Search results

Weather is often referred as an uncontrollable factor, which influences customer’s buying decisions and causes the demand to move in any direction. Such a risk usually leads to loss to industries. However, only few research studies about weather and retail shopping are available in literature. The purpose of this paper is to develop a model and to analyze the relationship between weather and retail shopping behavior (i.e. store traffic and sales).

The data set for this research study is obtained from two food retail stores and a fashion retail store located in Lower Bavaria, Germany. All these three retail stores are in same geographical location. The weather data set was provided by a German weather service agency and is from a weather station nearer to the retail stores under study. The analysis for the study was drawn using multiple linear regression with autoregressive elements (MLR-AR). The estimated coefficients of weather variables using MLR-AR model represent corresponding weather impacts on the store traffic and the sales.

The snowfall has a significant effect on the store traffic and the sales in both food and fashion retail stores. In food retail store, the risk due to snowfall varies depending on the location of stores. There are also significant lagging effects of snowfall in the fashion retail store. However, the rainfall has a significant effect only on the store traffic in the food retail stores. In addition to these effects, the sales in the fashion retail store are highly affected by the temperature deviation.

Limitations in availability of data for the weather variables and other demand influencing factors (e.g. promotion, tourism, online shopping, demography of customers, etc.) may reduce efficiency of the proposed MLR-AR model. In spite of these limitations, this study can be able to quantify the effects of weather variables on the store traffic and the sales.

This study contributes to the field of retail distribution by providing significant evidence of relationship between weather and retail business. Unlike previous studies, the proposed model tries to consider autocorrelation property, main and interaction effects between weather variables, temperature deviation and lagging effects of snowfall on the store traffic or the sales. The estimated weather impacts from this model can act as a reliable tool for retailers to explain the importance of different non-catastrophic weather events.

Fewer researchers have investigated the climatic and economic drivers of land-use change simultaneously and the interplay between drivers. This paper aims to investigate the nonlinear and interaction effects of price and climate variables on the rice acreage in high-latitude regions of China.

This study applies a multivariate adaptive regression spline to characterize the effects of price and climate expectations on rice acreage in high-latitude regions of China from 1992 to 2017. Then, yield expectation is added into the model to investigate the mechanism of climate effects on rice area allocation.

The results of importance assessment suggest that rice price, climate and total agricultural area play an important role in rice area allocation, and the importance of temperature is always higher than that of precipitation, especially for minimum temperature. Based on the estimated hinge functions and coefficients, it is found that total agricultural area has strong nonlinear and interaction effects with climate and price as forms of third-order interaction. However, the order of interaction terms reduces to second order after absorbing the expected yield. Additionally, the marginal effects of driven factors are calculated at different quantiles. The total area shows a positive and increasing marginal effect with the increase of total area. But the positive impact of price on the rice area can only be observed when price reached 50% or higher quantiles. Climate variables also show strong nonlinear marginal effects, and most climatic effects would disappear or be weakened once absorbing the expected rice yield. Expected yield is an efficient mechanism to explain the correlation between crop area and climate variables, but the impact of minimum temperature cannot be completely modeled by the yield expectation.

To the best of the authors’ knowledge, this is the first study to examine the nonlinear response of land-use change to climate and economic in high-latitude regions of China using the machine learning method.

The purpose of this paper is to develop and apply accurate and original models to understand and analyze the effects of the fabrication temperatures on thermal-induced stress and speed performance of nano positively doped metal oxide semiconductor (pMOS) transistors.

The speed performances of nano pMOS transistors depend strongly on the mobility of holes, which itself depends on the thermal-induced extrinsic stress σ. The author uses a finite volume method to solve the proposed system of partial differential equations needed to calculate the thermal-induced stress σ accurately.

The thermal extrinsic stress σ depends strongly on the thermal intrinsic stress σ₀, thermal intrinsic strain ε₀, elastic constants C11 and C12 and the fabrication temperatures. In literature, the effects of fabrication temperatures on C11 and C12 needed to calculate thermal-induced stress σ₀ have been ignored. The new finding is that if the effects of fabrication temperatures on C11 and C12 are ignored, then, the values of stress σ₀ and σ will be overestimated and, then, not accurate. Another important finding is that the speed performance of nano pMOS transistors will increase if the fabrication temperature of silicon-germanium films used as stressors is increased.

To predict correctly the thermal-induced stress and speed performance of nano pMOS transistors, the effects of fabrication temperatures on the elastic constants required to calculate the thermal-induced intrinsic stress σ₀ should be taken into account.

There are three levels of originalities. The author considers the effects of the fabrication temperatures on extrinsic stress σ, intrinsic stress σ₀ and elastic constants C11 and C12.

This study aims to explore the link between mortality and climate change. The focus is in particular on individuals’ adaptation to temperature changes. The authors analyze the relationship between climatic change (measured by temperature rate) and mortality in 23 Organisation for Economic Co-operation and Development countries during 1970-2010.

This study performs the adaptation regression model in the level form as a dynamic panel fixed effects model. The authors use a non-linear threshold estimation approach to examine the extreme temperature changes effect on the temperature–mortality relation. More specifically, the study explores whether the large increases/decreases in temperature rates affect mortality rates more than the modest changes.

This study indicates that the temperature–mortality relation is significant in early part of the sample period (before 1990) but insignificant during the second part (after 1990). After including controlling factors, as well as nation and year fixed effects, the authors provide evidence that people do adapt to the most of the temperature-related mortalities. Also, this study provides evidence of the non-linear relationship between national temperatures and mortality rates. It is observed that only after 5 per cent increase in the annual temperature, the relation between temperature and overall mortality is significant.

Most studies cover only one specific country, hence making it difficult to generalize across countries. Therefore, the authors argue that the best estimation of the health effects of temperature change can be found by modeling the past relationships between temperature and mortality across countries for a relatively long period. To the authors’ knowledge, previous studies have not systemically tested the adaptation effect across countries.

Due to the predicted global temperature rise and local expansion and densification of cities, Urban Heat Islands (UHI) are likely to increase in the Netherlands. As spatial characteristics of a city influence its climate, urban design could be deployed to mitigate the combined effects of climate change and UHIs. Although cities are already experiencing problems during warm-weather periods, no clear spatial means or strategies are available for urban designers to alleviate heat stress. The paper aims to discuss these issues.

There is a lack of knowledge on cooling effects that can be achieved through urban design in Dutch neighbourhoods. In this paper, the cooling effects of various design measures are compared on the level of urban blocks and neighbourhoods, with a focus on a 1960s neighbourhood in Amsterdam-West. The cooling effects are simulated by means of the microclimate model ENVI-met, here the effects on air temperature and physiological equivalent temperature will be evaluated.

The use of green, and a higher roof albedo in particular, seem to perform well as cooling measures. Combinations of cooling measures do not necessarily result in better performance and might even counteract other cooling effects. However, combinations of measures that lead to an increase in the environmental temperature show the largest heating.

Effects of green roofs and facades are beyond the scope of this study, though future suggestions for this research will be included.

The results add to the body of knowledge in the area of climate design enabling policy makers and designers to estimate the effect of simulated measures in comparable neighbourhoods and thus improve thermal comfort in outdoor spaces.

The purpose of this paper is to explore the impacts of climate change on crop net revenue by region. Particularly, the authors focus on the impact differences between north and south regions.

The authors applied the Ricardian approach which assumes that each farmer wishes to maximize revenue subject to the exogenous conditions of their farm. The climate data are based on actual measurements in 753 national meteorological stations and the socio-economic data covers 8,405 farms across 28 provinces in China.

On average, the rise of annual temperature will hurt farms both in the north or south. The impacts of climate change on both precipitation and temperatures have different seasonal impacts on producers in the north and the south of China. As a consequence, the impact on net farm revenues varies with farms in the north and the south being adversely affected (to different degrees) by a rise in the temperature, but both benefiting from an anticipated increase in rainfall. The results also reveal that irrigation is one key adaption measure to dealing with climate change. Whether in the north or south of China, increasing temperature is beneficial to irrigated farms, while for rainfed farms, higher temperature will result in a reduction in net revenues. The results also reveal that farms in the north are more vulnerable to temperature and precipitation variation than that in the south. Irrigated farms in the south are more vulnerable to precipitation variation than that in the north; but rainfed farms in the north are more vulnerable to precipitation variation than that in the south.

Applying empirical analysis to identify the differences of climate change impacts between north and south regions will help policy makers to design reasonable adaptation policies for various regions.

The breakdown of laminar flow in the clearance space of a journal is considered, and the point of transition is considered in relation to experiments carried out with ‘bearings’ of large clearance. Experiments involving flow visualization with very large clearance ratios of 0.05 to 0.3 show that the laminar regime gives way to cellular or ring vertices at the critical Reynolds number predicted by G. I. Taylor for concentric cylinders even in the presence of an axial flow and at a rather higher Reynolds number in the case of eccentric cylinders. The effect of the transition on the axial flow between the cylinders is small. The critical speed for transition as deduced by Taylor, is little affected by moderate axial flows and is increased by eccentricity. The effect of critical condition on the axial‐flow characteristics of the bearing system appears to be negligible, again for moderate axial flows. Assuming that the results can be extrapolated to clearances applicable to bearing operation, the main conclusion of this paper is that the breakdown of laminar flow, which is a practical possibility in very high‐speed bearings, is delayed by eccentric operation.

This paper investigates whether weather affects stock market returns in Fiji's stock market.

The author employed an exponential general autoregressive conditional heteroskedastic (EGARCH) modeling framework to examine the effect of weather changes on stock market returns over the sample period 9/02/2000–31/12/2020.

The results show that weather (temperature, rain, humidity and sunshine duration) have robust but heterogenous effects on stock market returns in Fiji.

It is useful for scholars to modify asset pricing models to include weather-related variables (temperature, rain, humidity and sunshine duration) to better understand Fiji's stock market dynamics (even though they are often viewed as economically neutral variables).

Investors and traders should consider their mood while making stock market decisions to lessen mood-induced errors.

This is the first attempt to examine the effect of weather (temperature, rain, humidity and sunshine duration) on stock market returns in Fiji's stock market.

The purpose of this paper is to investigate the mediating effect of online reviewers' affect (ORA) on the relationship between weather and online review ratings (ORR).

The consumers' online review data were collected from the third-party restaurant website, and the weather data were obtained from the weather part of Chinese e-government website. SnowNLP was utilized to analyze sentiment and further extract ORA. Furthermore, the mediating effects of ORA on temperature and ORR, rain and ORR were explored separately using PROCESS 3 Macro Model 4, and the interaction effect of temperature and rain was tested through PROCESS 3 Macro Model 7.

The findings of this work demonstrate that ORA mediates the relationship between temperature and ORR and the relationship between rain and ORR. Besides directly leading to higher ORR, a higher temperature can bring about higher ORR by elevating ORA. On the other hand, little rain and heavy rain have a direct negative influence on ORR, and they can also lead people into a bad mood state, thus leading to lower ORR. Furthermore, temperature moderates the effect of rain on ORA. When the temperature is higher, the differences of ORA are larger between different types of rain than that of lower temperature.

This study appears to be the first to investigate the relationship among weather, ORA and ORR using online data. The results could help managers understand when consumers are more likely to provide negative eWOM under corresponding weather conditions and adopt appropriate strategies to improve ORR.

While climatic conditions are believed to have some influence on triggering conflicts, the existing empirical results on the nature and statistical significance of their explanatory role are not conclusive. We construct a dataset for a sample of 139 countries which records the occurrence of an armed conflict, the annual average temperature and precipitation levels, as well as the relevant socioeconomic, demographic, and geographic measures over the 1961–2011 period. Using this dataset and controlling for the effect of relevant nonclimate variables, our comprehensive econometric analyses support the influencing role of climatic factors. Our results are robust and consistent with the hypothesis that climate warming is instrumental in raising the probability of onset of internal armed conflicts and suggests that, along with regulating population size and promoting political stability, controlling climate change is an effective factor for inducing peace by way of curtailing the onset of armed conflicts.