The Chinese government has actively promoted artificial intelligence (AI) in health care, with momentum building in 2016 through the Healthy China 2030 Initiative. This reform plan aims to modernize health care infrastructure, reduce the burden of chronic diseases, and expand access to medical services in rural areas using digital technologies such as AI. Health expenditure (HE) and digital financial inclusion (DFI) play a crucial role in improving health outcomes. HE improves access to medical services and the quality of care, while DFI allows individuals to afford health care, save for emergencies, and manage health-related financial risks. Therefore, this study examines the impact of AI, health expenditure and DFI on life expectancy (LE) in China from 2013Q1 to 2023Q4. This study employed autoregressive distributed lag (ARDL) and quantile regression analyses to ensure the robustness of the results. The finding shows that gross domestic product (GDP), AI, health expenditure, DFI and government effectiveness have positive effect on LE. This study recommended that the government expand AI integration in health care to improve diagnostics and treatment efficiency. It also emphasized promoting DFI to help low-income groups access health care and manage medical expenses. As well, the study suggested increasing public health expenditure to enhance health care infrastructure and service quality, ultimately improving LE.

Reducing carbon dioxide emissions (CO2e) is essential to achieving sustainable development objectives, safeguarding the environment, reducing the effects of climate change, and maintaining biodiversity for a future that is cleaner and more resilient. Nowadays, environmentalists also focus on how the environment reacts to society's increasing level of education. Increasing public awareness of environmental deterioration through environmental education, moral sermons, and higher tertiary enrollment can be a crucial policy in the fight against global warming, along with other measures to reduce CO2e. The effort to combat climate change necessitates improving energy efficiency (EE) and information and communication technology (ICT). Therefore, this study examines the impact of higher education (HED), EE and ICT on CO2e under the N-shaped EKC hypothesis. Using the panel data for five BRICS nations between 1991 and 2023, an empirical analysis is carried out, and the coefficients of the variables are estimated using the Second generation techniques (cross-sectional augmented distributed lag (CS-ARDL), Common Correlated Effects Mean Group (CCEMG) and Augmented mean group (AMG) approach. The estimates confirm the Inverted N-shaped EKC hypothesis between the GDP and CO2e. Moreover, the long-run estimates reveal that higher education, energy efficiency and ICT have negative effects on CO2e. BRICS countries should promote environmental education across all tiers, with an emphasis on conservation, climate change mitigation, and sustainable development, to help improve environmental awareness and literacy. Moreover, they should decouple energy use from economic growth to simultaneously achieve both economic and environmental goals, which can be facilitated by increasing ICT utilization, promoting higher tertiary enrollment, and improving energy efficiency.

Air pollution lowers life expectancy (LEX) by increasing the risk of respiratory, cardiovascular, and other chronic diseases. However, digitalization can help monitor air quality and support public health awareness through real-time data. Institutional quality ensures effective enforcement of environmental regulations and pollution control policies. Together, they help reduce pollution exposure and protect public health. Therefore, the current study aims to analyze the impact of Air pollution on LEX, with the role of urbanization, digitalization, and institutional quality in BRICS economies. This study employs the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model, the Fixed Effects estimator, and the Fully Modified Ordinary Least Squares (FMOLS) technique. The findings indicate that CO2 emissions have a negative impact on LEX, whereas GDP, urbanization, digitalization, and institutional quality have a positive effect on LEX. This study recommended that policymakers focus on reducing CO2 emissions by promoting clean energy and enforcing environmental regulations to mitigate their harmful effects on life expectancy. It also emphasized the importance of enhancing urban infrastructure to improve access to healthcare and sanitation. Additionally, the study highlighted the role of digitalization in strengthening health awareness and service delivery, and the need to improve institutional quality for effective health and environmental governance.

Life expectancy in China has been influenced by several environmental, technological, and institutional factors over the past decades. Air pollution, particularly CO2 emissions from rapid industrialization and urbanization, has been a major concern, contributing to respiratory and cardiovascular diseases that reduce population health. Meanwhile, the expansion of information and communication technology (ICT) has improved access to health services and medical information, supporting better health outcomes. Increased health expenditure has enhanced health care infrastructure, service delivery, and preventive programs, further promoting longevity. Strong institutional quality ensures effective policy implementation, transparency, and equitable access to health care, which collectively support higher life expectancy in China. Therefore, this study examines the impact of air pollution, ICT, health expenditure, and institutional quality on life expectancy in China. This study first examined the stationarity properties of the variables using the Augmented Dickey-Fuller and Phillips-Perron unit root tests and then tested for cointegration to identify the existence of long-run relationships. After confirming cointegration, the autoregressive distributed lag approach was used to estimate both short-run adjustments and long-run effects. To ensure the robustness of the long-run results, Fully Modified Ordinary Least Squares was applied as an additional estimation technique to check the stability and consistency of the estimated coefficients. The finding shows that CO2 emissions has negative effect on life expectancy, while ICT, health expenditure and institutional quality have positive effect on life expectancy. To enhance life expectancy in China, the government should reduce CO2 emissions through stricter environmental regulations and the promotion of renewable energy. Expanding ICT infrastructure and improving digital health literacy can increase access to health care services. Higher health expenditure and investment in preventive programs will strengthen health care quality and disease management. Strengthening institutional quality and ensuring transparent governance will improve the efficiency and equity of health service delivery.

The Government of Pakistan is accelerating digital transformation through major initiatives such as the Digital Economy Enhancement Project (DEEP), which helped boost IT exports to 3.223 billion in FY 2023–24 and expanded broadband access to over 139 million users. Programs like DigiSkills.pk have trained more than 600,000 individuals, while digital governance tools such as e-office systems and public health apps have significantly improved service delivery and efficiency. The rise of startups, adoption of AI policies, and preparations for 5G rollout reflect a shift toward a technology-driven, inclusive, and innovative economy. Therefore, the present study investigates the effects of digitalization, education, institutional quality, and technological innovation on inclusive growth (IG) in Pakistan from 2000 to 2023. The research employs both regression techniques (FMOLS, DOLS, and CCR) and supervised machine learning models, including Support Vector Machine, Lasso Regression, Ridge Regression, Random Forest, Gradient Boosting, KNN, and Decision Tree. The finding from machine learning models indicate ICT as the most influential driver of IG, followed by technological innovation, education, and institutional quality. Regression estimates similarly confirm the positive and significant role of all four predictors in fostering IG. Among the machine learning models, Support Vector Machine demonstrated the highest predictive accuracy, while Decision Tree performed the weakest. The study provides important policy recommendations aimed at achieving inclusive and sustainable economic development aligned with the UN Sustainable Development Goals.

Financial inclusion (FI) and technological innovation (TI) are pivotal in advancing SDG 13 (Climate Action) by enabling access to sustainable solutions and promoting low-carbon technologies. FI allows marginalized communities and businesses to invest in renewable energy (RE) and energy-efficient technologies, while TI drives the development of clean energy solutions and CO2 emissions (CO2E) reducing innovations. Together, they empower societies to take significant action against climate change, fostering a global transition to a low-carbon economy and helping achieve the targets of SDG 13. Previous studies have focused exclusively on the impact of either FI or TI on CO2E in China under the N-shaped Environmental Kuznets Curve (EKC). To address this gap, the current study examines the combined effects of FI and TI on CO2E within the EKC framework for the Chinese economy. This study utilizes the Autoregressive Distributed lag (ARDL), fully Modified ordinary least square (FMOLS), and Dynamic ordinary least square (DOLS) methods by using the time series quarterly data from 2006Q1 to 2022Q4. The ARDL long-run and short-run results confirm that there is an inverted N-shaped EKC between GDP and CO2E. While FI, TI, and RE have negative effects on CO2E. This study has several policy recommendations for policymakers to promote environmental sustainability in China.

The World Bank acknowledged that energy efficiency is a key factor for advancing multiple Sustainable Development Goals, highlighting its role in fostering sustainable economic growth, reducing environmental pressures, and promoting social well-being. During the recent decade, the identification of factor influencing energy efficiency has gained significant attention from policymakers. The present research extends the scope of the ongoing debates by incorporating additional prospective factors that may influence energy efficiency in Organisation for Economic Co-operation and Development (OECD) economies. Accordingly, this study aims to examine how eco-friendly technology, financial development, clean energy investment, energy price, and human capital affect energy efficiency for 25 OECD countries during the period of 1990–2020. To achieve these objectives, this study addresses critical research gaps by integrating ecological and technological innovations, a refined human capital index, and utilizing a comprehensive measure of energy efficiency. In addition to this, the study employs advanced econometric techniques, including Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL), Fully Modified Ordinary Least Square (FMOLS), and Dynamic Ordinary Least Square (DOLS), to ensure robust and reliable analysis. The findings reveal that all variables exert a positive influence on energy efficiency, while energy prices exhibit negative relationship. Hence, the study recommends fostering the development of new environmental technologies and increasing investment in the clean energy sector to strengthen energy efficiency. Additionally, it underscores that eco-friendly technology, sustainable financial development, and clean energy investment are critical components of improved energy efficiency. Furthermore, policymakers are encouraged to reassess existing energy policies with the aim of reducing energy prices to increasing energy efficiency and supporting both sustainable development goals and economic growth.

Inclusive growth (IG) aims to create decent work and economic opportunities for all, including marginalized populations, which aligns with the SDG-8 objective of promoting sustained, inclusive and sustainable economic growth. Many studies measure IG through a single dimension, overlooking its multidimensional economic, social, health, and sustainability aspects. This study contributes by constructing a multidimensional IG index and using the four dimensions and 15 indicators, which cover all the aspects of IG. Moreover, the study investigates the determinants of IG with a particular focus on foreign remittances and institutional quality in Pakistan from 1996 to 2021. The bootstrap auto regressive distributive lag (BARDL), fully modified ordinary least-squares and dynamic ordinary least-squares techniques are employed for estimating the results. The long-run estimates reveal that remittances, institutional quality, trade openness and financial development showed positive effects on IG. While inflation, population growth and unemployment exert a negative impact on IG. Moreover, BARDL short-run estimates reveal that remittances, institutional quality and financial development have a positive effect on IG. In contrast, population growth and trade openness have a negative impact on IG. The policy advice is that the government develop strategies to optimize the inflow of international remittances and enhance the institutional quality to promote the IG in Pakistan.

Health is directly aligned with Sustainable Development Goal (SDG) 3: Good Health and Well-Being, which emphasizes ensuring healthy lives and promoting well-being for all at all ages. The present study investigates the determinants of life expectancy (LEX) by incorporating a comprehensive set of factors: CO₂ emissions as an environmental factor; GDP, health expenditure, and research and development (R&D) as economic factors; education and individual internet use as social factors; and rule of law and government effectiveness as institutional factors. Using panel data for the top 20 high-life-expectancy countries covering the period 2001–2023, this study applies both traditional econometric techniques namely, PMG, fixed effects, and FMOLS estimators and advanced machine learning approaches, specifically Gradient Boosting and Random Forest. The regression results reveal that CO₂ emissions negatively affect LEX, whereas GDP, health expenditure, education, internet use, rule of law, government effectiveness, and R&D exert positive influences. The machine learning results further indicate that GDP, health expenditure, and education are the three most critical predictors of LEX in both Gradient Boosting and Random Forest models, with GDP emerging as the most dominant factor. Institutional variables such as rule of law, government effectiveness, and R&D display moderate importance, while CO₂ emissions and individual internet use consistently rank as the least influential. In terms of predictive performance, Gradient Boosting outperforms Random Forest across evaluation metrics, demonstrating lower errors and higher explanatory power. In light of these findings, this study also provides important policy implications to enhance LEX.

Inclusive growth is closely linked to Sustainable Development Goal (SDG-8), which aims to achieve sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all. Inclusive growth focuses on reducing inequalities and promoting human well-being by ensuring equal access to economic opportunities and the benefits of growth. This study investigates the factors influencing inclusive growth and the role of remittances (RM) and renewable energy in inclusive growth for Pakistan from 1991 to 2023. Machine learning models, namely Gradient Boosting and Random Forest, and Regression Algorithms (Ridge, Lasso), have been employed. In addition, we applied the fully modified ordinary least squares (FMOLS) and the dynamic OLS (DOLS) as analytical techniques for analysis. The empirical results of the machine learning models highlight the significant impact of remittances on inclusive growth, followed by carbon emissions, life expectancy, renewable energy, and inflation. The estimates of Ridge, Lasso, FMOLS, and DOLS show that remittances, renewable energy, and life expectancy have a positive effect on inclusive growth, while carbon emissions and inflation have a negative impact in Pakistan. The study provides valuable policy implications to achieve inclusive and sustainable economic growth in line with UN sustainable development goals.

This study is directly aligned with UN Sustainable Development Goal 06, which aims to “Ensure access to water and sanitation for all.” The primary objective of our research is to empirically assess the factors that contribute to water stress, with a particular emphasis on the role of environmental degradation/distress, such as CO2 emissions and ecological footprint, as well as energy consumption (both renewable and non-renewable) and industrialization in Pakistan (1975–2020). To ensure the accuracy of our findings and avoid any potential misspecification of the empirical model, we have included additional key regressors. Furthermore, we have adopted a comprehensive empirical strategy that takes into account the time-series nature of the data, employing various techniques such as unit root tests, autoregressive distributed lag (ARDL), Fully Modified Ordinary Least Squares, Dynamic OLS, and Granger causality analysis. The ARDL analysis reveals that environmental degradation (CO2 emissions and ecological footprint), non-renewable energy use, and industrialization, positively contribute to water stress. Conversely, renewable energy use, rainfall, forest area, and temperature have a negative impact on water stress. These findings suggest that the government of Pakistan should implement effective regulatory policies to control environmental degradation, develop robust water infrastructure, and promote water conservation awareness to address the water stress issue in the country.

Although Pakistan has taken commendable steps in the course of realization of the Sustainable Development Goals (SDGs), given the disproportionally high rate of women illiteracy, it is important to inquire how educational attainment of women can be focused on the optimal attainment of the environmental sustainability goal. Therefore, the objective of this study is to fill in this gap and analyze the effect of women’s education on environmental sustainability in Pakistan for the period 1995–2021. The Autoregressive Distributed Lag (ARDL) method demonstrates that women’s education (WED), education expenditure (EEX), technical innovation (TIN), and renewable energy (RE) all help reduce carbon emissions (CO2e). At the same time, population dynamics and industrialization impact the country’s CO2e. This study also employed granger causality technique and found that women’s education and localized CO2e has bi directional relationship. And one directional relationship exists between CO2e and population growth, CO2e and renewable energy and then CO2e and technological progress. The findings suggest the adoption and implementation of affirmative action programs aimed at improving the women’s STEM (science, technology, engineering and mathematics) education system and policy design. This method may assist in building a large and comprehensive basis concentrating on increasing R D allocation, especially in the sustainable context.

This study examines the validity of the environmental Kuznets curve (EKC) hypothesis and the role of environmental regulation, renewable electricity, industrialization, economic complexity, and technological innovation in sustainable environment for the G-10 economies, namely, Belgium, Canada, Germany, Italy, Japan, Netherlands, Sweden, Switzerland, the United Kingdom, and the USA, from 1994 to 2020. We employed CS-ARDL (cross-sectional augmented distributed lag (CS-ARDL), FMOLS (fully modified ordinary least squares), and DOLS (dynamic ordinary least squares) for the analysis of the data. The estimates confirm the N-shaped EKC hypothesis between the GDP and CO2 emission. Moreover, the long-run estimates exhibit that environmental tax, renewable electricity, economic complexity, and technological innovation have negative effect on CO2 emission, while GDP, industrialization and arable land have positive effect on CO2 emission. Based on these findings, we propose that governments must implement large-scale government plans and initiatives to encourage the development of environmentally friendly technologies and ideas based on renewable energy. Moreover, further growing renewable energy, environmental policies like a carbon tax, investments in green technologies, subsidies, and rewards for renewable energy infrastructure investment should be taken into account.

This study highlights the important role of strategic policy interventions in promoting sustainable economic prosperity and social equity in OECD economies. It specifically focuses on the positive effects of technological development, energy efficiency, renewable electricity, and human capital on inclusive growth across 35 OECD economies from 1990 to 2019. We employed cross-sectional augmented distributed lag, fully modified ordinary least squares, and dynamic ordinary least squares to analyze the data. The long-run and short-run estimates show that technological development, energy efficiency, renewable electricity, human capital, and renewable energy consumption have positive effects on inclusive growth. Based on these findings, we propose that the government should provide funds for innovation and technological development, and it improves access to information and services, it increases productivity and efficiency, creates new job opportunities, and promotes inclusive growth. Moreover, the government should promote energy efficiency, which will help in cost savings, job creation, improved access to energy, inclusive growth, and environmental benefits.

In the modern era, environmental degradation emerges as a critical global issue. Extensive studies have examined numerous socio and macro-economic determinants of environmental deterioration, yet the contributions of institutional quality and energy productivity justify further investigation. To bridge this gap, the current study addresses the impacts of geopolitical risk and economic policy uncertainty, with a keen emphasis on institutional quality and energy productivity within the BRICS countries from 1992 to 2021. By utilizing advanced econometric techniques such as CS-ARDL, FMOLS, DOLS, and Augmented Mean Group, the research reveals that institutional quality and energy productivity, along with foreign direct investment, exert a mitigating effect on CO 2 emissions. However, geopolitical risk and economic uncertainty correlate with an increase in environmental degradation. In conclusion, this study offers valuable policy recommendations derived from its findings, aiming to contribute to pursuing environmental sustainability in the BRICS nations.

We examined the impact of globalization, governance, and financial development on per capita income representing economic growth for 156 countries across the globe during 2002–2018. The analysis is categorized into full samples, and sub samples (i.e., low, lower, upper middle-, & high-income countries). The empirical methodology consists of 1 st and 2 nd generation panel unit root tests, panel co-integration test, Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and panel Dumitrescu Hurlin Granger causality test. The FMOLS and DOLS estimate indicated that financial development and human capital had a positive effect on economic growth for lower-income, lower-middle-income, upper-middle-income, high-income countries, and full sample. Governance factor namely control of corruption has a positive effect on economic growth in all income groups’ countries except lower-middle-income and full sample. The impact of governance factors namely political stability & absence of violence had a positive effect on economic growth in all income groups’ countries and full sample except upper-middle-income countries. Likewise, globalization had a positive effect on economic growth for all income groups’ countries, except full sample. These findings suggest that the developed financial sector, good governance, and globalization need to be strengthened to boost economic growth.

Environmental degradation rates have been on a concerning upward trajectory in recent decades, directly threatening the well-being of global populations. Responding to this urgent matter, scholars have been driven to explore its nuances, particularly emphasizing lowering energy consumption and carbon emissions amidst the growing demands of growing economies. Achieving the targets outlined in the 2015 Paris Climate Agreement has also become a priority for many countries. Therefore, this study scrutinizes the Environmental Kuznets Curve (EKC) hypothesis, specifically focusing on the role of energy productivity, technological advancement, and human capital in fostering a sustainable environment across 35 OECD economies from 1990 to 2018. Utilizing three robust econometric techniques, Cross-Sectional Autoregressive Distributed Lag (CS-ARDL), Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS), we have drawn insightful conclusions from our data. The analysis substantiates an N-shaped EKC hypothesis relationship between GDP and CO2 emissions, pointing towards an initially increasing, then decreasing, and finally an increasing again trend of emissions with GDP. Furthermore, the long-term projections underscore that energy productivity, technological progression, and human capital formation harm the environment. These findings culminate in a call for governments to orchestrate extensive plans and initiatives. This involves promoting green technologies, renewable energy–based ideas, and comprehensive education and awareness programs. These efforts should span all educational levels, highlighting climate change, sustainable practices, and the need for CO2 reduction, empowering societies to contribute to a sustainable future.

Institutional quality (IQ) plays a crucial role in achieving the Sustainable Development Goals. IQ is fundamental to SDG’s 16, which promotes peaceful and inclusive societies, provides access to justice, and builds effective, accountable, and transparent institutions. Countries with strong institutions that uphold the rule of law, protect human rights, and combat corruption are more likely to achieve this goal and promote economic development. Therefore, this study examines the relationship between IQ and economic development, as measured by the human development index (HDI), in 70 developing countries between 2002 and 2018. To achieve the above mention objective, various econometric techniques were employed, including CIPS unit root, Westerlund (2007) co-integration, and Cross-sectional Augmented Autoregressive Distributed Lag, and robustness analysis conducted using Fully Modified Ordinary Least Square, Dynamic Ordinary Least Square, Augmented Mean Grouped, Impulse Response Function, and Variance Decomposition Analysis panel estimators. The study found that IQ and globalization have a positive, while inflation, unemployment, and corruption have a negative impact on HDI. The estimates from the Impulse Response Function show that IQ positively influences HDI from 2019 to 2028. Additionally, the Variance Decomposition Analysis reported that around 38% of the variations in HDI can be attributed to changes in IQ. To improve IQ, transparency must be prioritized as it is the foundation of IQ enhancement. Similarly, measures must be taken to combat corruption, and strong administrative intervention is required to advance economic development. These findings suggest that policymakers must prioritize improving institutional quality and fighting corruption to promote economic development in developing countries.

Geopolitical risk (GPR) and other social indicators have raised many somber environmental-related issues among government environmentalists, and policy analysts. To further elucidate whether or not these indicators influence the environmental quality, this study investigates the impact of GPR, corruption, and governance on environmental degradation proxies by carbon emissions (CO2) in BRICS (Brazil, Russia, India, China, and South Africa) countries, namely Brazil, Russia, India, China, and South Africa, using data over the period 1990 to 2018. The cross-sectional autoregressive distributed lag (CS-ARDL), fully modified ordinary least square (FMOLS), and dynamic ordinary least square (DOLS) methods are used for empirical analysis. First and second-generation panel unit root tests report a mixed order of integration. The empirical findings show that government effectiveness, regulatory quality, the rule of law, foreign direct investment (FDI), and innovation have a negative effect on CO2 emissions. In contrast, geopolitical risk, corruption, political stability, and energy consumption have a positive effect on CO2 emissions. Based on the empirical outcomes, the present research invites the concentration of central authorities and policymakers of these economies toward redesigning more sophisticated strategies regarding these potential variables to protect the environment.