For the purpose of this study, the role of technological innovation is examined. Few studies have examined empirically and theoretically the relationship between technological innovation and ecological footprint in conjunction with other factors, such as the human capital index and renewable energy sources, such as biofuels and nuclear power. This study examines the impact of technological innovation on G-7 countries' ecological footprints from 1990 to 2020. A cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model is used in the study. The results of the study show that technological innovation minimizes the ecological footprint. A lower ecological footprint is also associated with increased usage of human capital and renewable energy. Depletion of the natural environment is a short-term and long-term consequence of increased GDP growth. Our results confirm that ecologically sustainable technology enhances the quality of the environment. Consistent panel causality results were achieved. In the context of the G-7 countries, our study's results could support the idea that there are new policy ideas that could help achieve the Sustainable Development Goals (SDG 3, 4, 7, 8, 9, and 13).
This study draws the link between COVID-19 and air pollution (ground ozone O3) from February 29, 2020 to July 10, 2020 in the top 10 affected States of the US. Utilizing quantile-on-quantile (QQ) estimation technique, we examine in what manner the quantiles of COVID-19 affect the quantiles of air pollution and vice versa. The primary findings confirm overall dependence between COVID-19 and air pollution. Empirical results exhibit a strong negative effect of COVID-19 on air pollution in New York, Texas, Illinois, Massachusetts, and Pennsylvania; especially at medium to higher quantiles, while New Jersey, Illinois, Arizona, and Georgia show strong negative effect mainly at lower quantiles. Contrarily, COVID-19 positively affects air pollution in Pennsylvania at extreme lower quantiles. On the other side, air pollution predominantly caused to increase in the intensity of COVID-19 cases across all states except lower quantiles of Massachusetts, and extreme higher quantiles of Arizona and New Jersey, where this effect becomes less pronounced or negative. Concludingly, a rare positive fallout of COVID-19 is reducing environmental pressure, while higher environmental pollution causes to increase the vulnerability of COVID-19 cases. These findings imply that air pollution is at the heart of chronic diseases, therefore the state government should consider these asymmetric channels and introduce appropriate policy measures to reset and control atmospheric emissions.
The Belt and Road Initiative (BRI) is closely linked to the ecological sustainability of the infrastructure ventures that intrinsically include the aspects of climate change and pollution. Though there exists literature on the environmental Kuznets curve (EKC) and pollution haven hypothesis (PHH), very few explore the scope in the light of Belt and Road host countries (B&RCs). Therefore, the study examines the income-induced EKC and Chinese outward foreign direct investment (FDI)-based PHH in the multivariate framework of people's connectivity and technology innovation in B&RCs from 2003 to 2018. The outcome of the study reveals that the observed relationship is quantile-dependent, which may disclose misleading results in previous studies using traditional methodologies that address the averages. Utilizing the novel "Method of Moments Quantile Regression (MMQR)" of Machado and Silva (J Econom 213:145-173, 2019), the findings confirm an inverted U-shape association between economic growth and CO2 emissions only at lower to medium emission countries, thus validating the EKC hypothesis. The Chinese outward FDI flows increase carbon emissions at medium to high emission countries, thereby confirming PHH. The findings also indicate that people's connectivity contributes to increasing emissions while innovation mitigates carbon emissions at lower to medium polluted countries. Moreover, the outcomes of Granger causality confirm one-way causality between economic growth and CO2 emissions, between FDI and CO2 emissions, between people's connectivity and CO2 emissions, and between innovation and CO2 emissions. The results offer valuable insight for legislators to counteract CO2 emissions in B&RCs through innovation-led energy conservation in infrastructure projects while adopting green and sustainable financing mechanisms to materialize mega construction projects under the BRI.
Toxoplasmosis is a protozoal infection of zoonotic potential with worldwide geographical distribution which affects nearly all warm-blooded animals including mammals and birds. Keeping in view, this study was conducted to determine the seroprevalence of toxoplasmosis along with associated risk factors and its haematological impacts in small ruminants of district Multan, Pakistan. In this study, a total of 250 sera samples collected from sheep (n=125) and goats (n=125) from three tehsils of Multan were examined using commercially available Latex agglutination test kit for the presence of anti-T. gondii antibodies. The haematological profiles of Toxoplasma seropositive and seronegative animals were determined by using automated haematology analyser. Overall seroprevalence of toxoplasmosis in small ruminants was 42.80% with a higher prevalence rate (44.80%) in sheep as compared to goats (40.80%). Sex, existence of co-morbid conditions, feeding pattern and presence of pet cats and dogs were identified as significant (P<0.05) risk factors associated with the presence of antibodies against toxoplasmosis. The breed was found to be a significant (P=0.026) risk factor for the seroprevalence of toxoplasmosis in goats but not in sheep. Haematological analysis revealed significantly altered leukocytic counts (P<0.05) in seropositive sheep and goats as compared to seronegative ones. Our findings showed that small ruminants of the Multan District in Pakistan are toxoplasma seropositive and may pose a serious threat of public health concern in the region.
Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy.
The emergence of new coronavirus (SARS-CoV-2) has become a significant public health issue worldwide. Some researchers have identified a positive link between temperature and COVID-19 cases. However, no detailed research has highlighted the impact of temperature on COVID-19 spread in India. This study aims to fill this research gap by investigating the impact of temperature on COVID-19 spread in the five most affected Indian states. Quantile-on-Quantile regression (QQR) approach is employed to examine in what manner the quantiles of temperature influence the quantiles of COVID-19 cases. Empirical results confirm an asymmetric and heterogenous impact of temperature on COVID-19 spread across lower and higher quantiles of both variables. The results indicate a significant positive impact of temperature on COVID-19 spread in the three Indian states (Maharashtra, Andhra Pradesh, and Karnataka), predominantly in both low and high quantiles. Whereas, the other two states (Tamil Nadu and Uttar Pradesh) exhibit a mixed trend, as the lower quantiles in both states have a negative effect. However, this negative effect becomes weak at middle and higher quantiles. These research findings offer valuable policy recommendations.