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.

High-throughput sequencing (HTS) has instigated a paradigm shift in plant pathology, showcasing its transformative role in the management of plant diseases. As a powerful tool, HTS aids in identifying pathogens and enhances disease management strategies by detecting novel and emerging pathogens, tracking disease outbreaks, and contributing to developing disease-resistant cultivars. Despite these benefits, the implementation of HTS faces obstacles due to the complexity of data interpretation and economic factors that affect its widespread adoption. This comprehensive review summarizes the strengths, limitations, and opportunities associated with using HTS in managing plant diseases. The article also delves into the prospects of HTS, incorporating technological advancements, synergy with complementary methodologies, capacity-building initiatives, and the development of best practices and guidelines. By acknowledging and addressing these obstacles while harnessing the full capabilities of HTS, we advocate for a refined approach to managing plant diseases. This approach is critical for ensuring global food security, especially in the context of a growing global population and climate change.

The extensive use of plastic products and rapid industrialization have created a universal concern about microplastics (MPs). MPs can pose serious environmental risks when combined with heavy metals. However, current research on the combined effects of MPs and hexavalent chromium [Cr(VI)] on plants is insufficient. Herein, a 14-day hydroponic experiment was conducted to investigate the impact of PVC MPs (100 and 200 mg/L) and Cr(VI) (5, 10, and 20 μM) alone and in combination on sweet potato. Results showed that combined Cr(VI) and PVC MPs affected plant growth parameters significantly, but PVC MPs alone did not. The combined application of PVC MPs and Cr(VI) resulted in a decrease in plant height (24-65%), fresh biomass per plant (36-71%), and chlorophyll content (16-34%). Cr(VI) bioaccumulation increased with the increase in its doses, with the highest concentration of Cr(VI) in the leaves (16.45 mg/kg), stems (13.81 mg/kg), and roots (236.65 mg/kg). Cr(VI) and PVC MPs-induced inhibition varied with Cr(VI) and PVC MPs doses. Osmolytes and antioxidants, lipid peroxidation, and H₂O₂ contents were significantly increased, while antioxidant enzymes except CAT were decreased with increasing Cr(VI) concentration alone and mixed treatments. The presence of PVC MPs promoted Cr(VI) accumulation in sweet potato plants, which clearly showed severe toxic effects on their physio-biochemical characteristics, as indicated by a negative correlation between Cr(VI) concentration and these parameters. PVC MPs alone did not significantly inhibit these parameters. The findings of this study provide valuable implications for the proper management of PVC MPs and Cr(VI) in sweet potato plants.

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. PM10 and O3, on average, increased by 6%, and 2%, while PM2.5, SO2 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 PM10, followed by 19% of PM2.5, and a minimum decrease of 2% observed in O3. 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 PM10 and PM2.5, however, its correlation with other air pollutants is weak. Regression analysis shows a similar pattern of there being a strong relationship between AQI and PM10(r2 = 0.97) and PM2.5(r2 = 0.93). The government must implement policies to control the environmental issues which are causing poor air quality in post-COVID-19.