Search

Summary: With around 15% of all described freshwater fish species in the world, the Amazon Basin is by far the most fish species‐rich freshwater ecosystem on the planet. In this opinion paper, a rough evaluation is given on just how vulnerable Amazonian freshwater fishes are to ongoing climate change. And to argue that current anthropogenic threats through rapid expansion of human infrastructure and economic activities in the basin could be a far greater threat to fish communities than those anticipated by any future climate change. Conservation actions in the Amazon Basin should focus preferentially on reducing the impacts of present‐day anthropogenic threats. [ABSTRACT FROM AUTHOR]

The bioeconomy model provides an alternative view of global economic systems by putting sustainable practices combined with digital approaches at the forefront to tackle issues such as climate change. To address this new business trends, financial institutions began to set up the environmental, social, and governance (ESG) business units to evaluate their business strategies. This paper is aimed at examining the nonfinancial effect created by the digital transformation (DT) activities, highlighting the role of enterprise heterogeneity after the implementation of Environmental Protection Law (EPL) in China. We employ the panel data of A-share listed companies from 2010 to 2020, selecting DT and ESG indicators as the important representations of "Industry 5.0." Our empirical results demonstrate a positive impact of EPL on the ESG performance in sight of resource enterprises (REs), environmental enterprises (EEs), and polluting enterprises (PEs), but a negative impact of EPL on the DT indicators among those environmental related industries. Additional causal relationship regression reveals that enterprise DT has an intrinsic promoting effect on the ESG performance, emphasizing on the high risk of digitization process being the shock transmitters to enterprise nonfinancial indices. Notably, the connectedness of environmental policy illustrates dynamic patterns by parallel trend test and propensity score matching (PSM) DID regression. This paper is prone to benefit lawmakers, regulators, and firm executives responsible for analyzing and assessing enterprise digitization behavior by exploring the influence of macrolevel environmental policy. [ABSTRACT FROM AUTHOR]

Summary: In the modern world, only conventional energy resources cannot fulfil the growing energy demand. Electricity is a fundamental building block of a technological revolution. Today, most of the electricity demand is met by the burning of fossil fuels but at the cost of adverse environmental impact. In order to bridge the gap between electricity demand and supply, nonconventional and eco‐friendly means of energy generation are considered. Renewable energy systems (RESs) offer an adequate solution to mitigate the challenges originated due to greenhouse gasses (GHG). However, they have an unpredictable power generation with specific site requirements. Grid integration of RESs may lead to new challenges related to power quality, reliability, power system stability, harmonics, subsynchronous oscillations (SSOs), power quality, and reactive power compensation. The integration with energy storage systems (ESSs) can reduce these complexities that arise due to the intermittent nature of RESs. In this paper, a comprehensive review of renewable energy sources has been presented. Application of ESSs in RESs and their development phase has been discussed. Role of ESSs in increasing lifetime, efficiency, and energy density of power system having RESs has been reviewed. Moreover, different techniques to solve the critical issues like low efficiency, harmonics, and inertia reduction in photovoltaic (PV) systems have been presented. Unlike most of the available review papers, this article also investigates the impact of FACTS technology in RESs‐based power system using multitype flexible AC transmission system (FACTS) controllers. Three simulation models have been developed in MATLAB/Simulink. The results show that FACTS devices help to maintain the stability of RESs integrated power system. This review paper is believed to be of potential benefit for researchers from both the industry and academia to develop better understanding of challenges and solution techniques for REs‐based power systems and future research dimensions in this area. [ABSTRACT FROM AUTHOR]

Turkey has been one of the earliest participants of the international climate policy process, since the Ministerial Conference on Atmospheric Pollution and Climate Change held in 1989. The country has prepared strategy documents, actions plans, sectoral policies, and projects to detect and adapt climate change effects. However, any of this has not turned into a main plan to support climate change mitigation on an international scale. The purpose of this paper is to identify local climate change mitigation strategies of Sakarya city, Turkey, by strengths, weaknesses, opportunities, and threats (SWOT) analysis. For this purpose, relevant information were gathered from Covenant of Mayors, greenhouse gas inventories of the city, National Energy Efficiency Action Plan, online workshop with 44 local stakeholders from private sector and local universities held on 13.10.2020. The results emphasized the importance of the cross-link between local adaptation and mitigation in terms of energy demand and energy-based emissions on national and international scale. [ABSTRACT FROM AUTHOR]

Summary: Computationally intelligent energy forecasting methods for appropriate energy management at the consumer/producer side have a positive impact on the preservation of energy and play a constructive role in tackling global climate change. The energy production and consumption are very high worldwide, demanding intelligent methods with real‐world implementation potentials for appropriate energy management. In this paper, we survey the existing intelligent load forecasting (ILF) systems, highlight their advantages and downsides, and briefly discuss the workflow of the employed literature. Furthermore, we debate on the existing load forecasting datasets and their features along with a brief overview of the challenges confronted by researchers using these datasets. Distinct from previous survey papers, we provide a detailed review of performance evaluation metrics and comparison of employed methods for energy load forecasting, thereby concluding the need of efficient, effective, and adoptable ILF methods functional in real‐world scenarios. Finally, we assess the employed techniques and deliver future research opportunities based on the derived conclusions from existing research works. This paper delivers the overall energy forecasting literature in a compact form with possible future insights for researchers working in ILF domain. [ABSTRACT FROM AUTHOR]

The climate of Sri Lanka has been fluctuating at an alarming rate during the recent past. These changes are reported to have pronounced impacts on the livelihoods of the people in the country. Water is central to the sustainable functioning of ecosystems and wellbeing of mankind. It is evident that pronounced variations in the climate will negatively impact the availability and the quality of water resources. The ecosystem-based adaptation (EbA) approach has proved to be an effective strategy to address the impact of climate change on water resources in many parts of the world. The key aim of this paper is to elaborate the wide range of benefits received through implementation of EbAs in field level, watershed scale, and urban and coastal environments in the context of Sri Lanka. In addition, this paper discusses the benefits of utilizing EbA solutions over grey infrastructure-based solutions to address the issues related to water management. The wide range of benefits received through implementation of EbAs can be broadly classified into three categories: water supply regulation, water quality regulation, and moderation of extreme events. This paper recommends the utilization of EbAs over grey infrastructure-based solutions in adaptation to climate change in the water management sector for the developing region due its cost effectiveness, ecofriendliness, and multiple benefits received on long-term scales. The findings of this study will unequivocally contribute to filling existing knowledge and research gaps in the context of EbAs to future climate change in Sri Lanka. The suggestions and opinions of this study can be taken into account by decision makers and water resources planning agencies for future planning of actions related to climate change adaptation in Sri Lanka. [ABSTRACT FROM AUTHOR]

Climate change and youth participation are emerging as important clarion calls today. Indeed, very few individuals will possibly counter a call for the involvement of young people in decisions and actions which (in)directly affect their lives. Presently, some of the greatest social problems faced by young South Africans are COVID-19, employment, and climate change. These challenges require the active participation of young people—locally known as the "born frees"—in the construction and operationalization of interventions, especially in light of the insufficient (sub)national response. That being said, policymakers often adopt top-down over bottom-up approaches, with the young generation often excluded or at best given a tokenistic role in climate decision-making processes. Therefore, this paper suggests some new ways of conceptualizing youth agency and brings to light how the born frees could efficiently take part or have a say in negotiating the path to climate adaptation, resilience, and mitigation. By drawing from the existing literature, the paper concludes that effective engagement with youth is essential in empowering them to key stakeholders or partners in adapting and/or mitigating climate change. [ABSTRACT FROM AUTHOR]

Transnational power grid interconnection is an important measure to promote the construction of energy Internet. It can meet the global power demand in a clean and green way, promote the UN's concept of "sustainable energy," and tackle climate changes. But, transnational power grid projects face many complex and variable risks due to their complex background and lacking experience. According to the characteristics of transnational power grid interconnection projects, a risk assessment index system including 10 indexes such as national relation, public participation, and available transmission capacity is constructed. Then, in order to overcome the shortcomings of traditional risk assessment methods, this paper proposes a risk assessment method combined with risk theory and probabilistic model, which can not only consider the uncertainties but also integrate the probability of accidents with consequences. Therefore, it can effectively assess the transnational power grid interconnection projects under diversified risks. In addition, in order to further magnify the impact of higher risks on such projects, a method combining traditional weighting method with the variable weight theory is proposed. The study in this paper provides certain guidance and decision-making support for different participants such as the government power sector, construction enterprises, and investment enterprises when they launch on sustainable development of the transnational project business, which have obvious practical significance. [ABSTRACT FROM AUTHOR]

From the microperspective, climate change restricts human life in many aspects, and it affects the regional economic system from the macroperspective. The paper presents an inoperability input-output model (IIM) that is an extension approach of the Leontief input-output model. The IIM is able to provide a feasible methodology for measuring the impact of vulnerable economic factors on the whole economic system and identifying the key adaptation trajectory of the economic system. The IIM is applied in Tianjin to explore its dilemmas facing the increased demand for electricity, water, and public health service sectors under the RCP2.5, RCP4.5, and RCP8.5 climate scenarios. The results indicated that the inoperability ranking of all economic sectors is the same under the three climate scenarios. The key adaptation trajectory in Tianjin is S40, S27, S25, S17, S12, S02, S21, S16, S09, S24, S29, S33, S19, S13, and S15 sector in order. The costs required by the key adaptation trajectory to adapt to climate change account for more than 90% of that required by the whole economic system. These results can be helpful for policy-makers to prioritize sectors in terms of climate adaptation and understand the efficacy of climate change risk mitigation strategies. [ABSTRACT FROM AUTHOR]

In this paper, we present a nonlinear deterministic mathematical model for malaria transmission dynamics incorporating climatic variability as a factor. First, we showed the limited region and nonnegativity of the solution, which demonstrate that the model is biologically relevant and mathematically well-posed. Furthermore, the fundamental reproduction number was determined using the next-generation matrix approach, and the sensitivity of model parameters was investigated to determine the most affecting parameter. The Jacobian matrix and the Lyapunov function are used to illustrate the local and global stability of the equilibrium locations. If the fundamental reproduction number is smaller than one, a disease-free equilibrium point is both locally and globally asymptotically stable, but endemic equilibrium occurs otherwise. The model exhibits forward and backward bifurcation. Moreover, we applied the optimal control theory to describe the optimal control model that incorporates three controls, namely, using treated bed net, treatment of infected with antimalaria drugs, and indoor residual spraying strategy. The Pontryagin's maximum principle is introduced to obtain the necessary condition for the optimal control problem. Finally, the numerical simulation of optimality system and cost-effectiveness analysis reveals that the combination of treated bed net and treatment is the most optimal and least-cost strategy to minimize the malaria. [ABSTRACT FROM AUTHOR]

Summary: Human activities are directly related to fossil fuel consumption and cause global warming and climate change effects by emitting greenhouse gas into the lower atmosphere. To reduce and adapt to such effects, renewable energy sources such as surface dams, including hydroelectric energy (HE) generation units, are gaining importance. In the current literature, HE potential calculations are based on formulations that are quite simple, clear and do not distinguish between different situations. In this paper, two classification methodologies are proposed for better assessment of HE production. The first one is based on the concept of the hypsographic curve to distinguish different drainage basin features into "Young," "Mature" and "Old." The second classification takes into account the newly defined energy index (EI) principle, which helps to classify the HE generation capability of drainage basins into "Very low," "Low," "Normal," "High" and "Very high." The application of the proposed methodology is presented for three drainage basins from the upstream sub‐basins of the Tigris River in Turkey. A comparison of the results shows that the proposed methodologies not only give numerical values but also sets linguistically rational and logical rules for the HE potential of a drainage basin. In the upstream basin of the Tigris River, the EI values vary between 0.705 and 0.122, corresponding to the "High" and "Low" categories, respectively. It is recommended that these two classification procedures be considered before the construction of any dam for HE production. [ABSTRACT FROM AUTHOR]

Water level (WL) forecasting has become a difficult undertaking due to spatiotemporal fluctuations in climatic factors and complex physical processes. This paper proposes a novel hybrid machine learning model based on an artificial neural network (ANN) and the Marine Predators algorithm (MPA) for modeling monthly water levels of the Tigris River in Al-Kut, Iraq. Data preprocessing techniques are employed to enhance data quality and determine the optimal input model. Historical data for water level and climatic factors data are utilized from 2011 to 2020 to build and assess the model. MPA-ANN algorithm's performance is compared with recent constriction coefficient-based particle swarm optimization and chaotic gravitational search algorithm (CPSOCGSA-ANN) and slime mold algorithm (SMA-ANN) to reduce uncertainty and raise the prediction range. The finding demonstrated that singular spectrum analysis is a highly effective method to denoise time series. MPA-ANN outperformed CPSOCGSA-ANN and SMA-ANN algorithms based on different statistical criteria. The suggested novel methodology offers good results with scatter index (SI) = 0.0009 and coefficient of determination (R2 = 0.98). [ABSTRACT FROM AUTHOR]

Alpine glaciers are vulnerable to climate changes, and their recession due to warming has already induced a large number of geohazards closely related to the glacial motion, such as debris flow. Strong coupling of the geology, geomorphology, climate, and glacial action controls the type, size, development, and frequency of debris flow along the Parlung Zangbo River, Southeastern (SE) Tibetan Plateau. Field investigation in recent years indicates that the north bank is prone to much more frequenter debris flows than the south bank. The sharp contrast between the two river banks is due to the different formation conditions of debris flows, especially glaciers and geomorphology. The present paper examines the differences in the glacier and geomorphology conditions for glacier-related debris flows to occur, through a combination of field investigation, interviews with local residents, and geomorphological parameter, and glacier distribution analysis in the ArcGIS platform. The result indicates that, compared to the south bank of the Parlung River, the north bank has more favorable conditions in the glacier and geomorphology south, which is more conducive to the occurrence of debris flow. In conclusion, three kinds of mechanisms due to different formation conditions, especially glacier and geomorphology, are analyzed. The glacier-related debris flows on the south and north banks occur through different mechanisms due to different formation conditions. The debris flows on the north bank primarily occur in modes I and III, while the south bank is dominated by modes II and III debris flows. Based on the result, effective measures are proposed for mitigating damage to roads and railways within this area. [ABSTRACT FROM AUTHOR]

Summary: Recently, due to climate change and global warming, carbon free power generation is becoming important. Among various heat sources to replace fossil fuels, a Molten Salt Reactor (MSR) can be a successful alternative power source. The MSR uses liquid nuclear fuel instead of solid fuel which enables to have high power density, compact size, and inherent safety features. The MSR Experiment (MSRE) which was conducted by Oak Ridge National Laboratory (ORNL) had demonstrated these possibilities. However, since the MSRE focused on the nuclear reactor design and operation rather than generating electricity, many issues are still needed to be addressed to fully evaluate potential of an MSR. One of the key issues is to calculate the optimal pinch temperature range of the primary and secondary heat exchangers of the MSR heat transport system. Thus, in this study, to calculate the optimal pinch temperature range of the heat exchangers of the MSR heat transport system, heat transport system, and power conversion system for MSRE are designed conceptually. A Plate Fin type Heat Exchanger (PFHE) is newly suggested and evaluated in this paper for MSR heat transport system application to achieve high compactness and satisfactory performance. Furthermore, to calculate the optimal pinch temperature range from an economic point of view, a PFHE annual cost model is utilized to estimate the cost of PFHE with respect to the pinch temperature. An optimal pinch temperature range is recommended from the study for the early design phase of MSR, which is 5 ~ 15 K. [ABSTRACT FROM AUTHOR]

In the context of rapid urbanization, the reduction of urban ecological land area, space chaos, and fragmentation have led to a series of climate and environmental problems. This paper takes the ecological patches of the urban climate environment compensation space as the research object and establishes several urban canopy models of several cases based on geographic information data. Then, we input them into the mesoscale WRF-UCM model and simulate after loading the meteorological data to derive the meteorological indicators of each case, quantitatively analyze the meteorological conditions in the ecological patch boundaries with different surface attributes, and conduct research on the "quality-efficiency" correspondence of the physical characteristics of the ecological patches and the spillover effects of climate regulation. Studies have shown that the highest surface temperature difference between water patches and patches with other attributes ∆ Tsk is up to 6.5°C in winter and 11.5°C in summer. The highest temperature difference at a somatosensory height of 2 meters ∆ T 2 is up to 2.0°C in winter and 3.0°C in summer, which can improve the surrounding environment. Under light wind conditions, water patches will have a positive spillover effect within 5.5 km of the downwind direction. Based on the data, an optimization strategy for the spatial pattern of urban ecological patches is proposed: the best distribution of water patches that are beneficial to the climate adjustment of Wuhan in winter and summer is in the north or northeast of the city, followed by the south or southwest. The north and northeast of the city should keep the woodland patches and reduce the setting of vegetation/arable land and wetland patches. This research is an interdisciplinary study of urban spatial planning that adapts to climate change, to supplement the deficiencies of urban planning theory, and to provide guidance for the promotion of green and ecological city construction. [ABSTRACT FROM AUTHOR]

One of the most important and continuously growing research areas in marine ecology is assessing the sustainability of ecosystems. The concept is traditionally approached by quantifying different anthropogenic pressures, such as top-down effects, overexploitation, invasive species, overfishing, and pollution. However, this perspective has been criticized for its inability to capture the complex interactions between natural factors (such as climate change), human factors, and sociopolitical factors that shape ecosystems. In this paper, we present a new structural model for assessing the sustainability of marine ecosystems, PCA-DEA-based model, as an extension to current models that incorporates these key interfaces into dynamic system modeling by including trade-offs on horizontal scales (e.g., decision making). [ABSTRACT FROM AUTHOR]

The most significant global challenge is global climate change; carbon emission reduction and carbon neutralization are the last way to solve global climate change. Achieving carbon neutrality will significantly impact the global economy and the national economy. The "dual carbon goals" proposed by China will also substantially impact the global economy and regional economic development. Focusing on regional economic development, achieving carbon neutrality is not only an opportunity for regional economic development but also a challenge. To achieve the goal of carbon neutrality, there must be institutional innovation and management innovation, which will provide new opportunities for balanced development among regions. With the development of inclusive finance, financial services for many disadvantaged groups and small and medium-sized enterprises have been further developed, thereby promoting economic development. With the development of Internet finance, big data, cloud computing, and other technologies, the development of inclusive finance, etc., the concept has also emerged. However, due to the short time of digital financial inclusion, the imperfect regulatory system, and the low degree of public participation, it is worth investigating whether the development of digital financial inclusion has a catalytic effect on the improvement of economic growth. Using the data of 31 provinces to build a panel regression model, this paper studies the market development, the development degree of digital financial inclusion, and the impact of digital financial inclusion on the relationship between factor markets and product markets and regions from the perspective of factor markets and product markets. [ABSTRACT FROM AUTHOR]

Alpine wetlands in western Sichuan plateau (WSCP) are located on the eastern edge of the Qinghai-Tibet Plateau (QTP), where the ecological environment is very sensitive to global climate change. Being naturally driven coupled with unreasonable human development activities, alpine wetlands have experienced serious ecological and environmental issues such as drought, inversion, and desertification. However, due to the limitations of data sources and calculation models, it is impossible for us to deeply understand the change mechanism and spatial difference of the ecological environment of the alpine wetland (EEAW) in previous studies. In view of this, an innovative approach for monitoring the EEAW change has been proposed in this paper. We employ the approach to perform the EEAW change trend analysis, and some meaningful characteristics were founded. Specifically, it includes the fol1owing aspects. The air temperature increase is relatively significant, while the precipitation change has obvious spatial differentiation, and even some region's precipitation experienced a decrease especially in plot1. In Haizishan, Lugu Lake, and Bari Lake, we explored an interesting phenomenon that the precipitation increases first and then decrease, and the turning point occurred around 1999. Increases in air temperature and evaporation have aggravated the drought in high-latitude areas. The drought situation has been alleviated in high-altitude areas due to the acceleration of snow melt water. Wetland vegetation and biomass presented an overall increasing trend, but the degradation also occurs in some area, including Zoige and Lugu Lake area. The human activity disturbances of wetland degradation mainly include the settlements expansion, agricultural development, and the ecotourism prosperity. Among them, targeted poverty alleviation projects have accelerated the urbanization in WSCP, and the development of agriculture and tourism has increased the interference of wetlands. Additionally, we have used Landsat images and national wetland survey data (1999, 2013, and 2020 year) from the past two decades to verify the EEAW trend and confirm the reliability of the analysis results using this approach. [ABSTRACT FROM AUTHOR]

In the Netherlands, waste electric and electronic equipment (e-waste) is an important point for discussion on the circular economy agenda. This paper shows the Dutch example of how “waste” can be turned into a resource, and the climate change benefits from appropriate collection and recycling. It describes the avoided emissions of CO2-equivalents due to e-waste recycling and appropriate removal and destruction of (H)CFCs contained in cooling and freezing appliances. Six different e-waste categories were included, and the results of 2016 were compared to previous years (2009–2015). In 2016, 110,000 tonnes of e-waste were collected. 80% of this was recycled to useful materials. Additionally, it resulted in 17% energy recovery. That year, the recycling of e-waste and the removal of (H)CFKs resulted in approximately 416,000 tonnes of avoided emissions of CO2-equivalents. Although the phasing out of cooling and freezing appliances with (H)CFKs led to a general decrease in the quantity of avoided CO2 emissions over time, removal of (H)CFKs still explained most of the avoided CO2 emissions. Material recycling appeared particularly beneficial for cooling and freezing appliances and small and large household appliances. The paper ends with reasons to further close the loop and ways forward to do so. [ABSTRACT FROM AUTHOR]

Summary: This paper is concerned with innovative approaches to renewable energy sources computation methodologies, which provide more refined results than the classical alternatives. Such refinements provide additional improvements especially for replacement of fossil energy usages that emit greenhouse gas (GHG) into the atmosphere leading to climate change impact. Current knowledge gap among each renewable energy source calculation is rather missing fundamentals of plausible, rational, and logical explanations for the interpretation of results. In the literature, there are rather complicated and mechanically applicable methodologies, which require input and output measurement data match with missing physical explanations. The view taken in this review paper is to concentrate on quite plausible, logical, rational, and effectively applicable innovative energy calculation methodologies with simplistic fundamentals. For this purpose, a set of renewable energy methodological approaches is revisited with their innovative structures concerning solar, wind, hydro, current, and geothermal energy resources. With the increase in the renewable energy utilizations to combat the undesirable impacts of global warming and climate change, there is a need for better models that will include physical environmental conditions and data properties in the probabilistic, statistical, stochastic, logical, and rational senses leading to refined and more reliable estimations with application examples in the text. Finally, new research directions are also recommended for more refined innovative energy system calculations. [ABSTRACT FROM AUTHOR]

Data from published studies were combined and analyzed to develop best-fit equations and curves quantifying the change in sick building syndrome (SBS) symptom prevalence in office workers with ventilation rate. For each study, slopes were calculated, representing the fractional change in SBS symptom prevalence per unit change in ventilation rate per person. Values of ventilation rate, associated with each value of slope, were also calculated. Linear regression equations were fitted to the resulting data points, after weighting by study size. Integration of the slope–ventilation rate equations yielded curves of relative SBS symptom prevalence vs. ventilation rate. Based on these analyses, as the ventilation rate drops from 10 to 5 l/s-person, relative SBS symptom prevalence increases approximately 23% (12% to 32%), and as ventilation rate increases from 10 to 25 l/s-person, relative prevalence decreases approximately 29% (15% to 42%). Variations in SBS symptom types, building features, and outdoor air quality may cause the relationship of SBS symptom prevalence with ventilation rate in specific situations to differ from the average relationship predicted in this paper. Practical Implications On average, providing more outdoor air ventilation will reduce prevalence rates of sick building syndrome (SBS) symptoms. However, given the costs of energy use, including increased risks of climate change, it is important to balance the benefits and risks of increased ventilation. This paper provides initial estimates of how the incremental health benefits per unit of increased ventilation diminish at higher levels of ventilation. [ABSTRACT FROM AUTHOR]

This paper shows the effects of changes in the spatial-temporal behavior and phase shift of climate variables on rainfed agriculture in the Lerma-Chapala-Santiago Basin in central Mexico. Specifically, changes in rainfall (R), maximum temperature (Tmax), and minimum temperature (Tmin) were analyzed over two 25-year periods (1960 to 1985 and 1986 to 2010). Climate surfaces were generated by interpolation using the thin-plate smoothing spline algorithm in the software ANUSPLIN. Climate data were Fourier-transformed and fitted to a sinusoidal curve model, and changes in amplitude (increase) and phase were analyzed. The temporal behavior (1960–2010) indicated that rainfall was the most stable variable at the monthly level and presented no significant changes. However, Tmax increased by 2°C in the final period, and Tmin increased by 0.7°C at the end of the final period. The basin was discretized into ten rainfed crop areas (RCAs) according to the extent of changes in the amplitude and phase of the climate variables. The central and southern portions (55% of the area) presented more significant changes in amplitude, mainly in Tmin and Tmax. The remaining RCAs were smaller (14.6%) but presented greater variation: the amplitude of the Tmin decreased in addition to showing a phase shift, whereas Tmax increased in addition to showing a phase shift. These results translate into a delay in the characteristic temperatures of the spring and summer seasons, which can impact the rainfed crop cycle. Additionally, rainfall showed an annual decrease of approximately 50 mm in all RCAs, which can affect the phenological development of crops during critical stages (emergence through flowering). These changes represent a significant threat to the regional economy and food security of Mexico. [ABSTRACT FROM AUTHOR]

Climatic conditions play a major role in wine production. Given the increasing impacts and risks posed by climate change, it is important to understand the effects it will have on the wine sector and different wine-producing regions worldwide. This study provides an in-depth examination of the scientific discourse on wine and climate change from 2000 to 2022 by conducting a bibliometric analysis of the literature published on the Web of Science database, which included 1,314 publications. The use of quantitative and qualitative methods allows us to investigate how research has evolved over the years. Our analysis uncovers the most productive countries, institutions, and journals leading the research in this domain, while emphasising the multifaceted approach to studying wine and climate change. Nevertheless, numerous research areas are yet to be adequately explored. Through co-citation analysis and bibliographic coupling, we identify dominant thematic clusters in previous and current scientific literature and reveal emerging research trajectories in this field. The main thematic clusters found include the assessment of climate change effects on viticultural regions worldwide, climate change's impact on grape composition, and the impact on grape phenology. Our results can be useful not only to understand the main themes studied until now but also to orientate researchers towards less explored aspects and disciplines in scientific research. [ABSTRACT FROM AUTHOR]

Summary: The performance of the Photovoltaic (PV) system is highly affected by fluctuating climatic conditions. Due to these changing climatic conditions, the irradiance diminishes, and it results in the non‐linearity in voltage‐power (P‐V) curves of the PV system. Therefore, it is one of the reasons for the occurrence of the multiple peaks on P‐V curves instead of a single peak. Therefore, it is challenging to track the global maximum power point tracking (MPPT) under these circumstances with traditional MPPT techniques viz. perturb and observe (P&O) and fuzzy logic control (FLC). Concerning global MPP tracking, this paper proposes a novel MPPT method based on a Rao‐1 optimization algorithm. The proposed algorithm is used to overcome the drawbacks of the classical P&O, FLC, and PSO based MPPT techniques under partical shading conditions (PSCs) and rapidly changing atmospheric conditions viz. slow convergence speed and oscillation around the MPP. The proposed MPPT tracks the global MPP under any abnormal conditions like PSCs. To show the efficiency of the proposed MPPT a comparative assessment is performed with well‐established MPPT methods in the literature. The simulation results witnessed that the proposed MPPT is more superior in improving the PV system performance under normal as well as under PSCs. The proposed MPPT over the PSO technique decreases the tracking time by 72.7% for pattern 1, 29.8% for pattern 2, 66.7% for pattern 3, 61.9% for pattern 5, 27.8% for pattern 6, and improves the tracking efficiency in all shading patterns. [ABSTRACT FROM AUTHOR]

Summary: This present paper considers a novel strategy for energy management and intelligent power control of a stand‐alone electric generation system (EGS). The considered system consists of a wind energy conversion system, which contains a wind turbine with permanent magnet synchronous generator associated with a battery storage system and a direct current (DC) load. According to different climatic changes, load variations, and battery state of charge, the considered EGS is studied as a switched and uncertain nonlinear system. The main control objectives are, first, to regulate the wind power generation to satisfy the required power and second, to maintain the battery state of charge within a certain limits to extend its life cycle. Thus, a strategy of energy management is proposed based on different modes of system operation. Furthermore, a neural network‐based integral sliding mode controller (NN‐ISMC) is developed, as a robust and intelligent control method, to satisfy the reference power of each operation areas and to improve the robustness and the stability of such stand‐alone system under high perturbations and external disturbances. The analytical stability of the proposed approach is demonstrated and assured using Lyapunov function method. In addition, the simulation results, realized using Matlab software, show that the suggested NN‐ISMC control strategy ensures faster transient response and smaller steady‐state error performances, compared to the other presented methods. [ABSTRACT FROM AUTHOR]

Summary: A worldwide shift headed for a greener and low emissions will necessitate remarkable advancement in the way in which the energy is being produced and used. The factors such as climate changes induced by pollution, progressively more strict emissions norms for vehicles, depletion of petrol/diesel along with instability in their prices for transportation systems, play a vital role in the improvisation of technology involved in conventional vehicles. The hybrid electric vehicles (HEVs) are on the peak of the list of choices available for clean vehicle technologies. The various architectures of HEV, different methodologies of hybrid vehicle, are focused in this paper. The design criteria and optimization techniques with reference to the driving cycle is also elucidated. The various electric drives used for HEV are discussed in this paper. Also, the different electric propulsion systems are explained. To improve the fuel economy and emission of hybrid power system, control strategies are very significant. Researchers concentrate in optimizing the performance of HEV. [ABSTRACT FROM AUTHOR]

The contents of free sulphydryl groups (SH), disulphide bonds (SS), and free amino groups (NH2) were determined in order to estimate the extent of climatic condition influence on gluten quality. The analysis included four bread wheat varieties grown in two production years (2011 and 2012) with different climatic conditions in different locations. According to our previously reported results, the working hypothesis was that enzyme activity for breadmaking purpose was insufficient. The aim of this paper was to study the influence of naturally present enzymes on the bread quality by the addition of previously extracted and freeze-dried albumins to the base flour as an additive. The selection of samples was made on the basis of different combinations of proteolytic and α-amylolytic enzymes activity levels. For samples from 2012 production year, the content of SH groups was significantly higher. Regarding the SS content, the obtained results exhibited the opposite trend. Variations in NH2 content were dominantly caused by temperature treatment of tested samples. The addition of freeze-dried albumins to bread improved its specific volume in a lesser extent, while bread crumb texture was significantly improved. [ABSTRACT FROM AUTHOR]

Summary: The Middle East region is a key player in the world energy market today. It holds approximately over 50% of the world's proven fossil fuel reserves. Yet, the region is significantly challenged by the large dependence on finite fossil fuel resources in its primary energy supply. The intricate relationship between climate change mitigation and the development of energy systems underlines great uncertainty over the future of energy development in the Middle East. Such uncertainty is greatly linked to growing energy demands and the region's capacity to transition to low‐carbon energy systems. Over the past 20 years, the total primary energy demand in the Middle has almost tripled due to rapid population growth and economic development. Notably, most of the growing energy demand was concentrated in 5 countries, Iran, Saudi Arabia, Iraq, Kuwait, and the UAE. These 5 countries represented around 82% of the total primary energy demand in 2015, with Saudi Arabia and Iran alone accounted for 60%. The core question of this paper is what are the possible implications of growing energy demands in these countries and which sectors will entail significant increases in the projected energy requirements? The significance of the work presented here stems from analyzing 4 major countries that constitute the largest share in Middle East's total energy consumption and associated emissions. Examining these 4 countries together is important to highlight how future increase in these countries could largely affect the overall energy demand from the Middle East region in the next 20 years. Thus, the scope of the paper is looking at energy demand implications in 4 countries, Iran, Saudi Arabia, Kuwait, and the United Arab Emirates (UAE). Iraq is excluded from the analysis due to the large political uncertainty associated with Iraq's energy development. Here, a regression model is used to forecast energy demand from 5 economic sectors across the 4 countries using projected increase in population and gross domestic product (GDP) by 2030. Results indicate that most of the projected energy demand will be from Iran and Saudi Arabia. In addition, industry and transportation sectors will witness the largest increase among the 5 sectors examined in the paper. For instance, industry and transportation sector will collectively account for 52% and 67% of the projected energy demand in Iran and Saudi Arabia, respectively. Such results are important to highlight when ascertaining sectoral level implications of future energy demands and to determine potential areas where energy savings can be made. [ABSTRACT FROM AUTHOR]

The research of the runoff structure and its influencing factors in the Xilinhe River Basin not only provides indispensable basic data for the economic development, but also has long-term significance for the protection of grasslands. Based on the runoff data of Xilinhot Hydrological Station from 1960 to 2010 and the daily meteorological data of three surrounding weather stations from 1960 to 2010, the paper calculated the potential evapotranspiration with Penman's formula and used the combination of Mann-Kendall and Pettitt to diagnose the variation points of characteristic value of runoff distribution during the year. The cumulant slope change rate method is used to quantitatively analyze the contribution rate of climate change and human activities to the uneven distribution coefficient and the complete adjustment coefficient of runoff during the year. The results show that (1) the monthly distribution of runoff in the Xilinhe River Basin is obviously "bimodal" during the year, and the uneven coefficient, complete adjustment coefficient, and concentration in the 2000s are significantly higher than those of 60s-90s. (2) In 1998, the coefficient of uneven distribution of runoff in the Xilinhe River Basin and the coefficient of complete adjustment both showed abrupt changes. (3) Climate change and human activities contributed 11.48% and 88.52% and 9.35% and 90.65% to the uneven distribution coefficient and the complete adjustment coefficient, respectively, of the runoff in the Xilinhe River Basin. Human activities are the main driving factors for changes in the distribution of runoff in the Xilinhe River Basin during the year. [ABSTRACT FROM AUTHOR]

Organic matter is the material basis for shales to generate hydrocarbon, as well as the main reservoir space and seepage channel for shale gas. When the thermal evolution degree is consistent, the organic carbon content in present shales is subject to the abundance of primitive sedimentary organic matter. Deep geofluids significantly influence the sedimentary organic matter's enrichment, but the mechanism remains unclear. This paper is aimed at determining how hydrothermal and volcanic activities affected the enrichment of sedimentary organic matter by studying lower Cambrian shales in the lower Yangtze region and upper Ordovician-lower Silurian shales. Oxidation-reduction and biological productivity are used as indicators in the study. The result shows that hydrothermal or volcanic activities affected the enrichment of sedimentary organic matter by influencing climate changes and the nutrients' sources on the waterbody's surface and reducing water at the bottom. In the lower Cambrian shales of the Wangyinpu Formation in the lower Yangtze region, hydrothermal origin caused excess silicon. During the sedimentary period of the lower and middle-upper Wangyinpu Formation, vigorous hydrothermal activities increased the biological productivity on the waterbody's surface and intensified the reducibility at the bottom of the waterbody, which enabled the rich sedimentary organic matter to be well preserved. During the sedimentary period of the lower upper Ordovician Wufeng Formation and the lower Silurian Longmaxi Formation in the upper Yangtze region, frequent volcanic activities caused high biological productivity on the waterbody surface and strong reducibility at the bottom of the waterbody. As a result, the abundant organic matter deposited from the water surface can be well preserved. During the sedimentary period of the upper Longmaxi Formation, volcanic activities died down gradually then disappeared, causing the biological productivity on the water surface to decrease. Besides, the small amount of organic matter deposited from the water surface was destroyed due to oxidation. [ABSTRACT FROM AUTHOR]

Precipitation and temperature are among major climatic variables that are used to characterize extreme weather events, which can have profound impacts on ecosystems and society. Accurate simulation of these variables at the local scale is essential to adapt urban systems and policies to future climatic changes. However, accurate simulation of these climatic variables is difficult due to possible interdependence and feedbacks among them. In this paper, the concept of copulas was used to model seasonal interdependence between precipitation and temperature. Five copula functions were fitted to grid (approximately 10 km × 10 km) climate data from 1960 to 2013 in southern Ontario, Canada. Theoretical and empirical copulas were then compared with each other to select the most appropriate copula family for this region. Results showed that, of the tested copulas, none of them consistently performed the best over the entire region during all seasons. However, Gumbel copula was the best performer during the winter season, and Clayton performed best in the summer. More variability in terms of best copula was found in spring and fall seasons. By examining the likelihoods of concurrent extreme temperature and precipitation periods including wet/cool in the winter and dry/hot in the summer, we found that ignoring the joint distribution and confounding impacts of precipitation and temperature lead to the underestimation of occurrence of probabilities for these two concurrent extreme modes. This underestimation can also lead to incorrect conclusions and flawed decisions in terms of the severity of these extreme events. [ABSTRACT FROM AUTHOR]

The North Atlantic Oscillation (NAO), which manifests as an irregular atmospheric fluctuation, has a profound effect on the global climate change. The NAO index (NAOI) is the quantitative indicator that can reflect the intensity of the NAO events, and its traditional definition is the normalized sea level pressure (SLP) difference between Azores and Iceland. From the variation tendency of the NAOI, we found that it is difficult to predict the NAO with the characteristics of variability and complexity. As a data-driven approach, the deep neural network presents great potential in learning the mechanisms of climate forecasting. In this paper, we adopt long short-term memory (LSTM) and ConvLSTM to predict the NAO from two aspects, NAOI and SLP, respectively. In previous studies, LSTM has been regarded as a resultful method for time series prediction. ConvLSTM can capture both the temporal and spatial interdependencies of the SLP field; then, the NAOI can be calculated from the SLP output. In order to improve the prediction reliability, we utilize the discrete wavelet transform (DWT) as a preprocessing technique to decompose original data into different frequencies, considering the local time dependency. It can effectively preserve the features of high-frequency data and forecast extreme events more accurately. The proposed DWT-LSTM and DWT-ConvLSTM models are compared against multiple advanced models, such as LSTM, Holt-Winters, support vector regression (SVR), and gated recurrent unit (GRU). The results indicate that both DWT-LSTM and DWT-ConvLSTM perform better, particularly at peak values. As for the 31 NAO events from 2006 to 2015, our models achieve the lowest prediction error and the best stability. Compared with the forecast products of CPC named Global Forecast System (GFS) and the ensemble forecasts (ENSM), our models are much closer to observation in multistep forecasting. [ABSTRACT FROM AUTHOR]

Background and Aims: A region's suitability for viticulture is commonly categorised by thermal‐based bioclimatic indices during the growing season, of which the growing season temperature (GST) is the most widely used. Growing season temperature thresholds, however, were developed for a specific region and therefore may not translate to other established wine regions, including Australia. This paper aims to investigate and refine the application of GST in modelling spatial suitability of existing wine regions in south‐east Australia. Methods and Results: Locations suitable for winegrapes according to traditional GST were mapped and compared with established wine regions. Inconsistencies were identified, and a range of refinements was applied in order to improve the spatial representation. Conclusions: This study highlights the benefit of three improvements to the GST parameter in order to successfully represent Australian wine regions. First, a latitude adjustment was applied to the GST to account for varying day length. Second, significant temporal variability in GST over the last 15–20 years was identified, highlighting the challenge of selecting a period that represents 'average conditions' for such studies. Third, in order to model the spatial extent of Australian wine regions, the inclusion of a 'hot' GST classification (19–22°C) was needed. Significance of the Study: These refinements allow for the successful quantification of climatic conditions for Australian viticulture. Refined GST thresholds can be applied to future climate projections, providing the industry with vital information and guidance on how to react and adapt to the challenges of climate change. [ABSTRACT FROM AUTHOR]

Bamboo forests are undoubtedly one of the most abundant nontimber plants on Earth and cover a wide area of tropical and subtropical regions around the world. This amazing plant has unique rapid growth and can play an important role in protecting our planet from pollution and improving the soil. Bamboo can be used as a biofuel, food, and for architecture and construction applications and plays a large role in the local economy by creating job opportunities. The aim of this paper is to review the extraordinary tropical plant bamboo by explaining the mechanisms related to the growth and strength of bamboo and identifying ways to utilize bamboo in industry, employment, climate change mitigation, and soil erosion reduction. [ABSTRACT FROM AUTHOR]

This paper reinvestigated the climate-crop yield relationship with the statistical model at crops' growing stage scale. Compared to previous studies, our model introduced monthly climate variables in the production function of crops, which enables separating the yield changes induced by climate change and those caused by inputs variation and technique progress, as well as examining different climate effects during each growing stage of crops. By applying the fixed effect regression model with province-level panel data of crop yields, agricultural inputs, and the monthly climate variables of temperature and precipitation from 1985 to 2015, we found that the effects of temperature generally are negative and those of precipitation generally are positive, but they vary among different growth stages for each crop. Specifically, GDDs (i.e., growing degree days) have negative effects on spring maize's yield except for the sowing and ripening stages; the effects of precipitation are negative in September for summer maize. Precipitation in December and the next April is significantly harmful to the yield of winter wheat; while, for the spring wheat, GDDs have positive effects during April and May, and precipitation has negative effects during the ripening period. In addition, we computed climate-induced losses based on the climate-crop yield relationship, which demonstrated a strong tendency for increasing yield losses for all crops, with large interannual fluctuations. Comparatively, the long-term climate effects on yields of spring maize, summer maize, and spring wheat are more noticeable than those of winter wheat. [ABSTRACT FROM AUTHOR]

Desertification is a major global environmental issue exacerbated by climate change. Strategies to combat desertification include prevention which seeks to reverse the process before the system reaches the stable desertified state. One of these initiatives is to implement early warning tools. This paper presents SAT (the Spanish acronym for Early Warning System), a decision support system (DSS), for assessing the risk of desertification in Spain, where 20% of the land has already been desertified and 1% is in active degradation. SAT relies on three versions of a Generic Desertification Model (GDM) that integrates economics and ecology under the predator-prey paradigm. The models have been programmed using Vensim, a type of software used to build and simulate System Dynamics (SD) models. Through Visual Basic programming, these models are operated from the Excel environment. In addition to the basic simulation exercises, specially designed tools have been coupled to assess the risk of desertification and determine the ranking of the most influential factors of the process. The users targeted by SAT are government land-use planners as well as desertification experts. SAT tool is implemented for five case studies, each one of them representing a desertification syndrome identified in Spain. Given the general nature of the tool and the fact that all United Nations Convention to Combat Desertification (UNCCD) signatory countries are committed to developing their National Plans to Combat Desertification (NPCD), SAT could be exported to regions threatened by desertification and expanded to cover more case studies. [ABSTRACT FROM AUTHOR]

Summary: The produced energy from varied sources in modern power systems is to be optimally planned for planning and operating of power system under the determined limit conditions. Recently, the rising overall people population of the world, the increasing of people requirements, improvements of technology, and ecosystem and global climate changes have caused with the increasing of electric energy demand. One of the most important solution methods to meet this energy demand is considered as utilization of renewable energy sources (RESs) in power systems. The structure of power systems has become with the usage of RESs more complex. The optimal power flow (OPF) from planning and operation problems has converted to difficult problem with RESs integrated into modern power systems. This paper presents the OPF problem of power systems with a high penetration of controllable renewable sources. These kinds of sources are able to inject a determined power since they have a back‐up unit (storage). Uncertain solar irradiance and wind speed are simulated via log‐normal and Rayleigh probability distributions, respectively. The proposed OPF problem with controllable renewable sources is solved by the differential evolutionary particle swarm optimization (DEEPSO) algorithm. Simulations conducted on various test systems illustrate the effectiveness and efficiency of DEEPSO as compared with other algorithms including moth swarm algorithm, backtracking search algorithm, and differential search algorithm. In addition, the Wilcoxon signed‐rank test is applied to show the supremacy, effectiveness, and robustness of DEEPSO algorithm. [ABSTRACT FROM AUTHOR]

Summary: In recent years, the attention towards the utilization of solar and wind power systems are becoming more popular because of its nonpolluted and nondepleting nature. However, these power sources rely on environmental conditions for power generation. So, extracting maximum power available from the wind and solar systems is essential. Many of the existing maximum power point tracking (MPPT) techniques' performances are not convenient in the hybrid operation of wind and solar power systems. So, to provide an optimum MPPT operation, this paper proposes a fuzzy logic controll er (FLC)–based elephant herding optimization (EHO) technique for MPPT in the hybrid wind‐solar system. The proposed fuzzy‐EHO (FEHO) method has the tendency to track the maximum power under the partial shading condition of the photovoltaic (PV) system and low wind speed situations. The results are compared with the conventional MPPT methods, and it shows that the proposed optimization method efficiently tracks the maximum power point of the wind‐solar power systems even with the variations in the climatic conditions. MATLAB tool is used in the design and operation of the proposed method. Furthermore, the performance of the proposed system is tested with an experimental setup and provides better efficiency. [ABSTRACT FROM AUTHOR]

In recent decades, global and local vegetation phenology has undergone significant changes due to the combination of climate change and human activities. Current researches have revealed the temporal and spatial distribution of vegetation phenology in large scale by using remote sensing data. However, researches on spatiotemporal differentiation of remote sensing phenology and its changes are limited which involves high-dimensional data processing and analysing. A new data model based on data cube technologies was proposed in the paper to efficiently organize remote sensing phenology and related reanalysis data in different scales. The multidimensional aggregation functions in the data cube promote the rapid discovery of the spatiotemporal differentiation of phenology. The exploratory analysis methods were extended to the data cube to mine the change characteristics of the long-term phenology and its influencing factors. Based on this method, the case study explored that the spring phenology of Qinba Mountains has a strong dependence on the topography, and the temperature plays a leading role in the vegetation green-up date distribution of the high-altitude areas while human activities dominate the low-altitude areas. The response of green-up trend slope seems to be the most sensitive at an altitude of about 2000 meters. This research provided a new approach for analysing phenology phenomena and its changes in Qinba Mountains that had the same reference value for other regional phenology studies. [ABSTRACT FROM AUTHOR]

This paper examines the impact that climate change may have on the lodging of oats in the Republic of Ireland and the UK. Through the consideration of a novel lodging model representing the motion of an oat plant due to the interaction of wind and rain and integrating future predictions of wind and rainfall due to climate change, appropriate conclusions have been made. In order to provide meteorological data for the lodging model, wind and rainfall inputs are analysed using 30 years' time series corresponding to peak lodging months (June and July) from 38 meteorological stations in the United Kingdom and the Irish Republic, which enables the relevant probability density functions (PDFs) to be established. Moreover, climate data for the next six decades in the British Isles produced by UK climate change projections (UKCP18) are analysed, and future wind and rainfall PDFs are obtained. It is observed that the predicted changes likely to occur during the key growing period (June to July) in the next 30 years are in keeping with variations, which can occur due to different husbandry treatments/plant varieties. In addition, the utility of a double exponential function for representing the rainfall probability has been observed with appropriate values for the constants given. [ABSTRACT FROM AUTHOR]

This paper examines photocatalytic hydrogen production as a clean energy solution to address challenges of climate change and environmental sustainability. Advantages and disadvantages of various hydrogen production methods, with a particular emphasis on photocatalytic hydrogen production, are discussed in this paper. Social, environmental and economic aspects are taken into account while assessing selected production methods and types of photocatalysts. In the first part of this paper, various hydrogen production options are introduced and comparatively assessed. Then, solar-based hydrogen production options are examined in a more detailed manner along with a comparative performance assessment. Next, photocatalytic hydrogen production options are introduced, photocatalysis mechanisms and principles are discussed and the main groups of photocatalysts, namely titanium oxide, cadmium sulfide, zinc oxide/sulfide and other metal oxide-based photocatalyst groups, are introduced. After discussing recycling issues of photocatalysts, a comparative performance assessment is conducted based on hydrogen production processes (both per mass and surface area of photocatalysts), band gaps and quantum yields. The results show that among individual photocatalysts, on average, Au-CdS has the best performance when band gap, quantum yield and hydrogen production rates are considered. From this perspective, TiO2-ZnO has the poorest performance. Among the photocatalyst groups, cadmium sulfides have the best average performance, while other metal oxides show the poorest rankings, on average. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Utilising climate funds properly to reduce the impact of potential risks of climate change at the local level is essential for successful adaptation to climate change. Climate change has been disrupting the lives of millions of households along the coastal region of Bangladesh. The country has allocated support from its national funds and accessed international funds for the implementation of adaptation interventions. With the focus of the scientific community on climate finance mechanisms and governance at the global and the national level, there is a lacuna in empirical evidence of how climate finance affects risk appraisal and engagement in adaptation measures at the local level. This paper aims to examine how the support from climate finance affects risk appraisal in terms of the perceived probability and severity and the factors which influence risk appraisal. A field survey was conducted on 240 climate finance recipient households (CF HHs) and 120 nonclimate finance recipient households (non-CF HHs) in Galachipa Upazila of Patuakhali District in coastal Bangladesh. The results indicate that both CF and non-CF HHs experience a high probability of facing climatic events in the future; however, CF HHs anticipated a higher severity of impacts of climatic events on different dimensions of their households. With higher income and social capital, the overall risk appraisal decreases for CF HHs. CF HHs have higher engagement in adaptation measures and social groups and maintain alternative sources of income. Climate finance played a critical role in supporting households in understanding the risks that they were facing, assisting them in exploring as well as enhancing their engagement in adaptation options. [ABSTRACT FROM AUTHOR]

Regional biogeographical models are considered to be important tools for supporting decisions relating to sustainable agricultural planning for climate change. These models are useful for a better understanding of the impact of climate change on individual crop species due to their sensitivity to regional ecological conditions. This paper deals with the application of a regional biogeographical model in order to predict the impact of climate change on growing conditions for grain maize in Central Europe. The model is based on a detailed knowledge of the relationships between the climatic characteristics of vegetation zones in landscapes with ecological growing conditions suitable for grain maize in the region under study. The results gained from using the model indicate a substantial increase in the total area suitable for growing of grain maize in the study region. By 2070, this area is expected to be triple the size it is today. Special maps are used to visualize prediction scenarios in order to support decision-making in regional planning in the study region, where grain maize is an important agricultural crop. This biogeographical model can be used in other European regions, where basic data related to vegetation zones are available. [ABSTRACT FROM AUTHOR]

Accurate remote-sensed precipitation data are crucial to the effective monitoring and analysis of floods and climate change. The Global Precipitation Measurement (GPM) satellite product offers new options for the global study of precipitation. This paper evaluates the applicability of GPM IMERG products at different time resolutions in comparison to ground-measured data. Based on precipitation data from 107 meteorological stations in the Beijing-Tianjin-Hebei region, GPM products were analysed at three timescales: half-hourly (GPM-HH), daily (GPM-D), and monthly (GPM-M). We use a cumulative distribution function (CDF) model to correct GPM-D and GPM-M products to analyse temporal and spatial distributions of precipitation. We came to the following conclusions: (1) The GPM-M product is strongly correlated with ground station data. Based on five evaluation indexes, NRMSE (Normalized Root Mean Square Error), NSE (Nash-Sutcliffe), FAR (False Alarm Ratio), UR (Underreporting Rate), and CSI (Critical Success Index), the monthly GPM products showed the best performance, better than GPM-HH products and GPM-D products. (2) The performance of GPM products in summer and autumn was better than in winter and spring. However, the GPM satellite's precision in undulating terrain was poor, which could easily lead to serious errors. (3) CDF models were successfully used to modify GPM-D and GPM-M products and improve their accuracy. (4) The range of 0–100 mm precipitation could be corrected best, but the GPM-M products were underestimated. Corrected GPM-M data in the range >100 mm were overestimated. According to this analysis, the GPM IMERG Final Run products at daily and monthly timescales have good detection ability and can provide data support for long-time series analyses in the Beijing-Tianjin-Hebei region. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the effects of temperature on the production performance values, as well as on sex differentiation and gonad development, of juvenile sterlet were investigated. For this purpose, fry were reared in the recirculating aquaculture system at three different temperatures (15, 20, and 25°C) in four replicate groups each for 140 days (10–150 dph). Fish were fed mainly with commercial diet. The growth rate in this period was significantly affected by temperature. The best performance values in terms of daily growth rate, thermal growth coefficient, and yield were registered at 20°C, indicating that this value to be near, or close to, the optimal for juvenile sterlet. Gonad samples were taken for histological analysis which revealed that the increased temperature did not cause significant differences in the sex ratio between treatments, but at 25°C resulted in significant deviation from the theoretically expected 1:1 gender ratio to favouring males. We have also found that increased temperature at 25°C accelerated germ cell development. These results indicate that rearing of sterlet juveniles at about 20°C temperature could be considered optimal both from aquaculture, as well as from species conservation viewpoints. [ABSTRACT FROM AUTHOR]

SUMMARY For several years recently, the price of oil has fluctuated due to the weak US dollar and financial risks. In particular, the WTI crude oil price reached $147 per barrel in July of 2008, which is the highest price thus far. An awareness of an impending crisis and concern over climate change are driving an increase in R&D for alternative energy sources instead of fossil-based energy. However, the researches based on traditional method show negative options about the economic value of new and renewable energy. This paper evaluates the value of new and renewable energy through a real option method which considers the uncertainty associated with fossil energy and the uncertainty of the success of R&D. The evaluating model assumes that the fossil energy price follows a geometric mean reverting process and that the probability of success with R&D on renewal forms of energy follows a binomial probability model. The model considers four options: the option to continue R&D, the option to delay R&D, the option to deploy R&D, and the option to abandon R&D. Finally, the value of Korean R&D on renewal forms of energy is analyzed by the model. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The climate continentality or oceanity is one of the main characteristics of the local climatic conditions, which varies with global and regional climate change. This paper analyzes indexes of continentality and oceanity, as well as their variations in the middle and high latitudes of the Northern Hemisphere in the period 1950–2015. Climatology and changes in continentality and oceanity are examined using Conrad’s Continentality Index (CCI) and Kerner’s Oceanity Index (KOI). The impact of Northern Hemisphere teleconnection patterns on continentality/oceanity conditions was also evaluated. According to CCI, continentality is more significant in Northeast Siberia and lower along the Pacific coast of North America as well as in coastal areas in the northern part of the Atlantic Ocean. However, according to KOI, areas of high continentality do not precisely correspond with those of low oceanity, appearing to the south and west of those identified by CCI. The spatial patterns of changes in continentality thus seem to be different. According to CCI, a statistically significant increase in continentality has only been found in Northeast Siberia. In contrast, in the western part of North America and the majority of Asia, continentality has weakened. According to KOI, the climate has become increasingly continental in Northern Europe and the majority of North America and East Asia. Oceanity has increased in the Canadian Arctic Archipelago and in some parts of the Mediterranean region. Changes in continentality were primarily related to the increased temperature of the coldest month as a consequence of changes in atmospheric circulation: the positive phase of North Atlantic Oscillation (NAO) and East Atlantic (EA) patterns has dominated in winter in recent decades. Trends in oceanity may be connected with the diminishing extent of seasonal sea ice and an associated increase in sea surface temperature. [ABSTRACT FROM AUTHOR]