Common Correlated Effects Mean Group Research Articles

Analyzing the Dynamics: Asymmetric Effects of Economic Growth, Technological Innovation, and Renewable Energy on Carbon Emissions in Africa

This study examines the interactions among economic growth, technological innovation, renewable energy consumption, and carbon emissions in Africa using advanced econometric methods. Panel data analysis reveals both symmetric and asymmetric impacts on carbon emissions, showing how economic and technological variables contribute to environmental outcomes. The augmented mean group (AMG) and common correlated effects mean group (CCEMG) estimators address cross-sectional dependence and heterogeneity, ensuring robust results. Findings indicate that positive economic growth increases emissions, while economic downturns significantly reduce emissions. The study underscores the need for strategic policies that leverage technological innovations and renewable energy to promote sustainable development in African economies, balancing economic growth with environmental protection.

Leveraging AI for Promoting Sustainable Environments in G-7: The Impact of Financial Development and Digital Economy via MMQR Approach

This study investigates the role of Artificial Intelligence (AI) in promoting a sustainable environment within the G-7 countries by testing the Load Capacity Curve (LCC) hypothesis. Additionally, it examines the effects of financial development, the digital economy, and urbanization on the load capacity factor using data from 2010 to 2022. The research employs cross-sectional dependence and slope homogeneity tests, revealing issues of cross-sectional dependence and heterogeneity. Panel unit root tests, both first and second generation, confirm that the variables are free from unit root problems. Furthermore, panel cointegration tests indicate that the variables are cointegrated in the long run. To assess the impact of the explanatory variables on the load capacity factor, the study utilizes the Method of Moments Quantile Regression (MMQR). The findings reveal a U-shaped relationship between income and the load capacity factor, supporting the LCC hypothesis in the G-7 region. The results also indicate that AI innovation and financial development have a significant positive correlation with the load capacity factor. In contrast, the digital economy and urbanization are found to significantly reduce the load capacity factor. Robustness checks, including the Driscoll-Kraay standard error, Augmented Mean Group, and Common Correlated Effect Mean Group estimation approaches, validate the findings obtained from the MMQR method. Moreover, the Dumitrescu-Hurlin (D-H) causality assessment is utilized to explore the causal connections between variables. The results reveal a unidirectional causal relationship between income and the load capacity factor. Additionally, bidirectional causal relationships are the remaining explanatory variables and load capacity factors.

Greenflation, a myth or fact? Empirical evidence from 26 OECD countries

Among the various effects of the green transition on the macroeconomy, we examined the unintended effects of the green transition on inflation, the so-called “greenflation”. We analyzed panel data from 26 OECD countries covering the period from 2005 to 2019 using four models based on the New Keynesian Phillips curve model. To date, only a few studies have examined greenflation, particularly in terms of empirical investigation. The main contribution of this study to the literature is to provide empirical evidence of greenflation. We also provide empirical evidence on the issues in the NKPC literature that the output gap better explains inflation dynamics than unit labor costs and confirm the demise of the Phillips curve. Estimates from the Common Correlated Effect Mean Group (CCEMG), Augmented Mean Group (AMG), and Panel Corrected Standard Error (PCSE) models indicate that the green transition has an upward inflationary impact on headline inflation, but no significant impact on core inflation. In addition, we have examined four circumstances under which the green transition may affect inflation that have not been sufficiently discussed in the literature: increased green transition spending, carbon pricing, increased demand for critical minerals, and the low energy density of renewable energy. Each of these circumstances has the potential to exert both upward and downward pressures simultaneously. Our suggestion to policymakers is that they do not yet need to change their current view of monetary policy with respect to greenflation, but they should carefully consider policy designs that take into account the unintended effects of the energy transition.

Climate Change and Food Security Causality in ECOWAS Region: Do Countries Interdependence Matter?

Studies abound on the causal relationship between climate change and food security; however, the ECOWAS sub-region, despite its unique climatic, agricultural, and socioeconomic characteristics, remains insufficiently explored. Utilizing data from 14 ECOWAS countries spanning 1986 to 2022, this study departs from earlier studies by incorporating the Dumitrescu–Hurlin Panel Causality test, the Pooled Mean Group (PMG), Common Correlated Effect Mean Group (CCEMG), Augmented Mean Group (AMG) and the Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL) technique to analyze the complex relationship between climate change and food security in the ECOWAS sub-region. Unlike the prior empirical studies, the findings reveal a robust mutual influence of climate change and food security in the ECOWAS sub-region. Furthermore, the findings reported a positive impact of climate change on food security, aided by increased cropland area and population growth. At the same time, drought incidence and erratic rainfall between July and September annually remained a severe concern in the region. However, while the conversion of forests and natural habitats into cropland in the sub-region can lead to heightened carbon emissions and reduced ecosystem services, policy recommendations advocate the need for ECOWAS sub-regional policymakers to carefully balance the benefits of food security through sustainable practices and environmental considerations to ensure long-term food security benefits.

The role of financial inclusion and tourism in tackling environmental challenges of industrialization and energy consumption: Redesigning Sustainable Development Goals policies

AbstractThe Next 11 (N‐11) countries are facing many challenges in achieving the objectives of Sustainable Development Goals (SDGs), as preserving environmental quality has become a major challenge for them. Specifically, achieving SDG 3, SDG 7, SDG 9, and SDG 13, the current research scrutinizes the influence of industrialization, tourism, and renewable and fossil fuel energy consumption with the moderating role of financial development on the ecological footprint in the N‐11 countries during 1995–2018. By testing all nine hypotheses through the augmented mean group, common correlated effects mean group estimators, and the Dumitrescu and Hurlin causality approach, various interesting findings are discovered. The findings verified that industrialization, tourism, and non‐renewable energy play major roles in driving environmental pollution. However, renewables and financial development increase environmental quality. Moreover, the interacting role of financial expansion with industrialization and non‐renewables significantly deteriorates the environment. In contrast, the moderating role of financial development with tourism and renewable energy protects environmental excellence. In addition, the growth hypothesis is discovered from tourism and renewables to ecological footprint, and the feedback hypothesis is discovered between industrialization, financial development, and ecological footprint. Following the empirical findings, we recommend several policy implications to address the objectives of SDG 3, SDG 7, SDG 9, and SDG 13.

Unraveling consumption‐based carbon dioxide emissions: Sectoral analysis and temporal dynamics in G7 economies

AbstractThis research investigates consumption‐based carbon dioxide emissions (CBE) within the Group of Seven (G7) economies from 1990 to 2022, particularly under the Paris Agreement. The main focus of the study is on key determinants such as trade, income, renewable energy consumption, foreign direct investment, and technological innovation. The study aims to unravel the complexities underlying the environmental footprint of consumption. A novel aspect of this research is its intent to fill existing gaps in the literature by providing a sectoral analysis and exploring the temporal dynamics of consumption patterns within these advanced industrial nations. Advanced econometric methods, specifically the Cross‐Section Augmented Autoregressive Distributed Lag (CS‐ARDL), are employed to enhance the robustness and reliability of the analysis. Cointegration relationships among CBE and its determinants shed light on the short and long‐term interdependencies among these factors. Using the Augmented Mean Group (AMG) and Common Correlated Effect Mean Group (CCEMG) methods to conduct robustness checks makes the models even more substantial and ensures the results' validity. The implications of this research are far‐reaching, extending to various stakeholders, including policymakers, businesses, industries, and supply chain decision makers. Our study identifies significant long‐term relationships between CBE and key determinants within G7 economies. The findings emphasize the urgency of adopting strategies that promote the sustainable management of natural resources. As global efforts intensify to address climate change, this research provides practical implications for shaping sustainable policy strategies and environmental initiatives on a global scale.

Sustainable development in ASEAN: The role of trade diversification, government revenue, and natural resources

AbstractOver the past few years, the Association of Southeast Asian Nations (ASEAN) has experienced great economic expansion, which has resulted in varied degrees of diversified commerce, an elevated level of government revenue, and an increase in the demand for energy. The purpose of this study is to provide a solution to this conundrum by analyzing the effects of trade diversification (TDF), government revenues (GRN), gross domestic product (GDP), and natural resource rent (NTR) on the sustainable development of the ASEAN countries between the years 1981 and 2022. In order to accurately portray the concept of environmental sustainability, the ecological footprint (EFP) is utilized to represent sustainable development. The quantile‐based econometrics technique known as the Method of the Moments Quantile Regression (MMQR) has been utilized in order to investigate the direction and amplitude of the asymmetric correlation that exists between the interaction of GRN, TDF, NTR, and EFP. According to the estimations of the MMQR, it is proposed that government revenues, which include significant financial incentives that promote the stringent execution of environmental rules, hence avoiding deleterious impacts on the environment, have negative coefficients at all quantiles (Q0.25 − Q0.90). Conversely, TDF and GDP have a positive and statistically strong significant correlation across all quantiles (Q0.25 − Q0.90), revealing that TDF reduces environmental sustainability and expands the size of the world's EFP by making energy‐intensive products more accessible. In addition, the Augmented Mean Group (AMG) and the Common Correlated Effect Mean Group (CCEMG) both provide evidence that supports the correlation study by demonstrating that there is a similar pattern of causality across variables.

Technology Oriented Struggle Against Climate Change in Transportation Sector: An Empirical Investigation

The transportation sector is one of the most important sectors in which greenhouse gas emissions (GHG) are the highest, thus causing the global warming problem to rise. One of the most effective and international solutions to this problem is considered to be a technology-oriented struggle, and the development of green technologies is encouraged by global authorities. The study aims to investigate the success of the technology-oriented struggle against global warming in the transport sector. In analyses, data on transportation-related greenhouse gas emissions, the number of patents (transport-related climate change mitigation technologies), trade openness, and GDP per capita of 12 OECD countries years between 1999-2017 were used. To identify the long-run and short-run relationship among variables, the Cross-Sectional Autoregressive Distributed Lags Estimator (CSARDL) and also the Mean Group (MG), Augmented Mean Group (AMG), and Common Correlated Effects Mean Group Estimators (CCE) were applied. According to the estimators' findings, no evidence was found that the number of patents and trade openness affected greenhouse gas emissions, but it was determined that GDP positively affected greenhouse gas emissions. As a result, it can be said that the technology-oriented struggle against climate change in the transportation sector alone isn't sufficient to reduce transportation-related GHG emissions.

The combine impact of stock market, international investment and clean energy consumption on ecological footprint in emerging countries

Purpose Emerging nations strive to diminish their ecological impact to meet net-zero targets, yet encounter formidable hurdles in curbing their environmental footprint. This purpose necessitated the study into impact of stock market, renewable energy and international investment on the ecological footprint in emerging countries from 1990 to 2020. Design/methodology/approach The study used augmented mean group (AMG) estimator, cointegration and heterogenous panel causality approach. Findings Results from the AMG show that renewable energy consumption reduces environmental pollution in most countries except Mexico. The study disclosed that stock market capitalization decreases ecological footprint in emerging countries. Using both the Kao and Pedroni cointegration methods, the study affirms the existence of stable equilibrium relationship in the long term. The causality test concluded a bidirectional relationship between stock market and ecological footprint and a unidirectional link between international investment, clean energy and ecological footprint. Research limitations/implications The research is limited to only emerging countries. Therefore, future research should examine the environmental impacts of renewable energy consumption in different countries and regions, taking into account the local environmental conditions, policies and practices. This would help to identify the best practices and standards for minimizing the ecological footprint of renewable energy technologies and maximizing their benefits for environmental sustainability. Practical implications The study found that stock market capitalization reduces ecological footprint in Brazil, China, Turkey and India. To foster a culture of sustainability in stock market development impact, academic policies should emphasize the integration of environmental education across disciplines. By promoting awareness of the ecological consequences of stock market activities, societies can cultivate a mindset that values responsible economic practices. This, in turn, can lead to informed decision-making at individual and institutional levels. Social implications First, since the study found that clean energy reduces ecological footprint, advocating for utilization of clean energy sources could be a key priority in emerging countries. Governments should incentivize the development and adoption of renewable energy technologies, such as wind and solar power, by providing subsidies and tax benefits. Furthermore, increasing awareness among residents about the benefits of clean energy and promoting its utilization in both residential and commercial environments can expedite the transition to a more environmentally friendly energy combination. Originality/value First, it pioneers an exploration into the interplay between stock market capitalization, international investment, clean energy and ecological footprint in emerging countries. Secondary unlike, unlike prior research, this study uses methodologies that account for cross-sectional dependencies and a unique characteristic specific to each country. In addition, by using common correlated effects mean group, AMG, cointegration and causality procedures, this study distinctly isolates and analyzes empirical findings for each country, leading to policy-oriented outcomes.

Econometric Advances in the Estimation of Housing Price Determinants

This paper aims to help improve the estimations produced by researchers who rely on conventional housing market pricing models to determine housing prices. The widespread use of panel data in estimating housing prices is justified by the richness of cross-sectional regional or metropolitan data analysed over several periods. Unfortunately, panel data has slope coefficient heterogeneity and cross-sectional dependence, producing inconsistent and misleading estimates of the coefficients using the Ordinary Least Squares (OLS) estimator. Recent advances in econometrics address these panel data limitations, producing better estimates. We analysed the empirical application of these new estimators on housing market panel data, showing that the Fully Modified Ordinary Least Squares Augmented Mean Group (FMOLS-MG) estimator produces the best estimates of the long-term housing market equilibrium and that the Dynamic Common Correlated Effects Mean Group (DCCE-MG) estimator produces the best estimates of the housing market's short-term dynamics. Adopting a trending methodology like Difference-in-Differences (DID) in housing market research to explain the effects of policy decisions on housing prices also has complications related to using the OLS estimator with fixed effects when the data has serial correlation. We show these problems can be overcome using the Feasible Generalised Least Squares estimator in a Seemingly Unrelated Regression Equations (FGLS-SURE) system. Recent econometric developments produce more accurate housing price determinant estimates than conventional econometric methods. These new methodologies can help researchers better estimate the effects of fundamental economic changes and policy decisions on housing prices, which can, in turn, support policymakers in implementing better housing policies.

Bridging the digital divide: the impact of technological innovation on income inequality and human interactions

This study seeks to understand the nuanced relationship between technological innovation and income inequality with an emphasis on the broader implications of this interplay on human–technology interactions in diverse socioeconomic settings. Using cross-country panel data from 59 nations (31 developed and 28 developing) from 1995 to 2020, the study employed the common correlated effect mean group (CCEMG) estimator. The robustness of our findings was validated using the augmented mean group (AMG) estimator and the panel causality test. The results indicate that technological innovation, while heralded for its potential to bridge communication and operational gaps, inadvertently exacerbates income disparities, with a pronounced effect in developed economies. Moreover, interactions between technological innovation and variables such as economic growth, globalisation and export trade introduce additional complexities, including both buffering and acceleration effects on the primary relationship. These findings shed light on the double-edged nature of technological advancements, underscoring the need for informed policy-making that harnesses the benefits of innovation while mitigating its unintended socioeconomic consequences. The study sets the stage for domain-specific explorations such as in education, public health and business. It also invites interdisciplinary discourse on the ethical and behavioural dimensions of technology adoption, especially user experiences and societal outcomes.

A Panel Analysis on the Nexus between Financial Development, Oil Production, and Trade-Openness and Its Impact on Sustainable Economic Growth: Evidence from Selected Arab Economies

In accordance with the United Nations Sustainable Development Goals agenda for decent and sustainable economic growth highlighted in the UNSDGs-8, several economies over the years have been on the quest for drivers for decent and sustainable economic growth, of which the Arab bloc is no exception. To this end, the present study draws strength from the classical growth model while exploring the dynamic nexus between oil production and economic growth while accounting for other key growth drivers like gross capital formulation accumulation, labour, trade openness, and financial development for a balanced panel of selected Arab economies. To operationalise the study objectives, the present study leverages second-generational panel econometric approaches. The econometrics techniques applied circumvent the cross-sectional dependency and slope heterogeneity in the sampled bloc. For co-integration analysis, the Westerlund’s panel co-integration test affirms a long-run equilibrium relationship between the study’s outlined variables. Furthermore, for long-run estimates, the present study leverages the common correlated effects mean group (CCEMG) methodology and the augmented mean group (AMG) method for robustness and soundness of the results and coefficients. The present study corroborates the trade-induced growth hypothesis in the entire panel at a p < 0.001 statistical level, which resonates with the mercantilism school of thought. Additionally, the present study also affirms the Solow–Swan hypothesis, where gross capital formation accumulation and labour drive economic growth. Interestingly, the panel bloc shows that oil production is a key driver to the nation’s economic growth, at a p < 0.05 statistical level. However, from a policy standpoint, there are policy suggestions for diversification of the Arab economies to move from a mono-economy dependent on oil production to other sectors like service, industry, and manufacturing, which require labour, capital accumulation, and more. Further policy caveats are outlined in the concluding section.

Does fiscal expenditure matter for agricultural development? Examining the impact of technological progress on food production

AbstractRice is a staple food for vast population of China and it remarkably contributes toward China's food security. Several fiscal programs have been implemented by Chinese central government for sustainable agriculture production. In this study, we examine the long‐run influence of agricultural public investment (API) and (agro)chemical inputs use (i.e., fertilizer and pesticide) on rice yield in main rice‐producing provinces of China. Second‐generation panel unit root tests, Westerlund error correction model cointegration test, Augmented‐Mean Group (AMG) and Common Correlated Effect Mean Group (CCEMG) estimation techniques have been used in this article. The co‐integration test results reveal strong evidence of the existence of the long‐run co‐integrating link between the considered series. The results of the AMG and CCEMG methods show that (i) API, (ii) fertilizer use, and (iii) power consumption for agricultural machinery positively and significantly impact rice productivity, whereas pesticide usage affects it negatively. Panel quantile regression showed that API, fertilizer use, power consumption, and sown area are significant and positive at 0.10–0.90 quantiles. The results also indicate that pesticide usage increases rice production at 0.10–0.30 quantiles but decreases rice production at 0.40–0.90 quantile levels. Based on these findings, we derive pertinent implications for China's food security.

Do circular economy, renewable energy, industrialization, and globalization influence environmental indicators in belt and road initiative countries?

This paper is the first comprehensive research to examine the effect of circular economy on environment employing two environmental degradation indicators (CO2 emissions, ecological footprint) and one environmental quality indicator (load capacity factor) for 57 Belt and Road Initiative (BRI) countries during 2000-2019. The effect of other variables such as renewable energy, industrialization, and globalization was also controlled. The study applied the cross-sectional autoregressive distributed lag method (CS-ARDL), the augmented mean group (AMG), and common correlated effects mean group (CCEMG) methods as a robustness checks. The empirical findings reveal that circular economy and renewable energy have pro-environmental effects by decreasing carbon emissions and ecological footprint and increasing the load capacity factor in BRI countries. However, industrialization and globalization have detrimental effects on the environment. The result of causality shows a bidirectional causality between renewable energy, circular economy, industrialization, and three environmental indicators, but the relationship of globalization with CO2 emissions and the load capacity factor is unidirectional and with the ecological footprint is bidirectional. All the results are confirmed by the robustness tests. The study suggests policy implications for the BRI government.

The remittance‐growth nexus in leading remittance‐earning nations, controlling regulatory quality, trade openness, energy use, and financial expansion

AbstractMany emerging countries rely on incoming remittances to finance their economic growth, as remittance inflow has a variety of positive knock‐on effects. This study looks at the “remittance‐growth nexus” among the top 20 nations that earn remittances, considering regulatory quality, trade openness, energy consumption, and financial development as controlling variables. It makes use of panel data covering the years 1996–2021, the “Common Correlated Effects Mean Group (CCEMG)” and “Augmented Mean Group” estimation techniques, and the “Dumitrescu‐Hurlin (D‐H)” causality assessment. The primary estimation findings demonstrate a unique outcome that incoming remittance hurts economic growth (EG). However, EG benefits from trade openness, energy use, financial expansion, and regulatory quality, which have a beneficial impact on EG. The D‐H causality assessment results support the primary estimation findings and also reveal that despite both regulatory quality's and financial expansion's influence on EG, they don't cause remittances, rather remittance inflow causes them. The findings have significant policy ramifications for inbound remittance to be routed to saving and investment, guaranteeing reliable energy availability, enhancing trade openness, a prudential banking system, and improving regulatory quality to affect EG positively.

Advancing environmental sustainability in the G-7: The impact of the digital economy, technological innovation, and financial accessibility using panel ARDL approach

This study examines the impact of the digital economy, technological innovation, financial accessibility, and urbanization on CO2 emissions in the G-7 region from 1990 to 2019. The analysis employed Cross-Sectional Dependence (CSD) and Slope Homogeneity tests, revealing the presence of CSD issues and heterogeneous slope coefficients. First- and second-generation panel unit root tests indicated no unit root problem within the dataset, with variables showing mixed integration orders. Panel cointegration tests confirmed that the variables are cointegrated over the long run. To assess the short-run and long-run impacts of the explanatory variables on CO2 emissions, the study utilized the Panel Autoregressive Distributed Lag (ARDL) model. The findings indicate that the digital economy significantly reduces CO2 emissions, while economic growth, technological innovation, financial accessibility, and urbanization increase CO2 emissions in the G-7 region. The robustness of the Panel ARDL results was validated using Driscoll-Kraay standard errors, Augmented Mean Group (AMG), and Common Correlated Effects Mean Group (CCEMG) estimations. Additionally, the Dumitrescu-Hurlin causality test revealed a unidirectional causal relationship between the digital economy and CO2 emissions, GDP and CO2 emissions, and CO2 emissions and technological innovation. Furthermore, bidirectional causality was found between financial accessibility and CO2 emissions, as well as between urbanization and CO2 emissions. These findings provide comprehensive insights into the dynamic interactions between economic, technological, and environmental variables in the G-7 region, highlighting the complexity of achieving sustainable development.

Role of resources rent, research and development, and information and communication technologies on CO2 emissions in BRICS economies

The continuous increase in pollution levels has become a severe threat to the globe, and practitioners are trying their best to deal with environmental challenges to attain for attaining the targets of sustainable development goals (SDGs). Under the environmental aspects, the world has introduced numerous solutions to become clean and green while the problem remains intact. Therefore, this study is an attempt that investigate the impact of natural resources rent, research & development expenditures (R&D), information and communication technologies, and renewable energy consumption on carbon emissions under the framework of the STIRPAT model for BRICS economies during 1998–2021 by using CS-ARDL method. The empirical outcome shows that economic growth (EG), urbanization, and natural resources increase environmental pollution. On the other hand, R&D expenditures, renewable energy and digitalization support the sustainability theme. Furthermore, this study uses the Augmented Mean Group, Common Correlated Effect Mean Group and Panel Quantile Regression to validate the prior outcomes. Finally, the inverted connection between natural resource rents and emissions is validated in BRICS economies. Based on the empirical outcomes, this study suggests policy implications for attaining the targets of SDG-13 (climates change), SDG-07 (affordable and clean energy), SDG-08 (decent economic growth), SDG-09 (innovations, research and development), SDG-11 (sustainable cities and societies), and SDG-12 (responsible consumption).

Exploring the impact of green finance and technological innovation on green economic growth: Evidence from emerging market economies

AbstractThe sustainable future of emerging market economies (EMEs) lies within achieving sustainable development goals (SDGs), specifically SDG 8, which is linked with green economic growth (GEG) and its determinants, such as green finance (GF) and technological innovation (TI), along with some control variables. However, the current literature has insufficiently addressed these determinants in the context of emerging and developing economies. In this regard, this study deployed the most effective and advanced techniques like cross‐sectional auto‐regressive distributed lags, augmented mean group, and common correlated effects mean group to handle the issues of endogeneity, cross‐sectional dependence, and heterogeneity, which is not an easy task for conventional methods used in existing studies. This study has compiled a panel data set spanning from 2000 to 2019, across 13 EMEs. The main outcomes revealed that GF, TI, and human capital (HC) help in fostering significant GEG, whereas financial development (FD) and economic globalization (ECOGLB) fail. The study underscores the need for policymakers to focus on the promotion of diffusion of GF and enhancement of TI and HC while regulating the FD and ECOGLB process to steer emerging nations toward sustainable development.

Is artificial intelligence technology innovation a recipe for low-carbon energy transition? A global perspective

Against the background of global warming, the low-carbon energy transition (LCET) has become one of the top concerns of governments around the world. Artificial intelligence (AI) is serving an increasingly relevant role in the energy sector by facilitating the development of cleaner energy. Thus, based on the panel data of 44 countries from 2000 to 2022, this study employs the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) methods to explore the impact of AI technology innovation on LCET. Moreover, we explore the moderating and spatial spillover effects between AI technology innovation and LCET. The main results show that: (1) AI technology innovation significantly promotes LCET. A 1 % increase in the AI technology innovation index causes a 0.176 % increase in the level of LCET using the AMG method, and a 0.198 % increase using the CCEMG method. (2) Financial incentives and energy efficiency effectively amplify the positive influence of AI technology innovation on LCET. (3) AI technology innovation generates discernible spillover effects on LCET through bilateral trade influence, particularly in countries with closer “trade distances.” This study recommends that countries adequately strengthen AI technology resources to realize new situations for the synergistic development of technology and green energy.

Reconsidering the Long-Term Impacts of Digitalization, Industrialization, and Financial Development on Environmental Sustainability in GCC Countries

Gulf Cooperation Council (GCC) countries have faced environmental challenges in recent decades. This study aims to identify the contribution of digitalization, industrialization, and financial development to the ecological footprint (EF) in GCC countries between 2000 and 2021. The empirical investigation involves estimating the STochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model using the augmented mean group (AMG), common correlated effects mean group (CCEMG) and cross-sectionally augmented autoregressive distributed lag (CS-ARDL) estimators. The findings reveal the existence of long-term linkages between EF and the factors mentioned above. Furthermore, there is evidence that adopting digitalization and information and communication technologies (ICT) improves long-term environmental quality. In contrast, both industrialization and financial development exert detrimental effects on the environment. Finally, the JKS Granger non-causality test revealed that all variables, except financial development, predict environmental degradation in GCC countries. These findings can assist in formulating efficient strategies to reduce ecological degradation and achieve environmental sustainability in GCC countries.