Concerns regarding environmental sustainability have generally been an important element in achieving long-term development objectives. However, developing countries struggle to deal with these concerns, which all require specific treatment. As a result, this study explores the interaction between financial development, renewable energy consumption, technological innovations, and CO2 emissions in India from 1980 to 2019, taking into account the critical role of economic progress and urbanization. The Autoregressive Distributed Lag (ARDL) model is used to quantify long-run dynamics, while the Vector Error Correction Model is used to identify causal direction (VECM). According to the study’s conclusions, financial development has a considerable positive impact on CO2 emissions. The coefficient of renewable energy consumption and technical innovations, on the other hand, is strongly negative in both the short and long run, indicating that increasing these measures will reduce CO2 emissions. Furthermore, economic expansion and urbanization have a negative impact on environmental quality since they emit a significant amount of CO2 into the atmosphere. The results of the robustness checks were obtained using the Fully Modified Ordinary Least Squares (FMOLS), the Dynamic Ordinary Least Squares (DOLS), and the Canonical Cointegration Regression (CCR) approaches to verify the findings. The VECM results reveal that there is long-run causality in CO2 emissions, financial development, renewable energy utilization, and urbanization. A range of diagnostic tests were also used to confirm the validity and reliability. This study delivers new findings that contribute to the existing literature and may be of particular interest to the country’s policymakers in light of the financial system and its role in environmental issues.

Following the outbreak of the novel coronavirus in early 2020, to effectively prevent the spread of the disease, major cities across China suspended work and production. While the rest of the world struggles to control COVID-19, China has managed to control the pandemic rapidly and effectively with strong lockdown policies. This study investigates the change in air pollution (focusing on the air quality index (AQI), six ambient air pollutants nitrogen dioxide (NO 2 ), ozone (O 3 ), sulphur dioxide (SO 2 ), carbon monoxide (CO), particulate matter with aerodynamic diameters ≤10 μm (PM 10 ) and ≤2.5 μm (PM 2.5 )) patterns for three periods: pre-COVID (from 1 January to May 30, 2019), active COVID (from 1 January to May 30, 2020) and post-COVID (from 1 January to May 30, 2021) in the Jiangsu province of China. Our findings reveal that the change in air pollution from pre-COVID to active COVID was greater than in previous years due to the government's lockdown policies. Post-COVID, air pollutant concentration is increasing. Mean change PM 2.5 from pre-COVID to active COVID decreased by 18%; post-COVID it has only decreased by 2%. PM 10 decreased by 19% from pre-COVID to active COVID, but post-COVID pollutant concentration has seen a 23% increase. Air pollutants show a positive correlation with COVID-19 cases among which PM 2.5 , PM 10 and NO 2 show a strong correlation during active COVID-19 cases. Metrological factors such as minimum temperature, average temperature and humidity show a positive correlation with COVID-19 cases while maximum temperature, wind speed and air pressure show no strong positive correlation. Although the COVID-19 pandemic had numerous negative effects on human health and the global economy, the reduction in air pollution and significant improvement in ambient air quality likely had substantial short-term health benefits; the government must implement policies to control post-COVID environmental issues.

Lamioideae comprised the second‐largest subfamily in Lamiaceae. Although considerable progress has recently been made in the taxonomic study of Lamioideae, the subfamily remains one of the most poorly investigated subfamily in Lamiaceae. Therefore, the present study was designed with the aim to document the pollen micromorphology of some selected Lamioideae taxa and its taxonomic significance from Pakistan. Pollen micromorphological features were observed using scanning electron microscopy. The pollen grains are monad, tricolpate, radially/bilateral symmetrical. The pollen grains were small to medium‐sized having oblate, oblate/subspheroidal, and subspheroidal shape. Exine sculpturing was observed as reticulate, microreticulate, and bireticulate. The colpus surface ornamentation was found as verrucate, gemmate, scabrate, and psilate. There was a considerable variation between the species in the micromorphology, that is, the coarseness of the reticulum, thickness of the muri comprising the reticulum and the number of secondary lumina per primary lumen. Hence, this study documented the pollen morphology of some selected taxa of the subfamily Lamioideae from Pakistan and strengthens the taxonomic identification of subfamily based on pollen characters, which helps in the correct identification, discrimination of the species of Lamioideae at generic and species level.

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During the epidemic period, primary emissions across the world were significantly reduced, while the response to secondary pollution such as ozone differed from region to region. To study the impact of the strict control measures of the new COVID-19 epidemic on the air quality of Anhui in early 2020, the air quality monitoring data of Anhui, from 2019 to 2021, specifically 1 January to 30 August, was examined to analyze the characteristics of the temporal and spatial distribution. Regression and path analysis were used to extract the relationship between the variable. PM 10 and O 3 , on average, increased by 6%, and 2%, while PM 2.5 , SO 2 decreased by 15% and 10% in the post-COVID-19 period. All air quality pollutants decreased during the active-COVID-19 period, with a maximum decrease of 21% observed in PM 10 , followed by 19% of PM 2.5 , and a minimum decrease of 2% observed in O 3 . Changes in air pollutants from 2017 to 2021 were also compared, and a decrease in all pollutants through 2020 was found. The air quality index (AQI) recorded a low decrease of 3% post-COVID-19, which shows that air quality will worsen in the future, but it decreased by 16% during the active-COVID-19 period. A path analysis model was developed to further understand the relationship between the AQI and air quality patterns. This path analysis shows a strong correlation between the AQI and PM 10 and PM 2.5 , however, its correlation with other air pollutants is weak. Regression analysis shows a similar pattern