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In the face of global warming, air pollution, and other difficulties, electric vehicles have become an industry strongly supported by various countries due to their good environmental protection characteristics. In the context of big data, people are exposed to more and more information, and the convenience brought by big data is also increasing. Based on this background, the development of green and intelligent vehicles is getting faster and faster. This paper is aimed at studying the application of machine learning algorithms in the development and consumption trends of green and intelligent vehicles in the context of big data. This paper proposes machine learning algorithms based on big data, as well as support vector machine algorithms and so on. Machine learning algorithms specialize in how computers simulate or implement human learning behaviors to acquire new knowledge or skills and to reorganize existing knowledge structures to continuously improve their performance. The test results of this paper show that, starting from 2014, China has begun to vigorously develop green and intelligent vehicles. In 2014, the production volume of green and intelligent vehicles in China was 3,675, and the sales volume was 2,790. The development of green and intelligent vehicles is not very good and has not been fully accepted by the public. However, since 2017, the production and sales of green and intelligent vehicles have been slowly increasing. By 2020, the production of green and intelligent vehicles will be 24,360 and the sales will be 24,090. It can be seen that with the development of time, green and intelligent vehicles are gradually being recognized. [ABSTRACT FROM AUTHOR]

As the unpredictable nature of the Earth's climate persists, the scholarly attention dedicated to climate research has undergone a notable transition, shifting its emphasis from the conventional notion of global warming to a greater disconcerting occurrence commonly referred to as "global boiling." The present article endeavors to elucidate the scientific evidence that posits a discernible alteration in climate patterns, specifically towards an exacerbation of extreme heat events. Furthermore, this study aims to delve into the various factors that are believed to be instrumental in precipitating this noteworthy phenomenon. Furthermore, we engage in a comprehensive examination of the potential ramifications on ecological systems, human communities, and the imperative necessity for proactive measures aimed at both mitigating and adapting to these challenges. This paper endeavors to elucidate the potential issues presented by the period of global boiling through a thorough examination of existing research and data. Furthermore, it seeks to underscore the significance of concerted efforts to effectively tackle this pressing matter. [ABSTRACT FROM AUTHOR]

Based on the importance of energy saving in terms of reducing the carbon impact and global warming problems, smart street lighting systems have been proposed in the past few years with different specifications. These systems include sensors for controlling the light intensity and connectivity for recording weather conditions and diagnosing lamp failure remotely. This paper discusses many published research studies regarding smart street lighting systems, providing a comparison between these systems which emphasizes the limitations of each one of them. Current and future trends are highlighted. [ABSTRACT FROM AUTHOR]

Transportation is the leading source of logistics, and people contribute the significant primary emissions for increasing global warming, so we should avoid such a situation and concentrate on zero emissions. The range of the vehicle is the major problem (range-anxiety) in electric vehicles. Nowadays, researchers focus on range extender optimization since range extenders significantly improve the range of the vehicle with an auxiliary power unit (APU), which can prove consumer satisfaction. However, range extenders can recover energy by proposing the various configurations and systems of extended-range electric vehicles (EREV). Many industries and researchers summarize these efforts to optimize and find the solution for range-extenders. This paper reviewed the most suitable technologies for energy recovery and state-of-the-art topological constructions. The analysis of the study primarily concentrated on optimizing the cost of the systems, fewer emissions, and more fuel economy. The research is based on the range-extenders evaluation and summary for electric vehicles to guide automakers and researchers to invent other state-of-the-art configurations and topologies to obtain optimized EV ranges, namely, enhanced functionality and fewer emissions. A survey on specific extend-range electric vehicles and classification of EREV technologies, the performance of APUs, different control strategies of energy management in EREVs, and various existing difficulties are discussed and further enhanced for the future researcher EREV forecast offered by advanced technologies. [ABSTRACT FROM AUTHOR]

Global warming has become the focus of attention of the international community, and the control of carbon dioxide emissions has become one of the necessary choices for the development strategies of countries around the world. Cities are places where carbon dioxide emissions are concentrated. The key to controlling carbon emissions is to control the carbon emissions of cities. My country is currently in the process of rapid urbanization. Quantitative studies of the carbon cycle at the city level will help to take stock of carbon dioxide emissions in cities. On the other hand, it is helpful to understand the status and role of the urban carbon cycle in the process of the regional carbon cycle. Through the analysis and prediction of the elements influencing the carbon cycle of smart cities, this paper first determines the factors affecting smart cities in the carbon cycle process as industrial carbon emission strength factors, industrial structure effects, economic development factors, and population elements. It is found that the major positive factors affecting the significant add of CO2 emissions in smart cities from 2010 to 2019 are economic development factors and demographic factors, including economic development factors GDP/per capita GDP. The per capita contribution to CO2 emissions is higher than the model established by adjusting the affecting elements of overall CO2 emissions, except that the proportion of economic development factors in total CO2 emissions from 2013 to 2015 was lower than the increase in total CO2 emissions. The comparison can better reflect the relation between CO2 emissions and influencing elements. The main determinants affecting CO2 emissions are the expansion of the financial condition, the increase in the average daily population, and the increase in construction work. The adaptation index is judged to be consistent, indicating that the model adjustment effect is good; finally, the green computing in the smart city predicts the carbon cycle process, and the actual value trend line and the predicted value trend line are not much different from the practical value, the forecast error is small, and the prediction results are credible. Global warming has become the focus of attention of the international community, and carbon emission control has become one of the necessary options in the development strategies of countries around the world. Cities are the places where carbon emissions are concentrated. The key to controlling carbon emissions is to control urban carbon emissions. At present, my country is in the process of rapid urbanization. Quantitative research on the carbon cycle at the city level will help to establish an inventory accounting of urban carbon emissions. On the other hand, it is convenient to deeply understand the status and role of the urban carbon cycle in the process of the regional carbon cycle. [ABSTRACT FROM AUTHOR]

Smart technology has potential in tracking the key challenges based on population based on the sustainable future. In today's cultures, a smart approach enables for the integration of information needed to address crucial concerns. The critical challenge is to limit the effects of global warming while maintaining a balanced economic growth. The impact of global warming mitigates the fundamental problem while ensuring the balance economic development. Intense research efforts should be directed toward balanced resource utilization, renewable energy system integration, efficient energy conversion technologies, effective process integration, effective techniques to enable a circular economy framework, and other issues that are important to the population. This paper finds the latest technology in the field of smart grid technologies which focused on the effective enhancement and efficient utilization of resource. The issues and challenges in using sustainable future are discussed and bring new ideas towards the sustainable future base on the application of smart technologies. [ABSTRACT FROM AUTHOR]

The growing world population is challenging the animal products supply system, particularly in developing countries, where demand for meat and milk in 2050 is estimated to increase to 109% and 116%, respectively, amidst deteriorating livestock feed sources. Globally, adequate production and availability of animal feed products to subsistence farmers has been declining due to factors such as global warming, growth in population, and low economic growth. This paper seeks to examine the existing scientific literature on the utilization of some unconventional feed resources, to abate the challenge of feed deficit and thus improve animal nutrition. The use of fruit waste and agricultural farm residues affords alternative, nutritive livestock dietary supplements; it has been proven they contain a spectrum of vital bioactive phytochemicals essential for sustainable growth and development of animals. The biochemical composition of the plant wastes and residues include carbohydrates, proteins, nucleic acids, and fatty acids, while the common bioactive phytochemicals are polyphenols, alkaloids, carotenoids, and flavonoids, along with tannins, terpenes, and saponins, which play vital roles in reducing disease-causing agents, such as bacteria, viruses, and free radical-associated diseases. The phytochemicals exhibit antioxidant, antimicrobial, antifungal, and anti-inflammatory, as well as anti-parasitic and antiprotozoal properties. However, it is important to guard against antinutritive and toxicity levels in animal feed products. The paper concludes that agroresidues'/wastes' nutritive and therapeutic potential could serve as alternative livestock feed resource, while also acting as additional job and income generator for communities. [ABSTRACT FROM AUTHOR]

Cold front is an important weather system that produces precipitation in East Asia. Under the background of global warming, extreme precipitation caused by cold fronts presents a significant increasing trend. Hence, it is very important to quantify the cold-front precipitation that may cause great damages. In this study, an objective identification method is proposed for cold-front precipitation, which can objectively identify the precipitation area affected by cold fronts. Then, the climatological characteristics and trends of cold-front precipitation over East Asia in the winter half years from 1989 to 2018 are investigated by using the ERA-5 reanalysis dataset. Based on the dataset of cold fronts and frontal zones, this method automatically distinguishes the precipitation area affected by cold fronts to quantitatively estimate cold-front precipitation. The results show that this identification method can well describe cold-front activities and associated precipitation during an extreme cold wave event that occurred in southern China in January 2016. In the past 30 years, cold fronts have significantly contributed to the precipitation in East Asia in winter half years. The areas with the maximum cold-front precipitation and maximum contribution rate of cold-front precipitation to total precipitation are located in the North Pacific storm track, cold-front precipitation exceeds 700 mm, and the contribution of cold-front precipitation to total precipitation exceeds 60%. In addition, the contribution rates of cold-front precipitation are also relatively large in the midlatitudes of East Asia, especially in North China and Northeast China, where cold-front precipitation accounts for 50%–60% of total precipitation. In East Asia, the total precipitation in autumn is greater than that in winter; however, cold-front precipitation and its contribution rate in winter are significantly more and larger than those in autumn. As the cold-frontal activity is more frequent and intense in winter, the rainfall in winter depends more on cold fronts. In the past 30 years, the trends of cold-front precipitation and total precipitation are consistent in most parts of East Asia, indicating that cold-front precipitation makes a great contribution to the trend of total precipitation in winter half years. [ABSTRACT FROM AUTHOR]

Global warming caused by carbon emissions has become increasingly prominent. As the world's second-largest economy, China is under enormous pressure to cut down its carbon dioxide emissions. It is urgent to seek effective methods to forecast carbon emissions and put forward the pointed and effective measures to reduce emissions. In this paper, we first use the software Eviews to make an analysis of randomness on data of carbon emissions in the four representative provinces and city, Beijing, Henan, Guangdong, and Zhejiang, in terms of their carbon emissions data from 1997 to 2017. Then, according to their distinct characteristics, the methods of stationary processing of the difference, moving average, and substituting strong impact points, respectively, are adopted to perform the data preprocessing. Then, model identification, parameter estimation, and model test are carried out to establish the model of ARIMA(p , d, q) for the prediction of the carbon emissions of the four regions, respectively. Finally, the model is used to forecast the data and analyze their tendency for their carbon emissions in the next three years. The results can provide guidance for decision-makers to set reasonable carbon emission reduction targets and take appropriate energy conservation and emission reduction measures. [ABSTRACT FROM AUTHOR]

This paper analyzed neighborhood residents’ cognition of and participation in low-carbon behaviors, basing on a questionnaire survey launched in a neighborhood in Wuhan, China. Results indicate that most respondents concerned the low-carbon impact on their daily lives and expected the government to make differences in low-carbon transition. Neighborhood residents’ participation in low-carbon behaviors was mainly reflected in three aspects: home energy conservation (HEC), efficient resource consumption (ERC), and recycling habits (RH), which were extracted from the five categories out of the 15 observed variables. Many interviewees had high level of participation in low-carbon behaviors that affect their economic interests. But these neighborhood residents rarely participated in public low-carbon behaviors such as planting trees or cooperative low-carbon behaviors. Therefore, these neighborhood residents’ participation in low-carbon behaviors was still on the initial stage. Specific proposals were put forward to promote urban low-carbonization further. [ABSTRACT FROM AUTHOR]

Due to the increasing awareness of global warming and environmental protection, many practitioners and researchers have paid much attention to the low-carbon supply chain management in recent years. Green supplier selection is one of the most critical activities in the low-carbon supply chain management, so it is important to establish the comprehensive criteria and develop a method for green supplier selection in low-carbon supply chain. The paper proposes a fuzz-grey multicriteria decision making approach to deal with these problems. First, the paper establishes 4 main criteria and 22 subcriteria for green supplier selection. Then, a method integrating fuzzy set theory and grey relational analysis is proposed. It uses the membership function of normal distribution to compare each supplier and uses grey relation analysis to calculate the weight of each criterion and improves fuzzy comprehensive evaluation. The proposed method can make the localization of individual green supplier more objectively and more accurately in the same trade. Finally, a case study in the steel industry is presented to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

With the continuous growth of global warming and traffic volume, rutting deformation is common on road surfaces. A new type of pavement mixture, a semiflexible mixture, was proposed to solve the easy deformation and low riding quality of traditional pavement mixtures. It can be divided into pouring type and mixing type according to the construction method. However, there is little research on mixed semiflexible mixtures (i.e., multiplex organic hydraulicity (MOH) mixtures) in the current literature, so the deformation of pavement under driving loads was studied based on the MOH mixture in this paper. Four types of pavement composed of the traditional mixture and MOH mixture (i.e., AC13-AC20, AC13-MOH, MOH-MOH, and MOH-AC20) were selected to achieve the purpose of the research. Based on these four types of pavement, the deformation of the pavement was studied by simulations and experiments. The results show that the deformation of the upper layer is almost the same for these four types of pavement and that the deformation of the middle layer changes substantially for these four types of pavement. In regard to the deformation, the relationship is MOH-MOH < AC13-MOH < MOH-AC20 < AC13-AC20. In addition, the contribution rate of the deformation of the middle layer is much greater than that of the upper layer. The antideforming ability of the whole pavement can be improved by using the MOH mixture as the middle layer of the pavement, which means that the performance of the MOH mixture can achieve the best performance when it is applied to the middle layer. [ABSTRACT FROM AUTHOR]

In recent years, rainstorm disasters caused by global warming have frequently occurred in China. It has caused serious damage to artificial high embankments. In this paper, the influence of rainfall intensity, slope, and reinforced layers on the erosion and destruction of the artificial high embankment is deeply analyzed. Through the model test, the rainfall erosion prediction model is established. The results show that (1) the gully width, depth, and erosion amount increased with the increase in rainfall intensity and slope and decreased with the increase in reinforcement layers; (2) the final ditch shape of the embankment is influenced by steel bars; and (3) according to the model test data, the mathematical model of dike scouring is established. Rainfall intensity and the coupling between slope and reinforced layers are considered in the model. It can be used for predicting erosion during rainfall. [ABSTRACT FROM AUTHOR]

In recent years, natural disasters and public health events caused by global warming have occurred frequently around the world. It has become a global consensus to actively respond to climate change. Firms are the main source of greenhouse gas emissions. The disclosure of carbon information is one of the most important ways for firms to respond to climate change. The effect of female directors on carbon information disclosure is still unclear. Considering that China is the largest country in greenhouse gas emissions and the social status of females in China is different from western countries, this paper explores the effect of female directors on carbon information disclosure by firms in China. Based on the sample of listed Chinese firms in high carbon industries during the period of 2012–2017, our empirical results show that female directors have a positive association with carbon information disclosure. In addition, we find that the power, educational level, and financial background of female directors have positive impacts on firms' carbon information disclosure. Our findings make a significant contribution to the ongoing debate on the role of female directors and provide new insights and policy implications for firms, regulators, and other stakeholders. [ABSTRACT FROM AUTHOR]

Global warming is accelerating the sea level to rise, which increases the risk of major coastal areas being submerged. Then, the residents may become environmentally displaced persons (EDP). A method of prediction and allocation of EDP is proposed in this paper. Firstly, the gray model is used to predict sea level and the amount of EDP. Then, the evaluation criterion for the responsibility ability of the EDP migration target countries is given based on the entropy and fuzzy comprehensive evaluation method. In addition, a two-way selection mechanism for EDP is constructed. Finally, the amounts of EDP for the next 10 years are predicted, and the allocation plan in 2030 is made by applying the proposed method. [ABSTRACT FROM AUTHOR]

The term minerality is often used to describe high-quality still white wines produced in cooler regions, such as Chablis. What minerality means in sensory terms and what is responsible for its presence is the subject of debate, however. This study explored the concept of minerality by analysing 16,542 Chablis Premier Cru tasting notes entered into CellarTracker between 2003 and 2022 on wines three to seven years old, together with weather, topography, and soil data for the Chablis area. The top three words used to describe Chablis Premier Cru wine were citrus, minerality, and acidity. Mentions of minerality declined between 1999 and 2019 vintages, whereas those of acidity, salinity, floral, orchard fruit, and stone fruit increased. The trends for minerality and salinity were slightly stronger with the year of tasting (2005 to 2022) than vintage. Bigram analysis indicated that consumers were more than 1.5 times as likely to refer to a stony kind of minerality as a saline one and only rarely smoky minerality. Use of the term minerality was correlated with growing season temperature and sunshine hours (negatively with each), as well as vineyard aspect (negatively with percentage vineyard area facing South or South-West), but not with Kimmeridgian soil type. The results imply that soils and geology are not a principal source of minerality in Chablis wine, but growing season warmth and sunshine are relevant to minerality. There is no simple explanation of minerality in Chablis wine; however, the recent decline in the use of this term for Chablis wine may be a consequence of three factors in combination: (i) it has become less fashionable; (ii) consumers are choosing "saline" instead of "mineral" when appropriate, but retaining it for "stony" sensations; and/or (iii) warming from climate change has reduced minerality. [ABSTRACT FROM AUTHOR]

Air pollution has a significant impact on environment resulting in consequences such as global warming and acid rain. Toxic emissions from vehicles are one of the primary sources of pollution. Assessment of air pollution data is critical in order to assist residents in locating the safest areas in the city that are ideal for life. In this work, density-based spatial clustering of applications with noise (DBSCAN) is used which is among the widely used clustering algorithms in machine learning. It is not only capable of finding clusters of various sizes and shapes but can also detect outliers. DBSCAN takes in two important input parameters—Epsilon (Eps) and Minimum Points (MinPts). Even the slightest of variations in the parameter values fed to DBSCAN makes a big difference in the clustering. There is a need to find Eps value in as minimum time as possible. In this work, the goal is to find the Eps value in less time. For this purpose, a search tree technique is used for finding the Eps input to the DBSCAN algorithm. Predicting air pollution is a complex task due to various challenges associated with the dynamic and multifaceted nature of the atmosphere such as meteorological variability, local emissions and sources, data quality and availability, and emerging pollutants. Extensive experiments prove that the search tree approach to find Eps is quicker and efficient in comparison to the widely used KNN algorithm. The time reduction to find Eps makes a significant impact as the dataset size increases. The input parameters are fed to DBSCAN algorithm to obtain clustering results. [ABSTRACT FROM AUTHOR]

Due to the unique topography and geographical location, severe snowfall is the main disastrous weather in winter in the Hunan Province of China. Based on the daily precipitation data in Hunan Province from 1961 to 2021, the regional heavy snowfall processes are classified by using the synoptic diagnostic method. In addition, the water vapor transport characteristics of typical heavy snowfall processes are analyzed by the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) air mass backward trajectory model. Then, the responses of the differences in water vapor transport to heavy snowfall under different weather situations are discussed. The results show that the spatial distribution of climatic mean heavy snowfall days in Hunan Province is extremely uneven, and the heavy snowfall days decrease from north to south, with the most in the Dongting Lake area and the least in the Nanling Mountains. In the past decades, snowstorms mainly occur in local areas, and there are fewer widespread snowstorms. The frequency of heavy snowfall days generally shows a decreasing trend, with three peaks all appearing before 1990. After the 2010s, the number of days and stations of heavy snowfall decreased noticeably, and so did the number of regional heavy snowfall processes. This result indicates that global warming has remarkable effects on the snowstorm events in Hunan Province. Heavy snowfall mainly occurs from December to February, and peaks from mid-January to early February. Over the past 61 years, more than 50% of heavy snowstorm events occurred after 2000. According to the main weather systems affecting regional heavy snowfall processes, these weather processes in Hunan Province can be classified into three categories: southern branch trough (SBT) type, blocking high collapse (BHC) type, and stepped trough type. Among them, the SBT type accounts for more than 60% of the heavy snowfall events in Hunan. In terms of the SBT type and the stepped trough type, the water vapor from the high-latitude inland and low-latitude sea surface accounts for a comparable proportion, each accounting for nearly 50%. For the SBT type, the proportion of the water vapor from warm-humid airflows is slightly higher than that from cold-humid airflows. However, in terms of the stepped trough type, the water vapor transported by cold-humid airflows from the north contributes more than that by warm-humid airflows. For the BHC type, the specific humidity and the water vapor from the high-latitude inland contribute 70% of heavy snowfall processes. In addition, the contribution of the two southwesterly water vapor channels to heavy snowfall processes is small. The water vapor sources differ remarkably for different heavy snowfall types, but all of them are dominated by the water vapor transport in the middle and lower troposphere, which is the main reason why the formation of snowfall areas under different weather types is obviously different. [ABSTRACT FROM AUTHOR]

Climate projections under the high-emission representative concentration pathway "RCP-8.5" scenario are used to show the effects of global warming on the energy production of two similar concentrated solar tower power plants in Spain and Chile from 2025 to 2060. Results show a reduction of the annual energy production of almost 1.8% for Chile and an increase of almost 6% in the Spanish plant, but an increase of 10% water consumption due to both the increase in dry- and wet-bulb temperatures and changes in the direct normal irradiation caused by climate change. When the effect of water restrictions is included in the model caused by the foreseen reduction in water availability in Spain, the increase of the annual energy production is only 2%. Finally, the levelized cost of energy (LCOE) calculated during the whole power plant lifetime (35 years) with respect to the LCOE obtained at the beginning of the project is increased by 1.1% in the Chilean plant, whereas in the Spanish plant, it can be reduced more than 2.5% if there are no water restrictions, while a reduction of 0.4% is expected in a scenario with water restrictions. Results show that careful consideration of climate projections should be considered to properly estimate the economic viability of solar tower plants. [ABSTRACT FROM AUTHOR]

Maize is one of the most important cereal food crops, and it can be grown all year in various agroecological zones. However, its vegetative growth and yield are susceptible to rainfall and temperature variability. As a result, the analysis of rainfall and temperature variability and trend was urgently needed in maize-growing agroecology zones to restructure the production system. The aim of the study was to examine rainfall and temperature variability and trends for developing a climate-resilient maize farming system in major agroecology zones in northwest Ethiopia. The study was implemented in low productive agroecology zones (LPZ), medium productive agroecology zones (MPZ), and high productive agroecology zones (HPZ) of northwest Ethiopia using daily time series climate data during the period 1987–2018. The coefficient of variation (CV), precipitation concentration index (PCI), rainfall anomaly index (RAI), and standardized precipitation (SPI) were applied to examine rainfall variability. Mann–Kendall's and Sen's slope estimator trend tests were used to detecting the statistical significance of changes in rainfall and temperature. Statistically significant increasing trends for annual maximum and minimum temperatures were recorded for all maize-producing agroecology zones. The mean annual temperature has exhibited a significant warming trend of 0.12 to 0.54°C per decade. The average annual rainfall has decreased by 38 to 67 mm per decade in all maize agroecology zones. Our research also showed that droughts now happen every one to three years; even consecutive droughts were seen in 2009, 2010, and 2011. For this reason, it could be required to develop a system of climate-resilient maize farming to address the issues of both global warming and the sub-Saharan countries that make up our study area. Climate-resilient maize agronomic activities have been determined by analyzing the onset, length of the growth period (LGP), and cessation date. Accordingly, the lower and upper quartiles of the date of onset of rainfall were in a range of May 9 to June 2, respectively; the length of the growth period (LGP) during the rainy season ranges from 97 to 232 days, and the cessation date of rainfall was November 1. Therefore, the short- to long-maturing maize varieties can be planted from May 9 to June 2 and can begin to be harvested in the first week of November under the current climatic circumstances. [ABSTRACT FROM AUTHOR]

Global warming is a vital issue addressed to every sector worldwide, including the construction industry. To achieve the concept of green technology, many attempts have been carried out to develop low-carbon footprint products. In the construction sector, Autoclaved Aerated Concrete (AAC) has become more popular and been manufactured to meet the construction demand. However, errors from manufacturing process accounted for approximately 3 to 5% of the AAC production. The development of AAC waste as lightweight aggregate in concrete is one of the potential approaches which was extendedly studied in this paper. The results showed that the compressive strength of AAC-LWA concrete was decreased with an increase in volume and coarse size. The optimum mix proportion was the AAC aggregate size of 1/2′′ to 3/8′′ with 20 to 40% replacement to normal weight aggregate. Internal curing by AAC-LWA was also observed and found to provide sufficient water inside the specimens, leading to an achievement in higher compressive strength. The main goal of this study is not only utilising unwanted wastes from industry (recycling of waste materials) but also building up a new knowledge of using AAC-LWA as an internal curing agent as well as the production of value-added lightweight concrete products. [ABSTRACT FROM AUTHOR]

Global warming increases global average precipitation and evaporation, causing extreme climate and hydrological events to occur frequently. Future changes in temperature, precipitation, and runoff from 2021 to 2050 in the upper reaches of the Minjiang River were analyzed using a distributed hydrological model, the SWAT (Soil and Water Assessment Tool), under a future climate scenario. Simultaneously, future variation characteristics of extreme climate hydrological elements in the upper reaches of the Minjiang River were analyzed using extreme climate and runoff indicators. The research shows that the frequency and intensity of the extreme temperature warming index will increase, while those of the extreme temperature cooling index will increase and then weaken in the upper reaches of the Minjiang River under a future climate scenario. The duration of precipitation, the intensity of continuous heavy precipitation, and the frequency of heavy precipitation will increase, whereas the intensity of short-term heavy precipitation and the frequency of heavy precipitation will decrease. However, spatial distribution of flood in the upper reaches is different, and thus flood risk in the upstream source area will still tend to increase. Particular attention should be given to the increase in autumn flood risk in the upper reaches of the Minjiang River. [ABSTRACT FROM AUTHOR]

Diesel engine emissions pose a serious threat to the environment and cause inevitable hazards to the atmosphere. The technique that is used to control and combat nitrogen oxide (NOX) emissions is selective catalytic reduction. However, the various experimental conditions in the SCR technique will reduce NOX emissions. Former studies experimentally discovered the decrease of NOx discharges at a higher temperature. In this study, a two-cylinder diesel engine with a 34 kW rated power at 3750 rpm under low-temperature operating conditions underwent hydrogen-assisted selective catalytic reduction of NOx. The experiment was performed by differing the hydrogen injection duration (0–40°CA) and NOX emissions were studied. Variation in emissions with H2 injection was investigated. On varying the hydrogen injection duration, NOX, HC, CO2, CO, and smoke emissions are reduced (mention the % of reduction) and it can be applied in real time to reduce global warming. [ABSTRACT FROM AUTHOR]

Nowadays in order to meet the increase in power demands and to reduce the global warming, renewable energy sources based system is used. Out of the various renewable energy sources, solar energy is the main alternative. But, compared to other sources, the solar panel system converts only 30–40% of solar irradiation into electrical energy. In order to get maximum output from a PV panel system, an extensive research has been underway for long time so as to access the performance of PV system and to investigate the various issues related to the use of solar PV system effectively. This paper therefore presents different types of PV panel systems, maximum power point tracking control algorithms, power electronic converters usage with control aspects, various controllers, filters to reduce harmonic content, and usage of battery system for PV system. Attempts have been made to highlight the current and future issues involved in the development of PV system with improved performance. A list of 185 research publications on this is appended for reference. [ABSTRACT FROM AUTHOR]

Based on the analysis of the feature of Asian continent becoming cold in the early 21st century winter under the background of global warming, the Asian Warming Hole (AWH) index is proposed in this paper to measure the intensity during the cold events process. Result shows that the Asian continent is indeed experiencing a cold stage in the early 21st century winter, but it is just in a cold phase and will become warmer in the future. In recent years the activity of the winter cold events has an obvious quasi-four-year cycle, which can be reflected by atmospheric circulation, and AO (Arctic Oscillation) may play a very important role. It is credible that Asian surface temperature will be higher in the coming 3-4 years compared with 2011 winter, signifying that the precipitation may increase correspondingly. [ABSTRACT FROM AUTHOR]

With the complex presence of important natural reserves, energy is considered as the main key ingredient to facilitate economic development in the Middle East, GCC, and Maghreb regions. Expectations for a rapidly growing economy in the next decade will likely cause an increase in the fraction of energy consumed domestically, limiting what is available for export. Considered as the home of global oil and gas reserves, the Middle East and North Africa (MENA) region is the cornerstone of the global energy architecture, and the global low-carbon energy transition poses critical questions to MENA oil and gas producers. Unfortunately, as the coronavirus (COVID-19) pandemic sweeps across the world, growth in the MENA region was projected to fall to 2.8% in 2020, lower than the growth rates during the 2008 global financial crisis and the 2015 oil price shock. Before the coronavirus (COVID-2019), the sharp drop in oil and gas prices that began in 2015 fostered MENA hydrocarbon producers to launch ambitious economic reform programs in all regions. The main goal of these programs was not only to increase the diversification of investment sectors to boost national and regional economies but also to encourage the development of the nonhydrocarbon sector. This article argues for a new challenge investigation and analysis to figure out with current MENA policy options and future prospects, as well as the present impact of COVID-19, in addition to the public policies that encourage diversification economy sector to avoid entire dependence on oil and gas in export are dressed. It also deals with the investigation of the pressing need to create job opportunities for a large and youthful population and the new definition of the possibility of the world moving more aggressively towards low-carbon integration. [ABSTRACT FROM AUTHOR]

Nowadays, the transports vehicles are required improvement or alternative source for fuel requirement, because of fuel shortage and reducing the global warming. Due to this reason, the electrical vehicle is more concentrated now. The electrical vehicle reduces the carbon emission and reduces the global warming. The main drawback for electrical vehicle is charging time and battery size. To overcome this problem, we use nanomaterial using batteries (like Li-ion battery and Li-based batteries). The nanomaterial particles increase the performance and storage battery capacity and reduce the size of the batteries. [ABSTRACT FROM AUTHOR]

Improving carbon reserves is considered to be an important way to alleviate global warming. However, there is a lack of research work based on the perspective of metropolitan area, and there is also a lack of analysis on the leading influencing factors of spatial distribution of carbon storage in subregions of metropolitan area. In this study, Nanjing metropolitan area (NMA) is taken as the research area, and the InVEST model is used to calculate the spatial distribution of regional carbon reserves, and the evolution of carbon reserves distribution in recent 20 years is analyzed. Then, based on the random forest (RF) model, taking the whole study area and subareas as the research scope, a regression model of each selected impact factor and carbon reserves is established, and the leading factors of spatial distribution of carbon reserves in NMA are obtained. The results show that the overall carbon reserves level in the study area is in a downward trend. Through the application of the RF model, the leading factors of the spatial distribution of carbon reserves in NMA and its subareas are derived. The research proves that the application of the RF model in the analysis is helpful for city planners and governments to make plans and improve regional carbon storage more effectively. [ABSTRACT FROM AUTHOR]

Climate change significantly affects global forest ecosystems, and the alpine treeline is sensitive to global warming. The alpine treeline data using high-resolution remote sensing images during four periods from 1987 to 2020 at Taibai Mountain are extracted. The dynamic change process of the alpine treeline and the response of vegetation near the alpine treeline to climate change are analyzed. The results showed that the alpine treeline of Taibai Mountain was primarily distributed in the range of 2266-3692 m, and the average elevation of the alpine treeline showed a trend of "rising-decreasing." The elevation of the alpine treelines of the sunny slopes were much higher than that of the shady slopes, and the highest values typically appeared on slopes and steep slopes of 15-35°. Vegetation is sensitive to temperature changes, and it typically displays a synchronous increase and decrease. However, there was a lag in the precipitation of approximately one year, and the rising temperatures of the sunny slopes were not conducive to vegetation growth. The vegetation will be dominated by a trend of unsustainable improvement in the future, and there exists a risk of continuous vegetation degradation on the northern slopes. However, a warm and humid climate will be conducive to vegetation growth above the alpine treeline. [ABSTRACT FROM AUTHOR]

Both the “urban minus rural” (UMR) and the “observation minus reanalysis” (OMR) methods were used to quantify the potential impacts of urbanization on regional temperature changes in the Beijing-Tianjin-Hebei metropolitan area in China. DMSP/OLS nighttime light imagery and population census data were used to classify stations into four classes: rural, small city, medium city, and large city groups. The regional average annual mean temperature was estimated to increase by 0.12°C decade−1 derived from urban warming, accounting for 40% of total climate warming from 1960 to 2009 using the UMR method. The OMR method also indicates that rapid urbanization has a significant influence on surface warming trend although the urban warming intensity is dependent on reanalysis dataset. The seasonal cycle patterns from all three datasets are consistent with each other, which is contrary to the UMR result owing to the cooling effect of agriculture activity in the rural stations confusing the UMR result. So in this paper we found a deficiency of the UMR method where it would underestimate the effects of urbanization in summer. In this regard, the results from the OMR method are relatively more convincing although we admit it still has many other problems. [ABSTRACT FROM AUTHOR]

In January 2008, extreme freezing rain struck South China. At the same time, the Tibetan Plateau (TP) was experiencing pronounced surface heating. The characteristics of this extreme weather and its linkage to the TP surface heating anomaly were analyzed in this paper. The results show that (1) anomalous heating of the TP helps to form and sustain the Siberian blocking high, which is important for persistent southward flow of dry and cold Siberian air; (2) TP heating helps the moisture flux move more north and strengthens the southerly wind above 850 hPa; (3) there are two Rossby wave trains at 500 hPa and the layers above it (at about 20∘N–40∘N). Correlation analysis reveals that TP heating anomalies are closely associated with these Rossby wave trains; (4) the Rossby wave propagates downstream from the TP to South China in the mid and high layers of the atmosphere when the TP changes swiftly from a heat sink to a heat source. This implies that anomalous heating of the TP may stimulate the Rossby wave train to propagate downward in midlatitudes. [ABSTRACT FROM AUTHOR]

Renewable wind power is productive and feasible to manage the energy crisis and global warming. The wind turbine's blades are the essential components. The dimension of wind turbine blades has been increased with blade sizes varying from approx. 25 m up to approx. 100 m or even greater with a specific purpose to increase energy efficiency. While wind turbine blades tend to be highly stressed by environmental conditions, the wind turbine blade must be constantly tested, inspected, and monitored for wind turbine blades safety monitoring. This research presents a methodology adaptation on machine learning technique for appropriate classification of different failure conditions on blade during turbine operation. Five defects were reported for the diagnosis study of defective wind turbine rotor blades, and the considered defects are blade crack, erosion, loose hub blade contact, angle twist, and blade bend. The statistical features have been drawn from the recorded vibration signals, and the important features was selected through J48 classifier. Eight tree-dependent classifiers were used to categorize the state of the rotor blades. Among the classifiers, the least absolute deviation tree performed better with the classification percentage of 90% (Kappa statistics = 0.88 , MAE = 0.0362 , and RMSE = 0.1704) with a computational time of 0.06 s. [ABSTRACT FROM AUTHOR]

The use of nanoparticles in concrete increases the material's strength and durability, making it useful in the building sector. Nanomaterials can lower the amount of cement in a building since cement releases carbon dioxide, which contributes to global warming. The mechanical response of concrete is studied in this study by replacing cement with various dosages of nanotitanium dioxide and nanoalumina. Nanotechnology has attracted a lot of attention in recent years because of its potential uses for particles. Cementitious materials at the nano/atomic level are the primary focus of current research. The mechanical characteristics of cementitious materials have been significantly improved by introducing nanotitanium dioxide and nanoalumina into cement. Nanotitanium dioxide and nanoalumina have been added to concrete in this study to examine its sorptivity and water absorption properties. To lower the carbon footprint of concrete, nanotitanium dioxide and nanoalumina can be used in place of cement. [ABSTRACT FROM AUTHOR]

Environmental problems caused by global warming are becoming alarming with every passing day. To curb greenhouse gas (GhG) emissions, promoting alternative fuel vehicles (AFVs) and sustainable transport mode choices is critical. This study explores the predictors of eco-social purchase, use, and conservation intentions in a developing country by employing an extended model of the theory of planned behaviour (TPB). Using a self-administered survey technique, data were collected from a nationally representative sample of 1372 customers of three leading automobile manufacturers from eight big cities of Pakistan. Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied to test the measurement and structural models. Results showed that the extended model of TPB received support in the context of Pakistani culture and for the targeted behaviours related to eco-social purchase, use, and conservation intentions concerning AFVs. Implications for marketers and theoretical contribution are discussed at the end. [ABSTRACT FROM AUTHOR]

With the rapid development of the social economy, people's demand for energy is increasing. Global warming has made people pay more attention to the importance of energy saving and emission reduction. The power grid is the system responsible for the transmission and distribution of electric energy. Therefore, in the context of energy saving and emission reduction, there are higher requirements for substations in the power grid. For this reason, an intelligent switch with built-in electronic voltage transformer is designed. The concept and key technical characteristics of the electronic voltage transformer intelligent switch are introduced; the overall design scheme of the built-in electronic voltage transformer intelligent switch is expounded; the hardware and software system of the device is designed; the management and control strategy of the design device is such as transformer tangent control, voltage and reactive control; and the operating system is ported. Finally, the simulation test of the system proves that the system has high effectiveness and stability. The research can provide reference for the realization of efficient, economic, stable, safe, and environmentally friendly distribution of power in a power grid. [ABSTRACT FROM AUTHOR]

Floods after monsoon rains are frequent disasters that affect millions of lives in Pakistan. Human lives are lost, agriculture economies are destroyed, and livestock animals, houses, fruit farms, and crops are lost which are the major livelihoods of thousands of people in Punjab. Each year there are heavy rains in the monsoon season and, due to global warming, there is the rapid melting of snow in northern glaciers; these factors subsequently cause floods. There is also loss of life due to the spread of waterborne diseases and snake bites. Flood monitoring provides early detection of a flood and the calculation of its intensity, which results in reduced human life losses and economic losses. Most casualties are caused by the lack of timely real-time, authentic information about the high-risk areas, and flood intensity, speed, and direction. Therefore, the proposed approach is centered on formal modeling and verification of safety and liveness properties of flood monitoring perceivers. Each flood perceiver has several sensors. It requires the collection of information starting from the flood perceiver, observer, and environmental forecast. This information is processed to determine the flood intensity level. We have developed a CP-Nets' formal model and model-checked it. We have verified the safety and liveness properties of correctness by exhaustive verification of the system using model-based proof obligations (Event-B method using Rodin). Our objective in this research is to propose a correct, reliable, and efficient flood warning, monitoring, and rescue (WMR) SoS based on formal methods. We have used formal modeling and model-checking based on state-of-the-art hierarchical CP-Nets supported by exhaustive formal proof obligations of Event-B. [ABSTRACT FROM AUTHOR]

Nowadays, it is well known that global warming is a great hazard to the planet, and the carbon emissions are a principal source of global warming. For this reason, the customers have become more environment and quality conscious than before, and as a result, they request the firms to be ecofriendly. In this context, it is desirable that companies develop and implement inventory models which consider sustainability issues. Furthermore, the companies face problems of shortages and setting prices in order to persist in a competitive and challenging business. Besides, there exists a kind of items different than the traditional products that it is necessary to feed them until a target weight is reached in order to slaughter and sell to customers. These are named as growing items. In this sense, this research work proposes an inventory model for growing items with imperfect quality when the demand is price sensitive under carbon emissions and shortages. The shortages are fully backordered. The demand is price sensitive according to a polynomial function. The proposed inventory model determines jointly the optimal policy for the selling price of perfect-quality growing items, the order quantity, and the backordering quantity which maximize the expected total profit per unit of time. Some numerical examples are resolved in order to illustrate the use and the applicability of the inventory model. Finally, a sensitivity analysis is conducted and some managerial insights are given. [ABSTRACT FROM AUTHOR]

As global warming has received widespread attention, the disclosure of firms' carbon information has been expected by increasing stakeholders. This study extends the previous literature on the determinants of firms' carbon information disclosure by examining the influence of academic independent directors and Confucianism on the quality of carbon information disclosure. Using a sample of Chinese listed firms in the CSI 300 Index during the period of 2012–2018, our empirical results show that academic independent directors have a significantly positive association with the quality of carbon information disclosure. The results also reveal that Confucianism positively affects the quality of carbon information disclosure. Moreover, Confucianism positively moderates the relationship between academic independent directors and the quality of carbon information disclosure. The results imply that Confucianism, as an informal system, can promote the governance effect of academic independent directors on firms' carbon information disclosure behaviour. Our findings offer shareholders, regulators, and other stakeholders an integrating perspective on motivating firms to disclose high quality carbon information. [ABSTRACT FROM AUTHOR]

Extreme precipitation events, which have intensified with global warming, will have a pernicious influence on society. It would be desirable to understand how they will evolve in the future as global warming becomes more serious with time. Thus, the primary objective of this study is to provide a comprehensive understanding of the changing characteristics of the precipitation extremes in the 21st century over Shaanxi Province, a climate-sensitive and environmentally fragile area located in the east of northwestern China, based on a consecutive simulation of the 21st century conducted by the regional climate model RegCM4 forced by the global climate model HadGEM2-ES at high resolution under middle emission scenario of the Representative Concentration Pathway 4.5 (RCP4.5). Basic validation of the model performance was carried out, and six extreme precipitation indices (EPIs) were used to assess the intensity and frequency of the extreme precipitation events over Shaanxi Province. The results show that RegCM4 reproduces the observed characteristics of extreme precipitation events over Shaanxi Province well. Overall for the domain, the EPIs excluding consecutive dry days (CDD) have a growing tendency during 1980–2098 although they exhibit spatial variability over Shaanxi Province. Some areas in the arid northern Shaanxi may have more heavy rainfalls by the middle of the 21st century but less wet extreme events by the end of the 21st century. And the humid central and southern regions would suffer more precipitation-related natural hazards in the future. [ABSTRACT FROM AUTHOR]

Ultraviolet radiation (UVR) which could induce skin damage and skin disease is a growing concern due to the increase in global warming. Brown macroalgae Sargassum cristaefolium has been recognized to exhibit UV protective activities. However, the mechanism of its photoprotective activity remains unclear. The purpose of this study is to investigate the potential mechanism of S. cristaefolium's photoprotective activity against UV radiation. Phytochemical analyses revealed valuable bioactive compounds in SCE, such as fucoxanthin which is widely known as an anti-inflammatory carotenoid. Treatment with SCE before UV-A radiation show reduced levels of wrinkles and desquamation. Interestingly, SCE treatment induces the skin healing process after UV radiation. SCE effectively inhibited proinflammatory TNF-α and IL-6 expression while increasing IL-10 production in the BALB/c mice skin. Current results suggest that SCE potentially protects the skin by attenuation of inflammatory cytokines. In addition, SCE demonstrates promising antibacterial activity (MIC = 1.302 µg/mL) against Staphylococcus aureus. Overall, SCE could be a source of an effective anti-inflammatory agent protecting against UV irradiation-induced skin damages. [ABSTRACT FROM AUTHOR]

In recent years, environmental awareness has increased considerably, and in order to decrease endangerments such as air and water pollution, and also global warming, green procurement should be employed. Therefore, in the assessment of suppliers, their environmental performance should be taken into consideration along with other criteria for supplier selection. Raising awareness of sustainability in production and conservation and protection of the environment is very important both for the whole environment and for the company itself by increasing its competitive advantage. And, one of the steps to achieve this is for the companies to try to select green suppliers. So, the purpose of this study is to raise awareness and tackle the need for green supplier selection and, using multiple-criteria decision-making models, to elaborate a case study regarding this. A survey was conducted in a manufacturing firm. The data were analysed, and fuzzy MCDM (multicriteria decision-making) methods and artificial neural networks were implemented. Fuzzy methods are the fuzzy analytic hierarchy process (fuzzy AHP), fuzzy TOPSIS, and fuzzy ELECTRE. ANN supports the result of fuzzy MCDM models from the profit side. ANN can make the best estimate of the current year based on historical data. Fuzzy MCDM methods will also find good solutions using the available data but will produce different solutions as there are different decision-making methods. It is aimed to produce a synergy from the solutions obtained here and to produce a better solution. Instead of a single method, it would be more accurate to produce a better solution than the solution provided by all of them. The dominant result has been obtained using the committee fuzzy MCDM and ANN to select the best green supplier. [ABSTRACT FROM AUTHOR]

Two alternative techniques, the lime stabilization technique (LST) and the geogrid reinforcement technique (GRT), are both useful to stabilize expansive soil slopes, but their impacts on the environment need be further evaluated. Based on a case study, two techniques as well as their construction processes were introduced. The energy consumption and carbon dioxide (CO2) emissions were investigated by the life cycle assessment (LCA). The sensitivity analyses were carried out, including the lime content for LST, the reinforcement spacing for GRT, the embankment height, delivery distance, and treatment width for both techniques. From the LCA results, with the GRT, the energy consumption and CO2 emissions can be reduced by 7.52% and 57.09%, respectively. The main sources of two techniques are raw material production, soil transportation, and paving stage while the CO2 emissions of lime production are about 11.68 times of those of geogrid production. From the sensitivity analysis results, as the lime content of LST increases by 1%, the total energy consumption and CO2 emissions increase by 8.27% and 13.16%, respectively; as the reinforcement spacing of GRT increases by 0.05 m, the total energy consumption and CO2 emissions increase by 1.63% and 0.69%, respectively; as the embankment height increases by 1 m, the increase rates of energy consumption and CO2 emissions of LST are 1.68 and 1.61 times of those of GRT, respectively. In this project, when the embankment height is less than 10 m, the geogrid technique has the advantages of energy-saving and emission-reduction. It was found that the GRT is not sensitive to the change of delivery distance and treatment width and significantly reduces the environmental impacts, especially in reducing the impact of global warming. [ABSTRACT FROM AUTHOR]

Generally, the warm mix asphalt (WMA) technology can reduce the mixing and paving temperature effectively as compared with that of hot mix asphalt (HMA), which is considered more environment-friendly. In this study, the environmental impacts and resource consumptions of WMA and HMA pavements were analyzed comparatively using the life cycle assessment (LCA) method. A LCA model of pavement was built; meanwhile, the relevant life cycle inventory (LCI) of WMA and HMA pavements was also collected and analyzed. The midpoint impact categories including Global Warming Potential (GWP), Chinese Abiotic Depletion Potential (CADP), and Particulate Matter Formation (PMF) were assessed for five cases. The assessment results showed that the resource consumptions of both WMA and HMA pavements in entire life were almost at the same level, while the environmental impacts of WMA pavement related to greenhouse gases and PM2.5 emissions were significantly less than that of HMA pavement, except for the case where the long-term performance of WMA pavement is much worse than that of HMA pavement. In final, it could be concluded that WMA pavement is more environment-friendly compared with HMA pavement although they have the same-level resource consumptions. [ABSTRACT FROM AUTHOR]

Nonequilibrium molecular dynamics simulations are employed to study the adsorption and blockage properties of a 3D graphene membrane. Specifically, we are interested in the mixtures of carbon dioxide (CO2) and sulfur dioxide (SO2), two of the most relevant pollutant gases for the anthropogenic effect in global warming. We simulate cases with distinct proportion of gases in the mixture. Our results indicate that the 3D graphene slit is able to absorb 90% or more of the gas molecules. We show that this property came from the fact that both CO2 and SO2 molecules are attracted by the graphene pore, which compensates for the entropic barrier that exists when leaving the bulk state to the confined state. Also, the simulation results show that changing the interlayer separation between the graphene sheets is possible in order to change the membrane properties, from absorbent to blockage. These results help to understand the properties of 3D graphene nanoslits and their application as highly selective filters. [ABSTRACT FROM AUTHOR]

We study summer heat waves and winter cold spells and their links to atmospheric circulation in an ensemble of EURO-CORDEX RCMs in Central Europe. Results of 19 simulations were compared against observations over 1980–2005. Atmospheric circulation was represented by circulation types and supertypes derived from daily gridded mean sea level pressure. We examined observed and simulated characteristics of hot and cold days (defined using percentiles of temperature anomalies from the mean annual cycle) and heat waves and cold spells (periods of at least three hot/cold days in summer/winter). Although the ensemble of RCMs reproduces on average the frequency and the mean length of heat waves and cold spells relatively well, individual simulations suffer from biases. Most model runs have an enhanced tendency to group hot/cold days into sequences, with several simulations leading to extremely long heat waves or cold spells (the maximum length overestimated by up to 2-3 times). All simulations also produce an extreme winter season with (often considerably) higher number of cold days than in any observed winter. The RCMs reproduce in general the observed circulation significantly conducive to heat waves and cold spells. Zonal flow reduces the probability of temperature extremes in both seasons, while advection of warm/cold air from the south-easterly/north-easterly quadrant plays a dominant role in developing heat waves/cold spells. Because of these links, the simulation of temperature extremes in RCMs is strongly affected by biases in atmospheric circulation. For almost all simulations and all circulation supertypes, the persistence of supertypes is significantly overestimated (even if the frequency of a given supertype is underestimated), which may contribute to development of too-long heat waves/cold spells. We did not identify any substantial improvement in the EURO-CORDEX RCMs in comparison to previous ENSEMBLES RCMs, but the patterns of the biases are generally less conclusive as to general RCMs' drawbacks. [ABSTRACT FROM AUTHOR]

Trend and an abrupt regime shift of temperature extremes were investigated based on diurnal data at 116 meteorological stations in the Northeast China region during 1957–2015. A total of 10 temperature indices divided into two categories: extremely cold and warm indices, were used in this study. The Mann–Kendall (MK) test was employed to evaluate the trend in temperature while changepoint, an R package for changepoint analysis, was used to detect changes in the mean levels of temperature extreme data series. The results of this study reveal that occurrence frequencies of the extreme cold night (TN10p) and extreme warm night (TN90p) have decreased and increased by −1.67 and 1.79 days/decade, respectively. Moreover, variations in temperature extremes have not been uniform with warming trends in minimum temperature being rapidly compared to maximum temperature extremes. The diurnal temperature range (DTR) depicted a remarkable decrease as a result of rapid warming in the minimum temperature. Warming in the region led to a reduction in the number of frost days (FD) and icing days (ID) and an increase in the number of growing season length (GSL) and tropical nights (Tr). Seasonally, TN10p largely decreased in winter and spring, while TNn and TN90p largely increased in winter and summer, respectively. Spatially, most of the stations with a significant warming trend in minimum temperatures were located in the Changbai Mountain, Greater Khingan Range, and Lesser Khingan Range. This implies that the mountainous regions are more sensitive and vulnerable to warming than the plain regions. On the contrary, most stations located in the Songnen Plain, Sanjiang Plain, and Liao River Plain displayed significant positive trend GSL and Tr. These climate extreme trends show that the region is experiencing warming which may have an impact on the hydrological process, ecological process, and agricultural production capacity. [ABSTRACT FROM AUTHOR]

Globally, industries and economies have undergone rapid development and expansion over the last several decades. As a result, global warming and environmental contaminations have resulted in climate change and jeopardized food security. In many developing countries, already decreasing crop yields are threatened by extreme weather and soil damaged by genetically modified food, making environmental problems worse and increasing food and organic product prices. For these reasons, this study proposes a hybrid multicriteria decision-making (MCDM) model for new product development (NPD) in the light-emitting diode- (LED-) based lighting plant factory. First, literature reviews and expert interviews are employed in constructing a list of decision-making objectives and criteria for new product development. Then, a fuzzy Delphi method (FDM) is used to screen the elements of the objectives and criteria, while a fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is used to determine the relationships among the objectives and criteria. Finally, a fuzzy analytic network process (FANP) and a composite priority vector (CPV) are manipulated to determine the relative importance weights of the critical objectives and criteria. Results show that the proposed method can create a useful and assessable MCDM model for decision-making applications in new product development, and a case study is herein performed to validate the feasibility of the proposed model in a Taiwanese LED-based lighting plant factory, which not only provides the decision-makers with a feasible hierarchical data structure for decision-making guidance but also increases the competitive advantages of trade-offs on developing novel products. [ABSTRACT FROM AUTHOR]

The emission of greenhouse gases (GHGs) results in global warming and climate change. The extent to which developing countries contribute to GHG emissions is not well known. This study reports findings on the effects of different land-use systems on GHG emissions (CO2 in this case) from two locations in the southern zone of Ghana, West Africa. Site one (located at Kpong) contained a heavy clay soil while site two (located at Legon) contained a light-textured sandy soil. Land-use systems include cattle kraals, natural forests, cultivated maize fields, and rice paddy fields at site one, and natural forest, woodlots, and cultivated soya bean fields at site two. CO2 emissions were measured using the gas entrapment method (PVC chambers). Trapping solutions were changed every 12–48 h and measurement lasted 9 to 15 days depending on the site. We found that, for the same land-use, CO2 emissions were higher on the clay soil (Kpong) than the sandy soil (Legon). In the clay soil environment, the highest average CO2 emission was observed from the cattle kraal (256.7 mg·m−2·h−1), followed by the forest (146.0 mg·m−2·h−1) and rice paddy (140.6 mg·m−2·h−1) field. The lowest average emission was observed for maize cropped land (112.0 mg·m−2·h−1). In the sandy soil environment, the highest average CO2 emission was observed from soya cropped land (52.5 mg·m−2·h−1), followed by the forest (47.4 mg·m−2·h−1) and woodlot (33.7 mg·m−2·h−1). Several factors influenced CO2 emissions from the different land-use systems. These include the inherent properties of the soils such as texture, temperature, and moisture content, which influenced CO2 production through their effect on soil microbial activity and root respiration. Practices that reduce CO2 emissions are likely to promote carbon sequestration, which will consequently maintain or increase crop productivity and thereby improve global or regional food security. [ABSTRACT FROM AUTHOR]