Human capital and ICT have a significant role in determining human development. The impacts of ICT and human capital on green growth and environmental sustainability should be explored for sustainable economic development. This research contributes to the literature on the role of ICTs and human capital in the determination of green growth and environmental performance. Based on time-series data 1990–2019, the study intends to investigate the impact of ICTs and human capital on environmental and green growth performance for China. The study reports that ICTs tend to reduce CO2 emissions and improve green growth in the long-run. However, education reduces CO2 emissions in the long-run but does not produce any significant impact on green growth in the long-run. It is suggested that government should invest in environmental efficiency and environmental technologies simultaneously with human capital that could significantly contribute to pollution reduction. Lastly, policies to increase human capital should be implemented simultaneously with policies to promote ICTs contribution in order to confirm green growth and environmental protection.

To achieve environmental sustainability, the role of human capital and financial inclusion has been debated in limited empirical studies. Employing a reliable ARDL model approach, this study examines the dynamic link between human capital and ICT, financial inclusion, and CO2 emissions using the China economy dataset over the period 1998–2020. The vivacious side of human capital shows that literacy rate and average year of schooling curb CO2 emissions in long run. The results of human capital are also based on facts in magnitude as well as in direction. Also, empirics unfold that digital financial inclusion significantly increases CO2 emissions. Based on these novel findings, a wide set of economic policies are repaired for environmental quality. Environmental education should be considered at early levels of education. The authorities and policymakers should fix energy-related issues through education. The China government should stimulate the educational sector to conduct a clean and green revolution that acts as a mechanism for a green and clean economy. This study's finding is more effective than the previous unlike empirical studies for policy-making because of the advanced econometric method.

This study was conducted to evaluate public awareness about COVID with aimed to check public strategies against COVID-19. A semi structured questionnaire was collected and the data was analyzed using some statistical tools (PLS-SEM) and artificial neural networks (ANN). We started by looking at the known causal linkages between the different variables to see if they matched up with the hypotheses that had been proposed. Next, for this reason, we ran a 5,000-sample bootstrapping test to assess how strongly our findings corroborated the null hypothesis. PLS-SEM direct path analysis revealed HRP -> PA-COVID, HI -> PA-COVID, MU -> PA-COVID, PM -> PA-COVID, SD -> PA-COVID. These findings provide credence to the acceptance of hypotheses H1, H3, and H5, but reject hypothesis H2. We have also examined control factors such as respondents' age, gender, and level of education. Age was found to have a positive correlation with PA-COVID, while mean gender and education level were found to not correlate at all with PA-COVID. However, age can be a useful control variable, as a more seasoned individual is likely to have a better understanding of COVID and its effects on independent variables. Study results revealed a small moderation effect in the relationships between understudy independent and dependent variables. Education significantly moderates the relationship of PA-COVID associated with MU, PH, SD, RP, PM, PA-COVID, depicts the moderation role of education on the relationship between MU*Education->PA-COVID, HI*Education->PA.COVID, SD*Education->PA.COVID, HRP*Education->PA.COVID, PM*Education -> PA.COVID. The artificial neural network (ANN) model we've developed for spreading information about COVID-19 (PA-COVID) follows in the footsteps of previous studies. The root means the square of the errors (RMSE). Validity measures how well a model can predict a certain result. With RMSE values of 0.424 for training and 0.394 for testing, we observed that our ANN model for public awareness of COVID-19 (PA-COVID) had a strong predictive ability. Based on the sensitivity analysis results, we determined that PA. COVID had the highest relative normalized relevance for our sample (100%). These factors were then followed by MU (54.6%), HI (11.1%), SD (100.0%), HRP (28.5%), and PM (64.6%) were likewise shown to be the least important factors for consumers in developing countries struggling with diseases caused by contaminated water. In addition, a specific approach was used to construct a goodness-of-fit coefficient to evaluate the performance of the ANN models. The study will aid in the implementation of effective monitoring and public policies to promote the health of local people.