to environmental degradation have received limited attention from studies focused on OIC countries. Consequently, by employing the panel VAR model with a two-step system GMM approach, the present research assists policymakers and decision-makers in these nations by addressing various aspects of this issue and filling gaps in the existing literature. This study offers crucial insights that enable policymakers to tackle the significant pollution levels in OIC countries and to pursue effective mitigation strategies. It encourages the promotion and support of diverse activities that utilize clean energy sources across the region.\u003c/p\u003e"},{"header":"2. Data and methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Sample selection\u003c/h2\u003e \u003cp\u003eThis study utilizes a balanced panel dataset of 35 OIC countries observed over five non-overlapping five-year periods from 2000 to 2024 (2000\u0026ndash;2004, 2005\u0026ndash;2009, 2010\u0026ndash;2014, 2015\u0026ndash;2019, and 2020\u0026ndash;2024). The five-year averaging transformation mitigates short-term cyclical fluctuations and yields a panel structure with 35 cross-sectional units and five time periods, comprising 175 total observations. This \"small T, large N\" configuration is ideally suited for the System GMM estimator employed in the analysis (Roodman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Moreover, the selection of these nations is particularly relevant to this research, as many are heavily dependent on fossil fuels and other environmentally damaging energy sources to drive their economic expansion. This reliance makes them highly vulnerable to the impacts of environmental degradation. Data for this analysis was systematically compiled from reputable international sources, including the World Bank's Open Data, the International Monetary Fund (IMF), and the Global Footprint Network (GFN).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Definition of variables\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Dependent variables\u003c/h2\u003e \u003cp\u003eReferring to Al-kubati et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Dogan et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); and Chu (2022), this study utilizes the Ecological Footprint of Consumption (ECF), which is the sum of various land types (built-up, carbon, cropland, fishing grounds, forest products, and grazing land) and is sourced from the Global Footprint Network (GFN).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Independent variables\u003c/h2\u003e \u003cp\u003eReferring to Gupta et al. (2022); Zambrano-Monserrate et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Dogan et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Dogan and Shah (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); and Al-kubati et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the independent variables were selected based on their relevance to the study's objectives and their role in explaining the dependent variables. These variables and their sources are as follows: Gross Domestic Product per capita (GDP) and its squared term (SGDP) are used to test the environmental Kuznets curve hypothesis. GDP is sourced from the World Bank (WB), and the squared term is derived from this data. The Financial Development Index (FD) is a comprehensive index compiled from the International Monetary Fund (IMF), which summarizes the development of financial institutions and markets in terms of depth, accessibility, and efficiency. Energy Intensity (EE) is a proxy for technology, representing GDP per unit of energy use, sourced from the World Bank (WB). Trade Openness (TO) is measured as the ratio of exports and imports of goods and services as a percentage of GDP, sourced from the World Bank (WB). Per capita industrial value-added (IV), agricultural value-added (AV), and services value-added (SV) and their squared values are all sourced from the World Bank (WB) and measure the value-added per worker in each respective economic sector. A binary dummy variable, D, is included to account for a country's adoption of green investment and renewable energy policies. It takes a value of 1 if a country has implemented such a policy in a given year and 0 otherwise.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Model specification and estimation method\u003c/h2\u003e \u003cp\u003eThe current study examines the impact of renewable energy consumption on environmental degradation in OIC countries for the period from 2000 to 2024. To achieve this goal, the study used the two-step system GMM estimation. In order to estimate a model with large N, for example, a dynamic panel data model with N larger than T, then the GMM (Generalized Method of Moments) estimation by Arellano-Bond is a two-step estimator commonly used for this target. This method addresses endogeneity by using lagged dependent variables as instruments. Furthermore, it\u0026rsquo;s a popular method for estimating models with unobserved country effects and serial correlation in the errors. Moreover, GMM is usually used to calculate the consistent estimates of the below equation, especially in small T and large N (Arellano and Bond (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e); Blundell and Bond (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)). Finally, the estimation strategy of the two-step System GMM estimator, following Arellano and Bover (1995) and Blundell and Bond (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), combines equations in both first-differences and levels. In the first step, the model is estimated under the assumption of homoscedastic errors to obtain consistent residuals. In the second step, these residuals are used to construct the optimal weighting matrix, and the model is re-estimated to produce more efficient two-step GMM estimates that are robust to heteroskedasticity and autocorrelation. This approach addresses endogeneity by using lagged levels as instruments for the differenced equation and lagged differences as instruments for the level\u0026rsquo;s equation, while also accounting for unobserved country-specific fixed effects (Roodman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe equation captures the dynamic nature of panel data, where the current value of the dependent variable is regressed on its lagged value, along with other independent variables and country-specific effects. The equation of general dynamic panel data of the two-step system GMM model as established by Blundell and Bond (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) is specified as follows:\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\text{E}\\text{C}\\text{F}}_{it}={{\\gamma\\:}\\text{E}\\text{C}\\text{F}}_{it-1}+{X}_{it}\\beta\\:+{{\\mu\\:}}_{i}+{{\\epsilon\\:}}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere i\u0026thinsp;=\u0026thinsp;1, 2, \u0026hellip;., N, t\u0026thinsp;=\u0026thinsp;1, 2, \u0026hellip;., T, ECF\u003csub\u003e\u0026#119946;\u0026#119957;\u003c/sub\u003e is the Ecological Footprint of Consumption for country i in period t, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{E}\\text{C}\\text{F}}_{it-1}\\:\\)\u003c/span\u003e\u003c/span\u003eis the lagged dependent variable, capturing the persistence and dynamic nature of environmental degradation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003e is a vector of independent variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\mu\\:}}_{i}\\)\u003c/span\u003e\u003c/span\u003e is represents the unobserved time-invariant country-specific effects (unobserved heterogeneity), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\epsilon\\:}}_{it}\\)\u003c/span\u003e\u003c/span\u003e the idiosyncratic error term, assumed to be white noise, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e are the parameters to be estimated. By incorporating all selected independent variables, the current study will estimate four models; the full reduced-form for five models for the two-step System GMM estimation is explicitly given as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{ECF}_{i,t}={{\\gamma\\:}\\text{E}\\text{C}\\text{F}}_{it-1}+{{\\beta\\:}}_{1}{REC}_{i,t}+{{\\beta\\:}}_{2}{GDPC}_{i,t}+{{\\beta\\:}}_{3}{SGDPC}_{i,t}+{{\\beta\\:}}_{4}{FD}_{i,t}+{{\\beta\\:}}_{5}{TO}_{i,t}+{{{\\beta\\:}}_{6}EE}_{i,t}+{{\\beta\\:}}_{7}{GI}_{i,t}+{{\\mu\\:}}_{i}+{{\\epsilon\\:}}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{ECF}_{i,t}={{\\gamma\\:}\\text{E}\\text{C}\\text{F}}_{it-1}+{{\\beta\\:}}_{1}{REC}_{i,t}+{{{\\beta\\:}}_{2}VA}_{i,t}+{{\\beta\\:}}_{3}SVA+{{\\beta\\:}}_{4}{FD}_{i,t}+{{\\beta\\:}}_{5}{TO}_{i,t}+{{\\beta\\:}}_{6}{EE}_{i,t}++{{\\beta\\:}}_{7}{GI}_{i,t}+{{\\mu\\:}}_{i}+{{\\epsilon\\:}}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{ECF}_{i,t}={{\\gamma\\:}\\text{E}\\text{C}\\text{F}}_{it-1}+{{\\beta\\:}}_{1}{REC}_{i,t}+{{{\\beta\\:}}_{2}VI}_{i,t}+{{\\beta\\:}}_{3}SVI+{{\\beta\\:}}_{4}{FD}_{i,t}+{{\\beta\\:}}_{5}{TO}_{i,t}+{{\\beta\\:}}_{6}{EE}_{i,t}+{{\\beta\\:}}_{7}{GI}_{i,t}+{{\\mu\\:}}_{i}+{{\\epsilon\\:}}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:{ECF}_{i,t}={{\\gamma\\:}\\text{E}\\text{C}\\text{F}}_{it-1}+{{\\beta\\:}}_{1}{REC}_{i,t}+{{{\\beta\\:}}_{2}VS}_{i,t}+{{\\beta\\:}}_{3}SVS+{{\\beta\\:}}_{4}{FD}_{i,t}+{{\\beta\\:}}_{5}{TO}_{i,t}{+{\\beta\\:}}_{6}{EE}_{i,t}+{{\\beta\\:}}_{7}{GI}_{i,t}+{{\\mu\\:}}_{i}+{{\\epsilon\\:}}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere ECF is the dependent variable and refers to the ecological footprint of consumption, REC is renewable energy consumption, GDP and SGDP per capita refer to GDP and the square of GDP per capita, respectively, FD refers to financial development, T is trade openness, EE is energy efficiency, VI and SVI are industrial value-added and its square, VS and SVS refer to service value-added and its square, VA and SVA are agriculture value-added and its square, and GI refers to green investment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.971109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.267045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4567642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.080758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.68404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.87703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37e\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22e\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198.5914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.16e\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2213087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1664733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0373484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7515666\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.059028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.5464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1600.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.51976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.28052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.69699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.76221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e205.5394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6252.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8278.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198.5914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47487.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17782.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27040.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198.5914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e204648.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9841.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8434.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198.5914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38094.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4342857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4970851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Descriptive statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides descriptive statistics for the dataset, which includes 175 country-year observations across 35 of the Organisation of Islamic Cooperation (OIC). A key finding is the significant role of ecological footprint (ECF), which is the main variable in the current study. The data reveals that the average ECF for the OIC region is 1.971, with values ranging from a minimum of 0.456 to a maximum of 7.080 across 175 observations. This substantial variance highlights the diverse levels of environmental impact and biocapacity existing among the various countries in the sample. Furthermore, Renewable Energy Consumption (REC) shows a significant presence with a mean of 41.68%, though it fluctuates widely from near zero to 95.46%, indicating a heterogeneous transition toward sustainable energy sources within the group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Model Selection and Estimation Strategy\u003c/h2\u003e \u003cp\u003eOur analysis employs a fixed-effects panel regression to examine the dynamics of the ecological footprint (ECF) over time. The results, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, reveal a significant and highly persistent effect of the lagged ecological footprint (L.ECF), with a coefficient of 0.741. This strong autocorrelation suggests that ECF is a near-unit-root process, a finding that critically informs our choice of estimation strategy. Given this high persistence, traditional methods like difference GMM would likely suffer from a weak instruments problem (Blundell and Bond, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Therefore, we utilize the system GMM estimator, which is better suited for dynamic panel models with highly persistent dependent variables and is robust to potential endogeneity (Arellano and Bover, 1995; Blundell and Bond, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Furthermore, the F-test for the fixed effects is highly significant, F (34,104)\u0026thinsp;=\u0026thinsp;3.37, decisively rejecting the null hypothesis that all panel-specific effects are zero. The rho value of 0.571 indicates that 57.1% of the total variance is attributable to unobserved, time-invariant, country-specific factors. This confirms the presence of significant unobserved heterogeneity, further justifying the use of a dynamic panel model that accounts for these fixed effects. Given that our number of cross-sections is small (N\u0026thinsp;\u0026lt;\u0026thinsp;100), a one-step system GMM is a robust and efficient choice for our primary estimation, though a two-step estimator could also be employed (Arellano and Bond, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Roodman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, the limited time dimension of the panel, with only five observations per country, provides strong justification for employing System GMM over Difference GMM, as Difference GMM estimators are known to suffer from significant finite sample bias and weak instrument problems when the number of time periods is small, whereas System GMM was specifically developed for (small T, large N) dynamic panels (Roodman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), precisely the structure observed in this study, with 35 countries observed over four periods, making it the more appropriate and robust choice for obtaining consistent and efficient parameter estimates under these conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFixed-effects (within) regression estimation results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL.ECF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000 (***)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000 (***)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e------------\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF test that all u_i\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000 (***)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: Standard errors in parentheses. ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Main regression results\u003c/h2\u003e \u003cp\u003eThis section presents and interprets the findings from the system generalized method of moments (Sys-GMM) estimations conducted to analyse the impact of renewable energy consumption, beside other variables, on the ecological footprint of consumption (ECF) in 35 OIC countries for five years. The study estimated four distinct models to test the core hypotheses, including the environmental Kuznets curve (EKC), and to explore the nuanced effects of economic structure.\u003c/p\u003e \u003cp\u003eTo address potential endogeneity and unobserved country-specific effects, this study employs the two-step system generalized method of moments (GMM) estimator. The results, presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, are robust across four model specifications, each exploring different dimensions of economic structure. The consistency of the findings is supported by the stable number of observations (175) and a number of instruments, which mitigates the risk of instrument proliferation and ensures the reliability of the specification tests (Roodman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFirstly, the lagged dependent variable ECF\u003csub\u003et\u0026minus;1\u003c/sub\u003e exhibits a strong positive and statistically significant coefficient across all four models. This finding confirms the highly persistent nature of ecological footprint in OIC member states, indicating that current environmental degradation levels are substantially conditioned by historical patterns. The coefficient magnitudes, consistently above 0.7, suggest a near-unit-root process characteristic of environmental indicators with strong inertial properties (Blundell \u0026amp; Bond, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This persistence implies that without aggressive policy interventions, OIC countries are likely to remain locked into existing environmental degradation trajectories. The results align with previous dynamic panel studies of environmental sustainability that document strong temporal dependence in ecological footprint measures (Salahuddin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). From a policy perspective, this path dependence underscores the urgency of early and substantive interventions, as delaying action makes subsequent environmental improvements increasingly difficult to achieve.\u003c/p\u003e \u003cp\u003eSecondly, Renewable energy consumption demonstrates a consistently negative relationship with ecological footprint across all specifications, with coefficients ranging from \u0026minus;\u0026thinsp;0.002 to -0.004. The effect is statistically significant at 10% and 5%, respectively, in Models (1), (2), and (3), though it is insignificant in Model (4). The negative coefficient confirms that greater adoption of renewable energy sources contributes to reducing environmental pressure, consistent with the theoretical expectation that cleaner energy portfolios mitigate ecological degradation (Bhattacharya et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dogan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; and Dogan and Shah, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the modest magnitude of the coefficient suggests that while renewable energy adoption is beneficial, its current impact within OIC countries remains limited. This may reflect the relatively low share of renewables in the overall energy mix across many OIC member states or inefficiencies in renewable energy deployment. The finding corroborates previous research demonstrating that the environmental benefits of renewable energy are contingent upon scale, technological efficiency, and the displacement of fossil fuel consumption (Sharif et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For OIC policymakers, these results suggest that accelerating renewable energy transition and improving conversion efficiencies should remain policy priorities.\u003c/p\u003e \u003cp\u003eThirdly, the relationship between economic output and ecological footprint is examined through both linear (GDP) and quadratic (GDPS) specifications in Model (1). GDP exhibits a positive and statistically significant coefficient of 0.054, indicating that higher economic output is associated with greater environmental pressure. This finding is consistent with the scale effect literature, which posits that economic expansion typically increases resource throughput and waste generation unless accompanied by sufficient technological or compositional changes (Grossman and Krueger, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Kongbuamai et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; and Khan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The squared GDP term (GDPS) is a positive and statistically significant coefficient of 0.036. This positive quadratic term indicates an upward slope, as it suggests a U-shaped relationship rather than the inverted U-shape predicted by the Environmental Kuznets Curve (EKC) hypothesis. This finding implies that within the OIC sample, environmental degradation does not automatically decline after surpassing an income threshold; instead, it continues to rise with economic development. This result aligns with recent critical assessments of the EKC hypothesis that question its universal applicability, particularly in developing and emerging economy contexts (Stern, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The absence of automatic environmental decoupling suggests that OIC countries cannot rely on economic growth alone to resolve environmental challenges and must pursue deliberate green growth strategies.\u003c/p\u003e \u003cp\u003eFourthly, models from (2) to (4) delve deeper by disaggregating the economy into its core sectors, agriculture (AV), industry (IV), and services (SV), and their squared terms to test for non-linear effects. These sectoral composition variables reveal nuanced patterns regarding the environmental intensity of different economic activities as suggested by the Environmental Kuznets Curve hypothesis at the sectoral level (Grossman and Krueger, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). For example, agricultural value-added (AV) and its squared term (AVS) in Model (2) fail to achieve statistical significance, suggesting that the agricultural sector's contribution to ecological footprint in OIC countries is not clearly delineated through a simple linear or quadratic specification. This may reflect the heterogeneity within the agricultural sector across OIC member states, ranging from traditional subsistence farming to modern, input-intensive agricultural systems with vastly different environmental footprints (Ali et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, industrial value-added (IV) in Model (3) is positive and insignificant, while its squared term (IVS) is also insignificant. This finding may indicate that the relationship between industrial activity and ecological footprint is mediated through other variables already included in the model, or that industrial composition effects are better captured through trade and GDP variables (Akyol and Soyyigit, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Khan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It may also reflect that within the OIC sample, countries at different stages of industrial development exhibit varying environmental profiles that aggregate to insignificant net effects (Khan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, services sector value-added (SV) in Model (4) demonstrates a positive and statistically significant coefficient of 0.271 at 1%, while its squared term (SVS) is also positive and significant at 1%. The results suggest that in the OIC context, services sector expansion is associated with an increased ecological footprint. This may reflect the energy and resource demands of modern service sectors, including transportation, logistics, tourism, and data centres, all of which have substantial environmental footprints (Ehigiamusoe et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Murshed et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, the transport and building subsectors within services are major energy consumers, and without a transition to clean energy, their expansion leads to higher emissions (Ehigiamusoe et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The positive quadratic term indicates that this relationship strengthens as the services sector expands. which underscores the need for robust climate policies and increased investment in clean energy specifically targeting the service sector (Murshed et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ehigiamusoe et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, these results agreed with Al-Kubati et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who found a U-shaped relationship in the service sector in lower-middle-income and lower-income countries. For OIC countries pursuing services-led development strategies, these results suggest that such transitions should be accompanied by deliberate greening of services sectors.\u003c/p\u003e \u003cp\u003eMoreover, the energy efficiency variable, proxied by GDP per unit of energy use, fails to achieve statistical significance in any model specification. The absence of a significant relationship may reflect several underlying dynamics. First, it may indicate the presence of rebound effects (or Jevons' Paradox), where improved efficiency lowers the effective cost of energy services, spurring greater consumption and overall resource use that partially or fully offsets the initial efficiency savings (Sorrell, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Polimeni et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Empirical research has documented rebound effects ranging from modest to substantial across different sectors and regions, with developing economies often exhibiting larger rebounds due to suppressed energy demand (Greening et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Second, it may suggest that energy efficiency improvements in the OIC region have not been sufficiently large or widespread to translate into measurable reductions in aggregate ecological footprint. This interpretation aligns with research demonstrating that technological efficiency gains often fail to deliver absolute environmental impact reductions without complementary policy measures such as carbon pricing, regulatory standards, or behavioural interventions (York and McGee, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For OIC policymakers, this result implies that energy efficiency policies should be designed and implemented alongside measures that address potential rebound effects to ensure that efficiency gains translate into genuine environmental improvements.\u003c/p\u003e \u003cp\u003eTrade openness exhibits a positive and statistically significant coefficient across all four model specifications at the 1% level. This robust finding indicates that greater integration into global trade markets is associated with higher ecological footprint in OIC member countries. The result provides empirical support for the pollution haven hypothesis, which posits that trade liberalization can lead to the spatial relocation of environmentally intensive production from countries with stringent environmental regulations to those with weaker standards (Copeland and Taylor, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This result agreed with Zambrano-Monserrate et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Kongbuamai et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, and Ali et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For many OIC countries, comparative advantage remains concentrated in resource-intensive and primary commodity sectors, and trade expansion has often intensified specialization in these environmentally demanding activities. The finding is consistent with research documenting the environmental costs of trade liberalization in developing and emerging economies (Shahbaz et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). From a policy perspective, this result underscores the importance of complementary environmental provisions within trade agreements and the need for OIC countries to upgrade their productive structures toward cleaner segments of global value chains.\u003c/p\u003e \u003cp\u003eThe financial development index demonstrates a consistently negative relationship with ecological footprint across all four models. However, none of these coefficients are statistical significance at conventional levels. This finding contributes to the mixed literature on finance-environment linkages. The non-significant result in the OIC context may reflect the offsetting nature of these competing channels. It may also indicate that the financial sectors in many OIC member states have not yet reached the level of sophistication required to effectively channel capital toward environmentally beneficial activities. Alternatively, the relatively low mean financial development index score of 0.221 reported in the descriptive statistics suggests that the overall level of financial sector development across the sample remains modest, potentially limiting its environmental impact.\u003c/p\u003e \u003cp\u003eFinally, the green investment demonstrates a negative and statistically significant effect on ecological footprint across all four model specifications at 1% and 5%. This finding represents one of the most policy-relevant results of the analysis. The negative coefficient indicates that periods during which specific renewable energy policies were active are associated with approximately 7.5\u0026ndash;8.1% lower ecological footprint compared to periods without such policies. This result provides empirical validation for the effectiveness of targeted green investment policies in the OIC context. Moreover, it robustly demonstrates that deliberate government policy for green investment and renewables is a potent tool for mitigating environmental impact, regardless of the underlying economic structure. The finding aligns with a growing body of evidence demonstrating that well-designed policy interventions can successfully accelerate environmental improvements (Popp et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Abdou et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Importantly, the mean value of the green investment dummy (0.434) indicates that such policies were active during only 43.4% of the period studied, suggesting considerable scope for policy expansion and intensification. For OIC policymakers, this result offers clear evidence that renewable energy support mechanisms, feed-in tariffs, clean technology subsidies, and related policy instruments represent effective tools for reducing environmental pressure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTwo-Step System GMM estimation results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel (1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel (2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel (3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel (4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: Ecological Footprint Consumption (ECF)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECF\u003csub\u003et-1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.809***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.896***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.833***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.722***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.054*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.036***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.081***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.081***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.077***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.075**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAVS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.271***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.221***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCons.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstruments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Standard errors in parentheses. ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic Tests and Model Validity results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel (1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel (2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel (3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel (4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArellano-Bond test for AR (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.07 (0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.81 (0.070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.31 (0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.94 (0.053)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArellano-Bond test for AR (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.54 (0.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.55 (0.120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.63 (0.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.72 (0.085)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSargan test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.47 (0.480)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.32 (0.313)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37 (0.503)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64 (0.726)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHansen test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.50 (0.777)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69 (0.707)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54 (0.765)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.66 (0.720)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Standard errors in parentheses. ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWald chi-square statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWald chi-square (Chi2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eprob\u0026thinsp;\u0026gt;\u0026thinsp;Chi2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e930.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1586.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1071.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1017.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: Standard errors in parentheses. ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Diagnostic Tests and Model Validity\u003c/h2\u003e \u003cp\u003eThe diagnostic tests and model validity reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provide strong evidence that our models are well-specified and that the employed instruments are valid. First, the Arellano-Bond tests for autocorrelation are fundamental to the consistency of the GMM estimator. The test for AR (1) in first differences consistently rejects the null hypothesis of no autocorrelation across all four models, with p-values ranging from 0.021 to 0.070, as the first-differencing process naturally introduces first-order serial correlation (Roodman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). More critically, the test for AR (2) fails to reject the null hypothesis in all specifications, with p-values ranging from 0.085 to 0.124. The absence of second-order serial correlation in the error terms validates the assumption that the moment conditions are correctly specified, confirming that the initial conditions of the dynamic process are exogenous and that the study instrument set is internally consistent (Blundell and Bond, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecond, the Sargan and Hansen tests of over-identifying restrictions assess the joint validity of the study instruments. The null hypothesis for both tests is that the instruments are valid, i.e., uncorrelated with the error term. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the p-values for the Hansen test are consistently high across all models, ranging from 0.707 to 0.777, providing robust evidence that we cannot reject the null hypothesis. This indicates that the full instrument set is exogenous and appropriate for estimation. While the Sargan test is also insignificant across all models, with p-values ranging from 0.313 to 0.726, it is sensitive to heteroskedasticity. Therefore, we place greater reliance on the Hansen test, which is robust in the presence of heteroskedasticity, further strengthening our confidence in the results (Roodman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The consistently non-significant results from both tests suggest that our models do not suffer from over-identification, and the dynamic panel bias has been effectively controlled for.\u003c/p\u003e \u003cp\u003eFinally, the Wald chi-square statistics for all four models, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, are highly significant at 1%. This indicates that the independent variables included in each model are jointly significant and that the models, as a whole, have strong explanatory power in predicting the ecological footprint. The Wald statistics range from 930.29 in Model (1) to 1,586.29 in Model (2), demonstrating substantial explanatory power across all specifications. Moreover, the consistently significant Wald statistics across all models provide strong confidence that the reported relationships are not due to random chance and that the model estimations are sound. In summary, the diagnostic tests collectively affirm that the study's empirical strategy is sound. The presence of AR (1) but not AR (2) and the failure to reject the null in the Hansen test provide a solid foundation for the interpretation of the substantive results that follows. Thus, the models are well-specified, the instruments are valid, and the results are reliable for inference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Robustness Test of Two-Step System GMM Model\u003c/h2\u003e \u003cp\u003eTo assess the robustness of the study's primary two-step System GMM findings, we re-estimated all four models using the one-step System GMM estimator with a restricted lag structure of (2 2), meaning the second lag of the dependent variable and predetermined variables were used as instruments. This robustness check serves multiple purposes: it addresses concerns about instrument proliferation in two-step GMM, evaluates the sensitivity of the study coefficient estimates to alternative estimator choices, and ensures that the study findings are not artifacts of the specific lag structure employed (Roodman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe one-step results presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrate remarkable consistency with the two-step estimates reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, reinforcing confidence in the stability of our findings. The lagged ecological footprint (ECFₜ₋₁) remains highly significant across all models with coefficients ranging from 0.758 to 0.816, confirming the persistent nature of environmental degradation in OIC member states (Blundell \u0026amp; Bond, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Renewable energy consumption (REC) maintains its negative and significant relationship with ecological footprint in all four models, with coefficients of -0.003 to -0.004, substantiating the environmental benefits of clean energy adoption (Bhattacharya et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe GDP variables in Model (1) retain their significance, with a GDP coefficient of 0.055 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1) and a GDPS coefficient of 0.035 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), reinforcing the U-shaped relationship between economic development and environmental pressure observed in the two-step results. Trade openness (TO) continues to exhibit a positive and statistically significant effect across all specifications at the 1% level, consistently supporting the pollution haven hypothesis (Copeland \u0026amp; Taylor, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe green investment (GI) remains negative in all models, though it achieves statistical significance only in Model (1) at the 5% level. This slight reduction in significance compared to the two-step results is expected, as one-step estimators are generally less efficient than their two-step counterparts (Arellano \u0026amp; Bond, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). However, the consistent sign and similar magnitude across specifications suggest that the substantive conclusion that green investment policies reduce ecological footprint remains valid.\u003c/p\u003e \u003cp\u003eFinally, the sectoral variables exhibit strong consistency with the two-step findings. Industrial value-added (IV) in Model (3) now achieves marginal significance at the 10% level with a coefficient of 0.112, while its squared term (IVS) remains insignificant. Most importantly, the services sector variables (SV and SVS) in Model (4) remain positive and highly significant at the 1% level, with coefficients of 0.267 and 0.209, respectively, strongly confirming that services sector expansion in OIC countries is associated with increased environmental pressure (Lenzen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOne-Step System GMM estimation results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel (1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel (2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel (3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel (4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: Ecological Footprint Consumption (ECF)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECF\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.758***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.816***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.776***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.776***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.004**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.004***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.055*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.075**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAVS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.112*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.267**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.209***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCons.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einstruments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Standard errors in parentheses. ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic Tests and Model Validity results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel (1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel (2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel (3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel (4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArellano-Bond test for AR (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.01\u003c/p\u003e \u003cp\u003e(0.044)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.83\u003c/p\u003e \u003cp\u003e(0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.82\u003c/p\u003e \u003cp\u003e(0.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.82\u003c/p\u003e \u003cp\u003e(0.069)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArellano-Bond test for AR (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003cp\u003e(0.115)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.61\u003c/p\u003e \u003cp\u003e(0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.58\u003c/p\u003e \u003cp\u003e(0.115)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.58\u003c/p\u003e \u003cp\u003e(0.115)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSargan test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003cp\u003e(0.353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003cp\u003e(0.233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003cp\u003e(0.503)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003cp\u003e(0.503)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHansen test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003cp\u003e(0.617)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003cp\u003e(0.594)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003cp\u003e(0.765)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003cp\u003e(0.765)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Standard errors in parentheses. ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWald chi-square statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWald chi-square (Chi2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eprob\u0026thinsp;\u0026gt;\u0026thinsp;Chi2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e798.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1933.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e939.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e939.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: Standard errors in parentheses. ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe diagnostic tests in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e provide strong support for the validity of the one-step specifications. The Arellano-Bond test for AR (1) appropriately rejects the null hypothesis in all models. Critically, the AR (2) tests fail to reject the null across all specifications, validating the moment conditions and confirming the absence of second-order autocorrelation (Arellano \u0026amp; Bond, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). The Hansen test statistics are insignificant across the four models. This robustly confirms that the instruments are exogenous and uncorrelated with the error term. The Sargan test similarly fails to reject the null in all specifications. Importantly, with instruments restricted to lags 2 and 2 only, the instrument count remains appropriately low (10\u0026ndash;11 instruments for 35 groups), satisfying the rule of thumb that instruments should not exceed the number of groups and mitigating concerns about instrument proliferation (Roodman, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Wald chi-square statistics presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e are highly significant across all models (p\u0026thinsp;\u0026lt;\u0026thinsp;0.000). These results confirm that the independent variables are jointly significant and that the models possess strong explanatory power. Thus, the one-step System GMM estimation with restricted lag structure (2 2) provides compelling evidence that our primary findings are robust to alternative estimator choices and instrument specifications. The coefficient estimates maintain consistent signs, magnitudes, and significance patterns across both estimation approaches. The diagnostic tests confirm that the restricted instrument set remains valid and that the models are free from second-order autocorrelation.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study utilizes a balanced panel dataset of 35 OIC countries observed over five non-overlapping five-year periods from 2000 to 2024 (2000\u0026ndash;2004, 2005\u0026ndash;2009, 2010\u0026ndash;2014, 2015\u0026ndash;2019, and 2020\u0026ndash;2024), comprising 175 total observations. is ideally suited for the System GMM estimator employed in the analysis. Employing a robust two-step System GMM estimator within a dynamic panel framework to address endogeneity, unobserved heterogeneity, and the persistent nature of ecological footprint, our findings challenge several conventional assumptions and offer pointed policy insights.\u003c/p\u003e \u003cp\u003eThe results confirm the highly persistent nature of ecological footprint across all specifications, indicating that current environmental degradation is substantially conditioned by historical patterns. Renewable energy consumption demonstrates a consistently negative and significant relationship with ecological footprint in three of four models, confirming that greater clean energy adoption contributes to reducing environmental pressure. The analysis results show that the Environmental Kuznets Curve (EKC) hypothesis is not valid for this context; instead, it reveals a U-shaped relationship between GDP and the ECF, with both linear GDP and squared GDP terms positive and significant. This indicates that economic growth continues to exacerbate environmental pressures without evidence of an automatic turning point. This trend is mirrored at the sectoral level, where services sector value-added demonstrates a positive and significant coefficient with a positive quadratic term, challenging the conventional assumption of a clean services sector and revealing that modern service activities carry substantial environmental footprints that intensify as the sector expands.\u003c/p\u003e \u003cp\u003eContrary to expectations, energy efficiency gains fail to achieve statistical significance in any model specification, providing empirical support for the Jevons Paradox and rebound effects. This implies that efficiency improvements, while beneficial for economic reasons, may even spur greater resource consumption through rebound effects if implemented without complementary policies. Furthermore, trade openness emerges as a persistent positive driver of environmental degradation across all models, reinforcing the 'pollution haven' dynamic. Financial development, while consistently negative in sign, fails to achieve statistical significance, reflecting the modest level of financial sector development across the sample and the offsetting nature of competing channels through which finance affects environmental outcomes. A green investment policy is one powerful and robust factor for mitigating environmental impact. The green investment exhibits a negative and highly significant coefficient across all four models, underscoring that periods with active renewable energy policies are associated with approximately 7.5\u0026ndash;8.1% lower ecological footprint compared to periods without such policies. This provides robust empirical validation that targeted, policy-driven investments are the most effective tool for directly reducing environmental pressure.\u003c/p\u003e \u003cp\u003eConsequently, the path to genuine sustainability requires a comprehensive and integrated policy package. This should include (1) the implementation of strict green investment mandates and renewable energy targets to channel capital towards sustainable infrastructure and technologies; (2) financial sector reforms that explicitly integrate sustainability criteria to enable effective channelling of resources toward green projects; (3) the deliberate greening of service sectors through green building codes, sustainable transportation, and tourism certification programs; (4) complementary environmental provisions within trade agreements to address pollution haven dynamics; and (5) the adoption of consumption-based environmental accounting to fully capture the global environmental cost of domestic economic activities and trade. Finally, only through such a concerted strategy can OIC countries stop sacrificing the environment on account of economic expansion and navigate toward a sustainable future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to the Faculty of Economics and Management and the Center for Sustainable and Inclusive Development Studies (SID) at Universiti Kebangsaan Malaysia (UKM) for providing the necessary facilities and research environment. Special thanks to samer Al-khatib for their valuable feedback and assistance with language proofreading and collecting data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaram Issam Khateeb: Writing original draft, software, formal analysis, data curation, investigation, methodology, and conceptualization. Fathin Faizah Said: Writing, review \u0026amp; editing, supervision, methodology, and validation. Norlida Hanim Mohd. Salleh: writing, review and editing, and supervision. Zulkefly Abdul Karim: Writing, review and editing, and supervision. Samer Al-Khatib: Writing, review, and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This is not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdou, A. 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Journal of Environmental Management, 272, 111069.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Renewable Energy Consumption, Ecological Footprint, Environmental Kuznets Curve, Green Investment, Trade Openness, Financial Development OIC Countries","lastPublishedDoi":"10.21203/rs.3.rs-9246399/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9246399/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the impact of renewable energy consumption (REC) and economic structure on environmental degradation, measured by the ecological footprint (ECF), across 35 Organization of Islamic Cooperation (OIC) countries over 5 years. Employing the two-step system GMM estimation framework to address endogeneity, the study analyses the dynamic relationships between ecological footprint, renewable energy consumption, GDP, the square of GDP, per capita industrial, agricultural, and services value-added, the square of per capita industrial, agricultural, and services value-added, financial development, trade openness, and green investment. The results demonstrate various key findings: (1) the results confirm the highly persistent nature of ecological footprint, with lagged ECF coefficients ranging from 0.722 to 0.896 across all specifications. (2) Renewable energy consumption demonstrates a consistently negative and significant relationship with ecological footprint, confirming that greater clean energy adoption reduces environmental pressure. (3) The analysis finds no evidence for the Environmental Kuznets Curve (EKC) hypothesis. Instead of an inverted U-shaped relationship, the results indicate a U-shaped curve is observed for GDP at the aggregate level and is further validated through a sectoral lens, with services value-added also demonstrating significant U-curve relationships with the ecological footprint. (4) Trade openness (TO) is also a persistent positive driver, supporting the 'pollution haven' hypothesis. (5) Finally, the green investment policy (GI) exhibits a consistently negative and highly significant coefficient across all four models, proving to be the most robust factor in reducing the ecological footprint. The study recommends that OIC countries move beyond isolated measures like promoting renewables or efficiency. Instead, it calls for a comprehensive policy package featuring strict green investment mandates, financial sector reforms for sustainability, consumption-based environmental accounting, and implementing deliberate greening of service sectors to genuinely decouple growth from degradation.\u003c/p\u003e","manuscriptTitle":"The Impact of Renewable Energy Consumption on Environmental Degradation: Evidence from OIC Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 08:58:10","doi":"10.21203/rs.3.rs-9246399/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-28T10:17:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-25T14:01:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T07:08:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158749840806986245408331640202199784497","date":"2026-04-14T11:03:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T09:33:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118645177872930036452055375971078920405","date":"2026-04-13T09:29:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166716548040661080379649810983289599688","date":"2026-04-13T06:10:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T01:26:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-10T05:50:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-28T14:23:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-28T14:23:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-03-27T15:19:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"00c0af5c-6468-48d5-8fb6-3cc41d183c87","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T13:08:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 08:58:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9246399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9246399","identity":"rs-9246399","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}