مطالب

INTRODUCTION: Since Iran is located in the semi-arid belt, it has faced such issues as drought, dust crisis, and intensified migration. The assessment of the effects of climate change includes identifying some key aspects of uncertainties used to estimate its impacts, such as uncertainties in the context of Atmosphere-Ocean General Circulation Models (AOGCMs): in regional-scale climatology, in statistical or dynamic downscaling methods, and parametric and structural uncertainties in different models. One of the most important sources of uncertainty in climate change is the use of different AOGCMs that produce different outputs for climate variables. METHODS: In this study, to investigate the uncertainty of AOGCM models, the downscaled data of the NASA Earth Exchange Global Daily Downscaled Projections dataset obtained from 21 AOGCMs with medium Representative Concentration Pathway4.5 scenario were downloaded from the NASA site for 81 cells in Hamadan Province, Iran. After the validation of the models, they were evaluated against the criteria of the coefficient of determination and model efficiency coefficient in comparison with the data of the Hamedan synoptic station in the statistical period of 1976-2005. To reduce the uncertainty of AOGCMs, the ensemble performance (EP) of models was used in Climate Data Operators software. FINDINGS: It was revealed that MRI-CGCM3, MPI-ESM-LR, BNU-ESM, ACCESS1-0, MIROC-ESM, MIROC-ESM-CHEM, and MPI-ESM-MR models had better performance than similar models. It was also found that IPSL-CM5A-LR, CNRM-CM5, CSIRO-Mk3-6-0, CESM1-BGC, and GFDL-ESM2M had the lowest correlation between observational and simulation data of mean monthly precipitation. CONCLUSION: According to the results, this method could provide a good estimate in the base period (1976-2005), compared to the data of the Hamedan synoptic station, and was more accurate compared to the single implementation method of each AOGCM model. The results of EP of models in the future period (2020-2049) showed that precipitation will not change considerably in the future and will increase by 0.23 mm. In addition, the average, maximum, and minimum annual temperatures will increase by 1.54°C, 1.7°C, and 1.40°C, respectively.

Today, different customer’s willingness to pay alongside the maturity of data analytics has made it possible to offer segmented prices and gain maximum consumer surplus. Price segmentation is defined as a strategy in which prices are free to adjust based on the "fences" of time of purchase, place of purchase, customer characteristics, product characteristics, etc. Since strategic pricing policies are not practically possible for every single customer, each of them falls into one section. Price segmentation is based on key concepts such as "willingness to pay" and "consumer surplus". However, it also causes "price unfairness perception". Since such feeling is triggered when people compare their deal to another’s, approaches to "make difference in transactions" would be helpful. This study aims to investigate the perceived fairness in three price segmentation methods. The present research is practical in terms of purpose and is descriptive survey research in terms of research method. The statistical population of the research is 384 customers of Tehran's online stores. Data collection was done by questionnaire and data analysis by one-way analysis of variance and least significant difference method using SPSS software. Cronbach's alpha was used to assess the validity of the questionnaires. Findings indicate that the method based on "customer characteristics", has the least level of perceived fairness.  The "product bundle" method has increased perceived fairness, but not much, and price segmentation based on "product features" results in the highest perceived fairness.