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Article

Pankaj Singh and Gaurav Agrawal

The purpose of this paper is to review research on weather index insurance (WII) for mitigating the weather risk in agriculture and to identify research gaps in current…

Abstract

Purpose

The purpose of this paper is to review research on weather index insurance (WII) for mitigating the weather risk in agriculture and to identify research gaps in current available literature through integrative review.

Design/methodology/approach

This paper is based on the integrative review method as proposed by Whittemore and Knafl. QualSysts tool was adopted for assessing the quality appraisal of articles. Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Findings

Detailed critical analysis of content reveals that WII studies are growing and shifting from traditional to the newest themes. Efficacy of WII is significantly influenced by the impacts of climate change. This paper generates a conceptual framework by synthesizing the published literature on WII.

Research limitations/implications

This paper will be used to improve the WII practices and influence public policy. It is also beneficial in research by contributing to the systematic body of knowledge and useful for researchers to analyze the past and present status with future prospects of further studies on WII.

Originality/value

The paper is the original work of the author. To the best of authors’ knowledge, this is the first paper on integrative review on the efficacy of WII. An attempt has been made in the current paper to critically examine the studies of WII.

Details

International Journal of Ethics and Systems, vol. 35 no. 4
Type: Research Article
ISSN: 2514-9369

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Article

Patrik Appelqvist, Flora Babongo, Valérie Chavez-Demoulin, Ari-Pekka Hameri and Tapio Niemi

The purpose of this paper is to study how variations in weather affect demand and supply chain performance in sport goods. The study includes several brands differing in…

Abstract

Purpose

The purpose of this paper is to study how variations in weather affect demand and supply chain performance in sport goods. The study includes several brands differing in supply chain structure, product variety and seasonality.

Design/methodology/approach

Longitudinal data on supply chain transactions and customer weather conditions are analysed. The underlying hypothesis is that changes in weather affect demand, which in turn impacts supply chain performance.

Findings

In general, an increase in temperature in winter and spring decreases order volumes in resorts, while for larger customers in urban locations order volumes increase. Further, an increase in volumes of non-seasonal products reduces delays in deliveries, but for seasonal products the effect is opposite. In all, weather affects demand, lower volumes do not generally improve supply chain performance, but larger volumes can make it worse. The analysis shows that the dependence structure between demand and delay is time varying and is affected by weather conditions.

Research limitations/implications

The study concerns one country and leisure goods, which can limit its generalizability.

Practical/implications

Well-managed supply chains should prepare for demand fluctuations caused by weather changes. Weekly weather forecasts could be used when planning operations for product families to improve supply chain performance.

Originality/value

The study focuses on supply chain vulnerability in normal weather conditions while most of the existing research studies major events or catastrophes. The results open new opportunities for supply chain managers to reduce weather dependence and improve profitability.

Details

International Journal of Retail & Distribution Management, vol. 44 no. 2
Type: Research Article
ISSN: 0959-0552

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Article

Da Liu, Wenbo Wang and Yinchuan Zhao

Weather affects consumer decision-making. However, academic research on how weather factors affect specific takeaway foods is limited. This paper aims to fill in the gap…

Abstract

Purpose

Weather affects consumer decision-making. However, academic research on how weather factors affect specific takeaway foods is limited. This paper aims to fill in the gap and therefore to contribute to online marketing and operation.

Design/methodology/approach

Web crawler techniques were first exploited to collect takeaway food ordering data from Meituan, the world’s largest GMV platform. Then statistics models and a time series regression model were selected to study the weather impact on online orders.

Findings

The findings highlight that certain weather factors, such as temperature, air quality and rainfall have clear effects on most category takeaway orders.

Originality/value

Quantitative analysis of weather impacts on the takeaway ordering business will help to guide the online service platforms for marketing promotion and the settled businesses to make reasonable arrangements for inventory and marketing tactics.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

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Article

Nari Sivanandam Arunraj and Diane Ahrens

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…

Abstract

Purpose

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).

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

International Journal of Retail & Distribution Management, vol. 44 no. 7
Type: Research Article
ISSN: 0959-0552

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Article

Rajiv Seth, Valeed A. Ansari and Manipadma Datta

Small farmers in developing countries have very little means of managing the weather‐risk to their agricultural produce. Weather derivatives could provide a solution, but…

Abstract

Purpose

Small farmers in developing countries have very little means of managing the weather‐risk to their agricultural produce. Weather derivatives could provide a solution, but the demand for such instruments and the willingness to invest in them needs to be researched. The purpose of this paper is to assess weather‐risk hedging by farmers, focusing on the willingness to pay in Rajasthan, India.

Design/methodology/approach

The paper presents results of a contingent valuation study done on the findings of a survey carried out in six villages in the state of Rajasthan. The survey was done on over 500 farmers after explaining the concept of weather derivatives and how they would work to help them hedge their weather‐related yield risk. The survey included questions on factors, which could have a bearing on the farmers' willingness to pay, and a bidding game where responses were solicited to premiums in a hypothetical market. Probit and logit models are used to determine probabilities of “Yes” responses to various bids and the mean willingness‐to‐pay.

Findings

The paper brings out a model, which uses nine independent variables affecting the probability of a farmer saying “Yes” to a price quoted to him for a weather derivative. Using the results from the probit and logit models, the farmers' mean willingness‐to‐pay is determined to be around 8.8 per cent of the maximum possible payout of a weather derivative contract.

Originality/value

With weather derivatives being accepted as a means of risk management for agriculture in developing countries, the willingness‐to‐pay figures determined in this paper would provide an insight to the structuring and pricing of weather derivatives, especially in developing countries.

Details

The Journal of Risk Finance, vol. 10 no. 1
Type: Research Article
ISSN: 1526-5943

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Article

Anil K. Sharma and Ashutosh Vashishtha

This article aims to examine the state of risk management in agriculture and power sector of India, evaluate the effectiveness of weather derivatives as alternative risk…

Abstract

Purpose

This article aims to examine the state of risk management in agriculture and power sector of India, evaluate the effectiveness of weather derivatives as alternative risk management tools and basic framework required to implement them.

Design/methodology/approach

Applications of traditional risk‐hedging tools and techniques in Indian agricultural and power sectors have proved to be costly, inadequate, and more importantly, a drag on the country's fiscal system. Mostly they offer a hedge against only the price risk. The volume related risk, which is rather more serious and highly weather‐dependent, remains practically unhedged. This study has used existing literature and empirical evidences for analyzing the various issues related to risk management in agriculture and power sector. Traditional derivative strategies have been used to construct weather derivatives contracts with different underlying weather indices.

Findings

The article suggests that how an appropriate weather‐based derivative contract system may be a more flexible, economical and sustainable way of managing the volume‐related weather risk in an economy, like India, having predominant agricultural and power sectors.

Originality/value

The article will be of value to all those who have some stakes in agricultural and power sectors of an economy and would like to mange the volume related risk in these sectors.

Details

The Journal of Risk Finance, vol. 8 no. 2
Type: Research Article
ISSN: 1526-5943

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Article

Indranarain Ramlall

The purpose of this paper is to delve into an extensive analysis of different food crops, ranging from bananas, beans, brinjals, cabbages, chillies, creepers, groundnuts…

Abstract

Purpose

The purpose of this paper is to delve into an extensive analysis of different food crops, ranging from bananas, beans, brinjals, cabbages, chillies, creepers, groundnuts, mixed vegetables, pineapples and tomatoes, over three decades. To maintain an ever-increasing population level, much stress is exerted on the production of food crops. However, till date, very little is known about how climate change is influencing the production of food crops in Mauritius, an upper-income developing country found in the Indian Ocean and highly vulnerable to climate risks.

Design/methodology/approach

Based on the interactions between production of crops, harvest area for crops and weather metrics, a vector autoregressive model (VAR) system is applied comprising production of each crop with their respective harvest area. Weather metrics are then entered into as exogeneous components of the model. The underlying rationale is that weather metrics are not caused by production or harvest area and should thereby be exogeneously treated. Should there be cointegration between the endogenous components, the vector error correction model (VECM) will be used. Diagnostic tests will also be entertained in terms of ensuring the endogeneity states of the presumed variables under investigation. The impact of harvest area on product is plain, as higher the harvest area, the higher is the production. However, a bi-directional causality can also manifest in the case that higher production leads towards lower harvest area in the next period as land is being made to rest to restore its nutrients to enable stable land productivity over time. Other dynamics could also be present. In case cointegration prevails, VECM will be used as the econometric model. The VAR/VECM approach is applied by virtue of the fact that traditional ordinary least squares (OLS) estimation approach will be biased and susceptible to trigger off unreliable results. Recourse is made towards the Johansen and Juselius (1990) technique. The Johansen and Juselius approach is based on the following VAR specification-bivariate VAR methodology. X1,t = A0 + A1,1X1,t – 1 + A1,2X1,t – 2+ […] .+ A1,p X1,tp + A2,1X2,t – 1 + A2,2X2,t – 2+ […] .+ A2,pX2,tp + ßjW + e1,t […] […]..(1) X2,t = B0 + B2,1X2,t – 1 + B2,2X2,t – 2+ […] .+ B2,p X2,tp + B1,1X1,t – 1 + B1,2X2,t – 2+ […] .+ B1,pX2,tp + ajW + e2,t […] […] […](2) X1,t is defined as the food crops production, while X2,t pertains to harvest area under cultivation for a given crop under consideration, both constituting the endogeneous components of the VAR. The exogeneous component is captured by W which consists of the nine aforementioned weather metrics, including the cyclone dummy. The subscript j under equation (1) and (2) captures these nine distinct weather metrics. In essence, the aim of this paper is to develop an econometric-based approach to sieve out the impacts of climate metrics on food crops production in Mauritius over three decades.

Findings

Results show weather metrics do influence the production of crops in Mauritius, with cyclone being particularly harmful for tomatoes, chillies and creepers. Temperature is found to trail behind bearish impacts on tomatoes and cabbages production, but positive impacts in case of bananas, brinjals and pineapples productions, whereas humidity enhances production of beans, creepers and groundnuts. Evidence is found in favour of production being mainly governed by harvest area. Overall, the study points out the need of weather derivatives in view of hedging against crop damages, let alone initiation of adaptation strategies to undermine the adverse effects of climate change.

Originality/value

To the best of the author’s knowledge, no study has been undertaken in Mauritius, let alone developing of an econometric model that properly integrates production, harvest area and weather metrics. Results show weather metrics do influence the production of crops in Mauritius, with cyclone being particularly harmful for tomatoes, chillies and creepers. Temperature is found to trail behind bearish impacts on tomatoes and cabbages production, but positive impacts in case of bananas, brinjals and pineapples productions, whereas humidity enhances production of beans, creepers and groundnuts. Evidence is found in favour of production being mainly governed by harvest area. Overall, the study points out the need of weather derivatives in view of hedging against crop damages, let alone initiation of adaptation strategies to undermine the adverse effects of climate change.

Details

International Journal of Climate Change Strategies and Management, vol. 6 no. 3
Type: Research Article
ISSN: 1756-8692

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Article

Srinivasa Ramanujam, R. Chandrasekar and Balaji Chakravarthy

The purpose of this paper is to develop an algorithm, using PCA‐based neural network, to retrieve the vertical rainfall structure in a precipitating atmosphere. The…

Abstract

Purpose

The purpose of this paper is to develop an algorithm, using PCA‐based neural network, to retrieve the vertical rainfall structure in a precipitating atmosphere. The algorithm is powered by a rigorous solution to the plane parallel radiative transfer equation for the atmosphere with thermodynamically consistent vertical profiles of humidity, temperature and cloud structures, together with “measured” vertical profiles of the rain structure derived from a radar.

Design/methodology/approach

The raining atmosphere is considered to be a plane parallel, radiatively participating medium. The atmospheric thermodynamic profiles such as pressure, temperature and relative humidity along with wind speed at sea surface and cloud parameters corresponding to Nargis, a category 4 tropical cyclone that made its landfall on May 2, 2008 at the Republic of Myanmar, are obtained by solving the flux form of Euler's equations in three‐dimensional form. The state‐of‐the‐art community software Weather Research and Forecasting has been used for solving the set of equations. The three‐dimensional rain profiles for the same cyclone at the same instant of time are obtained from National Aeronautics and Space Administration's space borne Tropical Rainfall Measuring Mission's precipitation radar over collocated pixels. An in‐house Micro‐Tropiques code is used to perform radiative transfer simulations for frequencies corresponding to a typical space borne radiometer, and hence to generate the database which is later used for training the neural network. The back propagation‐based neural network is optimized with reduced number of parameters using principal component analysis (PCA).

Findings

The results show that neural network is capable of retrieving the vertical rainfall structure with a correlation coefficient of over 0.99. Further, reducing the ill‐posedness in retrieving 56 parameters from just nine measurements using PCA has improved the root mean square error in the retrievals at reduced computational time.

Originality/value

The paper shows that combining numerically generated atmospheric profiles together with radar measurements to serve as input to a radiative transfer model brings in the much‐required synergy between numerical weather prediction, radar measurements and radiative transfer. This strategy can be gainfully used in satellite meteorology. Using principal components to reduce the ill‐posedness, thereby increasing the robustness in retrieving vertical rain structure, has been attempted for the first time. A well‐trained network can be used as one possible option for an operational algorithm for the proposed Indian climate research satellite Megha‐Tropiques, due to be launched in early 2011.

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Article

Mariam AlKandari and Imtiaz Ahmad

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on…

Abstract

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

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Article

Anthony R. Holt

Radar can provide valuable information on the spatial distribution of rainfall, but is not as yet able to provide accurate quantitative information on rainfall rate…

Abstract

Radar can provide valuable information on the spatial distribution of rainfall, but is not as yet able to provide accurate quantitative information on rainfall rate. Describes research on the use of polarization towards improving the radar monitoring of storms.

Details

Environmental Management and Health, vol. 7 no. 2
Type: Research Article
ISSN: 0956-6163

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