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The basic priority to neutralize carbon emissions (CE) is to achieve the sustainable development goal of climate action. In this regard, the role of renewable energy (RE) is widely debated. Transition to RE and environment-related innovation in technologies (ERIT) is a need of the hour. However, the challenge of uncertain economic policies in the transition process is interesting to study. Therefore, the present study intends to add a value to the literature by re-examining the interactive effect of economic policy uncertainty (EPU) in RE and ERIT and the transition process towards carbon neutrality. The second-generation econometric methodology is applied to empirically test the proposed interactive linkage of EPU, RE, ERIT, and CE. Findings reported the negative role of EPU in adopting RE and ERIT in seven emerging economies: Brazil, China, India, Indonesia, Mexico, Russia, and Turkey. However, the ERIT and RE are found to be supportive of neutralizing the CE. Emerging seven (E7) countries are suggested to be consistent in their economic policies to nurture the transition process toward a sustainable environment.

Abstract The vegetation optical depth (VOD), a vegetation index retrieved from passive or active microwave remote sensing systems, is related to the intensity of microwave extinction effects within the vegetation canopy layer. This index is only marginally impacted by effects from atmosphere, clouds and sun illumination, and thus increasingly used for ecological applications at large scales. Newly released VOD products show different abilities in monitoring vegetation features, depending on the algorithm used and the satellite frequency. VOD is increasingly sensitive to the upper vegetation layer as the frequency increases (from L-, C- to X-band), offering different capacities to monitor seasonal changes of the leafy and/or woody vegetation components, vegetation water status and aboveground biomass. This study evaluated nine recently developed/reprocessed VOD products from the AMSR2, SMOS and SMAP space-borne instruments for monitoring structural vegetation features related to phenology, height and aboveground biomass. For monitoring the seasonality of green vegetation (herbaceous and woody foliage), we found that X-VOD products, particularly from the LPDR-retrieval algorithm, outperformed the other VOD products in regions that are not densely vegetated, where they showed higher temporal correlation values with optical vegetation indices (VIs). However, LPDR X-VOD time series failed to detect changes in VOD after rainfall events whereas most other VOD products could do so, and overall daily variations are less pronounced in LPDR X-VOD. Results show that the reprocessed VODCA C- and X-VOD have almost comparable performance and VODCA C-VOD correlates better with VIs than other C-VOD products. Low frequency L-VOD, particularly the new version (V2) of SMOS-IC, show a higher temporal correlation with VIs, similar to C-VOD, in medium-densely vegetated biomes such as savannas (R ~ 0.70) than for other short vegetation types. Because the L-VOD indices are more sensitive to the non-green vegetation components (trunks and branches) than higher frequency products, they are well-correlated with aboveground biomass: (R ~ 0.91) across space between predicted and observed values for both SMOS-IC V2 and SMAP MT-DCA. However, when compared with forest canopy height, results at L-band are not systematically better than C- and X-VOD products. This revealed specific VOD retrieval issues for some ecosystems, e.g., boreal regions. It is expected that these findings can contribute to algorithm refinements, product enhancements and further developing the use of VOD for monitoring above-ground vegetation biomass, vegetation dynamics and phenology.

Drought is the most serious natural disaster causing severe damage to agriculture. Drought impacts on rice (Oryza sativa) production present a major threat to future global food security. In this paper, the Environmental Policy Integrated Climate (EPIC) model was used to simulate the growth of rice, in different periods (short-term (2019-2039), medium-term (2040-2069), long-term (2070-2099)), based on multiple Representative Concentration Pathways (RCP) scenarios. Drought intensity and rice physical vulnerability curves were assessed, based on the output parameters of EPIC, to evaluate global rice yield risk, due to drought. The results show that the average expected loss rate of global rice yield may reach 13.1% (±0.4%) in the future. The high-risk area of rice drought is mainly located in the north of 30°N. The fluctuation of rice drought risk and the proportion of increased risk areas will increase significantly. About 77.6% of the changes in rice drought risk are explained by variations in shortwave radiation (r = 0.88). Projections show that the average value of daily shortwave radiation increases by 1 W/m2 during the rice growth period, accompanied by an expected rice yield loss rate of about 12.7%. The rice drought risk methods presented in this paper provide plausible estimates of forecasting future drought risk under climate change, and address challenges of sparse data; we believe these methods can be applied to decisions for reducing drought-related crop losses and ensuring global food security.

As a sensitive indicator of climate change and a key variable in ecosystem surface-atmosphere interaction, vegetation phenology, and the growing season length, as well as climatic factors (i.e., temperature, precipitation, and sunshine duration) are widely recognized as key factors influencing vegetation productivity. Recent studies have highlighted the importance of soil moisture in regulating grassland productivity. However, the relative importance of phenology, climatic factors, and soil moisture to plant species-level productivity across China's grasslands remains poorly understood. Here, we use nearly four decades (1981 to 2018) of in situ species-level observations from 17 stations distributed across grasslands in China to examine the key mechanisms that control grassland productivity. The results reveal that soil moisture is the strongest determinant of the interannual variability in grassland productivity. In contrast, the spring/autumn phenology, the length of vegetation growing season, and climate factors have relatively minor impacts. Generally, annual aboveground biomass increases by 3.9 to 25.3 g∙m2 (dry weight) with a 1 % increase in growing season mean soil moisture across the stations. Specifically, the sensitivity of productivity to moisture in wetter and colder environments (e.g., alpine meadows) is significantly higher than that in drier and warmer environments (e.g., temperate desert steppes). In contrast, the sensitivity to the precipitation of the latter is greater than the former. The effect of soil moisture is the most pronounced during summer. Dominant herb productivity is more sensitive to soil moisture than the others. Moreover, multivariate regression analyses show that the primary climatic factors and their attributions to variations in soil moisture differ among the stations, indicating the interaction between climate and soil moisture is very complex. Our study highlights the interspecific difference in the soil moisture dependence of grassland productivity and provides guidance to climate change impact assessments in grassland ecosystems.

Large scale process-based modeling is a useful approach to estimate distributions of global net primary productivity (NPP). In this paper, in order to validate an existing NPP model with observed data at site level, field experiments were conducted at three sites in northern China. One site is located in Qilian Mountain in Gansu Province, and the other two sites are in Changbaishan Natural Reserve and Dunhua County in Jilin Province. Detailed field experiments are discussed and field data are used to validate the simulated NPP. Remotely sensed images including Landsat Enhanced Thematic Mapper plus (ETM+, 30 m spatial resolution in visible and near infrared bands) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, 15m spatial resolution in visible and near infrared bands) are used to derive maps of land cover, leaf area index, and biomass. Based on these maps, field measured data, soil texture and daily meteorological data, NPP of these sites are simulated for year 2001 with the boreal ecosystem productivity simulator (BEPS). The NPP in these sites ranges from 80 to 800 gCm(-2)a(-1). The observed NPP agrees well with the modeled NPP. This study suggests that BEPS can be used to estimate NPP in northern China if remotely sensed images of high spatial resolution are available.

Microbial single-cell-protein (SCP) production from high-organic-strength industrial wastewaters is considered an attractive method for both wastewater purification and resource utilization. In the last two decades, pollutant removal-oriented yeast SCP production processes, i.e., yeast treatment processes, have attracted a great deal of attention from a variety of research groups worldwide. Different from conventional SCP production processes, yeast treatment processes are characterized by higher pollutant removal rates, lower production costs, highly adaptive yeast isolates from nature, no excess nutrient supplements, and are performed under non-sterile conditions. Furthermore, yeast treatment processes are similar to bacteria-dominated conventional activated sludge processes, which offer more choices for yeast SCP production and industrial wastewater treatment. This review discusses why highly adaptive yeast species isolated from nature are used in the yeast treatment process rather than commercial SCP producers. It also describes the application of yeast treatment processes for treating high-carboxyhydrate, oil-rich and high-salinity industrial wastewater, focusing primarily on high-strength biodegradable organic substances, which usually account for the major fraction of biochemical oxygen demand. Also discussed is the biodegradation of xenobiotics, such as color (including dye and pigment) and toxic substances (including phenols, chlorophenols, polycyclic aromatic hydrocarbons, etc.), present in industrial wastewater. Based on molecular information of yeast community structures and their regulation in yeast treatment systems, we also discuss how to maintain efficient yeast species in yeast biomass and how to control bacterial and mold proliferation in yeast treatment systems.

The unprecedented economic growth in recent decades has cultivated the exploitation of natural resources and over-consumption, leading to ecological deterioration and sustainability. The ever-increasing consumption in developing countries is creating a significant environmental strain. Thus, the industry and consumers’ environmental issues and their harmful effects on human health have led to concerns among researchers, scientists, academic communities, and policymakers. The present work examines the impact of different consumption value factors on sustainable consumption behavior concerning consumer choice in Pakistan and China. A cross-sectional study is conducted, and data are collected through a primary source questionnaire. A sample of 431 respondents is chosen from different cities in Pakistan, and a sample of 342 respondents is selected from China. Estimation techniques like descriptive statistics, frequency distribution, multicollinearity, R square, independent sample t-test, the coefficient of correlation, and regression analysis are used for the data analysis. The comparative results show that knowledge values (KVs) and emotional values (EMVs) significantly influence the choice behavior of respondents toward environmentally friendly products both in Pakistan and China. In contrast, social values (SVs) and conditional values (CVs) show insignificant influence. Furthermore, functional values (FVs) are significant in Pakistan while insignificant in the context of China, and environmental values (EVs) are significant in China although insignificant in Pakistan with regard to sustainable consumption behavior.