GMS-MODFLOW application in the investigation of groundwater potential in Concepcion, Tarlac, Philippines

This study used GMS-Modflow to investigate the ten-year groundwater potential in Concepcion, Tarlac. This region in Central Luzon, Philippines, with limited surface water, depends on groundwater as its primary freshwater source. The water demand projection estimated an increase of 38.5% from 2020 to 2030; hence, higher groundwater abstraction is perceived in the next ten years. To deviate from the risk associated with reliance on groundwater, this study, through GMS-MODFLOW, developed a groundwater model to mimic the aquifer’s current condition and investigated its behavior in response to future spatial and temporal variables. The simulation results generally showed a sustainable groundwater supply in Concepcion, Tarlac, for the next ten years, with no significant decline in hydraulic heads.

Estimating the Reduction in Future Fleet-Level CO2 Emissions From Sustainable Aviation Fuel

With rising concerns over commercial aviation’s contribution to global carbon emissions, the aviation industry faces tremendous pressure to adopt advanced solutions for reducing its share of CO2 emissions. One near-term potential solution to mitigate this global emissions situation is to operate existing aircraft with sustainable aviation fuel (SAF); this solution requires almost no modification to current aircraft, making it the “quickest” approach to reduce aviation carbon emissions, albeit the actual impact will be determined by the degree to which airlines adopt and use SAF, the ticket price impact of SAF, and the future growth of travel demand. This article presents results that estimate the expected fleet-wide emissions of future airline operations using SAF considering various projected traveler demand and biofuel penetration/utilization levels. The work demonstrates an approach to make these predictions by modeling the behavior of a profit-seeking airline using the Fleet-Level Environmental Evaluation Tool (FLEET). Considering five future SAF scenarios and two future passenger demand projection scenarios, FLEET estimates future fleet-level CO2 emissions, showcasing the possible upper and lower bounds on future aviation emissions when SAF is introduced for use in airline fleets. Results show that the future fleet-level CO2 emissions for all scenarios with SAF are lower than the baseline scenario with no SAF, for all demand projection scenarios. The passenger demand served and the trips flown for a given SAF scenario depends on the SAF price and the biofuel penetration levels. This shows that even if airlines serve a higher passenger demand for some future scenarios, the carbon emissions could still be lower than the current baseline scenario where airlines only use conventional jet fuel.

Case Analysis: Financial viability for the commercialization of physicochemical testing services at the Antioquia Liquor and Alcohol Factory (ALAF)

The purpose of this work is to evaluate the profitability of the market for physicochemical testing of alcoholic beverages, which the Physicochemical Laboratory of the Liquor de Antioquia Factory (FLA) intends to enter, according to the transformation in its legal nature. The methodology that includes the costing of the tests using cost accounting and the analytical procedures used by the laboratory. For the construction of the cash flow of the project for the sale of the physicochemical tests, it was necessary to search and consolidate information concerning the inventory of fixed assets of the laboratory and the entry of annual samples from the different areas of the FLA. The AutoRegressive Moving Average (ARMA) model is used as the demand projection method. The results reveal that, although restricted and specialized, the physicochemical testing market may generate, under certain scenarios, a return on investment that allows the self-sustainability, viability and competitiveness of the physicochemical laboratory over time.

Strategies to Predict E-Commerce Inventory and Order Planning

This study examines the characteristics of a prediction model for businesses in the online marketplace by considering the market trend, prior sales and decision maker's preference on potential demand estimate. With the rapid growth of the electronic market, the main challenge for online sellers is the ability to analyze customer expectation, market data, and sales information to make the accurate procurement decision. The proposed model integrates a mathematical structure for a target season sale comprising upcoming demand projection by seller's internal team, data from past sales and the overall trend of seller's e-brand to determine the online customer demand. The study proposed a newsvendor model as a tool for sellers to make the instantaneous decision of ordering merchandise from the supplier when the quick response to the customer order is a priority for electronic market. Results of the study provide insights into the procurement dynamics and implications of the e-commerce inventory plan.

Supply-Demand Projection and Gap Analysis for Major Pulses in Assam

Pulses are important constituent in the food basket of people of Assam. The state produces 107.5 thousand tonnes of total pulse from an area of 146.4 thousand hectare with yield rate of 735kg/ha (2016-17). The present production is able to meet only 20 per cent of the present requirement. The major pulses grown in the state are gram, arhar, greengram, lentil, blackgram and pea. A study conducted with the objective of analysing the supply demand gap of pulses in Assam, it was found that there was huge gap in requirement and supply of most of the pulses except black gram and pea and the deficit continues upto 2025, as well. The government has been trying to increase the pulses production with increase area and enhanced yield through various schemes namely NFSM-Pulse. Bringing more pulse area under irrigation, adoption of recommended production technology is expected to increase the yield that might help in bridging the demand supply gap. As blackgram was found to be surplus, the people of Assam should gradually replace lentil with blackgram as dal. Establishment of mini dal processing plant might attract the youth to grow and process pulse and thereby increase the employment opportunity and enhanced production in the state.

The Role of Open Access Data in Geospatial Electrification Planning and the Achievement of SDG7. An OnSSET-Based Case Study for Malawi

Achieving universal access to electricity is a development challenge many countries are currently battling with. The advancement of information technology has, among others, vastly improved the availability of geographic data and information. That, in turn, has had a considerable impact on tracking progress as well as better informing decision making in the field of electrification. This paper provides an overview of open access geospatial data and GIS based electrification models aiming to support SDG7, while discussing their role in answering difficult policy questions. Upon those, an updated version of the Open Source Spatial Electrification Toolkit (OnSSET-2018) is introduced and tested against the case study of Malawi. At a cost of $1.83 billion the baseline scenario indicates that off-grid PV is the least cost electrification option for 67.4% Malawians, while grid extension can connect about 32.6% of population in 2030. Sensitivity analysis however, indicates that the electricity demand projection determines significantly both the least cost technology mix and the investment required, with the latter ranging between $1.65–7.78 billion.