Nonlinear Method Research Articles

Hysteresis modeling and analysis of piezoelectric actuators using a modified LuGre model at different preloads and frequencies

Despite a large of models proposed to characterize the nonlinear hysteresis of piezoelectric actuators, few of them can show the effects of different preloads on the nonlinear hysteresis. In this paper, a modified LuGre model (MLM) is presented for piezoelectric actuators under different preloads to accurately characterize its dynamic hysteresis nonlinearity at different frequencies. Based on the classical LuGre model (CLM), the presented model introduces infinite impulse response filters and smoothness coefficient to simplify it. The parameters of the models were identified by the non-linear least squares method. A series of experiments were conducted to verify the validity of the MLM under different preloads at different frequencies excitation signals.In the constant frequency experiments under different preloads, the MLM reduces the mean error by 68.1%−81.9% compared with the CLM; In the constant preload experiments at different frequencies, the MLM reduces the mean error by 72.2%−83.0% and 65.9%−83.2% compared with the classical Bouc–Wen model (CBM) and the CLM.

Optimal resource allocation in hierarchical organizations under uncertainty: An interval-valued n-person cooperative game approach

In many real production scenarios, departmental organizations often exhibit a hierarchical structure, where departments cooperate with subordinate departments to optimize resource allocation and maximize their respective benefits. However, due to a lack of information or data, many model parameters in the allocation process cannot be precisely defined. In response to this challenge, an interval n-person hierarchical resource allocation model is proposed to achieve maximum economic benefit in uncertain environments. Based on the concepts of satisfactory degrees of comparing intervals and interval-valued cores of interval-valued n-person cooperative games, an auxiliary nonlinear programming model and method are developed to solve the interval-valued cores of such cooperative games. The approach explicitly considers the inclusion and/or overlap relations between intervals, whereas the traditional interval ranking method may not guarantee the existence of interval-valued cores. The proposed method offers cooperative opportunities under uncertain conditions. Finally, the feasibility and applicability of the models and methods are demonstrated through a numerical example and comparison with other methods.

Comprehensive effects of aerodynamic performance and dynamic strength of aircraft wings after bird strike in numerical simulation

ABSTRCT To enhance the bird strike resistance of fixed-wing aircraft wings, a combined computational approach integrating nonlinear solid mechanics and fluid dynamics was employed to analyze the bird strike characteristics of both traditional aluminum alloy skins and novel composite materials skins. Additionally, a standardized procedure was established to simulate aircraft structural damage more comprehensively, swiftly, and accurately during bird strikes, along with diagnostic analysis of flow field changes near the wing post-impact. This method not only quantifies wing strength damage but also establishes corresponding relationships between damage severity and aerodynamic performance degradation. The research findings indicate that the deformation of composite material skin wings is only a quarter of that of aluminum alloy skin wings. Composite material skins absorb less harmful energy compared to aluminum alloy skins, with consistently lower stiffness reduction rates at various bird strike velocities. Velocity distribution on deformed wings with composite material skins exhibits minimal deviation compared to normal wings, thereby preventing airflow separation. The research results provide more comprehensive guidance for the selection of materials for bird strike resistance of aircraft wings and the airworthiness evaluation after bird strikes. Highlights This paper establishes a standardized procedure that enables a more comprehensive, faster, and higher-precision simulation of structural damage to the aircraft during a bird strike on the wing and allows for the diagnostic analysis of the flow field changes near the wing after bird strike. By coupling nonlinear solid mechanics and fluid dynamics computational methods, the aerodynamic performance of the wings at different angles of attack post-deformation is investigated. This approach not only quantifies the strength damage to the wings but also establishes corresponding relationships between the degree of damage and the deterioration in aerodynamic performance. The research yields insights into the aerodynamic performance degradation patterns with varying angles of attack for both composite material skin and aluminum alloy skin deformed wings. These findings can serve as valuable references for pilots in emergency situations, aiding in their decision-making regarding aircraft control.

Reentry trajectory planning for hypersonic vehicles via an improved sequential convex programming method

The reentry trajectory planning problem of hypersonic vehicles is generally a continuous and nonconvex optimization problem. The solution efficiency and convergence properties of the planning algorithm become an essential point when considering the practical application scenarios. In this paper, an augmented-Lagrange-based improved sequential convex programming algorithm is proposed to solve it and achieve superior solutions with higher accuracy and efficiency. Firstly, the mapped Chebyshev pseudo-spectral method is utilized to convert the continuous optimal control problem into a highly approximately discretized one. In this framework, the discretization technique is employed to discretize the states and control variables at the time interval, which leads to the improvement of ill-conditioned differential matrix and better approximation properties of the variables. Moreover, by introducing slack variables, automatically updated penalty parameters and Lagrange multipliers during the convexification processing, the original nonconvex problem is transformed into an augmented-Lagrange-function-formed relaxed convex one. Then, the arrived convex subproblem can be solved iteratively by the matured interior-point method until the predetermined precision is satisfied. Based on this transformation, it can effectively alleviate the numerical difficulty and improve the convergence property of the proposed improved sequential convex programming algorithm. Finally, Rigorous theoretical analysis is conducted to prove that the solution sequence generated by the proposed algorithm could converge to an approximate global solution of the original problem. Numerical simulations are also presented, and it is demonstrated that the solution time of the proposed algorithm is more than 95% less than that of the traditional nonlinear programming methods without losing the merits of high precision.

Global-Position Tracking Control for Multi-Domain Bipedal Walking with Underactuation

Abstract Accurate control of a humanoid robot's global position (i.e., its three-dimensional position in the world) is critical to the reliable execution of high-risk tasks such as avoiding collision with pedestrians in a crowded environment. This paper introduces a time-based nonlinear control method that achieves accurate global-position tracking (GPT) for multi-domain bipedal walking. Deriving a tracking controller for bipedal robots is challenging due to the highly complex robot dynamics that are time-varying and hybrid, especially for multi-domain walking that involves multiple phases/domains of full actuation, over actuation, and underactuation. To tackle this challenge, we introduce a continuous-phase GPT control law for multi-domain walking, which provably ensures the exponential convergence of the entire error state within the full and over actuation domains and that of the directly regulated error state within the underactuation domain. We then construct sufficient multiple-Lyapunov stability conditions for the hybrid multi-domain tracking error system under the proposed GPT control law. We illustrate the proposed controller design through both three-domain walking with all motors activated and two-domain gait with inactive ankle motors. Simulations of a ROBOTIS OP3 bipedal humanoid robot demonstrate the satisfactory accuracy and convergence rate of the proposed control approach under two different cases of multi-domain walking as well as various walking speeds and desired paths.

A Method for Stabilizing the Vibration Amplitude of a Flip-Flow Vibrating Screen Using Piecewise Linear Springs

The flip-flow vibrating screen (FFVS) is a novel multi-body screening equipment that utilizes vibrations to classify bulk materials in the field of screening machinery. The relative amplitude of FFVSs determines the tension and ejection intensity of elastic flip-flow screen panels, which is a critical operating parameter affecting the screening performance. However, FFVSs generally suffer from large variations of relative amplitude caused by the loading of materials and the changes in shear spring stiffness (the temperature changes of the shear springs lead to their stiffness changes), which significantly reduce the screening efficiency and lifespan of FFVSs. To address this problem, this paper proposes a nonlinear stiffness-based method for stabilizing the vibration amplitude of FFVSs using piecewise linear springs. By introducing these springs between the two frames, the sensitivity of the relative amplitude to shear spring stiffness is reduced, thereby achieving the stabilization of the relative amplitude of FFVSs. In this study, the variations of the vibration amplitude of the FFVS due to the loading of materials and the changes in shear spring stiffness were first demonstrated in a reasonable operating frequency range. Then the reasonable operating frequency range and dynamics of the resultant nonlinear flip-flow vibrating screen (NFFVS) with piecewise linear springs were investigated using the harmonic balance method (HBM) and the Runge–Kutta numerical method. The operating frequency region for the NFFVS lies between the critical frequency ωcs and the frequency ωlb corresponding to the saddle-node bifurcation point. Finally, a test rig was designed to validate the theoretical predictions. Theoretical and experimental results demonstrate that piecewise linear springs can effectively stabilize the relative amplitude of the FFVS.

Optimizing size and economic feasibility assessment of photovoltaic and energy storage setup in residential applications

Battery energy storage systems (BESSs) are essential in enhancing self-sufficiency, sustainability, and delivering flexibility services. However, adoption of this technology in residential applications is constrained, predominantly due to its suboptimal economic performance. A proper selection of design parameters and optimal resource utilization can significantly enhance performance and establish economic feasibility. This research introduces a photovoltaic (PV)-BESS optimization framework, formulated to ascertain optimal infrastructure sizing, and maximize economic performance. The proposed tool considers various elements, such as energy storage state of health status, renewable profiles, residential load profiles, and prevailing energy market conditions. A novel rule-based, non-linear optimization method is developed, with a focus on maximizing revenue while considering energy storage (ES) degradation to project more accurate and realistic scenarios and payback periods. Employing the Estonian energy market as an illustrative case study, the model probes the potential of integrating ES and PV technology in North European residences. The model achieved the shortest payback period of five years in our case study, underscoring the emerging potential of such technologies in behind-the-meter applications.

Optimizing methanol synthesis from CO2 using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability.

Assessment of the performance of linear and nonlinear regression-based methods for estimating in situ catalytic CO2 transformations employing TiO2/Cu coupled with hydrogen exfoliation graphene (HEG) has been investigated. The yield of methanol was thoroughly optimized and predicted using response surface methodology (RSM) and artificial neural network (ANN) model after rigorous experimentation and comparison. Amongst the different types of HEG loading from 10 to 40 wt%, the 30 wt% in the HEG-TiO2/Cu assisted photosynthetic catalyst was found to be successful in providing the highest conversion efficiency of methanol from CO2. The most influencing parameters, HEG dosing and inflow rate of CO2, were found to affect the conversion rate in the acidic reaction regime (at pH of 3). According to RSM and ANN, the optimum methanol yields were 36.3 mg g-1 of catalyst and 37.3 mg g-1 of catalyst, respectively. Through the comparison of performances using the least squared error analysis, the nonlinear regression-based ANN showed a better determination coefficient (overall R2 > 0.985) than the linear regression-based RSM model (overall R2 ∼ 0.97). Even though both models performed well, ANN, consisting of 9 neurons in the input and 1 hidden layer, could predict optimum results closer to RSM in terms of agreement with the experimental outcome.

Nonlinear seismic analysis of city gate architectural heritages using a sensitive-based model updating method

Due to the unknown accumulation of internal damage and severe degradation of structural mechanisms, updating numerical model related to current damage using Operational Modal Analysis and performing nonlinear seismic analysis pose significant challenges. In this paper, a nonlinear seismic analysis method is proposed involving a three-stage finite element model updating process. This method not only updates the global parameters and local parameters of substructure, but also updates the FEM to true current damage state based on elastic-plastic constitutive model considering current damage. First, on-site vibration and material performance tests were conducted to obtain modal data and material properties of the structure. Second, each component in the model was updated to detect damage, employing a sensitivity-based approach in Stage 1. Then, each element within the severely damaged components obtained from Stage 1 was updated for damage detection, again using a sensitivity-based approach in Stage 2. Finally, the current damage state was updated based on elastoplastic constitutive model considering the current damage and a seismic analysis was performed in Stage 3. The results indicate that, in Stage 1, updating the global parameters of the model aligns the calculated data with the measured data. In Stage 2, updating the local parameters to accurately identify the location and severity of damage within the heavily affected components is demonstrated by a simple example. These results can establish a scientific foundation for the nonlinear seismic analysis of city gate architectural heritages.

Dynamic Modeling and Improved Nonlinear Model Predictive Control of a Free-Floating Dual-Arm Space Robot

With the increasing demand for space missions, space robots have become the focus of research and attention. As a typical representative, the free-floating dual-arm space robot has the characteristics of multiple degrees of freedom, a floating base, and dynamic coupling between the manipulator and the base, so its modeling and control are very challenging. To address these challenges, a novel dynamic modeling and control method is proposed for a free-floating dual-arm space robot. First, an explicit dynamic model of a free-floating dual-arm space robot is established based on the explicit canonical multi-rigid-body dynamic modeling theory and combined with the concept of a dynamic equivalent manipulator. The establishment process of this model is not only simple and canonical to avoid the definition and calculation of many intermediate variables, but the symbolic result expression of the model also has the characteristics of iteration, which is convenient for computer automatic modeling. Next, aiming at addressing the problem of trajectory tracking and the base attitude stability of a free-floating dual-arm space robot with parameter perturbation and external disturbance, an improved nonlinear model predictive control method introducing the idea of sliding mode variable structure is proposed. Theoretical analysis shows that the proposed controller has better robustness than the traditional nonlinear model predictive controller. Then, an in-orbit service task is designed to verify the effectiveness of the proposed dynamic modeling and control strategy of the free-floating dual-arm space robot. Finally, the dynamic modeling and control methods proposed are discussed and summarized. The proposed methods can not only realize the tracking of the desired trajectory of the arms of the free-floating space robot, but can also realize the stable control of the base of the free-floating space robot. This paper provides new insights into the difficult problems regarding the dynamics and control of free-floating dual-arm space robots.

Non-linear behavior of functionally graded elastoplastic beam under torsion

The torsional behavior of beams graded in one and two directions under large displacements and angular deformations was analyzed using the power law and sinusoidal functions. Functionally graded material is elastoplastic, consisting of ceramic and metal. A nonlinear finite element method with isoparametric hexahedral elements was used. The finite element formulation was made using the updated Lagrangian formulation based on the virtual displacement principle. An iterative solution using Newton-Raphson and updated Newton-Raphson methods was used to solve the nonlinear equation system. The propagation of the plastic zone was calculated based on the flow theory of plasticity. Elastoplastic behavior and effective material properties were determined according to the TTO model. Numerical investigations have shown that functionally graded beams behave quite differently from homogeneous beams under torsion. Yielding of the material starts at the outer boundaries of the section of the homogeneous beams, and the plastic zone propagates symmetrically. On the other hand, yielding and propagation of plastic zones tend to shift to regions with more ceramic volume with higher effective Young modulus in functionally graded beams. Unlike homogeneous beams, the largest shear stresses can occur within the section rather than at the outer boundaries of the section. In beams graded from ceramic to metal using the power law, the section moves along the transverse direction in addition to the rotation. This transverse displacement occurs in the grading direction, and its magnitude is about 3% of the thickness at 12.5° rotation angle. Also, the shear stresses are not zero in the section's midpoint. The effects of material distribution on displacements, stresses, and plastic zone propagation were examined, and essential points were reported.

Improvement of Z-Weighted Function Based on Fifth-Order Nonlinear Multi-Order Weighted Method for Shock Capturing of Hyperbolic Conservation Laws.

Based on a 5-point stencil and three 3-point stencils, a nonlinear multi-order weighted method adaptive to 5-3-3-3 stencils for shock capturing is presented in this paper. The form of the weighting function is the same as JS (Jiang-Shu) weighting; however, the smoothness indicator of the 5-point stencil adopts a special design with a higher-order leading term similar to the τ in Z weighting. The design maintains that the nonlinear weights satisfy sufficient conditions for the scheme to avoid degradation even near extreme points. By adjusting the linear weights to a specific value and using the τ in Z weighting, the method can be degraded to Z weighting. Analysis of linear weights shows that they do not affect the accuracy in the smooth region, and they can also adjust the resolution and discontinuity-capturing capability. Numerical tests of different hyperbolic conservation laws are conducted to test the performance of the newly designed nonlinear weights based on the weighted compact nonlinear scheme. The numerical results show that there are no obvious oscillations near the discontinuity, and the resolution of both the discontinuity and smooth regions is better than that of Z weights.

New Weibull Log-Logistic grey forecasting model for a hard disk drive failures

Forecasting the failure of hard disk drives is important in server operation and has attracted increasing attention. However, current disk drive warning systems suffer from high false positive rates and high resource consumption when dealing with hard disk drive overall failure. Therefore, to accurately and stably predict hard disk drive overall failure, this paper develops a new Weibull Log-Logistic grey forecasting model with multiple interaction effects. Firstly, to capture and fit the trend of hard disk failure data flexibly and reduce the volatility, a new accumulation generation operator is established by introducing the Weibull Log-Logistic mixture distribution. Secondly, the proposed model constructs a multiple interaction term to describe the nonlinear relationship of the dependent variables on the system behavior series and the multiple interaction effects between the independent variables. Then, the parameter estimation method is designed to improve the fitting accuracy of the new model, in which linear estimation method, nonlinear estimation method, and meta-heuristic optimization algorithm are used to calculate the parameter values according to their categories. Finally, four varieties of hard disk drive data sets from BackBlaze are selected as study cases to validate the effectiveness of the proposed model. The results show that the mean MAPE of the proposed method is 2.1181 %, 2.2306 %, 8.1712 %, and 4.3417 %, respectively, corresponding to (hard disk drives ST8000NM0055, ST12000NM0007, ST10000NM0086, and ST14000NM0138), and the average value of the evaluation metrics are optimal in all competing models. Furthermore, it is observed that the number of reallocated sectors has the greatest influence in causing the failure of hard disk drives.

Electromagnetic telemetry signal denoising: An interference elimination via parameter estimation

Electromagnetic telemetry has emerged as a promising technique for the real-time acquisition of downhole data. However, the transmission of electromagnetic waves through the formation encounters attenuation, leading to a weakened received signal at the surface, typically registering at the microvolt level. The growing presence of surface electrical equipment in well-site environments has engendered heightened electromagnetic interference, thus deteriorating the signal-to-noise ratio (SNR) of received signals. The conventional linear filtering method can effectively eliminate the electromagnetic interference at the wellsite, but it has no effect on the low frequency interference close to the carrier frequency, especially the common low-frequency pulse interference (LPI) and power line interference (PLI). To mitigate the adverse effects of surface noise, we propose a nonlinear processing method based on parameter estimation algorithms. Generally, the proposed method entails reconstructing the interference by accurate parameter estimation and subtracting it from the received signal, aiming to enhance the SNR of the received signal. Simulation data and real noise data collected in the field are processed to verify the effectiveness of the proposed method. The results demonstrate the superiority of this approach over traditional filtering techniques in effectively eradicating surface interference.

Study of the kurtoses transmission of linear structures under stationary non-Gaussian random loadings

Recent research has discovered that the kurtoses of non-Gaussian stress loadings can significantly impact the fatigue life of in-service structures. To predict potential damage, a fatigue life estimation method based on Gaussian damage and a kurtosis-related corrective coefficient has been developed. However, the transmission of kurtoses from excitations to responses has not been well studied, particularly in multi-input cases. This hinders efforts to accelerate fatigue damage by controlling input kurtoses. Therefore, this paper aims to establish a kurtoses transmission model for a linear structure under multiple uncorrelated stationary non-Gaussian excitations generated by zero-memory non-linear method. Firstly, a single-input kurtosis transmission equation is proposed to establish the relationship between excitation kurtosis and response kurtosis. Using this equation, the response kurtosis is formulated with input kurtosis and the system parameters of a linear structure. Secondly, the response kurtosis of multi-input cases is deduced, and the study shows that the response kurtosis is the weighted sum of the kurtosis induced by each excitation. Finally, numerical validation and experimental studies are conducted to verify the accuracy of both the single-input and multi-input kurtoses transmission models. The validations demonstrate that the proposed models can predict the response kurtoses with satisfactory precision, regardless of the input spectral shape and the number of uncorrelated input forces.

Quantitative and intelligent methods for economic forecasts (birth rate time series (Ntl))

The models proposed in the field of time series forecasting are numerous. However, accurate forecasting is still one of the main challenging problems faced by decision makers in various fields, especially in financial markets. Anticipatory and intelligent methods play a crucial role in economic forecasting, helping to increase the accuracy and reliability of forecasts. This is the main reason that many researchers have in the focus of their research the development of methods for improving the performance of forecasts. Using only a linear or non-linear method is not enough to recognize and model all features and functions of the data. In addition to quantitative techniques as well as intelligent techniques that have been developed recently as a result of technological developments, a new technique or hybridization of these two techniques behaves as a very efficient approach. For this reason, in the study it is proposed to improve the modeling and forecasting results by combining a linear A RIMA method with an intelligent non-linear method, the artificial neural network -ANN. Several ARIMA-ANN hybrid model architectures are known, but in this thesis a new A RIMA-ANN technique with improving features of the existing hybrid methods is proposed. The time series under study are the birth rate in Albania obtained by INSTAT and the Bank of Albania with monthly frequencies. Time series analysis goes through three phases: defining characteristics, modeling and forecasting. The analysis is performed with ARIMA, ANN and the proposed ARIMA-ANN method. Data mining, sentiment analysis, and natural language processing (NLP) techniques are used to analyze unstructured data sources. The models, either quantitative or intelligent, give satisfactory results, but the hybrid model shows much higher performance in all series. From the measurement indicators of the prediction performance, the hybrid method has resulted with high efficiency. The ARIMA-ANN model produced much better results than the ARIMA and ANN models. These results are a consequence of the combination of the two techniques and from which results a good recognition of the different structures of the data modes. This study contributes to the field of time series forecasts in Albania and intelligent or quantitative modeling by developing a new forecast model, that of hybridization.

Nonlinear Conjugate Gradient Methods for Unconstrained Test Functions through an Approximate Wolfe Line Search

Nonlinear Conjugate Gradient Methods for Unconstrained Test Functions through an Approximate Wolfe Line Search

The Influence of Short-Term High Temperature Environment on the Non-Uniform Distribution of Ferrite Grain Size in 40 mm-thick Q345 Steel

In order to reveal the non-uniform distribution of grain size in thick direction for engineering heavy plate, microstructure of 40 mm-thick Q345 steel was observed and measured under different short-term high temperature environments formed by fire. Moreover, the influence of the short-term high temperature environment was revealed on the distribution of ferrite grain size in the Q345 steel. Under different fire service environments, there was a log-normal distribution relationship between the distribution parameter Nf (number of ferrite grains) and df (average grain diameter), as well as ρAf (area fraction density) and df, at different positions along the thickness direction. However, the statistical results are greatly affected by the length of the statistical interval. When df is about 4 to 6 times the length of the statistical interval, the statistical accuracy is higher. By using nonlinear fitting method, multiple non-uniform distribution empirical models including Nf-df empirical formulas and ρAf-df empirical formulas were established at different positions along thick direction under various fire environments. Furthermore, the interrelationships between fire temperature T and Nf , T and ρAf , fire duration t and Nf , t and ρAf were revealed, respectively.

Characterisation of coherent ultrasonic nonlinear imaging

Nonlinear ultrasonic imaging methods for non-destructive testing have shown sensitivity to early damage formation. Among those, the Fundamental wave Amplitude Difference (FAD) technique has been proposed in the literature as a nonlinearity mapping method for defect visualisation. It does so by calculating the residual between a low and high nonlinearity image, which is achieved by some modulation of the focal spot amplitude. This is realised either through different input voltages or alternating the element-wise firing scheme. This paper assesses the sensitivity of FAD through different techniques used for the generation of the low nonlinearity images, using both theoretical and experimental approaches. These techniques include (1) Different-Voltage FAD (DV-FAD); (2) Odd-Even FAD (OE-FAD) and; (3) Parallel–Sequential FAD (PS-FAD). Formulae are derived that allow the different firing sequences and image corrections to be compared. The FAD techniques are then experimentally evaluated against the theory, showing a 50% amplitude gain at the crack tip when comparing PS-FAD to OE-FAD and DV-FAD. Additionally, the instrument behaviour is assessed by considering the image correction required to improve the image contrast for the visualisation of the crack tip. Finally, it is shown that all FAD techniques are equivalent in terms of nonlinear detection but additional work is required in comparing the performance of other array controllers when using FAD.

Impact of environmental policy stringency on sectoral GHG emissions: evidence from Finland and Sweden by nonlinear quantile-based methods

ABSTRACT The growing societal concern regarding environmental matters has led to the implementation of many environmental measures intended to protect the environment and address global warming by lessening emissions and mitigating climate change. In line with this movement, this study scrutinizes the impact of these environmental measures on greenhouse gas (GHG) emissions to analyze the cases of Finland and Sweden. More specifically, the study employs the Environmental Policy Stringency (EPS) index as a proxy for environmental measures, explores sector-specific GHG emissions by employing nonlinear quantile-based methodologies (including quantile-on-quantile regression and Granger causality-in-quantiles methods as the primary model and quantile regression for robustness checking) spanning the period from 1991/Q1 to 2020/Q4. The findings show that: (i) EPS lessens GHG emissions from fuel exploitation, industrial combustion, and the power industry sector at lower and middle quantiles in Finland and Sweden; (ii) EPS decreases GHG emissions from processes, transportation, and waste sectors in Finland but increases them in Sweden at higher quantiles; (iii) EPS leads to an increase in GHG emissions from the agriculture and construction sectors at higher quantiles; (iv) EPS has a causal effect on sector-specific GHG emissions across different quantiles; (v) the robustness of the findings is largely confirmed. Hence, the study underscores the varying impacts of EPS on sectoral GHG emissions based on quantiles, sectors, and countries, emphasizing the need for policymakers to adopt environmental policies to comprise these differences and adjust the policy framework accordingly.