Actual Consumption Research Articles

Drive Cycle‐Based Estimation of Energy Consumption for Electric Two‐Wheeler

ABSTRACTThe transportation sector is the backbone of the economic growth of any country. However, the heavy dependence of this sector on petroleum fuel is a matter of concern for sustainable development. To address this issue countries are working toward green energy‐based transportation, and among all viable solutions electric vehicles (EVs) are emerging as front runners. Range anxiety is one of the most prominent concerns in EV adoption. The range of a vehicle depends on the energy consumption so it becomes crucial to estimate it very precisely. There are many standard drive cycles such as the New European Driving Cycle (NEDC) and Worldwide harmonized Light‐duty Vehicle Test Cycles (WLTC) which are used for the estimation of energy consumption. However, these standard cycles fail to capture the driving behavior of real traffic. Due to this reason, these standard cycles underestimate the energy consumption compared with actual consumption. For more realistic energy requirement estimations, researchers are focusing on the development of real‐world drive cycles specific to a particular geography. In this paper, a real‐world drive cycle of electric two‐wheeler has been developed for the city of Lucknow, India, and compared with the driving characteristics and energy consumption estimates of WLTC. The energy requirement per km for the Lucknow drive cycle and WLTC are found as 14.89 Wh/km and 11.95 Wh/km, respectively, which indicates per km energy required estimation for LDC is 24.60% higher than WLTC.

Dynamic Harmonic Distortion Analysis and Mitigation Strategies for DC Third Rail Systems

Harmonic distortions in DC third rail systems can cause overheating of electric motors and transformers. Single-tuned filters and shunt active harmonic filters (SAHFs) are often used to mitigate the harmonic distortions. However, there is a lack of studies on the effects of train dynamics on harmonic distortions. This paper aims to investigate the influence of dynamic train behaviors for a DC third rail system and provide recommendations for the design of single-tuned filters and SAHFs to mitigate the harmonic distortions. The traction power supply system of a DC third rail system in Malaysia is modeled using ETAP-eTraX and MATLAB-Simulink software for the investigation. The ETAP-eTraX software is used to accurately compute the dynamic behavior of the train, while MATLAB-Simulink allows for the assessment of the impact of train behavior on the rail track, as well as the harmonic effect of the railway power network on the train. The findings showed that the SAHF exhibits strong adaptability and superior filtering performance compared to the single-tuned filter in addressing dynamic harmonic distortion in traction power supply systems. This study emphasizes the significance of incorporating harmonic mitigation devices, particularly for managing dynamic harmonic distortion based on actual train consumption patterns.

Unfulfilled water impact forecasts, plans and projects

The article presents a retrospective review of some forecasts, plans and projects of anthropogenic impact on water resources that did not materialize, were not implemented by the planned date or were incomplete. Values of full (water intake) and irrevocable water consumption for 2000, predicted in the 1960s and 1970s by well-known domestic and foreign researchers, are compared with the actual water consumption in 2000 in the world, the USA, and our country. It is shown that most of water consumption forecast parameters turned out to be significantly higher than the actual one, which gives reason to consider these forecasts were not borne out. In the Water Strategy of the Russian Federation, developed in 2009 for the period up to 2020, these values were also significantly overestimated (by tens of percent). Incomplete implementation of various programs did not lead to the expected significant improvement in the water quality of rivers, including the Volga, and reservoirs in Russia. Such failed projects as NAWAPA in the USA and Canada, projects of interzonal redistribution of water resources in the USSR, projects of Nizhneobskaya and Turukhanskaya (Evenkijskaya) hydroelectric power plants, partially implemented projects of Cheboksary and Nizhnekamsk hydroelectric power plants, as well as a number of others are considered. Among the main reasons for unsuccessful forecasts, unfulfilled plans and projects are the lack of reliable data, incomplete knowledge about laws of nature and society development, financial problems, environmental demands, and the dramatically changed economic and political situation.

A new model for fuel consumption and route time computations – a case study in the Quebec forest industry

ABSTRACT In forest transportation in Quebec, transportation rates are typically based on an estimated trip or travel time duration during which a truck travels loaded from an origin to a destination and then returns empty to the origin. These transportation rates implicitly consider fuel consumption for fuel surcharge costs in negotiation, but for most shippers and carriers, fuel consumption remains a rough estimate, leading to underestimation or overestimation of actual consumption. In this paper, we propose a new fuel consumption model and a more detailed trip time computation to support accurate estimations. The model takes into account road network characteristics that affect fuel consumption, such as road elevation profiles, including slopes that significantly affect fuel consumption compared to flat roads, and curves where trucks change speed, as well as intersections where trucks need to stop or slow down. The road network of the province of Quebec (Canada) is represented in a route network that integrates all the characteristics considered by the fuel consumption model. The model is validated in a case study using a timber truck equipped with GPS and information about overall fuel consumption between a set of refueling points. Utilizing the fuel consumption model and a route planner enables accurate estimation of fuel consumption and, consequently, associated greenhouse gas (GHG) emissions and travel times. A case study involving three companies is then conducted to analyze how more detailed information can inform new transportation rates.

Momentum for organic food purchase intention and actual adoption- moderating effects of social media influencer and celebrity endorser

Despite extensive research into the factors influencing organic food consumption intention, a notable gap exists in understanding how this intention translates into actual consumption behavior. Addressing this gap, this study employs the Theory of Planned Behavior (TPB) to investigate consumer individual and business-related factors that shape the intention to purchase organic food and subsequently influence its actual acquisition. We collected primary data from organic food consumers in major departmental stores in Bangladesh through a questionnaire survey. We analyzed the data using structural equation modeling (SEM) with AMOS v-24 and PROCESS MACRO v-4.20 software. The findings reveal significant direct, mediating, and moderating effects within the pathways delineated by the model. By uncovering these effects, the study offers valuable insights into the interplay of consumer individual and business-related factors in shaping both intention and actual behavior toward organic food consumption. Our research contributes to validating and extending the Theory of Planned Behavior (TPB) framework and provides practical implications for scholars and policymakers managing organic food consumption. By exploring these factors, we are equipping them with fresh perspectives and insights that can be directly applied in their work. This study, by elucidating the dynamics between intention and behavior, aids in developing more effective strategies to promote sustainable consumption practices in the realm of organic food, thereby empowering and motivating them to make a real difference in this field.

A new voltage sensitivity-based distributed feedback online optimization for voltage control in active distribution networks

Rising sustainability, environmental, and decarbonization considerations have prompted a recent increase in the integration of inverter-based distributed energy resources (DER), such as photovoltaics (PV), into the power grid. This trend raises concerns about potential voltage violations within distribution systems, emphasizing the growing relevance of effective voltage regulation. Utilizing smart inverters for active and reactive power control proves highly efficient in addressing voltage rise issues within active distribution networks (ADN). This work proposes a distributed feedback online optimization (FOO) strategy for voltage regulation in ADNs. The FOO strategy utilizes online recursive least squares (RLS) sensitivity-based feedback, relying on local measurements to iteratively compute optimal reactive power set points for DERs, ensuring bus voltage regulation to within acceptable limits. The distributed control algorithm employs a primal-dual update approach, enabling coordinated information exchange between neighboring controllers. This online feedback-based control offers a promising solution for real-time coordinated voltage control in distribution grids, particularly with the increasing integration of inverter-based DERs at the grid's edge. The approach demonstrates robustness under varying conditions, communication dynamics, and topology changes, validated using real distribution feeders with actual solar PV production and consumption data. Advancements in sensing, communication systems, and smart grid tools further enhance the feasibility and scalability of this strategy for evolving power systems.

How exposure to natural scenes can promote weight control behaviors: A replication experiment

We performed a behavioral experiment to replicate published findings showing that exposure to natural scenes, i.e., viewing pictures of natural versus urban scenes, is associated with the choice of a “reward drink” containing less sugar (i.e., a healthier dietary choice). In total, 140 participants were randomly assigned to one of two study conditions (viewing natural or urban scenes). Participants completed a task measuring temporal discounting. Two measures related to weight control were used: the amount of ice cream consumed in a taste test (actual food consumption) and the amount of sugar chosen for the reward drink. Compared to the urban group, the natural scene group chose reward drinks with less sugar and ate less ice cream in the taste test. The discounting rate fully mediated the impact of exposure to natural versus urban scenes on the two measures. The association between experimental exposure to natural scenes and weight control behaviors was not contingent on the intention to lose weight or participant sex. This replication experiment suggests that exposure to natural scenes helps individuals to control sugar intake and food consumption.

Reinforcement learning for occupant behavior modeling in public buildings: Why, what and how?

Effective control of energy consumption in public buildings holds paramount significance for global sustainable development. However, uncertainty in occupant behavior during operational phase often leads to a substantial discrepancy between the designed and actual energy consumption. While there is existing research on occupant behavior in public buildings utilizing stochastic modeling, statistical modeling, data mining, and agent-based modeling methods, the connotations and formation mechanisms have not been systematically revealed, rendering these developed models weak in applicability and reliability in practical scenarios. Reinforcement learning, which can perceive aspects of external environment and learn from stochastic interactions spontaneously, has been distinguished as a promising method for occupant behavior modeling. This research aims to develop a paradigm for reinforcement learning application in occupant behavior modeling in public buildings. Semi-structured interviews and literature review were combined to collect relevant information, while reasons for using reinforcement learning and applicable algorithms were discerned through qualitative analysis. Finally, detailed frameworks were proposed and validated through focus groups, offering a comprehensive guide that will serve as a reference for future research. This includes steps for collecting occupant-related data, selecting appropriate algorithms, and training and deploying reinforcement learning agents effectively. This research innovatively proposed a “panorama” of reinforcement learning for occupant behavior modeling in public buildings, laying a solid foundation for indoor environment and energy efficiency improvement in public building operations.

Comparison of energy consumption prediction models for air conditioning at different time scales for large public buildings

Large public buildings have complex functions and high air conditioning energy consumption, and the prediction of energy consumption for air conditioning in different time scales can realise different energy saving management needs. Taking a large public building in Guangzhou as a research case, the long and short-term memory neural network (LSTM) is used as the basis to establish the machine learning model for energy consumption prediction for air conditioning at different time scales in terms of 10 min, hourly and daily, and a comparative analysis is carried out. The original data were analysed and processed by Z-Score and Local Outlier Factor (LOF) outlier detection methods. The relationship between meteorological parameters for Typical Meteorological Year (TMY) and the actual local meteorological parameters and energy consumption for air conditioning was analysed using linear regression, Pearson and Spearman correlation coefficients, taking meteorological parameters as input variables. The results show that comparing energy consumption for air conditioning with the meteorological parameters in TMY and actual local meteorological parameters, the actual meteorological parameters had a stronger correlation energy consumption for air conditioning. When the actual local meteorological parameters are taken as input variables, the mean absolute percentage error (MAPE) is reduced by about 0.85 %, and the coefficient of determination (R2) is increased by about 0.01. At the same time, the MAPE and R2 of the 10-min energy consumption prediction model are about 6.43 % and 0.9738, respectively, the MAPE and R2 of the hourly prediction model are about 9.29 % and 0.9568, respectively, while the MAPE and R2 of the daily prediction model are about 25.76 % and 0.7998. Meanwhile, an improved energy consumption prediction model is proposed to reduce the daily energy consumption prediction error to 6.36 % for MAPE, and the R2 of this model is 0.9927.

Development and Validation of the Meiji Nutritional Profiling System per Serving Size.

Serving size may be the appropriate reference for calculating food nutritional value. We aimed to assess the nutritional values of Japanese foods based on serving sizes rather than per 100 g by adapting the Meiji Nutritional Profiling System (Meiji NPS). Given the variability in serving sizes across countries, we used Japanese serving sizes to calculate the Meiji NPS scores. We confirmed the convergent validity of the Meiji NPS scores per serving size with the Nutrient-Rich Food Index 9.3 using Spearman's correlation coefficients (r = 0.51, p < 0.001). Food groups recommended by official guidelines, such as pulses, nuts and seeds, fish and seafood, fruits, vegetables, and milk and milk products, scored relatively high. Furthermore, the nutrient density scores of food items with small serving sizes, such as mushrooms, algae, seasonings, and fats and oils, were moderated when calculated by per serving size, despite having considerably higher or lower scores per 100 g. These results indicate that calculating NPS per serving size allows for the assessment of the nutritional value of food items in accordance with actual consumption quantities. Therefore, the Meiji NPS calculated per serving size, alongside the per 100 g version, may be useful for dietary management depending on specific purposes.

Impact of Non-Residential Users on the Energy Performance of Renewable Energy Communities Considering Clusterization of Consumptions

Renewable energy communities foster the users’ engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects.

Evidence-based food serving size labelling: Survey and laboratory analyses of consumer cooking spray usage.

Nutrition label serving sizes are determined primarily based on typical consumption when such data are available. However, such data are not available for certain foods such as spray cooking oil (cooking spray). Our study assessed cooking spray use by the United States (US) adults compared to the 0.25-s serving size used on US-sold cooking spray labels. Adults (n = 1041, aged 33 ± 16.7 years) completed a 13-question survey on cooking spray use and perceptions. In the survey, participants reported using cooking spray for 1.9 ± 0.9 s per use, and 42.3%-43.1% of participants reported being more likely to purchase products if they were labelled calorie- or fat-free. Next, 30 adults (aged 29.7 ± 11.0 years) completed a laboratory-based study which assessed cooking spray durations for seven cookware items. Spray times ranged from 1.0 ± 0.5 (smallest pan) to 2.5 ± 1.3 s (largest baking sheet), with 100% of sprays (210/210) exceeding the 0.25-s US serving size. Our results suggest that cooking spray serving size should be increased to 1 s to better reflect actual consumption, and this would have the added benefit of aligning better with cooking spray serving sizes in other developed countries (0.5-1.0 s). A 1-s serving size would also preclude cooking spray advertised as calorie- or fat-free, allowing consumers to make more informed choices on the dietary implications of using cooking spray.

A novel scheme for shore power data to enhance containership-at-berth emission estimation

Ship-at-berth emissions significantly affect air quality and health of human beings in a port and its neighbourhood. However, it is challenging to estimate these emissions precisely due to stringent data requirements. Shore Power (SP) data, including its actual energy consumption and duration, offers useful insights to refine these estimates, but has yet to be fully explored. This study proposes a novel scheme incorporating SP data to improve the accuracy of containership-at-berth emission estimates and evaluate emission reduction measures. The findings reveal substantial differences among existing emission estimates from identical case studies, highlighting the importance of SP data. Additionally, it demonstrates significant emissions from low-load main engines and confirms the efficacy of SP in emission reduction. These findings provide valuable insights into emission estimation methods and their potential applications in estimating emission reduction measures, underlining the importance of policy support in facilitating the SP implementation.

ASSESSMENT AND CURRENT TRENDS OF ENSURING FOOD SECURITY IN UKRAINE

The research on Ukraine's food security is of paramount importance, as it directly impacts the national agro-food systems. The article delves into the current state of internal food self-sufficiency in the Ukrainian population, conducting an analytical assessment of export-import transactions with agricultural raw materials and food products. The findings reveal a significant trend of increasing food exports in recent years, along with an evaluation of the export-import coverage for the main types of food products. Ukraine's place in the world rating of food security based on the Global Food Security Index was studied. The fundamental problems and reasons that determined Ukraine's decrease in the rating positions and the level of food security of its population were identified. In addition to the military conflict, Ukraine has a low economic availability of food products for the population and a low level of sustainability in the domestic agro-food system. Despite the war's consequences, the agricultural business remains one of the most profitable economic activities in Ukraine. This analysis of individual indicators of the effectiveness of the country's agrarian sector as a basis for ensuring food security underscores the sector's potential. The modern production structure of the main types of agricultural products by categories of producers was studied. The key trends accompanying ensuring food security in Ukraine under modern conditions are summarized. It was revealed that food security in the crop sector is achieved at the expense of the public sector agrarian formations, mainly at the expense of households in the livestock sector. Based on the average values ​​of the actual consumption indicators, the state of providing the population with the main types of food was analyzed. The results showed problematic aspects in providing the country's population with meat and dairy products, fish, fruits, berries, and grapes. The priority directions for ensuring and strengthening food security on a national scale are substantiated. Among the main ones, the following are highlighted: the need to implement sustainable farming and animal husbandry practices, further improvement of the food quality management and control system, protection of biodiversity of ecological agrosystems, optimization and reduction of value chains, increasing the level of social responsibility of food producers and consumers.

Assessing radon hazard in drinking water: A comprehensive approach integrating deterministic and probabilistic methods with water consumption routines

The research aimed to determine the spatiotemporal distribution patterns of radon activity concentrations in tap water of Yerevan city and assess radon-associated hazards using both deterministic and probabilistic approaches. This was accomplished by integrating one-year monitoring data of radon in water with water consumption habits among adult population clusters, which were identified through food frequency questionnaire in Yerevan.The study findings indicated variations in radon activity levels across administrative districts. The highest average activity concentrations were detected in Davtashen (7.07 Bq/L), while the lowest average was observed in Kanaker-Zeytun (1.57 Bq/L). The overall pattern of spatiotemporal variation during monitoring period revealed higher prevalence of radon in water in the northern and western parts of the city compared to the east and south, indicating different sources of drinking water. The radon-associated hazard assessment from water, using a deterministic approach (e.g., inhalation, ingestion, radon dissolution in blood, total effective dose), revealed values below the individual dose criterion (IDC) of 0.1 mSv/y. Monte Carlo simulation revealed a probability of exceeding IDC in specific water consumption-based groups. Residents of Yerevan who drink more than 3 L water daily with the highest observed activity concentration of 11.4 Bq/L, have an 86.26 % chance of exceeding IDC. Residents consuming 2.1 L water daily have a 7.02 % chance of exceeding IDC.The study highlights the importance of applied principles and methodologies for radon monitoring, particularly considering actual water consumption data and different risk assessment approaches. Considering the worst-case scenario results, it is recommended to keep tap water consumption up to 3 L per day, keeping the tap open longer to reduce radon levels. It also emphasizes the need for continued monitoring, given the variations in radon activity. The study provides valuable insights into radon exposure assessment, mitigation, and action plans in terms of water safety and public health.

Underwhelming pleasures: Toward a self-regulatory account of hedonic compensation and overconsumption.

Hedonic overconsumption (e.g., overconsumption of gratifying behaviors, e.g., eating, gaming) is common in daily life and often problematic, pointing to the need for adequate behavioral models. In this article, we develop a self-regulatory framework proposing that when an actual consumption experience falls short of hedonic expectations-such as when being distracted-people will want to consume more to compensate for the shortfall. In a preliminary meta-analysis, a small-scale field experiment on distraction during lunch and subsequent afternoon snacking (Study 1), and a preregistered experience sampling study (Study 2) involving more than 6,000 consumption episodes in everyday life across multiple consumption domains, we investigated the predictions from our hedonic compensation model. There was clear and consistent evidence across studies and analyses for the prediction that distraction during consumption compromises the actual enjoyment of a given consumption experience. Both empirical studies yielded consistent evidence for a positive association between actual enjoyment and consumption satisfaction but inconsistent and weaker evidence for the expected role of actual-expected enjoyment discrepancies for this part of the model. There was also consistent evidence for the expected negative association between consumption satisfaction and the need for further gratification. Finally, there was moderate and inconsistent support linking the need for further gratification to subsequent consumption across Study 1 (amount and frequency of snacking in the afternoon) and Study 2 (shorter duration to subsequent consumption). Taken together, the present framework provides initial support for the proposed link among compromising consumption contexts, consumption enjoyment, and subsequent hedonic compensation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Knowledge, Attitudes, and Practices towards Antibiotics, Antimicrobial Resistance, and Antibiotic Consumption in the Population of Kazakhstan.

During the COVID-19 pandemic, a ban on inspections of small businesses, including pharmacies, was imposed in Kazakhstan, which relaxed law enforcement efforts regarding the prohibition of over-the-counter antibiotic (AB) sales. This study aimed to investigate how this affected the knowledge, attitudes, and practices (KAP) related to AB and antimicrobial resistance (AMR), as well as to assess actual AB consumption at the community level. The study comprised two cross-sectional sub-studies: the first involved a KAP survey conducted in 2022 and 2024, utilizing the Special Eurobarometer questionnaire on AMR. The second sub-study analyzed AB consumption in 2021 and 2023, measured in defined daily doses per 1000 inhabitants. Results revealed an increase in the percentage of individuals reporting receipt of information about ABs and AMR in the past year (37.3% in 2022 vs. 52.9% in 2024, p < 0.001) and an increase in the percentage of individuals reporting AB use in the past year (49.0% in 2022 vs. 54.0% in 2024, p = 0.056). The most consumed ABs were from the Watch group, with azithromycin and ceftriaxone ranking highest. These findings support the hypothesis that the relaxation of law enforcement contributed to an increase in AB consumption and emphasize the need for public health policies to address this issue.

Impacts of carbon emissions allowance limitations on carbon price with power generation rights trading in China

Carbon pricing is a crucial mechanism for achieving carbon peaking and carbon neutrality through the carbon trading market. Recently, the price of carbon emissions allowances in China's national carbon market has shown a gradual upward trend. The limitations on carbon emissions allowances have also become increasingly stringent. Power generators, which primarily rely on thermal power generation, have a substantial demand for carbon emissions allowances. The rising prices and stricter carbon emissions constraints have increased operational costs. Therefore, exploring the carbon pricing mechanism under more stringent carbon emissions allowance constraints is vital. It encourages thermal power generators to engage in carbon trading and reduce carbon emissions. This paper focuses on an energy trading platform supply chain comprising three power generators, one energy trading platform, and one power retailer. We consider stringent carbon emissions constraints and power generation rights trading. Through the analysis of three scenarios, the study explores optimal pricing decisions for carbon emissions allowances to facilitate carbon emission reduction and promote renewable energy generation and consumption. The findings indicate that, under certain conditions (i.e., the carbon quotas allocation coefficient b1∈[0.73,0.8]), scenario 3, which excludes generation rights trading, yields the highest optimal carbon price. The energy trading platform can adjust various commissions to influence the carbon price, as well as the price and quantity of generation rights trading. Introducing power generation rights trading proves beneficial in reducing carbon emissions (nearly 400 tons less than that in scenario 3 without generation rights trading, when the realized total power demand is around 8600 MWh) and fostering renewable power generation and consumption (the actual renewable power consumption weight is 3% higher than that without generation rights trading). When considering generation rights trading, the high-emission generator and renewable power generator achieve the highest power profits, while for the low-emission generator, the profit in this case is the second best among these scenarios. Moreover, by decreasing the coefficient of carbon quotas allocation and increasing the reduction rate simultaneously, the carbon cost will pass through to the electricity price more sufficiently (e.g., when b1=0.76, the pass-through rate increases from 0.5015 to 0.7984), particularly within a specific range of the carbon quotas allocation coefficient.

An Overview of Livestock Role in Ensuring Food and Nutrition Security: The Case of Ethiopia

This review aimed to deliver and document summarized information on the contribution of livestock to food and nutrition security, both generally and specifically in the case of Ethiopia. Livestock have made both positive and negative contributions to food and nutrition security. Positively, they provide high biological value, palatable, and nutrient-dense food sources such as milk, meat, and eggs for human consumption. They also offer quick income, employment, source of fuel, social functions, raw materials for industry (hide and skin…. etc.), input for soil fertility (manure) and draft power, indirectly contributing to stable food cultivation. However, livestock also pose negative impacts on food and nutrition supply by competing with humans for grains, utilizing fodder crop lands, emitting greenhouse gases, and transmitting zoonotic diseases to humans. In Ethiopia, trends in livestock populations and food production levels reveal disparities between actual consumption and productivity compared to available resources. Energy, protein, and fat supply from the livestock sector has increased from 2010 to 2021. Yet, the country's per capita consumption of dairy, meat, and eggs remains below global averages, exacerbating undernutrition, particularly in rural areas. Livestock products are rich in high-quality protein and essential micronutrients crucial for growth and development, especially in children and infants. Enhancing nutritional outcomes in Ethiopia necessitates addressing barriers to livestock output and promoting the consumption of livestock-derived foods. Tackling underlying issues impeding livestock productivity is equally vital as efforts to enhance productivity and ensure access to livestock-based foods.

Comparative Analysis Of Cost Calculation With Activity-Based Costing And Traditional Methods

In the modern business world, accurate cost calculation is crucial for effective decision-making. Traditional methods often fail to reflect actual resource consumption, leading to inaccurate pricing and resource allocation. This research aims to analyze the use of Activity-Based Costing (ABC) and compare it with traditional methods. The research approach is qualitative, employing literature review methods. The findings show that ABC, which allocates costs based on activities performed, provides more realistic and accurate cost estimates. ABC helps companies identify non-value-added activities, improve efficiency, and reduce waste. Although ABC is more complex and expensive to implement, it provides more precise cost information, supporting optimal decision-making regarding pricing, product strategy, and resource allocation. Conversely, traditional methods are simpler but often result in less accurate cost allocation and do not reflect variations in activity consumption. Therefore, ABC is superior in terms of accuracy and effectiveness in identifying waste and inefficient activities, despite requiring technological support and higher initial costs.