This research investigates the dynamic interactive associations among sustainable investment in the energy sector, air pollution, and sustainable development. To this end, it employs a "one-step" system-generalized method of moments (GMM) and "one-step" differential-GMM estimators, covering the period between 1996 and 2017. In this context, it utilizes the simultaneous equations of the dynamic panel data model for panel data of 27 Chinese provinces and municipalities. We have developed a new model of sustainable development, which incorporates sustainable investment in the energy sector and air pollution to offer a robust theoretical foundation for considering the underlying relations. The system-GMM estimator is used for the full data set; however, differential-GMM is utilized for the subsets of data, in order to tackle the small sample bias problem. The empirical outcomes provide several vital insights in that they yield mixed findings for the aggregated sample and subsets of data. For example, a two-way causal relationship occurs for all the panels, except the central part (medium development regions), between sustainable investment in the energy sector and sustainable development. Contrary to this, causality runs from air pollution to sustainable investment in the energy sector in a full data set and the central part (medium dev.). Nevertheless, the opposite is true in the case of the eastern part (most developed regions) of China. Still, the same relationship runs in either direction in the case of the western part (least developed regions). On the other way around, the feedback hypothesis of causality is confirmed, across all the samples, between air pollution and sustainable development. Hence, sustainable development and air pollution are overwhelmingly interdependent, in the country as well as the province and municipality level of the Chinese economy.Biocovers are known for their role as key facilitator to reduce landfill methane (CH4) emission on improving microbial methane bio-oxidation. Methanotrophs existing in the aerobic zone of dumped wastes are the only known biological sinks for CH4 being emitted from the lower anaerobic section of landfill sites and even from the atmosphere. However, their efficacy remains under the influence of landfill environment and biocover characteristics. Therefore, the present study was executed to explore the suitability and efficacy of dumpsite soil as biocover to achieve enhanced methane bio-oxidation under the interactive influence of nutrients, carbon source, and environmental factors using statistical-mathematical models. The Placket-Burman design (PBD) was employed to identify the significant factors out of 07 tested factors having considerable impact on CH4 bio-oxidation. The normal plot and Student's t test of PBD indicated that ammonical nitrogen (NH4+-N), nitrate nitrogen (NO3--N), methane (CH4), and copper (Cu) concentration were found significant. A three-level Box-Behnken design (BBD) was further applied to optimize the significant factors identified from PBD. The BBD results revealed that interactive interaction of CH4 with NH4+-N and NO3--N affected the CH4 bio-oxidation significantly. The sequential statistical approach predicted that maximum CH4 bio-oxidation of 27.32 μg CH4 h-1 could be achieved with CH4 (35%), NO3--N (250 μg g-1), NH4+-N (25 μg g-1), and Cu (50 mg g-1) concentration. Conclusively, waste dumpsite soil could be a good alternative over conventional soil cover to improve CH4 bio-oxidation and lessen the emission of greenhouse gas from waste sector.The aim of this study is to assess the content of heavy metals and their potential health risk in consumed food crops. To this end, the samples from vegetables, rice, potato, onion, and black tea were derived from high sales and commonly consumed types. The noncarcinogenic health risk of heavy metals to the adults, teens, and children was estimated by target hazard quotients (THQs) and hazard index (HI) calculation. Sensitivity and uncertainty analyses were carried out using Monte Carlo simulations. Heavy metal pollution index (HMI) was used for ranking noncarcinogenic heavy metal pollution in sampled food crops. THQs showed that noncarcinogenic health risks to the local population were largely related to As (0.71 for adults, 0.87 for teens, and 2.4 for children), Mn (0.43 for adults, 0.28 for teens, and 0.64 for children), and Mo (0.12 for adults, 0.02 for teens, and 0.4 for children). HI for individual food crops (HIΣfi) in terms of different populations showed that the highest HIΣfi was for children while the highest HIΣTea was for adults. The arrangement of the calculated HIΣfi along with its highest value was in the order of HIΣRice (3.71) > HIΣTea (0.39) > HIΣBeans (0.2) > HIΣVegetables (0.13) > HIΣOnion (0.12) > HIΣPotato (0.11). The value of HI for all sampled food crops based on their daily ingestion rate achieved by deterministic and probabilistic (Monte Carlo simulations) approaches for adults, teens, and children was 1.63, 1.28, and 1.87, 1.67, 4.51, and 2.48 respectively, and revealed that all populations are vulnerable to the significant noncarcinogenic health risks and children are at more risk. https://www.selleckchem.com/products/i-191.html The sensitivity analysis revealed that the ingestion rate (IR) is the most influential factor that contributed to the total risk. The determined HMI showed no heavy metal pollution for all food crops, and rice had higher-order in HMI ranking. These results showed that heavy metals exposure due to food ingestion is a threat to human health and needs choosing a proper strategy to reduce heavy metal exposure.The study aims to analyze two objectives first is to explore the non-linear relationship between tourism development, economic growth, urbanization, and environmental degradation, and also to analyze the threshold level of the contribution of tourism development on environmental degradation in top tourist arrival destinations. We applied the newly proposed econometric method panel smooth transition regression (PSTR) framework with two regimes on yearly panel data from 1995 to 2017. Findings suggest that the relationship between tourism development and environmental degradation is non-linear and regime dependent. Furthermore, the findings indicated that the relationship above the threshold level is negative and significant, while below the threshold, tourism development is positive and significant effect on environmental degradation. Tourism development and environmental degradation also exhibit the inverted U-shape relationship meaning that at a particular point, increase in tourism development increases in environmental degradation but after a particular point, increase in tourism development decreases the environmental degradation.