Multidimensional analysis of global climate change: a review

Elucidating responses of the intertidal clam Ruditapes philippinarum to compound extreme oceanic events

Ocean acidification and heatwaves caused by rising CO2 affect bivalves and other coastal organisms. Intertidal bivalves are vital to benthic ecosystems, but their physiological and metabolic responses to compound catastrophic climate events are unknown. Here, we examined Manila clam (Ruditapes philippinarum) responses to low pH and heatwaves. Biochemical and gene expression demonstrated that pH and heatwaves greatly affect physiological energy enzymes and genes expression. In the presence of heatwaves, Manila clams expressed more enzymes and genes involved in physiological energetics regardless of acidity, even more so than in the presence of both. In this study, calcifying organisms' biochemical and molecular reactions are more susceptible to temperature rises than acidity. Acclimation under harsh weather conditions was consistent with thermal stress increase at lower biological organization levels. These substantial temporal biochemical and molecular patterns illuminate clam tipping points. This study helps us understand how compound extreme weather and climate events affect coastal bivalves for future conservation efforts.

Structural characteristics and drivers of greenhouse gas emissions at county-level and long-time scales: A case study of the Anji County, China

To achieve carbon neutrality, the Chinese government needs to gain a comprehensive understanding of the sources and drivers of greenhouse gas (GHG) emissions, particularly at the county level. Anji County in eastern China is a typical example of an industrial transformation from quarrying to a low-carbon economy. This study analyzed the decoupling types and structural characteristics of GHG emissions and the driving factors of carbon dioxide (CO2) emissions in the Anji from 2006 to 2019, and explored the differences between county-level and provincial-level or city-level results. It was observed that energy-related activities are the main source of GHG emissions in Anji and that economic development is the driving factor behind the increasing CO2 emissions. However, industrial transformation and upgradation coupled with the alternative use of clean energy limit the growth of GHG emissions. This study details the GHG emissions of county during the industrial transformation stage and provides corresponding policy recommendations for county governments.

The interplay between climate change and ageing: A systematic review of health indicators

Climate change and rapid population ageing pose challenges for communities and public policies. This systematic review aims to gather data from studies that present health indicators establishing the connection between climate change and the physical and mental health of the older population (≥ 65 years), who experience a heightened vulnerability to the impacts of climate change when compared to other age cohorts. This review was conducted according to the PICO strategy and following Cochrane and PRISMA guidelines. Three databases (PubMed, Scopus and Greenfile) were searched for articles from 2015 to 2022. After applying inclusion and exclusion criteria,nineteen studies were included. The findings indicated that various climate change phenomena are associated with an elevated risk of mortality and morbidity outcomes in older adults. These included cardiovascular, respiratory, renal, and mental diseases, along with physical injuries. Notably, the impact of climate change was influenced by gender, socioeconomic status, education level, and age-vulnerability factors. Climate change directly affected the health of older adults through ambient temperature variability, extreme and abnormal temperatures, strong winds, sea temperature variability, extreme El Niño-southern Oscillation (ENSO) conditions and droughts, and indirectly by air pollution resulting from wildfires. This review presents further evidence confirming that climate change significantly impacts the health and well-being of older adults. It highlights the urgency for implementing effective strategies to facilitate adaptation and mitigation, enhancing the overall quality of life for all individuals.

What influences the performance of carbon emissions in China?-Research on the inter-provincial carbon emissions' conditional configuration impacts

The severe global warming issue currently threatens humans' existence and development. Countries and international organizations have effectively implemented policies to reduce carbon emissions and investigate low-carbon growth strategies. Reducing carbon emissions is a hot topic that academics and government policy-making departments are concerned about.Through necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis(fsQCA), this paper investigates local governments' configuration linkage effect and path choice to improve carbon emission performance from six dimensions: energy consumption, industrial structure, technological innovation, government support, economic development, and demographic factors. The research findings include the following: (1) Individual condition does not represent necessary conditions for the government's carbon performance. Among the two sets of second-order equivalence configurations(S and Q) (five high-level carbon performance configurations), those dominated by economic development or low energy consumption can produce high-level carbon performance. Therefore, the six antecedent conditions dimensions work together to explain how the government can create high levels of carbon performance. (2)According to the regional comparison, China's eastern, central, and western regions exhibit similarities and differences in the driving forces behind high carbon emission performance. All three regions can demonstrate carbon emission performance when all the factors are combined. However, when constrained by the conditions of each region's resource endowment, the eastern region emphasizes the advantage of economic and technological innovation, the central region favors government support and demographic factors, and the western region prefers upgrading industrial structure based on a specific level of economic development.

Spatiotemporal climate variability and trends in the Upper Gelana Watershed, northeastern highlands of Ethiopia

The aim of this study was to evaluate the performance of CHIRPS and TAMSAT satellite rainfall data over the Upper Gelana watershed, where gauged meteorological data to understand the nature of the climate are scarce. In addition, variability and trends in rainfall and temperature were examined from 1983 to 2021. To evaluate satellite rainfall, categorical and continuous validation statistics were used. Trends were analyzed using Mann-Kendall, Sen's Slope estimator, and innovative trend analysis (ITA) methods. The study also utilized time-series geostatistical analysis techniques. The validation statistics show that TAMSAT performs better on the daily timescale, while the two products have comparable performance on the monthly timescale. TAMSAT was chosen for rainfall analysis because of its higher resolution and performance. The results reveal high inter-annual spatiotemporal variability and strong irregularities in monthly rainfall. The Mann-Kendall test indicates statistically significant positive trends in kiremt and annual rainfall, but belg rainfall exhibits an insignificant negative trend. In the kiremt season, we found a 96.1, 101.6, and 104.8 mm decadal rate of rainfall increment in the lower weina dega (LWD), upper weina dega (UWD), and dega agroecological zones, respectively. In contrast, belg season rainfall declined by 16.4, 16.2, and 14.0 mm per decade in the LWD, UWD, and dega agroecology zones, respectively. The pixel-wise trend analysis also revealed trends and magnitudes of monthly, seasonal, and annual rainfall that vary across the study area. In both LWD and UWD annual minimum and maximum temperatures, respectively, showed significant decreasing and increasing trends, but in dega agroecology the trends were insignificant. The findings of rainfall and temperature trends using the ITA method demonstrated its ability to discover some hidden trends that were not detected by the MK test.

Soil salinization and chemically mediated plant-insect interactions in a changing climate

Increase in soil salinization due to climate change is a global phenomenon that can induce significant changes in plant growth, physiology, and chemistry, exacerbating growing threats to insect biodiversity. Insects that rely on plants are likely to be indirectly impacted by changes in soil salt content through changes in plant chemistry, yet few studies link changes in plant metabolism to impacts on higher trophic levels. Some salinity-mediated changes in specialized metabolites may be predictable due to highly conserved metabolic pathways shared between herbivore defense and stress resistance, but recent studies also suggest substantial variation across plant species and habitats. To date, most of the research on salinity and chemically mediated plant-insect interactions has focused on herbivores, particularly in agricultural systems. Published effects of salinity on pollinators and parasitoids are scarce. Future research will need to focus more on the role of plant chemistry to bridge the divide between studies of plant and insect responses to salinization.

A comparative study of statistical and machine learning models on carbon dioxide emissions prediction of China

The escalating levels of carbon dioxide (CO2) emissions represent the primary driver of global warming, and addressing them is of paramount importance. Timely and accurate prediction, as well as effective control of CO2 emissions, are pivotal for guiding mitigation measures. This paper aims to select the best prediction model for near-real-time daily CO2 emissions in China. The prediction models are based on univariate daily time-series data spanning January 1st, 2020, to September 30st, 2022. Six models are proposed, including three statistical models: grey prediction (GM(1,1)), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average with exogenous factors (SARIMAX), and three machine learning models: artificial neural network (ANN), random forest (RF), and long short-term memory (LSTM). The performance of these six models is evaluated using five criteria: mean squared error (MSE), root-mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). Our findings reveal that the three machine learning models consistently outperform the three statistical models across all five criteria. Among them, the LSTM model demonstrates exceptional performance for daily CO2 emission prediction, boasting an impressively low MSE value of 3.5179e-04, an RMSE value of 0.0187, an MAE value of 0.0140, an MAPE value of 14.8291%, and a high R2 value of 0.9844. This underscores the robustness of the LSTM model in capturing and predicting complex emission patterns, positioning it as the most suitable option for near-real-time daily CO2 emission prediction based on the provided daily time series data. Moreover, our study's results provide valuable insights into emissions forecasting, enabling data-driven decision-making for policymakers and stakeholders. The accurate and timely predictions offered by the LSTM model can aid in the formulation of effective strategies to mitigate carbon emissions, contributing to a more sustainable future. Furthermore, the findings of this study can enhance our understanding of the dynamics of CO2 emissions, leading to more informed environmental policies and actions aimed at reducing carbon emissions.

Interactions of urbanisation, climate variability, and infectious disease dynamics: insights from the Coimbatore district of Tamil Nadu

Climate change and shifts in land use/land cover (LULC) are critical factors affecting the environmental, societal, and health landscapes, notably influencing the spread of infectious diseases. This study delves into the intricate relationships between climate change, LULC alterations, and the prevalence of vector-borne and waterborne diseases in Coimbatore district, Tamil Nadu, India, between 1985 and 2015. The research utilised Landsat-4, Landsat-5, and Landsat-8 data to generate LULC maps, applying the maximum likelihood algorithm to highlight significant transitions over the years. This study revealed that built-up areas have increased by 67%, primarily at the expense of agricultural land, which was reduced by 51%. Temperature and rainfall data were obtained from APHRODITE Water Resources, and with a statistical analysis of the time series data revealed an annual average temperature increase of 1.8 °C and a minor but statistically significant rainfall increase during the study period. Disease data was obtained from multiple national health programmes, revealing an increasing trend in dengue and diarrhoeal diseases over the study period. In particular, dengue cases surged, correlating strongly with the increase in built-up areas and temperature. This research is instrumental for policy decisions in public health, urban planning, and climate change mitigation. Amidst limited research on the interconnections among infectious diseases, climate change, and LULC changes in India, our study serves as a significant precursor for future management strategies in Coimbatore and analogous regions.

Aspergillus nomiae and fumigatus Ameliorating the Hypoxic Stress Induced by Waterlogging through Ethylene Metabolism in Zea mays L

Transient and prolonged waterlogging stress (WS) stimulates ethylene (ET) generation in plants, but their reprogramming is critical in determining the plants' fate under WS, which can be combated by the application of symbiotically associated beneficial microbes that induce resistance to WS. The present research was rationalized to explore the potential of the newly isolated 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase-producing fungal endophytic consortium of Aspergillus nomiae (MA1) and Aspergillus fumigatus (MA4) on maize growth promotion under WS. MA1 and MA4 were isolated from the seeds of Moringa oleifera L., which ably produced a sufficient amount of IAA, proline, phenols, and flavonoids. MA1 and MA4 proficiently colonized the root zone of maize (Zea mays L.). The symbiotic association of MA1 and MA4 promoted the growth response of maize compared with the non-inoculated plants under WS stress. Moreover, MA1- and MA4-inoculated maize plants enhanced the production of total soluble protein, sugar, lipids, phenolics, and flavonoids, with a reduction in proline content and H2O2 production. MA1- and MA4-inoculated maize plants showed an increase in the DPPH activity and antioxidant enzyme activities of CAT and POD, along with an increased level of hormonal content (GA3 and IAA) and decreased ABA and ACC contents. Optimal stomatal activity in leaf tissue and adventitious root formation at the root/stem junction was increased in MA1- and MA4-inoculated maize plants, with reduced lysigenous aerenchyma formation, ratio of cortex-to-stele, water-filled cells, and cell gaps within roots; increased tight and round cells; and intact cortical cells without damage. MA1 and MA4 induced a reduction in deformed mesophyll cells, and deteriorated epidermal and vascular bundle cells, as well as swollen metaxylem, phloem, pith, and cortical area, in maize plants under WS compared with control. Moreover, the transcript abundance of ethylene-responsive gene ZmEREB180, responsible for the induction of the WS tolerance in maize, showed optimally reduced expression sufficient for induction in WS tolerance, in MA1- and MA4-inoculated maize plants under WS compared with the non-inoculated control. The existing research supported the use of MA1 and MA4 isolates for establishing the bipartite mutualistic symbiosis in maize to assuage the adverse effects of WS by optimizing ethylene production.

Environmental influences on childhood asthma: Climate change

Climate change is a key environmental factor for allergic respiratory diseases, especially in childhood. This review describes the influences of climate change on childhood asthma considering the factors acting directly, indirectly and with their amplifying interactions. Recent findings on the direct effects of temperature and weather changes, as well as the influences of climate change on air pollution, allergens, biocontaminants and their interplays, are discussed herein. The review also focusses on the impact of climate change on biodiversity loss and on migration status as a model to study environmental effects on childhood asthma onset and progression. Adaptation and mitigation strategies are urgently needed to prevent further respiratory diseases and human health damage in general, especially in younger and future generations.

How to achieve carbon neutrality while maintaining economic vitality: An exploration from the perspective of technological innovation and trade openness

The significant drop in global carbon emissions in 2020 was credited to the enormous loss of economic activity from the impact of COVID-19. The challenge is now to reduce carbon emissions without causing massive disruption and damage to economic production. To achieve carbon neutrality while maintaining economic vitality, the impact of technological innovation and trade openness must be considered. This paper sets technological innovation and trade openness as core variables and establishes two extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) models. The first model focuses on carbon emissions and the second focuses on economic growth. Comparisons were made between the BRICS (i.e., Brazil, Russia, India, China, and South Africa) and G7 (i.e., Canada, France, Germany, Italy, Japan, the UK, and the USA) countries. The fully modified ordinary least squares (FMOLS) regression analysis was used to explore the impact of technological innovation and trade openness on low-carbon economic development. A Panel Granger Causality Test explores the causal relationship between the core, control and dependent variables. The results illustrate that: (1) technological innovation is the primary factor that inhibits carbon emissions and promotes economic growth in both international organizations, (2) trade openness promotes the growth of carbon emissions in BRICS countries, but restrains G7 growth, confirming the "Pollution Haven Hypothesis", (3) per capita GDP is the largest contributor to carbon emissions growth in both the G7 and BRICS countries, which illustrates that per capita GDP is the largest contributor to carbon emissions. It is proportional to G7 and BRICS carbon emissions. This paper provides several policy recommendations: breaking through basic research, adjusting the science and technology evaluation system, optimizing the export trade structure, and increasing the proportion of renewable energy.

Spatio-temporal variations, spatial spillover, and driving factors of carbon emission efficiency in RCEP members under the background of carbon neutrality

Improving carbon emission efficiency (CEE) has emerged as a critical way for Regional Comprehensive Economic Partnership (RCEP) members to promote carbon reduction in the context of climate change mitigation and carbon neutrality. The super-efficiency slacks-based measure (SBM) model, which considers non-desired outputs, is adopted to comprehensively assess the current state and trend of CEE in 15 RCEP countries from a spatio-temporal dynamic perspective, and the global Malmquist-Luenberger (GML) index is coupled to quantify the spatial and temporal differences and dynamic changes. Following that, taking into account the spatial characteristics of CEE, the extended STIRPAT model and the spatial Durbin model are combined to further investigate the primary influencing factors of CEE. It is found that (1) the CEE of RCEP members is generally poor and unevenly distributed in temporal and spatial dimensions, with significant room for improvement and an overall positive spatial autocorrelation; (2) CEE varies considerably among RCEP members, with developed countries far outstripping developing countries in terms of both the current status and trend of CEE; (3) on a dynamic level, the GML index exhibits W-shaped fluctuations, with technological progress acting as the dominant force; and (4) in terms of spillover effects, affluence and economic agglomeration inhibit CEE enhancement, whereas technology level and investment capacity facilitate it. The findings will be useful in developing carbon-neutral plans for various countries as well as coordinated sustainable development for RCEP regions.

Insights into accumulation of active ingredients and rhizosphere microorganisms between Salvia miltiorrhiza and S. castanea

Salvia miltiorrhiza is an important traditional herbal medicine, and its extracts could be used for treating cardiovascular disease. Although these medicinal compounds are functionally similar, their wild relative, S. castanea, produces significantly different concentrations of these compounds. The reason for their differences is still unknown. In a series of soil and plant-based analyses, we explored and compared the rhizosphere microbiome of S. miltiorrhiza and S. castanea. To further investigate the geographical distribution of S. castanea, MaxEnt models were used to predict the future suitable habitat areas of S. castanea in China. Results revealed the distributions and structure of the rhizosphere microbial community of S. miltiorrhiza and S. castanea at different times. In addition, differences in altitude and soil moisture resulting from changes in climate and geographical location are also critical environmental factors in the distribution of S. castanea. The findings of this study increase our understanding of plant adaptation to their geographical environment through secondary metabolites. It also highlights the complex interplay between rhizospheric factors and plant metabolism, which provides the theoretical basis for the cultivation of S. miltiorrhiza and the use of S. castanea resources.

Review: Climate change resilience disconnect in rural communities in coastal Kenya. A rhetoric communication discord proliferated by COVID-19 pandemic

The world has been hit by consequential pandemics in the past two millennia. The COVID-19 pandemic has taken center stage, paralyzing vulnerable communities in the global south impacted by unprecedented climate vagaries. The focus of this study is COVID-19 and climate resilience communication rhetoric. In this context, we embed this study in response to the resilience of rural livelihoods to the COVID-19 crisis and climate resilience education communication rhetoric. We posit our review based on the following questions: Has COVID-19 worsened the climate resilience pathway for rural communities in coastal Kenya? Is the COVID-19 pandemic a proxy for climate resilience in rural livelihoods? How does COVID-19 communication rhetoric undermine climate resilience for vulnerable coastal communities in Kenya? Through a resilient theoretical paradigm, we enclose our view based on the existing literature along with climate resilience and COVID-19 proliferation. In light of the current state of COVID-19, the focus has shifted to the pandemic that will cover climate resilience. From the review, climate resilience pathway has been impacted by corona virus with noted funding response variations, in addition, even with the corona virus pandemic, climate resilience communication should be on-going rather than sporadic. Increasing the discursive process about climate change challenges is critical among Kenyan coastal communities. We recommend inclusion of climate resilience communication in existing policy frameworks as a salient solution to notable information discourse bottlenecks.

Influencing factors of carbon emissions and their trends in China and India: a machine learning method

China and India are the largest coal consumers and the most populated countries in the world. With industrial and population growth, the need for energy has increased, which has inevitably led to an increase in carbon dioxide (CO2) emissions because both countries depend on fossil fuel consumption. This paper investigates the impact of energy consumption, financial development (FD), gross domestic product (GDP), population, and renewable energy on CO2 emissions. The study applies the long short-term memory (LSTM) method, a novel machine learning (ML) approach, to examine which influencing driver has the greatest and smallest impact on CO2 emissions; correspondingly, this study builds a model for CO2 emission reduction. Data collected between 1990 and 2014 were analyzed, and the results indicated that energy consumption had the greatest effect and renewable energy had the smallest impact on CO2 emissions in both countries. Subsequently, we increased the renewable energy coefficient by one and decreased the energy consumption coefficient by one while keeping all other factors constant, and the results predicted with the LSTM model confirmed the significant reduction in CO2 emissions. Finally, this study forecasted a CO2 emission trend, with a slowdown predicted in China by 2022; however, CO2 emission's reduction is not possible in India until 2023. These results suggest that shifting from nonrenewable to renewable sources and lowering coal consumption can reduce CO2 emissions without harming economic development.

Regional Differences, Distribution Dynamics, and Convergence of Air Quality in Urban Agglomerations in China

The urban agglomeration (UA), with a high concentration of population and economy, represents an area with grievous air pollution. It is vital to examine the regional differences, distribution dynamics, and air quality convergence in UAs for sustainable development. In this study, we measured the air quality of ten UAs in China through the Air Quality Index (AQI). We analyzed regional differences, distribution dynamics, and convergence using Dagum’s decomposition of the Gini coefficient, kernel density estimation, and the convergence model. We found that: the AQI of China’s UAs shows a downward trend, and the index is higher in northern UAs than in southern UAs; the differences in air quality within UAs are not significant, but there is a gap between them; the overall difference in air quality tends to decrease, and regional differences in air quality are the primary contributor to the overall difference; the overall distribution and the distribution of each UA move rightward; the distribution pattern, ductility, and polarization characteristics are different, indicating that the air quality has improved and is differentiated between UAs; except for the Guanzhong Plain, the overall UA and each UA have obvious σ convergence characteristics, and each UA presents prominent absolute β convergence, conditional β convergence, and club convergence.

Water Deficit May Cause Vegetation Browning in Central Asia

There is consistent evidence of vegetation greening in Central Asia over the past four decades. However, in the early 1990s, the greening temporarily stagnated and even for a time reversed. In this study, we evaluate changes in the normalized difference vegetation index (NDVI) based on the long-term satellite-derived remote sensing data systems of the Global Inventory Modelling and Mapping Studies (GIMMS) NDVI from 1981 to 2013 and MODIS NDVI from 2000 to 2020 to determine whether the vegetation in Central Asia has browned. Our findings indicate that the seasonal sequence of NDVI is summer > spring > autumn > winter, and the spatial distribution pattern is a semicircular distribution, with the Aral Sea Basin as its core and an upward tendency from inside to outside. Around the mid-1990s, the region’s vegetation experienced two climatic environments with opposing trends (cold and wet; dry and hot). Prior to 1994, NDVI increased substantially throughout the growth phase (April–October), but this trend reversed after 1994, when vegetation began to brown. Our findings suggest that changes in vegetation NDVI are linked to climate change induced by increased CO2. The state of water deficit caused by temperature changes is a major cause of the browning turning point across the study area. At the same time, changes in vegetation NDVI were consistent with changes in drought degree (PDSI). This research is relevant for monitoring vegetation NDVI and carbon neutralization in Central Asian ecosystems.

Assessing the impact of energy internet and energy misallocation on carbon emissions: new insights from China

With the deterioration of environmental quality caused by fossil energy use, the research on energy internet and energy misallocation is of critical relevance to achieve low-carbon sustainable development. However, we find that the relevant research that analyzes energy internet and energy misallocation on carbon emissions under the same framework is ignored. For this purpose, the generalized method of moments (GMM), panel threshold model, and spatial analysis (deviation ellipse, hotspot analysis, and geographically and temporally weighted regression (GTWR)) model were applied to investigate the impact of energy internet and energy misallocation on carbon emissions using panel data of 30 provinces in China from 2004 to 2018. The major statistical results include the following: (1) energy misallocation significantly contributes to carbon emissions, while energy internet inhibits carbon emissions. Energy internet can negatively moderate the positive effect of energy misallocation on carbon emissions. (2) The effect of energy misallocation on carbon emissions reveals an inverted "U-shaped" characteristic of first promoting and later inhibiting, but the inhibiting effect is insignificant. Moreover, the marginal effect of energy misallocation on carbon emissions decreases when the energy internet crosses the second thresholds consecutively, while the marginal effect of the energy internet on carbon emissions shows an inverted "N" shape. (3) Compared with the under-allocated regions, the promotion effect of energy misallocation on carbon emissions and the inhibitory effect of energy internet on carbon emissions are stronger in the over-allocated regions, while the energy internet has a more significant negative moderating effect on energy misallocation. (4) The gravity center of China's carbon emissions gradually shifts to the northwest with time. The longitude of the gravity center (east-west direction) changes greatly, while the latitude of the gravity center (north-south direction) changes less. Besides, the carbon emission hotspot regions centered on Shanxi spread to the neighboring provinces, which form a high-high agglomeration region, and the cold spot region dominated by Qinghai, Guangxi, and Guangdong forms low-low agglomeration characteristics. Finally, the GTWR model shows that the impact of energy internet and energy misallocation on carbon emissions shows significant hierarchical, banded, or block-like characteristics in spatial distribution.

US biopharmaceutical companies' stock market reaction to the COVID-19 pandemic. Understanding the concept of the 'paradoxical spiral' from a sustainability perspective

In an uncertain and finite world, actions towards the development of a green economy are attracting wider support. The damaging anthropogenic impact on earth systems is leading humanity to devastating situations, jeopardizing its very survival. A new world scenario has emerged with COVID-19, where the biopharmaceutical sector has arisen as a powerful effective solution for the health, economic and social crisis derived from the pandemic. This research aims to study the stock market reaction of the two US biopharmaceutical companies that first developed messenger RNA vaccines against COVID-19 (Pfizer and Moderna), considering two time periods, before and during COVID. In the analysis, the influence of the technological market index, market volatility, and investor sentiment are also considered. The results show an unequal influence of market volatility and market sentiment on the returns of both companies, as well as a different volatility behaviour. Furthermore, a contagion effect is observed during the COVID period between both companies and the technological market. The study's findings provide investors, organizations, policy-makers and society with useful information for the design of policies and strategies that ultimately are called to ensure sustainable growth for future generations.

Banana split: biomass splitting with flash light irradiation

Biomass splitting into gases and solids using flash light irradiation is introduced as an efficient photo-thermal process to photo-pyrolyze dried natural biomass powders to valuable syngas and conductive porous carbon (biochar). The photo-thermal reactions are carried out in a few milliseconds (14.5 ms) by using a high-power Xenon flash lamp. Here, dried banana peel is used as a model system and each kg of dried biomass generates ca. 100 L of hydrogen and 330 g of biochar. Carbon monoxide and some light hydrocarbons are also generated providing a further increase in the high heating value (HHV) with an energy balance output of 4.09 MJ per kg of dried biomass. Therefore, biomass photo-pyrolysis by flash light irradiation is proposed as a new approach not only to convert natural biomass wastes into energy, such as hydrogen, but also for carbon mitigation, which can be stored or used as biochar.

Biomass splitting into gases and solids using flash light irradiation is introduced as an efficient photo-thermal process to photo-pyrolyze dried natural biomass powders to valuable syngas and conductive porous carbon (biochar).

Responses of nitrogen efficiency and antioxidant system of summer maize to waterlogging stress under different tillage

Waterlogging was one of the main abiotic stresses affecting maize yield and growth in the North China Plain, while ridge tillage effectually improved soil environment, enhanced crop stress resistance to waterlogging, and increased grain yield of waterlogged maize. In order to explore the responses of nitrogen (N) efficiency and antioxidant system of summer maize to waterlogging stress under different tillage, a field experiment was conducted to explore N use efficiency, leaf activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), and malondialdehyde (MDA) content of waterlogged maize Denghai 605 (DH605) and Zhengdan 958 (ZD958) under different tillage system (ridge planting and flat planting). Our results showed that ridge tillage was beneficial to ameliorate waterlogging damages on antioxidant system by increasing SOD, POD, and CAT activities, and decreasing MDA content. Moreover, ridge tillage significantly increased N efficiency of waterlogged maize. N translocation amount (NTA), N translocation efficiency (NTE), N contribution proportion (NCP), N harvest index (NHI), and N use efficiency (NUE) of waterlogging treatment under ridge planting system (W-V3+R) for DH605 was increased by 108%, 69%, 60%, 8% and 16%, while ZD958 increased by 248%, 132%, 146%, 13% and 16%, respectively, compared to those of waterlogging treatment under flat planting system (W-V3). Ultimately, ridge tillage led to a significant yield improvement by 39% and 50% for DH605 and ZD958, respectively, compared to that of W-V3. In conclusion, ridge tillage was conducive to retard leaf aging, and enhance nitrogen efficiency, thereby resulting in a yield improvement of waterlogged summer maize.