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Previous literature on the exchange rate and household consumption failed to examine the effect of positive and negative changes in the exchange rate on household consumption expenditures. We extended the available literature by investigating the asymmetric impacts in African emerging economies (AEE). To attain this aim, we utilized the nonlinear ARDL model, which covers both negative and positive shocks in the exchange rate. Our results indicate that movements in the exchange rate have asymmetric impacts on household consumption in all the included emerging economies, excluding Nigeria. Our findings propose recommendations for the policymakers to ensure the alignment of the optimal exchange rate for countries of emerging African economies and propose relevant policies for the countries.JEL Classification: D12, D60, I30How to Cite:Gohar, R., Chang, B. H., Uche, E., & Bhatty, K. (2023). The Asymmetric Effect of Exchange Rate on the Household Consumption Expenditures. Etikonomi, 22(1), 31–44. https://doi.org/10.15408/etk.v22i1.23428.

Glacial landforms are pieces of evidence to comprehend the glaciological past and paleoclimatic conditions in the region. In paleo-glacial reconstructions, a critical geomorphological context is provided for determining glacial chronologies. The study provides a detailed geomorphological mapping and investigation of the Nehnar valley, NW Himalayas, Jammu and Kashmir, India. To map the glaciogeomorphological features, the study uses LISS-IV (5.8 m resolution) and Google Earth Pro (1 m resolution) images as well as morphological sketches and GPS data collected during field visits. The Advanced Spaceborne Thermal Emission and Reflection Radiometer Digital Elevation Model (ASTER-DEM, 30m resolution) has also been used to enhance the morphological and elevation characterization of glacial features. A host of glacial features like lateral and terminal moraines, serrate, debris cones, moulins and outwash plains are among several others mapped in the study region. Using dating techniques, the spatial data on glacial landform features in the area can be used to guide the reconstruction of paleo-glaciological setups. This fills in some of the gaps in our knowledge about glaciation in the Kashmir Himalaya during the Pleistocene.

The present study attempts to explore the efficacy of machine learning models in landslide predictions caused by rainfall events along Highway from Bandipora to Gurez, J&K, India. Random Forest (RF) and Logistic Regression (LR) Models were employed to find the optimal parameters for targeted feature i.e. landslide prediction. These models were evaluated for accuracy using the receiver operating characteristics, area under the curve (ROC-AUC) and false negative rate (FNR). The results reveal a positive correlation between antecedent precipitation and landslide occurrence rather than between single-day landslide and rainfall events. Comparing the two models, LR model’s performance is well within the acceptable limits of FNR and therefore could be preferred for landslide prediction over RF. LR model’s incorrect prediction rate is 8.48% without including antecedent precipitation data and 5.84% including antecedent precipitation data. Our study calls for wider use of Machinery Learning Models for developing early warning systems of landslides.

As glaciers recede in response to global climate change, a significant growth of glacial lakes is being witnessed in the Himalayan region. Owing to their high elevations, rapid growth and mostly unstable dams constituting of loosely consolidated glacial moraine deposits, these lakes pose a significant threat in the form of Glacial Lake Outburst Floods (GLOFs). This study focused on studying the evolution of glacial lakes in Shyok catchment of Upper Indus basin, over a period of 30 years (1992 to 2022) and assessing their GLOF hazard levels. The Inventorization and Spatio-temporal change detection of the glacial lakes (A≥0.001 km2) was done using multi-temporal medium and high-resolution satellite imagery from the years 1992, 2002, 2012 and 2022. Besides, we have used Google Earth and Planet imagery, to validate the glacier and glacial lakes outlines. The GLOF hazard level assessment was carried out through a multi-criteria approach using Analytical Hierarchy Process AHP to classify the lakes into very-low, low, medium, high and very-high hazard categories. The results reveal a significant increase in the total number of glacial lakes from 143 in 1992 to 240 in 2022 as well as the total glacial lake area that increased from 8.45 km2 (± 0.02 km2) in 1992 to 13.26 km2 (± 0.04 km2) in 2022. Furthermore, out of 63 glacial lakes (A>0.05 km2) selected for the hazard assessment, 7 lakes were identified as very-high hazard lakes with GLOF susceptibility indices greater than 7.0 while 19 more lakes were classified as High-hazard lakes with GLOF susceptibilities in the range of 0.6 to 0.7. Most of these lakes are proglacial lakes and are close to their mother glaciers. The majority of these hazardous lakes have witnessed a high growth rate, which suggests that the GLOF hazard of these lakes is going to increase further in the future owing to the warming climatic trends. This study will act as a precursor to a more detailed and in-depth GLOF risk assessment of highly hazardous glacial lakes in the region and will help in developing mitigation strategies for any potential disasters in future.

The National Highway 1D from Sonamarg to Kargil is known as one of the most landslide susceptible areas in Kashmir and Ladakh divisions. The aim of the present study is to delineate landslide susceptibility zones along National Highway 1D from Sonamarg to Kargil, north western (NH) Himalaya. Based on landslide influencing geo-environmental factors such as slope angle, landuse/landcover, distance to faults, precipitation, soil, slope aspect, lithology, altitude, distance to streams, and distance to road a detailed landslide susceptible map is prepared. A weighted pair wise comparison matrix is generated using Analytical Hierarchy Process (AHP). The geo-environmental factors and their derived weights through AHP were then overlaid using the index overlay module in ArcGIS 10.2 supplemented by MS Excel and MATLAB. The relationships between the conditional and trigger factors was carried out by analyzing landslide events. The results show that the area of 913.55 Km2 is covered by very high and high landslide susceptibility zones constituting 55% of the study area. These are the most tortuous and rugged zones with high occurrence and impact of landslides. The study also identified and mapped a total of 317 landslides along National Highway 1D through rigorous multiple field surveys and secondary sources. There is a frequent incidence of traffic disruptions along the road. Thus there is an urgent need to mitigate the landslide hazard particularly to avert the disruption from Sonamarg to Leh road, which causes huge inconvenience in terms of human and economic losses.

Climate change, a global challenge of our time has severely impacted mountain glaciers. This study presents a detailed and an updated glacier inventory of 2020 for the Upper Jhelum Basin (UJB), Kashmir Himalayas using Sentinel-2 data and documents 308 glaciers. The glaciers range in size from 0.01 to 10.51 km2 and cover an area of 102.1 km2. All glaciers are located in the elevation zone between 3500 and 5000 masl. About 86% of glaciers are small in size (

BACKGROUND Incisional hernias are a common occurrence following midline laparotomy. Numerous techniques have been employed in the management of this common condition. Randomized trials and evidence based medicine help us in deciding what the optimal treatment is in a particular condition. This is often not possible in case of hernia due to the heterogeneity of population. Laparoscopy is gaining popularity in the management of this condition as results are comparable to open surgery with lesser post-operative pain. The placement of mesh intraperitoneally as in laparoscopic intraperitoneal onlay mesh repair has its own disadvantage such as mesh erosion into viscera. Placement of mesh endoscopically as an onlay repair seems to be a good option, more so in the developing world, considering the prohibitive cost of composite mesh. The purpose of this study was to analyze the technical ease, reproducibility, outcome, postoperative pain and complications of laparoendoscopic hybrid technique in the management of abdominal wall hernia. METHODS This technique combines the benefits of minimal invasive surgery and ease of onlay mesh repair, while reducing wound related problems associated with open surgery. It involves first a laparoscopic approach in entry, reducing the contents, visualizing the sac followed by endoscopic dissection using the same trocar anterior to abdominal wall in the subcutaneous plane. This is followed by sac dissection, suturing the abdominal wall and placing an onlay mesh RESULTS Fifteen patients have been operated; no subcutaneous drains were placed. A polypropylene onlay mesh was used and fixation done with suturing. No major complications were seen. There was no subcutaneous emphysema. CONCLUSIONS The laparoendoscopic hybrid technique offers advantage of being minimally invasive and having lower wound related complications, with early ambulation and decreased hospital stay. It is also technically easier to suture the defect and allow complete excision of the sac. However a randomized trial is necessary and larger series are required to compare the results with other established techniques. KEY WORDS Incisional Hernia, Laparo - Endoscopic - Onlay Mesh, Surgical Techniques, SCOLA

This study aims to assess the short-temporal dynamics of glaciers in Sind watershed of Jhelum basin in North-western Himalayas. The spatio-temporal fluctuations in morphological parameters of glaciers were estimated for a period of 28 years from 1990 to 2018. This has been achieved by analyzing multi-temporal imageries from Landsat-5, Sentinel-2 and IRS LISS IV earth observation data using geospatial tools and supplemented with field observations. The total glaciated area of Sind basin has decreased from 56.73 Km2 (1990) to 43.94 Km2 (2018) with the loss of 22.5% during the period of 28 years. The number of glaciers has increased from 92 (1990) to 116 (2018) generally due to defragmentation of glaciers. Glaciers (> 1 Km2) have shown a maximum loss of −34.8% during the study period. Although contributing less to the total glacier area, glaciers found in low elevation zones (3800–4200 masl) have lost maximum area of around − 55.5%. Mean elevation of glaciers has increased from 4330 masl (1990) to 4390 masl (2018). Mean Equilibrium Line Altitude (ELA) of glaciers (> 0.5 Km2) has shifted from 4336 to 4432 masl with an annual upward shift of 3.4 m a−1. The trend analysis of meteorological parameters (Temperature and Precipitation) using Mann–Kendall test reveals that the seasonal and annual temperature (both Tmax and Tmin) has shown a significant positive trend. While as precipitation (both annual and seasonal) has shown a negative trend. The continued glacier recession in the region is largely due to decreasing precipitation in the winter months (November–March) and increasing mean annual temperatures. The persistence of warming trend will result in continuous shrinkage and thinning of glaciers with drastic impacts on the hydrological regime of the region.

Among all the natural and human forces acting on the land surface and shaping Landuse and Landcover (LULC), anthropogenic actions have been a dominant force, which has caused dramatic changes to the LULC throughout the globe. The situation of Kashmir Himalayas is no different. LULC changes are rampant and a cause for several environmental problems in the region. Under the changing climate regime and ever-growing anthropogenic pressures, the long-term analysis of LULC has become a necessity. The present study attempts to track the LULC change through a systematic review of literature, to analyse and discuss the trends and patterns of LULC dynamics in Kashmir Valley. The findings from several studies concerning the region that have applied geospatial technology to detect changes in LULC reveal that the changes have occurred at a faster pace in the last 20 years. Major landuse classes such as agriculture, forest, water, snow, and glaciers have shown a declining trend, in comparison to horticulture and built-up that has shown an increasing trend. These changes in LULC have far-reaching impact on the economy of the region. Climate change, population growth, economic compulsion, and unplanned development among others are the major causes /drivers of intensified LULC changes in the region.

Climate change is strongly affecting the Himalayas. Geologically young and fragile, the Himalayas are sensitive to even minor changes in the climate. Regional warming in Himalayas has been observed between 0.15 and 0.60°C per decade, which is higher than the mean global warming rate of 0.74°C per 100years. Consequent to this warming trend, the glaciological changes in Himalayas are obvious, which has resulted in the formation, expansion and disappearance of various types of glacial lakes. The dangerous lakes among these pose threat to downstream community and infrastructure. In this study, an attempt has been made to critically analyze the progress in Glacier Lake Outburst Floods (GLOF) research and understand its dynamism from multiple approaches through a meta-analysis of peer-reviewed scholarly literature for a period of 2001–2020. The study has found that the glacial lakes in the study region are increasing in number and expanding rapidly owing to the climate change and anthropogenic causes as the Glacier Lake Outburst Floods (GLOFs) are becoming common. The expansion rates of pro-glacial lakes connected to glaciers and moraine-dammed lakes are faster than other type of lakes. Findings from the studies on area change analysis in the region reveal that a number of expanding glacial lakes can emerge as potential sites for future GLOFs, hence need immediate monitoring and observation. Geospatial tools and techniques coupled with field investigations have been found as a potent tool for mapping the evolution and propagation of GLOF hazards resulting from accelerated shrinking and thinning of glaciers and continued lake expansion. Further, the satellite-based remote sensing and modeling has been found as an excellent tool for GLOF management as well as reconstruction of previous GLOF events and prediction of future outburst potential. The idea of this study is linked to the increased incidence of GLOF events in the Himalayan region. This study will help in better understanding of glacial lake expansion and provide scope for future research in devising risk management action plans of potential GLOFs by selecting expanding glacial lakes as case studies.

The present study endeavours to explore the efficacy of machine learning models in landslide predictions triggered by extreme rainfall events along Highway from Bandipora to Gurez, J&K, India. Random Forest (RF) and Logistic Regression (LR) Models were employed on account of their technical utility and logical procedure to transform the decision making into a series of algorithm steps that eventually culminate in low variance and minimum bias. The crux of these models was to find the optimal parameters for targeted feature i.e. landslide prediction. Hyper-parameter tuning was used to optimize the performance of the respective models. These models were evaluated for accuracy using the receiver operating characteristics, area under the curve (ROC-AUC) and false negative rate (FNR). When antecedent rainfall data is incorporated in the prediction models, both (LR and RF) performed better with an AUC of 0.825 and 0.843 respectively. The results reveal a positive correlation between antecedent precipitation and landslide occurrence rather than between single-day landslide and rainfall events. In case of FNR, LR and RF improved to14.32% and 15.92% respectively, when antecedent rainfall was included in the analysis. Comparing the two models, LR model’s performance is well within the acceptable limits of FNR and therefore could be preferred for landslide prediction and early warning over RF. LR model’s incorrect prediction rate is 8.48% without including antecedent precipitation data and 5.84% including antecedent precipitation data. Our study calls for wider use of Machinery Learning Models for developing early warning systems of landslides.Keywords: -Rainfall Triggered; Machine Learning Models; Area Under Curve; False Negative Rate.

Electricity usage plays a vital role in raising the massive growth in the economy; also, the industrial sector is the key factor of overall energy demand closely related to the economy. The study aims to contribute in two ways. First, the Vector Error Correction Model (VECM) estimates electricity consumption in Pakistan during 1970-2018 to find the relationship between electricity consumption, price, and real gross domestic product. Second, decomposing the overall impact of an unexpected shock on each variableos Dynamic Variance Decomposition Technique applied. The empirical analysis shows that the factors are co-integrated. The results also indicate the long-run relationship between electricity consumption, price, and real gross domestic product in the industrial sector. Further, the VECM analysis responses are also confirmed by the variance decomposition method. The findings confirm the potential of the industrial sector. We propose that formalized and proper assurance of electricity needs and demands at a reasonable price can boost the local industry's confidence and attract foreign investors. However, a strong governance structure should be extended to the public sector to ensure policies that priorities the distribution of energy to businesses for development.

The present work attempts to analyze the slope stability of road section along the national highway NH-1D from Sonamarg to Kargil in the erstwhile J&K, India. The rugged and treacherous terrain in this region is prone to frequent slope failures taking heavy toll of life, property and inconveniences to commuters due to disruption of vehicular movement. For slope study analysis twenty facets were chosen for detail based kinematic analysis and Rock Mass Rating (RMR) to identify the causes and types of failure and potential failure directions. The RMR values obtained from the study range from17 to 96, representing very poor to very good rating and the slopes range from completely unstable to completely stable. The kinematic analysis reveals that most joint planes intersect with each other in different directions consequently forming different potential failure modes. The study concludes that the existing slopes require urgent mitigation measures to thwart the failure.