Increase In Energy Consumption Research Articles

Examining the non-linear impact of fossil and renewable energy consumption on Vietnam’s ecological footprint: insights from the asymmetric ARDL approach

Vietnam’s economy is rapidly growing, marked by an increasing reliance on fossil fuels and renewable energy sources, resulting in unsustainable consumption patterns. The primary objective is to investigate the implications of renewable and non-renewable resource consumption on the overall resource availability over the long term, as indicated by the ecological footprint. Furthermore, the study incorporates economic growth to examine the Environmental Kuznets Curve hypothesis in Vietnam. Methodologically, the study adopts the asymmetric Autoregressive Distributed Lag model, utilizing time-series data ranging from 1986 to 2022. The findings highlight that Vietnam’s economic growth still use more natural resources while compromising the overall ecological reserve. Furthermore, the research findings suggest that the Environmental Kuznets Curve hypothesis does not hold in the context of Vietnam. Specifically, the increasing share of primary energy sourced from fossil fuels is observed to coincide with an increase in demand in ecological footprint. In contrast, an increase in primary energy consumption from renewable sources demonstrates a marked reduction in natural resource demand over the long term. Several strategies are recommended to enhance the resilience of environmental resources, such as adopting new and energy-efficient technologies is necessary within the industrial sector, promote the utilization of renewable energy sources, enhance waste management systems, transition from conventional energy to renewable sources in both production and consumption and the formulation of legislation on renewable energy usage.

Practical Application of 5G Networks: Opportunities and Vulnerabilities

5G is a revolutionary technology that provides ultra-speed, low latency and wide capacity, opening up new opportunities for all spheres of life. However, the introduction of 5G poses risks to cybersecurity and privacy, as well as increases energy consumption. It is necessary to ensure security, privacy and energy efficiency in order to fully realize the potential of 5G.

A sustainable study of competitive industrial performance amidst environmental quality: New insight from novel Fourier Perspective

This paper uses the novel Fourier Augmented Autoregressive Distributed Lag within the load capacity curve framework to investigate the effects of economic growth, trade openness, renewable energy consumption, and competitive industrial performance on environmental sustainability. The results of the empirical analysis provide evidence that the load capacity curve is valid in Turkiye. Moreover, while trade openness jeopardizes environmental sustainability, renewable energy consumption and industrial competitiveness increase environmental quality. Policymakers should focus intensively on policies that can achieve SDG 9 and industrialization activities. It is essential to give privileges, especially to companies operating in the industrial sector, regarding the transition to renewable energy. Energy efficiency policies that will accelerate the transition to clean energy sources are also an alternative to escape from fossil energy immediately. Environmental factors should be considered when importing intermediate goods used in industry, and these policies should be guaranteed by law as soon as possible.

Energy Losses or Savings Due to Air Infiltration and Envelope Sealing Costs in the Passivhaus Standard: A Review on the Mediterranean Coast

To obtain the Passivhaus Certificate or Passivhaus Standard (PHS), requirements regarding building envelope air tightness must be met: according to the n50 parameter, at a pressure of 50 Pa, air leakage must be below 0.6 air changes per hour (ACH). This condition is verified by following the blower door test protocol and is regulated by the ISO 9972 standard, or UNE-EN-13829. Some construction techniques make it easier to comply with these regulations, and in most cases, construction joints and material joints must be sealed in a complex way, both on façades and roofs and at ground contact points. Performing rigorous quality control of these processes during the construction phase allows achieving a value below 0.6 ACH and obtaining the PHS certification. Yet, the value can increase substantially with the passage of time: as windows and doors are used, opened, or closed; as envelope materials expand; with humidity; etc. This could result in significant energy consumption increases and losing the PHS when selling the house at a later point in time. It is therefore important to carefully supervise the quality of the construction and its execution. In this study, we focused on a house located in Sitges (Barcelona). The envelope air tightness quality was measured during four construction phases, together with the sealing of the joints and service ducts. The blower door test was performed in each phase, and the n50 value obtained decreased each time. The execution costs of each phase were also determined, as were the investment amortisation rates based on the consequent annual energy demand reductions. Air infiltration dropped by 43.81%, with the final n50 value resulting in 0.59 ACH. However, the execution costs—EUR 3827—were high compared to the energy savings made, and the investment amortisation period rose to a 15- to 30-year range. To conclude, these airtightness improvements are necessary in cold continental climates but are not applicable on the Spanish Mediterranean coast.

Multi‐objective GA to schedule task graphs on heterogeneous voltage frequency islands

SummaryEnergy consumption of multiprocessor's system is increasing day by day. The capability of multiprocessor systems and high compute‐intensive tasks play a major role in increasing energy consumption. Voltage frequency island (VFI) architecture partitioned the cores into groups for which voltage/frequency can be controlled by a single switch. VFI plays a major role in optimizing the energy consumption. We have generated the initial population by using the slot technique to VFI architecture. The genetic algorithm studied by many researchers to solve scheduling problems. So we combined the genetic algorithm with the VFI‐enabled architecture and slot approach called VFIGen. Then apply the VFIGen algorithm to optimize the energy consumption. When comparing the results of the proposed one with the existing state‐of‐art we achieved the performance gain by to .

Multi‐objective GA to schedule task graphs on heterogeneous voltage frequency islands

SummaryEnergy consumption of multiprocessor's system is increasing day by day. The capability of multiprocessor systems and high compute‐intensive tasks play a major role in increasing energy consumption. Voltage frequency island (VFI) architecture partitioned the cores into groups for which voltage/frequency can be controlled by a single switch. VFI plays a major role in optimizing the energy consumption. We have generated the initial population by using the slot technique to VFI architecture. The genetic algorithm studied by many researchers to solve scheduling problems. So we combined the genetic algorithm with the VFI‐enabled architecture and slot approach called VFIGen. Then apply the VFIGen algorithm to optimize the energy consumption. When comparing the results of the proposed one with the existing state‐of‐art we achieved the performance gain by to .

Energy poverty and developments in finance, economic complexity and economic conditions

Purpose Energy poverty presents substantial challenges for both developed and developing nations, with the latter experiencing more pronounced adverse effects due to issues related to the provision and equitable access of energy resources. This study aims to provide a deep understanding of how financial development, economic complexity and government expenditures can impact energy poverty. Design/methodology/approach This research employs generalized method of moments (GMM) estimation on panel data to investigate the economic determinants of energy poverty in 31 developing countries from 2000 to 2020. For a comprehensive analysis, the proxies for energy poverty include access to electricity, access to clean fuels and energy consumption. Findings The findings suggest that while financial development cannot facilitate access to clean fuels in developing countries, it contributes to an increase in energy access and consumption. Another finding is that energy poverty can be alleviated by enhancing economic complexity since economic complexity can result in increased access to electricity and increased use of clean energy sources. Furthermore, the results underscore the pivotal role of government expenditures, surpassing the influence of financial development. In other words, government expenditures have the potential to significantly improve energy poverty across all three indices. Originality/value This is a pioneering research that seeks to examine some economic dynamics including, financial development and economic complexity on energy poverty and provide valuable guidance for policymakers aiming to promote sustainable energy development with respect to economic dynamics.

Energy poverty and developments in finance, economic complexity and economic conditions

Purpose Energy poverty presents substantial challenges for both developed and developing nations, with the latter experiencing more pronounced adverse effects due to issues related to the provision and equitable access of energy resources. This study aims to provide a deep understanding of how financial development, economic complexity and government expenditures can impact energy poverty. Design/methodology/approach This research employs generalized method of moments (GMM) estimation on panel data to investigate the economic determinants of energy poverty in 31 developing countries from 2000 to 2020. For a comprehensive analysis, the proxies for energy poverty include access to electricity, access to clean fuels and energy consumption. Findings The findings suggest that while financial development cannot facilitate access to clean fuels in developing countries, it contributes to an increase in energy access and consumption. Another finding is that energy poverty can be alleviated by enhancing economic complexity since economic complexity can result in increased access to electricity and increased use of clean energy sources. Furthermore, the results underscore the pivotal role of government expenditures, surpassing the influence of financial development. In other words, government expenditures have the potential to significantly improve energy poverty across all three indices. Originality/value This is a pioneering research that seeks to examine some economic dynamics including, financial development and economic complexity on energy poverty and provide valuable guidance for policymakers aiming to promote sustainable energy development with respect to economic dynamics.

Study on cold start of fuel cell stack combined with liquid heating and fuel cell self-starting

Fuel cell vehicles have been paid more and more attention, while the low temperature cold start capability has become the main reason that hinders their large-scale commercialization, especially for the fuel cell bus. Generally, liquid heating (positive temperature coefficient thermistor (PTC) as the heat source) and fuel cell self-starting heating are the common cold start methods. However, these methods can bring about low start speed and high energy consumption. Therefore, a new cold start method combined with liquid heating and fuel cell self-starting is proposed to solve the problems. Besides, phase change material (PCM) is introduced to reduce energy consumption further. The PCM can absorb the waste heat of the fuel cell stack. Then the heat can be used for stack cold start by circulating liquid. In this study, a fuel cell bus cold start system simulating model is built. Based on this model, the fuel cell temperature and energy consumption during the cold start are analyzed. The results show that the method combined with liquid heating and fuel cell self-starting is superior to the liquid heating method. The cold start time and PTC consumption of the PTC-PCM-self-starting heating mode are 19.0 % and 19.2 % less than that of PTC-PCM heating mode. As for the combined heating method, dropping the self-starting temperature can not only increase the cold start speed but also reduce the proportion of electric heating. For every 1 °C decrease, the start-up time is shortened by 1 % on average, and the waste heat utilization rate is increased by 0.5 %. Although increasing the self-starting current can increase energy consumption, it will save more cold start time and reduce the proportion of electric heating. For every 1 A increase, the start-up time is shortened by 0.3 % on average, and the waste heat utilization rate is increased by 0.1 %. The PTC-PCM-self-starting heating method provides a way for fuel cell stack to start quickly and energy-efficiently in low temperature environment.

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

Techno-economic analysis of integrated MIDREX process with CO2 capture and storage: Evaluating sustainability and viability for iron production

This study offers a comprehensive evaluation of integrating carbon capture and storage (CCS) technology with the Midrex process for direct reduced iron (DRI) production. Employing rigorous simulation and validation against real plant data, we assess the impact of CCS integration on energy consumption, CO2 emissions, and economic feasibility. Our findings reveal a slight increase in energy consumption alongside a substantial reduction in CO2 emissions, with the cost of CO2 avoidance remaining competitive. Sensitivity analysis demonstrates the resilience of the proposed integration to market fluctuations. The findings of our study highlight the possibilities of integrating CCS with the Midrex process to address environmental issues and maintain economic sustainability in the iron manufacturing industry.

Hybrid Tasmanian Devil and Elephant Herding Model-Based Optimal Cluster Head Selection and Routing in MANET

MANETs (Mobile AdHoc Networks) have major problems due to the resource-constrained nature of mobile nodes. MANET topology is highly uncertain due to the mobility of MANET nodes that affect the stability of interconnected links. This scenario results in an increased traffic overhead which affects the routing protocol performance and results in increased energy consumption. A routing scheme designed for MANET should be able to handle energy depletion issues and nodes’ mobility. This paper designs two novel schemes to enhance energy efficiency and network lifetime by performing clustering and routing in MANET. The Hybrid Tasmanian devil and Elephant herding optimization (HTDEHO) is used for clustering the MANET nodes to minimize the overhead, enhance the stability of the network topology, and reduce collision. The HTDEHO selects the optimal cluster head (CH) from a set of nodes based on their fitness value calculated by various parameters such as mobility-based node ranking, residual energy, distance, node degree, and optimal next hop CH selection. Dwarf mongoose optimization algorithm with Fuzzy variable (FDMO) algorithm selects optimal routing cost from CH to the base station based on various parameters such as throughput, delay, and distance. The efficiency of the proposed model is evaluated with different performance metrics such as energy consumption, throughput, end-to-end delay, and packet-dropping analysis. The performance of the proposed HTDEHO-FDMO classifier is compared with different techniques such as RDOAICRP, BFOA, MARP-HO, and FEKHO-QBA. Even after 170 iterations, the HTDEHO model retains a total of 10 alive nodes.

Single Atom Catalysts Based on Earth-Abundant Metals for Energy-Related Applications.

Anthropogenic activities related to population growth, economic development, technological advances, and changes in lifestyle and climate patterns result in a continuous increase in energy consumption. At the same time, the rare metal elements frequently deployed as catalysts in energy related processes are not only costly in view of their low natural abundance, but their availability is often further limited due to geopolitical reasons. Thus, electrochemical energy storage and conversion with earth-abundant metals, mainly in the form of single-atom catalysts (SACs), are highly relevant and timely technologies. In this review the application of earth-abundant SACs in electrochemical energy storage and electrocatalytic conversion of chemicals to fuels or products with high energy content is discussed. The oxygen reduction reaction is also appraised, which is primarily harnessed in fuel cell technologies and metal-air batteries. The coordination, active sites, and mechanistic aspects of transition metal SACs are analyzed for two-electron and four-electron reaction pathways. Further, the electrochemical water splitting with SACs toward green hydrogen fuel is discussed in terms of not only hydrogen evolution reaction but also oxygen evolution reaction. Similarly, the production of ammonia as a clean fuel via electrocatalytic nitrogen reduction reaction is portrayed, highlighting the potential of earth-abundant single metal species.

Experimental and Numerical Heat Transfer Assessment and Optimization of an IMSI Based Individual Building Block System of the Kingdom of Bahrain

The increase in energy consumption in Bahrain is a significant issue. Insulation blocks are crucial for reducing heat transfer from outside to inside buildings. However, there’s limited research on the thermal performance of Bahrain’s insulation building blocks. No research to date has been conducted in Bahrain to study the effect of plaster and insulation inserts on the R-value of the blocks. This study examines and optimizes the thermal resistance (R-value) of an ‘Integrated Masonry System International, Ltd. (IMSI)’ block, chosen due to its common use in Bahrain’s commercial and residential construction. The study involves experimental analysis using a hot box setup and numerical analysis through the finite element method (FEM), along with assessing the impact of insulation inserts in the block’s cavities. R-values are calculated and validated for accuracy. The R-value discrepancy between numerical and experimental findings is 2.411%, and between numerical and manufacturer’s data is 5.743%. It is also observed that a 25 mm external plaster, as required by Bahrain’s government (EWA), enhances the R-value by 79.34%. Furthermore, optimizing the IMSI block’s height increased the R-value by 10.67%.

Vibrio neptunius-ULV11 cell-free supernatant as a promising antifouling approach in reverse osmosis systems

One of the most common problems observed in continuous operation of desalination plants, is the presence of biofouling in ultrafiltration (UF) modules and reverse osmosis membranes (ROMs), decreasing permeated water obtention and increasing energy consumption among other adverse effects. To solve this problem, desalination industry applied UF modules and ROMs cleaning procedures that aims to increase half-life operation time, however, many of the actual cleaning procedures are focused on the addition of chemical compounds that provokes secondary effects such as UF modules and ROMs disintegration leading to structural changes and a decrease in plant performance. Thus, new approaches to replace current cleaning compounds with new ecofriendly and less secondary effects technologies are necessary.In this study, we present a Vibrio neptunius strain ULV11 isolate, with a high capacity to remove fouling in RO systems. In detail, ULV11 cell-free supernatant (CFS) was used in replace of chemical agents as a cleaning solution according to standard operational protocol on 3-year-old membranes of continuous operation. The results showed that CFS can be used under operational conditions (such high pressure and flux) allowed us to recover permeate volumes similar to those observed in traditional chemical treatment with better conductivity. This recovery was accompanied by a reestablishment of membrane topology observed by atomic force microscopy and EPS remotion since concanavalin A signal trough confocal microscopy was decrease as compared with biofouling control.

Study on Cold Start of Fuel Cell Stack Combined with Liquid Heating and Fuel Cell Self-starting

Fuel cell vehicles have been paid more and more attention, while the low temperature cold start capability has become the main reason that hinders their large-scale commercialization, especially for the fuel cell bus. Generally, liquid heating (positive temperature coefficient thermistor (PTC) as the heat source) and fuel cell self-starting heating are the common cold start methods. However, these methods can bring about low start speed and high energy consumption. Therefore, a new cold start method combined with liquid heating and fuel cell self-starting is proposed to solve the problems. Besides, phase change material (PCM) is introduced to reduce energy consumption further. The PCM can absorb the waste heat of the fuel cell stack. Then the heat can be used for stack cold start by circulating liquid. In this study, a fuel cell bus cold start system simulating model is built. Based on this model, the fuel cell temperature and energy consumption during the cold start are analyzed. The results show that the method combined with liquid heating and fuel cell self-starting is superior to the liquid heating method. The cold start time and PTC consumption of the PTC-PCM-self-starting heating mode are 19.0% and 19.2% less than that of PTC-PCM heating mode. As for the combined heating method, dropping the self-starting temperature can not only increase the cold start speed but also reduce the proportion of electric heating. For every 1 °C decrease, the start-up time is shortened by 1% on average, and the waste heat utilization rate is increased by 0.5%. Although increasing the self-starting current can increase energy consumption, it will save more cold start time and reduce the proportion of electric heating. For every 1 A increase, the start-up time is shortened by 0.3% on average, and the waste heat utilization rate is increased by 0.1%. The PTC-PCM-self-starting heating method provides a way for fuel cell stack to start quickly and energy-efficiently in low temperature environment.

The ‘Carrot Test’: An approach to characterize individual differences in oral processing behaviour and eating rate

BackgroundEating rate is a modifiable risk factor of obesity and efficient methods to objectively characterise an individual’s oral processing behaviours could help better identify people at risk of increased energy consumption. Many previous approaches to characterise oral processing and eating rate have relied on specialised equipment or wearable devices but that are time consuming, expensive or require expertise to administer. The current trial tested the use of a standardised test food (the ‘carrot test’) to measure oral processing. ObjectiveWe sought (i) to test whether self-reported eating rate (SRER) is predictive of food oral processing derived from coded eating behaviours captured in the laboratory with a standardised test food, and (ii) to test whether differences in SRER are predictive of oral processing behaviours, eating rate and intake of a test meal. MethodsTwo hundred and fifty-three volunteers (86 male and 167 female, mean age 39.5 ± 13.6 years, mean BMI 22.2 ± 3.4 kg/m2) provided their SRER and anthropometric measurements of height, weight and dual-energy X-ray absorptiometry (DEXA) percentage fat mass. Participants were also v ideo recorded eating a fixed 50 g portion of carrot and an ad libitum lunch meal of fried rice. Average eating rate (g/min), bite size (g) and number of chews per bite for the carrot and lunch were derived through behavioural coding of the v ideos. Energy intake (kcal) was recorded at lunch and a later afternoon snack. ResultsFaster SRER significantly predicted faster eating rate, larger bite size and more chews per bite observed during intake of the carrot (ß = −0.26–0.21, p ≤ 0.001) and the lunch (ß = −0.26–0.35, p ≤ 0.014). SRER did not significantly predict intake at lunch or during the afternoon snack (ß = 0.05–0.07, p ≥ 0.265). Participants’ oral processing of the carrot significantly predicted oral processing of the lunch (ß = −0.25–0.40, p ≤ 0.047) and faster eating rate of the carrot significantly predicted increased lunch intake (ß = 0.119, p = 0.045). None of the oral processing behaviours predicted afternoon snack intake (ß = −0.01–0.05, p ≥ 0.496). None of these associations were moderated by BMI or body composition. ConclusionWe confirm that SRER is a valid measure of group level differences in individual oral processing behaviours, but did not predict an individual’s energy intake at a lunch-time meal. We demonstrate that it is possible to characterise differences in eating rate by coding eating behaviours for a standardized test food (in this case, a fixed portion of raw carrot). This approach could be used to provide an objective measure of a person’s habitual oral processing behaviour, and was shown to be a significant predictor of eating rate and energy intake for a later test meal.

The ‘Carrot Test’: An approach to characterize individual differences in oral processing behaviour and eating rate

BackgroundEating rate is a modifiable risk factor of obesity and efficient methods to objectively characterise an individual’s oral processing behaviours could help better identify people at risk of increased energy consumption. Many previous approaches to characterise oral processing and eating rate have relied on specialised equipment or wearable devices but that are time consuming, expensive or require expertise to administer. The current trial tested the use of a standardised test food (the ‘carrot test’) to measure oral processing. ObjectiveWe sought (i) to test whether self-reported eating rate (SRER) is predictive of food oral processing derived from coded eating behaviours captured in the laboratory with a standardised test food, and (ii) to test whether differences in SRER are predictive of oral processing behaviours, eating rate and intake of a test meal. MethodsTwo hundred and fifty-three volunteers (86 male and 167 female, mean age 39.5 ± 13.6 years, mean BMI 22.2 ± 3.4 kg/m2) provided their SRER and anthropometric measurements of height, weight and dual-energy X-ray absorptiometry (DEXA) percentage fat mass. Participants were also v ideo recorded eating a fixed 50 g portion of carrot and an ad libitum lunch meal of fried rice. Average eating rate (g/min), bite size (g) and number of chews per bite for the carrot and lunch were derived through behavioural coding of the v ideos. Energy intake (kcal) was recorded at lunch and a later afternoon snack. ResultsFaster SRER significantly predicted faster eating rate, larger bite size and more chews per bite observed during intake of the carrot (ß = −0.26–0.21, p ≤ 0.001) and the lunch (ß = −0.26–0.35, p ≤ 0.014). SRER did not significantly predict intake at lunch or during the afternoon snack (ß = 0.05–0.07, p ≥ 0.265). Participants’ oral processing of the carrot significantly predicted oral processing of the lunch (ß = −0.25–0.40, p ≤ 0.047) and faster eating rate of the carrot significantly predicted increased lunch intake (ß = 0.119, p = 0.045). None of the oral processing behaviours predicted afternoon snack intake (ß = −0.01–0.05, p ≥ 0.496). None of these associations were moderated by BMI or body composition. ConclusionWe confirm that SRER is a valid measure of group level differences in individual oral processing behaviours, but did not predict an individual’s energy intake at a lunch-time meal. We demonstrate that it is possible to characterise differences in eating rate by coding eating behaviours for a standardized test food (in this case, a fixed portion of raw carrot). This approach could be used to provide an objective measure of a person’s habitual oral processing behaviour, and was shown to be a significant predictor of eating rate and energy intake for a later test meal.

An Implementation on Energy Efficient Task Scheduling in Cloud Environment

Abstract: Cloud computing has revolutionized the way computing resources are utilized by providing scalable, on-demand access to a shared pool of resources over the internet. It offers significant advantages such as cost savings, flexibility, and accessibility, making it an essential technology for businesses and individuals alike. However, the increasing energy consumption associated with cloud data centres has become a critical concern, necessitating the development of energy-efficient task scheduling algorithms. This paper explores various algorithms designed to optimize task scheduling in cloud environments to reduce energy consumption. Among these, heuristic algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are popular for their ability to find near-optimal solutions in large search spaces. Additionally, machine learning-based approaches, such as Reinforcement Learning (RL), have shown promise in dynamically adapting to workload variations and improving energy efficiency. Other notable algorithms include Ant Colony Optimization (ACO) and Dynamic Voltage and Frequency Scaling (DVFS), each offering unique mechanisms to balance performance and energy usage. The focus of this paper is on the implementation and comparative analysis of these task scheduling algorithms in a cloud environment. We present a comprehensive evaluation of their performance, highlighting their strengths and limitations in achieving energy efficiency. The results demonstrate that while no single algorithm is universally optimal, tailored combinations of these approaches can significantly enhance energy savings in cloud data centres.

The Cointegration Between Energy Subsidy and Energy Accessibility in Eastern Africa Countries

Despite the significant potential, the energy industry in East Africa remains largely underdeveloped, with minimal modern energy access, low per capita consumption, and heavy reliance on biomass energy, which makes up over 90% of the region’s energy consumption. Less than 1% of rural areas have access to electricity. Introduction of subsidies on energy influences energy accessibility. The study found it clear that subsidies slightly improved energy accessibility in Eastern African countries in the long run. This can be explained by the fact that energy subsidies often result in lower prices for energy consumers. This makes energy more affordable for households, particularly those with lower incomes. Lower energy prices reduce the financial burden of meeting energy needs, making it easier for individuals and families to access essential energy services such as electricity, heating, and cooking. Lower energy prices due to subsidies can lead to increased energy consumption. It is therefore the responsibility of each member state to encourage the adoption of renewable energy sources by providing subsidies and incentives for renewable energy technologies such as solar panels, wind turbines, and energy-efficient appliances. Subsidizing renewable energy reduces reliance on fossil fuels, mitigates environmental impacts, and promotes sustainable energy practices.