Agricultural green production (AGP) and efficiency improvement of smallholders’ management are the objective requirements for the development of China’s modernized agriculture and the understanding of the rural vitalization strategy. Based on field survey data of 582 rice farmers in Shaanxi Province, this study used the Logit model to analyze the determinants of smallholders’ adoption of agricultural green production technology (AGPT) and used the propensity score matching (PSM) method to measure the effect of AGPT on the technical efficiency (TE) of rice production and the heterogeneity of this influence among smallholders. Results showed that the AGPT adoption rate and the mean of rice production TE were 15.1% and 0.312, both had a lot of room for improvement. Furthermore, it was found that household’s characteristics, family characteristics, agricultural management characteristics, social characteristics, and cognitive characteristics significantly affect smallholders’ AGPT adoption, and the AGPT adoption significantly increased the TE of rice production by 18.8 to 24.5%. Besides, farmers with older age, less education, more specialized planting, more fragmented land, and more off-farm employment adopting AGPT could significantly improve the TE; the increase proportion was 29.8%, 29.5%, 21.3%, 27.2%, and 16.8%. The study also showed that the AGPT could not significantly increase the rice output value of smallholders. In addition, considering the endogeneity problem caused by sample selection bias, the study re-estimated using the endogenous transformation regression (ESR) model which showed that the promotion of AGPT to TE was still robust. The study puts forward policy recommendations on how to further promote the adoption of AGPT and improve the TE by farmers.

It is important to evaluate the impact of undesirable energy output on the climate-carrying capacity of the power grid-based economy to promote the green development. Three indicators—climate natural capacity, urban climate pressure, and urban coordinated development capacity—are used as input factors to study the climate-carrying capacity. The Nemerow index method and comprehensive evaluation method based on entropy weight are employed to calculate inputs. Pollution emissions such as carbon dioxide emissions, waste gas, wastewater, and solid waste pollution are included as energy undesirable outputs, and industry output value is included as a desirable output to calculate the non-radial directional distance of the output of climate-carrying capacity that combines desirable and undesirable outputs. The total factor non-radial directional distance function and energy-environmental non-radial directional distance function are used to obtain the efficiency index of total factor climate-carrying capacity and the efficiency of climate-carrying capacity performance, respectively. These two indices are included in the analysis to estimate the impact of energy undesirable output on climate-carrying capacity. Results from empirical analysis showed that when two types of undesirable outputs, namely waste gas and wastewater outputs, in Shanghai are constrained, the efficiency and performance efficiency of climate environmental carrying capacity are both lower than 0.8, indicating that undesirable outputs had a substantial influence on the climate-carrying capacity. In Shanghai, the major approach to improve the regional climate-carrying capacity is to improve energy efficiency and reduce undesirable outputs of power grid-based economy.

The new coronavirus SARS-CoV-2 has infected more than 14 million people worldwide so far. Brazil is currently the second leading country in number of cases of COVID-19, while São Paulo state accounts for 20% of total confirmed cases in Brazil. The aim of this study was to assess environmental and social factors influencing the spread of SARS-CoV-2 in the expanded metropolitan area of São Paulo, Brazil. Firstly, a spatial analysis was conducted to provide insights into the spread of COVID-19 within the expanded metropolitan area. Moreover, Spearman correlation test and sensitivity analysis were performed to assess social indicators and environmental conditions which possibly influence the incidence of COVID-19. Our results reveal that the spread of COVID-19 from the capital city São Paulo—its epicenter in Brazil—is directly associated with the availability of highways within the expanded metropolitan area of São Paulo. As for social aspects, COVID-19 infection rate was found to be both positively correlated with population density, and negatively correlated with social isolation rate, hence indicating that social distancing has been effective in reducing the COVID-19 transmission. Finally, COVID-19 infection rate was found to be inversely correlated with both temperature and UV radiation. Together with recent literature our study suggests that the UV radiation provided by sunlight might contribute to depletion of SARS-CoV-2 infectivity.

This study investigated the impact of environmental engineering on existing venues, venues and operations management. First, the literature analysis and field survey method are used to summarize the current situation of the venue. Then, 420 questionnaires are collected for statistical verification analysis, combined with the interview method to understand the deepest feelings of the people, and finally discussed with a multi-inspection method. The researcher believes that although environmental construction can improve infrastructure and human quality of life, which, after all, are experiment after completion. If, before construction, a good communication channel; obtaining consensus from the people and businesses; acquiring precise information; spraying water to reduce dust; increasing the height of the peripheral fence; planning a substitutive plan; avoiding crowds; reducing mistakes in the process; avoiding influencing the water and electricity supply and soil, water, and environment sanitation; and planning for a substitutive route and parking space with subsidy for damages are not possible, a negative image will be produced, willingness to spend will be reduced in the people due to the blocking of the view by the building (75%), the billboard will lose its functionality (63.2%), and inconvenience will be caused by the line of movement (75%) and parking space (55.9%), which are not helpful for development.

Bioreduction of selenium oxyanions to elemental selenium is ubiquitous; elucidating the properties of this biogenic elemental selenium (BioSe) is thus important to understand its environmental fate. In this study, the magnetic properties of biogenic elemental selenium nanospheres (BioSe-Nanospheres) and nanorods (BioSe-Nanorods) obtained via the reduction of selenium(IV) using anaerobic granular sludge taken from an upflow anaerobic sludge blanket (UASB) reactor treating paper and pulp wastewater were investigated. The study indicated that the BioSe nanomaterials have a strong paramagnetic contribution with some ferromagnetic component due to the incorporation of Fe(III) (high-spin and low-spin species) as indicated by electron paramagnetic resonance (EPR). The paramagnetism did not saturate up to 50,000 Oe at 5 K, and the hysteresis curve showed the coercivity of 100 Oe and magnetic moment saturation around 10 emu. X-ray photoelectron spectroscopy (XPS) and EPR evidenced the presence of Fe(III) in the nanomaterial. Signals for Fe(II) were observed neither in EPR nor in XPS ruling out its presence in the BioSe nanoparticles. Fe(III) being abundantly present in the sludge likely got entrapped in the extracellular polymeric substances (EPS) coating the biogenic nanomaterials. The presence of Fe(III) in BioSe nanomaterial increases the mobility of Fe(III) and may have an effect on phytoplankton growth in the environment. Furthermore, as supported by the literature, there is a potential to exploit the magnetic properties of BioSe nanomaterials in drug delivery systems as well as in space refrigeration.

Biotic invasions can predominantly alter the dynamics, composition, functions, and structure of natural ecosystems. Social insects, particularly ants, are among the most damaging invasive alien species. Invasive ant species are among the supreme threats to ecosystems. There are about 23 species of invasive ants recorded worldwide, according to the ant invasive databases. The ecological impacts of invasive ants comprise predation, hybridization, and competition with native species that changes the ecosystem processes with the biodiversity loss and upsurge of pests. The effects of invasion on native fauna in the same habitats might be catastrophic for the native community through various ecological mechanisms, e.g., habitat disturbance, resource competition, limiting the foraging activity of native species, and various other indirect mechanisms of invasive species. Invasive species may have harmful impacts on habitats and devastating effects on natural flora and fauna, and stopping these new species from being introduced is the most effective way to deter future invasions and maintain biodiversity. This paper reviews the literature to evaluate the effects of invasive ant species on the native species, including vertebrates, invertebrates, and plants sharing the same habitats as the non-native species under global environmental changes. We also highlighted the various management strategies that could be adopted in minimizing the adverse effects of these invasive ant species on the natural ecosystem. To this end, strategies that could regulate the mode and rate of invasion by these alien ant species are the most effective ways to deter future invasions and maintain biodiversity.

Globally, climate change is an alarming threat to the livestock industry. Such changes in the climate can also adversely affect the returns of livestock farmers in Pakistan. Improvement in the production process could decrease the risk of losses. This study analyzes the efficacy of silage to abate the losses in livestock profitability resulting from the climate change. The study employed cross-sectional survey data of 492 livestock farmers collected from six districts of Punjab Province, Pakistan. The data are analyzed with endogenous switching regression, considering the possibility of selection bias and endogeneity in adopting silage as a climate change adaptation measure. The study findings show a significant difference in material well-being between silage users and conventional feed users. Furthermore, training programs such as silage training and livestock development programs could be meaningful provisions to abate climate change and improve food security. It is suggested that training and development programs should be incorporated in policy plans to improve the well-being of farmers in terms of their farm revenues.

Conflicts are socio-political pressures that alter wellbeing, social structure, and economic sustenance. However, very limited studies have assessed the long-term impact of conflicts on environmental sustainability. This study investigates the role of internal and external conflicts on ecological footprint in the Middle East and North African countries (MENA) over the period 1995–2016. Here, we test whether the environmental Kuznets curve (EKC) hypothesis is valid for MENA countries during the period of internal and external conflicts—characterized by energy disasters and deteriorating income levels. Using robust econometric tools based on 12 MENA countries, the results show that income growth has negative impact with evidence of inherent heterogeneity across quantile distribution of ecological footprint. However, the positive impact of the square term of income decreases ecological footprint, thus, confirming U-shaped relationship between income and environmental indicator across MENA countries. The results further show that excessive energy consumption is attributed to a rising level of urbanization, while increase in conflicts stimulates environmental degradation. These findings are essential for effective conflict resolution and environmental policies across conflict-prone countries.

Rotavirus A is the most common cause of infectious diarrhea worldwide. This study aimed to retrospectively study and analyze 4009 stool samples that were tested for viruses causing diarrhea, using multiplex reverse transcription PCR at Dankook University Hospital between 2010 and 2019. Furthermore, we determined the correlation between these factors and various climatic factors, including wind-chill temperature, relative humidity, rate of sunshine, and particulate matter. Rotavirus A infections occurred frequently in February, March, and April on an annual basis. Furthermore, during the study, the detection rate was highest at 17.0% (n=61/359) in 2011. Based on an analysis of weather big data, patient age, and period-specific infection during the summer, when the wind-chill temperature and relative humidity were high, the Rotavirus A infection rate was very low. Relative humidity (p=0.020) and particulate matter (p=0.049) were associated with the average number of monthly cases of Rotavirus A infection. However, wind chill temperature (p=0.074) and rate of sunshine (p=0.993) were not associated with the average monthly distribution of Rotavirus A cases. These results indicate that Rotavirus A infection was correlated with relative humidity and particulate matter during the study period and further the current understanding of the distribution of Rotavirus A infections resulting from climatic factors and particulate matter. This could help establish climate-related health policies to reduce the incidence of diarrhea and guide the development of vaccines against Rotavirus A.

The spatially varying relationship between land surface temperature (LST) and land-use factors at a large scale has been widely studied by geographically weighted regression (GWR) models. However, the directionally varying relationship caused by wind directions has not yet been considered. In this study, the wind directions in the summer and the winter of Wuhan in 2017 were extracted to build a geographically-directionally weighted regression (GDWR) to identify the spatially and directionally varying relationships between them. The results indicated that both the R² and the significance have been improved by the GDWR model in the summer and the winter. Specially, the GDWR performed best in the winter of 2017, increasing R² from 0.0688 to 0.6635 provided by ordinary least squares (OLS)-based multiple linear regression (MLR) and GWR, to 0.7839 by the GDWR, with P-value lower than 0.05 all across the study area. Furthermore, the residual has been dramatically reduced in the north and southeast part of Wuhan by GDWR in the winter. It’s probably due to the fact that in the winter, wind was flowed from south to north. But the GDWR did not reduce the residual in central Wuhan. It suggests that the wind would cause an obviously directionally varying relationship in the suburbs; while it would not make a significant impact on the relationship between LST and its driving factors in the central city where complex land uses existed.