Cost Of Degradation Research Articles

Performance Interference of Virtual Machines: A Survey

The rapid development of cloud computing with virtualization technology has benefited both academia and industry. For any cloud data center at scale, one of the primary challenges is how to effectively orchestrate a large number of virtual machines (VMs) in a performance-aware and cost-effective manner. A key problem here is that the performance interference between VMs can significantly undermine the efficiency of cloud data centers, leading to performance degradation and additional operation cost. To address this issue, extensive studies have been conducted to investigate the problem from different aspects. In this survey, we make a comprehensive investigation into the causes of VM interference and provide an in-depth review of existing research and solutions in the literature. We first categorize existing studies on interference models according to their modeling objectives, metrics used, and modeling methods. Then we revisit interference-aware strategies for scheduling optimization as well as co-optimization-based approaches. Finally, the survey identifies open challenges with respect to VM interference in data centers and discusses possible research directions to provide insights for future research in the area.

Cross-Layer Codebook Allocation for Uplink SCMA and PDNOMA-SCMA Video Transmission Systems and a Deep Learning-Based Approach

Sparse code multiple access (SCMA) and power-domain nonorthogonal multiple access (PDNOMA) have been considered for use with fifth-generation (5G) and beyond mobile communications, in order to increase the bandwidth efficiency, reduce the latency in terms of waiting for resources, and increase the numbers of connections. Existing codebook allocation schemes for SCMA and PDNOMA-SCMA systems consider only the physical layer. We propose an iterative cross-physical-and-application-layer codebook allocation for uplink video communications. We allocate the codebook according to the channel state information of the physical layer, and codebooks are exchanged among users to minimize the sum of the video mean square error in order to increase the average peak signal-to-noise ratio (PSNR). Furthermore, we propose a deep neural network (DNN) approach to reduce the computational complexity. We propose a novel postprocessing algorithm that is applied to the DNN output to ensure the constraints of codebook allocation are satisfied. Simulation results show that the proposed iterative PDNOMA-SCMA cross-layer codebook allocation scheme outperforms an existing scheme that uses only the physical layer by 1.27 dB in terms of PSNR (video quality) for 12 users, four subcarriers, and SNR = 21 dB. The proposed DNN-based approach has an execution time that is 67% lower than the proposed iterative PDNOMA-SCMA cross-layer codebook allocation scheme, at the cost of a performance degradation of 0.84 dB.

Improving Soil Fertility and Wheat Yield by Tillage and Nitrogen Management in Winter Wheat–Summer Maize Cropping System

Soil degradation and high environmental costs impede agricultural production in North China. A 6-year field experiment was conducted to determine the effects of tillage practice and nitrogen application rate on changes in soil fertility and wheat yield. Four tillage systems (rotary tillage without maize straw return through 6 years, RT; rotary tillage with maize straw return through 6 years, RS; deep tillage with maize straw return through 6 years, DS; and rotary tillage through 2 years followed by deep tillage next year with maize straw applied for two cycles, RS/DS) and three N levels (HN, 300 kg N ha−1, refers to traditional farming practice; MN, 0.75 × HN, 225 kg N ha−1, to recommended N rate; and LN, 0.5 × HN, 150 kg N ha−1, to reduced N rate) were tested. The soil organic carbon, labile organic carbon, inorganic N, available phosphorus, and available potassium under straw return treatments were significantly higher than RT in the 0–30 cm soil layer (p < 0.05). The microbial diversity, invertase, urease, and alkaline phosphatase activities also increased when maize straw was returned. Tillage practices could distribute maize straw in different depths of the soil and then affect soil nutrients, enzyme activity, and microbial diversity. The RS treatment presented the greatest effects in the 0–10 cm layer, while more significant impacts were observed in DS and RS/DS treatments at the 10–30 cm depths. The levels of soil nutrients and enzyme activity increased with an increased N rate. Compared to that under LN, wheat yields increased under HN and MN treatments, whereas there were no significant differences between HN and MN (p > 0.05). An increasing tendency of grain yield was observed in DS and RS/DS, while conversely so in RS. RS/DS had lower farm costs than DS during the study duration. Thus, RS/DS at 225 kg N ha−1 is the best method for improving soil fertility and wheat yield.

Multiobjective Optimization of Safety, Comfort, Fuel Economy, and Power Sources Durability for FCHEV in Car-Following Scenarios

Safety, comfort, and energy-saving oriented car-following issue for fuel cell/battery hybrid electric vehicle (FCHEV) is a comprehensive problem of vehicle dynamic and fuel economy. Combining adaptive cruise control (ACC) and energy management strategy (EMS) is proven to be one promising method to realize multiobjective co-optimization. However, in majority of existing researches, degradation of power sources is always ignored and trade-off among different objectives remains a burning issue, in this article, a Pareto-based EMS under car-following scenarios is proposed. Specifically, degradation models of fuel cell/battery are established and incorporated into multiobjective optimization functions. Then, based on back-stepping technique and equivalent consumption minimization strategy (ECMS), an integrated framework of ACC and EMS is developed, which realizes the coordination between vehicle external longitudinal dynamics control and internal powertrain energy management. For getting the trade-off among the abovementioned objectives, a Pareto-involved multiobjective optimization method is proposed to optimize control parameters of the integrated framework of ACC and EMS. The simulation results of Urban Dynamometer Driving Schedule (UDDS) test highlight that compared with the weighted-sum methods, the proposed method can utmost reduce average tracking error by 15.14%, variation of speed by 8.61%, equivalent fuel consumption cost by 7.56%, and comprehensive power sources degradation cost by 17.78%.

Day-Ahead Optimal Bidding for a Retailer With Flexible Participation of Electric Vehicles

The existing bidding models for retailers managing electric vehicles (EVs) in distribution-level day-ahead (DA) electricity markets have not fully addressed EVs’ temporal distribution, charging and discharging management, or bidding curves. To address these challenges, this paper proposes a DA optimal bidding model for a retailer with flexible participation of EVs. First, the investigating period for DA optimal bidding is modeled to cover the connecting periods of all EVs that may potentially impact the DA power demand prediction. Then, incentive mechanisms for charging and discharging are proposed to enable retailers to manage EVs under uncertain market prices and EV parameters while also considering the battery degradation cost and the preferences of EV users based on a social survey. The optimal charging model based on incentive mechanisms for charging-discharging helps EVs minimize their energy purchase costs under different price scenarios. Based on the minimum EV energy purchase cost, the optimal bidding model of a retailer aims to achieve the maximum bidding profit considering the conditional value at risk (CVaR), with the step bidding curves and the temporal differences for different time intervals considered. Simulations validate the proposed model under flexible incentive mechanism for charging and incentive mechanism for discharging participation of EVs.

Does environmental degradation matter for poverty? Clarifying the nexus between FDI, environmental degradation, renewable energy, education, and poverty in Morocco and Tunisia.

Poverty is the curse for sustainable and equitable development worldwide by detreating environmental sustainability, economic instability, and inequality. However, as a remedy for poverty reduction, researchers over the past decade have examined the key macro determinants and established positive associations, implying the contributory role in poverty reduction. The study explores the environmental, energy, education, and foreign direct investment (FDI) effects on poverty reduction in Morocco and Tunisia from 1991 to 2020. We employed autoregressive distributed lagged (ARDL) and nonlinear ARDL frameworks to document the explanatory variables' elasticity on poverty reduction in both the long- and short-run horizons. According to linear assessment, the study documented that education, energy, and FDI support poverty reduction. At the same time, the cost of environmental degradation has a detrimental effect on poverty augmentation. Referring to asymmetric assessment, the study established a long-run asymmetric association between asymmetric shocks of education, FDI, and energy with poverty. For directional association, the study has implemented a casualty test with the Fourier TY causality test and revealed a bidirectional association between education and poverty, and energy and poverty. Moreover, the unidirectional casualty was unveiled between FDI and poverty, and poverty and environment.

Integrated model for optimal energy management and demand response of microgrids considering hybrid hydrogen-battery storage systems

Hybrid hydrogen-battery (HHB) based energy storage technologies have recently become matters of significant interest to enable sustainable and net zero-emission microgrids characterized with high penetration levels of variable renewable energy sources (RES) such as solar and wind. In this regard, there are multiple objectives and constraints controlling the scheduled decision-making framework that manages these microgrid resources while supplying the demand. As such, this paper introduces an integrated energy management system (IEMS) to concurrently optimize the operation schedule of the HHB storage system and provide demand response (DR) via adequate shifting slots for the elastic loads of a microgrid setup. The proposed IEMS aims to optimally manage the microgrid’s energy by minimizing three conflicting objective functions: the electricity and battery degradation costs, customers’ discomfort, and peak-to-average ratio (PAR). The proposed IEMS is developed for microgrids containing a mix of variable RES, an HHB storage system, and a local power consumption considering dynamic grid tariff. The IEMS is formulated as a mixed-integer nonlinear problem and is solved using a multi-objective artificial hummingbird optimizer (MOAHA). To account for the variability and uncertainty of RES, the proposed IEMS is tested under four scenarios: good, bad, average weather scenarios, and a forecasted weather profile based on a stochastic model of RES. The performance of the proposed IEMS is evaluated via comprehensive comparative analyses with the conventional energy management strategy (CEMS) without and with consideration of the HHB storage system and DR. Moreover, the performance of MOAHA has been assessed versus the state-of-the-art multi-objective particle swarm optimizer (MOPSO). For the purpose of these comparisons, four quality metrics have been utilized to quantify the benefits of the proposed IEMS: renewable contribution factor (RCF), greenhouse gas (GHG) emission, loss of power supply probability (LPSP), and saving cost (SC). The results show that the proposed IEMS yields better-optimized values that help the microgrid operator identify the sweet spot of the conflicting objectives, where it has increased the customer saving to be (82.95%, 29.65%, 33.33%, and 33.00 %), (73%,23.94%, 25.49%, and 31.65%) and (10.69%,3.62%, 1.59%, and 1.67%) compared to the CEMS without/with including HHB. and IEMS based-MOPSO over the four studied scenarios, respectively. Also, the results show the superiority of the proposed IEMS in improving the identified four quality metrics compared to the CEMS without/with considering HHB storage systems and IEMS-based-MOPSO. Accordingly, integrating HHB and implementing optimized IEMS based-MOAHA with considering the DR approach plays a vital role in providing a profit for the customer, protecting the environment from emissions, and enhancing the system’s reliability with considering the weather uncertainty.

Optimal sizing of renewable energy storage: A techno-economic analysis of hydrogen, battery and hybrid systems considering degradation and seasonal storage

Energy storage is essential to address the intermittent issues of renewable energy systems, thereby enhancing system stability and reliability. This paper presents the design and operation optimisation of hydrogen/battery/hybrid energy storage systems considering component degradation and energy cost volatility. The study examines a real-world case study, which is a grid-connected warehouse located in a tropical climate zone with a photovoltaic solar system. An accurate and robust Multi-Objective Modified Firefly Algorithm (MOMFA) is proposed for the optimal design and operation of the energy storage systems of the case study. To further demonstrate the robustness and versatility of the optimisation method, another synthetic case is tested for a location in a temperate climate zone that has a high seasonal mismatch. The modelling results show thatthe system in the tropical zone always provides a superior return when compared to a similar system in the temperate zone due to abundant solar resources.When comparing battery-only and hydrogen-only systems, battery systems perform better than hydrogen systems in many situations, with a higher self-sufficient ratio and net present value. However, if there is high seasonal variation and a high requirement for using renewable energy (the penetration of renewable energy is >80 %), using hydrogen for energy storage is more beneficial. Furthermore, the hybrid system (i.e., combining battery and hydrogen) outperforms battery-only and hydrogen-only systems. This is attributed to the complementary combination of hydrogen, which can be used as a long-term energy storage option, and battery, which is utilised as a short-term option. This study also shows that storing hydrogen in a long-term strategy can lower component degradation, enhance efficiency, and increase the total economic performance of hydrogen and hybrid storage systems. The developed optimisation method and findings of this study can support the implementation of energy storage systems for renewable energy.

Transfer Deep Reinforcement Learning-Based Energy Management Strategy for Plug-In Hybrid Electric Heavy-Duty Trucks under Segmented Usage Scenarios

Energy management strategy (EMS) is a way to reduce the energy consumption of hybrid power systems. This article proposes a unique deep reinforcement learning- (DRL-) based EMS for plug-in hybrid electric heavy-duty trucks (PHETs), combining driving cycle pattern recognition (DPR) and deep transfer learning (DTL). The proposed EMS can cope well with the complex usage scenarios of PHETs and the difficulty of generating EMS. While ensuring the minimum overall driving cost, the strategy can improve the convergence speed of the DRL method and the generalizability under segmented usage scenarios. Firstly, representative driving cycles that reflect different usage scenarios are constructed based on a naturalistic data-driven method. Secondly, a plug-in hybrid electric heavy-duty truck (PHET) driving pattern recognizer based on a learning vector quantization neural network (LVQ) is built. Thirdly, the deep deterministic policy gradient (DDPG) algorithm is innovatively combined with the DTL algorithm. The pretrained neural network in the corresponding usage scenarios is transferred to the natural driving cycles based on DTL. Moreover, the proposed EMS gives an emphasized consciousness on the battery degradation cost. Finally, the strategy is tested under natural driving cycles in different usage scenarios and proven through comparison with the current state-of-the-art techniques, deep reinforcement learning-based strategy, and dynamic programming (DP). The results show that the proposed strategy outperforms existing cutting-edge deep reinforcement learning techniques in terms of convergence speed, battery life extension, fuel consumption, and overall driving cost reduction. The proposed control strategy can improve the convergence speed by nearly 50%, while effectively extending the battery life and reducing the overall driving cost compared to the existing state-of-the-art strategies. The battery degradation rate is reduced by 48.46%, 57.95%, and 36.99%, and the driving cost is reduced by 17.76%, 8.51%, and 7.12%, respectively, under each usage scenario.

Degradation of sulfamerazine using ultrasonic horn and pilot scale US reactor in combination with different oxidation approaches

The present study aimed at utilizing ultrasonic horn (USH) and ultrasonic reactor (USR) having provision for dual frequency operation for the successful and cost-effective degradation of sulfamerazine (SMZ). USH and USR were combined with UV, persulfate (PS), peroxymonosulfate (PMS), Fenton (Fe2+/H2O2) and Fenton like (Fe3+/H2O2) process in detailed investigation to establish the optimal loading for obtaining the improved degradation of SMZ quantified in terms of the synergistic index values. A mathematical model was also developed to explain the complex SMZ degradation kinetics for all the AOPs. Significantly higher SMZ degradation (>99 %) was achieved for various combined approaches as USH/PS, USH/PMS, USH/Fe2+/H2O2, USH/Fe3+/H2O2, USH/PS/UV, USH/PMS/UV, USH/Fe2+/H2O2/UV, and USH/Fe3+/H2O2/UV, demonstrating the significant role of SO4¯ and OH radicals in intensifying the oxidation of the SMZ molecules. The pilot scale study with USR also demonstrated effective SMZ degradation using combined AOPs with maximum removal as 94.14 % for USR (22 + 44 kHz)/Fe3+/H2O2/UV. The dual frequency mode of operation was observed to be more efficient than the single frequencies. The cost estimation study based on of cavitational yield revealed that the most cost-effective system was the USR (22 + 44 kHz)/Fe3+/H2O2/UV with a degradation cost of 0.57 ₹/L (0.0069 $/L).

A User-Friendly and Economical Six-Axis Force/Torque Sensor With Wireless Communication for Force Control and Monitoring

Six-axis force/torque (F/T) sensors have been actively developed owing to the increasing demand for force control and monitoring in many engineering applications. However, these sensors are expensive and complicated to use, particularly the wireless ones. This study presents a user-friendly and economical wireless six-axis F/T sensor for force control and monitoring applications. A newly designed elastic structure was used to overcome the reliability degradation and high machining cost problems of the elastic structures developed in our previous study. T-shaped structures were arranged symmetrically around the upper and lower parts, reducing the manufacturing cost. The electrical and electronic circuits for economical analog/digital signal processing, including zero calibration, were developed based on cheap microcontrollers. A user-friendly embedded wireless communication module was programmed at a low machine level for fast force control and monitoring. Additionally, we implemented a universal serial bus (USB)-wired F/T sensor similar to the wireless unit, except for wired communication. The effectiveness of the proposed F/T sensor was demonstrated by experimental tests and comparison with a commercial F/T sensor.

Insightful Electric Vehicle Utility Grid Aggregator Methodology Based on the G2V and V2G Technologies in Egypt

Due to the exponential expansion of the global fleet of electric vehicles (EVs) in the utility grid, the vehicle-to-grid paradigm is gaining more attention to alleviate the pressure on the grid. Therefore, an EV aggregator acts as a resilient load to enhance the power deficiency in the electrical grid. This paper proposes the vital development of a central aggregator to optimize the hierarchical bi-directional technique throughout the vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technologies. This study was implemented using three different types of EVs that are assumed to penetrate the utility grid throughout the day in an organized pattern. The aggregator determines the number of EVs that would participate in the electric power trade during the day and sets the charging/discharging capacity level for each EV. In addition, the proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner using the V2G technology and ensuring a sufficient grid peak load demand shaving based on the genetic algorithm (GA). Three case studies were investigated based on the parking interval time where the battery degradation cost was minimized to reach approx. 82.04%. However, the revenue of the EV owner increased when the battery degradation cost was ignored. In addition, the load demand decreased by 26.5%. The implemented methodology ensured an effective grid stabilization service by shaving the load demand to identify the average required power throughout the day. The efficiency of the proposed methodology is ensured since our output findings were in good agreement with the literature survey.

ADVANCES IN SOLAR COOKING IN DEVELOPING COUNTRIES - A REVIEW

Increased energy demand has forced societies to rely on traditional fuel sources with negative health andenvironmental impacts. In most developing countries, solar energy is available for long hours and in ranges thatif exploited can reduce dependence on biomass and fossil fuels. However, despite its potential, uptake of solarcooking devices is low due to intermittent radiation, technological challenges, lack of awareness and slowercooking rates compared to other energy sources. There are four traditional types of solar cookers, box, panel,parabolic and tube. The latest technology is the PV conversion to electricity. Since the advent of solar cooking,numerous experiments, designs and improvements of solar cookers have been successfully developed. Researchhas focused on improving solar cooker performance such as; improved design, sun tracking mechanisms, solarthermal storage or “hybrid” designs that remove limitation of nighttime cooking or periods of poor irradiance.These improvements have increased the efficiency, reduce the cost of the solar cookers, and addressedsociocultural challenges such as sheltered cooking, visual appearance, and the ability to cook traditionalrecipes. However, more needs to be done to increase ease of adoption, the capacity of energy storage andflexibility. The cost of domestic workloads and environmental degradation should also be a factor in energypolicy formulation.

An economic, self-supporting, robust and durable LiFe5O8 anode for sulfamethoxazole degradation

Electrochemically activating peroxydisulfate (PDS) to degrade organic pollutants is one of the most attractive advanced oxidation processes (AOPs) to address environmental issues, but the high cost, poor stability, and low degradation efficiency of the anode materials hinder their application. Herein, an economic, self-supporting, robust, and durable LiFe5O8 on Fe substrate (Fe@LFO) anode is reported to degrade sulfamethoxazole (SMX). When PDS is electrochemically activated by the Fe@LFO anode, the degradation rate of SMX is significantly improved. It is found that hydroxyl radicals (•OH), superoxide radical (O2•–), singlet oxygen (1O2), Fe(Ⅳ), activated PDS (PDS*), and direct electron transfer (DET) reactions synergistically contribute to the degradation of SMX, which can realize the degradation of SMX in four possible routes: cleavage of the isoxazole ring, hydroxylation of the benzene ring, oxidation of the aniline group, and cleavage of the S–N bond, as evidenced by a series of tests of radicals quenching, electron paramagnetic resonance (EPR), linear sweep voltammetry (LSV) and liquid chromatograph mass spectrometer (LC-MS). Furthermore, Fe@LFO has good structural stability, excellent cyclability and low degradation cost, demonstrating its great potential for practical applications. This work contributes to a stable and effective anode material in the field of AOPs.

Economic benefit evaluation model of distributed energy storage system considering custom power services

This paper proposes an economic benefit evaluation model of distributed energy storage system considering multi-type custom power services. Firstly, based on the four-quadrant operation characteristics of the energy storage converter, the control methods and revenue models of distributed energy storage system to provide reactive power compensation, new energy consumption, peak-valley arbitrage and other customized power services are analyzed. Secondly, an economic benefit evaluation model of custom power services is formulated, considering the life cycle degradation cost, investment payback period, net present value, and internal return rate of energy storage. Finally, demonstrate the effectiveness and superior performance of the proposed methodology through comparative studies on the economic benefits and practical value. The influence of reserve capacity ratio of energy storage converter, additional price for power quality management, peak-valley price difference, battery cost and project cycle on the annual return and internal rate of return is revealed through the sensitivity analysis, which provides the decision-making reference for battery selection and capacity allocation of distributed energy storage system participating in various custom power services.

Optimal sizing method with sensitivity analysis for hybrid energy storage system in electric vehicle using hybrid technique

This manuscript proposes a hybrid method for optimum sizing and energy management (EM) of hybrid energy storage systems (HESSs) in Electric vehicle (EV). The proposed hybrid method is combined performance of Honey Badger Algorithm (HBA) and recalling-enhanced recurrent neural network (RERNN), commonly called HBA-RERNN method. The major objective of proposed system is reducing the vehicle life time cost. The HESSs are incorporated with battery and super capacitor (SC). The proposed method is utilized to solve combined energy management and optimization size. Based on the variables, such as size of battery pack and super capacitor pack, HESS size is reflected. Depend on various sensitivity factors, optimum hybrid energy storage systems size and financial costs are analyzed. At last, the performance of proposed system is implemented on MATLAB site and compared with several existing systems. From this simulation outcome, it concludes that the proposed system diminishes the overall cost and battery degradation cost as 66625 USD than the existing systems. The efficiency of the proposed system achieves 94.8763%.

Operating Cost Comparison of a Single-Stack and a Multi-Stack Hybrid Fuel Cell Vehicle Through an Online Hierarchical Strategy

One of the recently suggested solutions for enhancing the fuel economy and lifetime in a fuel cell (FC) hybrid electric vehicle (HEV) is the use of a multi-stack (MS) structure for the FC system. However, to fully realize the potential of this structure, the design of an appropriate energy management strategy (EMS) is necessary. This paper aims to compare the operating cost, including hydrogen consumption and degradation of the FC, between a single-stack (SS) and an MSFC-HEV. To do so, a hierarchical EMS, composed of two layers, is devised for the MS system. In the first layer, a rule-based strategy determines how many FCs should be ON according to the requested power, battery state of charge (SOC), and FCs degradations. In the second layer, an equivalent consumption minimization strategy (ECMS) is developed to determine the output power of each activated FC according to the cost function and constraints. Regarding the SS structure, ECMS is employed for power distribution. The purpose of this strategy is to decrease fuel consumption and FC system degradation costs in both structures. The performance of the ECMS is compared with dynamic programming (DP) as a global optimization strategy for validation purposes. The obtained results using experimental data show that an FC-HEV with an MS structure reaches less hydrogen and degradation costs than an SS one.

Bi-Level Optimizing Model for Microgrids With Fast Lithium Battery Energy Storage Considering Degradation Effect

The nonlinear degradation effect of the fast lithium battery energy storage system (FLBESS) and the time-scale variability of peak shaving and frequency regulation both make the optimal operation of the microgrid more challenging. Therefore, this paper presents a bi-level optimization model to balance the degradation cost of lithium battery and the benefit of peak shaving and frequency regulation and improve the utilization of FLBESS while dealing with the multiple time scales optimization problems. The upper-level model mainly optimizes the day-ahead hourly power to minimize the power purchase cost and wind and solar power curtailment cost while calculating the internal parameters of the FLBESS to supply the lower-level model for the solution. The lower model optimizes the intra-hour economic cost and coordinates the frequency regulation benefit and lithium battery degradation cost. Finally, case studies are conducted using a regional microgrid to verify that the model solution satisfies the requirements for optimal system operation. In contrast, the model has the advantages of lower economic cost, more significant life extension effect, and higher energy storage utilization rate. The results show that the annual costs were reduced by 14.9%, and the battery life was increased by 19.0% than without considering degradation.

Assessing the economic cost of urban pollution: A case study of landfill waste in Kulonprogo, Indonesia

Introduction – Rapid urbanization in developing countries dramatically increases solid waste production, negatively impacting the socio-economy and ecology. Solid waste management is essential to minimize the associated risks, especially in health and the environment. Objective – This study assesses the economic cost of pollution from the disposal of household waste at the Banyuroto landfill, Kulonprogo, Indonesia. Methodology/Approach – This study uses the sickness and reimbursement cost approach. The subjects in this study were 100 households in Banyuroto, Kulonprogo, Indonesia, selected using cluster random sampling. Findings – The results show that the landfill has a negative impact on the community’s life because it causes air pollution (unpleasant odors), water pollution, and waste-related diseases, such as diarrhea, respiratory problems, flu, cough, cold, and itchy rash. The economic cost of pollution emerging from the landfill is IDR 2,355,000 per year. Meanwhile, using the replacement cost method, the estimated cost of environmental quality degradation is IDR 12,144,000 per year. For 1,230 households, the cost is IDR 125,941,304 per year, so the average economic cost due to environmental degradation is IDR 912,104,414 per year. Originality/Value/Implications – This study contributes to the literature by highlighting the environmental and health risks from the pollution caused by landfill waste in urban areas.

Environment and female entrepreneurship in Southeast Asia: investigation of the environmental Kuznets curve hypothesis

This paper examines the relationship between female entrepreneurship and environmental implications in ASEAN economies. While economic growth and affluence benefit society, they come at the cost of environmental degradation, particularly rising carbon dioxide levels and ecosystem concerns. Women entrepreneurs in Southeast Asia can potentially support their families and contribute to societal development by starting their own businesses. Their ventures provide fresh perspectives on societal challenges and enable active participation and contribution to creative endeavors, leading to economic and social empowerment with ripple effects on families and communities. To assess the environmental consequences of women’s entrepreneurial activities, the study applies the bias-corrected method of moments and tests the Environmental Kuznets Curve hypothesis across ten ASEAN nations from 1980 to 2021. Results show cross-sectional dependence in all variables and confirm a long-term relationship within the EKC model. The fixed-effects estimator with bias correction is deemed the most suitable model. Findings indicate that GDPpc leads to decreased emissions, while the square of GDPpc contributes to increased emissions; however, the cubic term of GDPpc leads to decreased CO2 emissions, supporting the “inverted N” curve distribution of emissions across ASEAN nations proposed by the EKC theory. Additionally, the study reveals that higher percentages of female entrepreneurs are associated with increased carbon dioxide emissions, potentially due to increased efforts to support and develop female entrepreneurs. Conversely, a reduction in the gender gap in marriage, divorce, remarriage, and domestic violence leads to a shift away from entrepreneurial activities among women, positively impacting the environment.