Slope Coefficient Research Articles

Spatio-temporal analysis of river flow master recession curve characteristics over Ardabil province rivers

The Master Recession Curve (MRC) is a crucial tool for evaluating, managing, and utilizing rivers, particularly under low-flow conditions. Analyzing changes in the MRC of river flow is useful for water resource planning, surface water utilization, and estimating and allocating environmental flow. This study utilized daily flow data from rivers in Ardabil province, Iran over a 48-year statistical period (1970–2015). The Visual Basic program was utilized to estimate the equations and coefficients of recession curves. The trends of the constant and slope coefficients of the MRC equations were determined using the Mann-Kendall test and linear regression. Higher constant coefficient values were attributed to main rivers with higher discharge (e.g., Borran, Mashiran, and Samian, having 17.98, 13.79, and 5.64 cms, respectively. The slope of the MRC in most of the rivers in the central part of the study area exhibited a decreasing trend (p-value<0.05), which attributed to climate change, alterations in precipitation patterns, reduced rainfall/snowmelt, and water abstraction leading to less water input during non-flood periods. Additionally, most rivers in different climatic regions and areas of the study region exhibited an increasing trend (p-value<0.05) in the MRC constants, indicating reduced groundwater recharge or increased groundwater abstraction, as a common issue in the Ardabil province. Moreover, human water use and reservoir regulation (Yamci, Sabalan, Saghezchi, Givi, and small regulating reservoirs) can influence river flow patterns during non-flood periods. The occurrence of droughts and intensified surface water use by farmers, as well as unauthorized extraction of water from wells, have exacerbated this trend.

Dynamics of Economic Growth and Health Outcomes Among the Northern States of India: A Panel Data Analysis

This article shows the relationship between health outcomes and economic growth for the period of 2002–2020 among the northern states of India. By using pooled ordinary least squares, fixed effect and random effect model, the study examines the impact of infant mortality, birth rate and death rate on gross state domestic product per capita at a constant price. The study takes gross capital formation and gross enrolment ratio as control variables. Data from the Reserve Bank of India and EPWRF states of India were retrieved and used in the study. The result shows that health outcomes and economic growth have a relatively substantial and significant association with each other. The results demonstrate that economic growth increases by 1% with a decrease in infant mortality rate by its elasticity coefficient –0.32. The results show that economic growth will increase by 1% with the increase in birth rate, gross enrolment ratio and gross fixed capital formation by its slope coefficients, that is, 0.10, 0.32 and 1.78 which are statistically significant at 1%, 5% and 1% level of significance. The finding of the study demonstrates that health outcomes remain a vital determinant in improving economic growth among the northern states. Our results advocate that the government spending on health should be improved; moreover, the government should frame health policies to boost health outcomes to level the disparities in health outcomes among the northern states of India.

Detection of arsenic and lead ions in water through validation of the electrothermal atomic absorption method

The validation of the electrothermal spectrometry method for Arsenic and lead determination in water samples guarantees the quality of analytical data. The study was carried out at AQLAB. A theoretical and technical investigation was applied to ensure the reliability and accuracy of the analytical method. Parameters such as INEN standards, Eurachem international standards, AQLAB internal documents, and environmental standards were used to validate the process. Several readings were performed on different samples to make calibration curves for As and Pb, evaluate the method's linearity, and obtain information on the slope, intercept, and determination coefficient. Statistical calculations were used to determine the limit of detection and quantification, repeatability, reproducibility, trueness, and uncertainty. The results show that the hypotheses were fulfilled, calibration curves with determination coefficient higher than 0.995, repeatability standard deviation lower than 16%, calculated F &lt; critical F (4.96), calculated t student &lt; theoretical (2.23), trueness was between 100.29-110.18 and 99.64-107.92%, uncertainty was 20% range 0.005 to 0.10 mg/L and 15% range 0.01- 0.20mg/L, limit of detection was 0.001 and 0.005 mg/L and limit of quantification was 0.01 and 0.02mg/L, respectively. Therefore, the validation method was robust and accurate. Keywords: water samples, analytical data, reliable results, Arsenic, Lead, validation.

Detection of arsenic and lead ions in water through validation of the electrothermal atomic absorption method

The validation of the electrothermal spectrometry method for Arsenic and lead determination in water samples guarantees the quality of analytical data. The study was carried out at AQLAB. A theoretical and technical investigation was applied to ensure the reliability and accuracy of the analytical method. Parameters such as INEN standards, Eurachem international standards, AQLAB internal documents and environmental standards were used to validate the process. Several readings were performed on different samples to make calibration curves for As and Pb, evaluate the method's linearity, and obtain information on the slope, intercept and determination coefficient. Statistical calculations were used to determine the limit of detection and quantification, repeatability, reproducibility, trueness and uncertainty. The results show that the hypotheses were fulfilled, calibration curves with determination coefficient higher than 0.995, repeatability standard deviation lower than 16%, calculated F &lt; critical F (4.96), calculated t student &lt; theoretical (2.23), trueness was between 100.29-110.18 and 99.64-107.92%, uncertainty was 20% range 0.005 to 0.10 mg/L and 15% range 0.01- 0.20mg/L, limit of detection was 0.001 and 0.005 mg/L and limit of quantification was 0.01 and 0.02mg/L, respectively. Therefore, the validation method was robust and accurate. Keywords: water samples, analytical data, reliable results, Arsenic, Lead, validation

Detection of arsenic and lead ions in water through validation of the electrothermal atomic absorption method

The validation of the electrothermal spectrometry method for Arsenic and lead determination in water samples guarantees the quality of analytical data. The study was carried out at AQLAB. A theoretical and technical investigation was applied to ensure the reliability and accuracy of the analytical method. Parameters such as INEN standards, Eurachem international standards, AQLAB internal documents and environmental standards were used to validate the process. Several readings were performed on different samples to make calibration curves for As and Pb, evaluate the method's linearity, and obtain information on the slope, intercept and determination coefficient. Statistical calculations were used to determine the limit of detection and quantification, repeatability, reproducibility, trueness and uncertainty. The results show that the hypotheses were fulfilled, calibration curves with determination coefficient higher than 0.995, repeatability standard deviation lower than 16%, calculated F &lt; critical F (4.96), calculated t student &lt; theoretical (2.23), trueness was between 100.29-110.18 and 99.64-107.92%, uncertainty was 20% range 0.005 to 0.10 mg/L and 15% range 0.01-0.20mg/L, limit of detection was 0.001 and 0.005 mg/L and limit of quantification was 0.01 and 0.02mg/L, respectively. Therefore, the validation method was robust and accurate. Keywords: water samples, analytical data, reliable results, Arsenic, Lead, validation.

Regional scale terrace mapping in fragmented mountainous areas using multi-source remote sensing data and sample purification strategy

As a stepped cross section of farmland built along the contour lines, terrace is widely distributed on hill-slopes. It changes the original surface slope and runoff coefficient, reduces soil nutrient loss, and has become the most important soil erosion control measure in China. Accurate terrace mapping at regional scale is crucial for soil conservation, agriculture sustainability and ecological planning. Due to the influence of cloudy and rainy weather, poor data availability makes it difficult to identify terrace distribution only using optical remote sensing images in mountainous areas. In this study, we incorporated multi-spectral optical and SAR data, features of terrain, texture and time sequence information, and proposed a pixel-based supervised classification method based on sample purification strategy to obtain a 10 m resolution terraced map in a plateau mountainous region. With 610 terrace/non-terrace validation sample data, 10-fold cross-validation was used to test the classification results. For identified terrace, the values of Overall Accuracy (OA), Producer's Accuracy (PA) and User's Accuracy (UA) stay stable above 90 %, the F1 score and Kappa coefficient show the smallest fluctuation and is stable in the range of 0.90–0.93 and 0.81–0.87, respectively. The accuracy evaluation of grid units show that the uncertainty of the terrace distribution is mainly concentrated in the north and south of the study area. Slope cultivated land, low-slope terrace and non-agricultural vegetation are easily mixed due to the heterogeneity of terrace features and the spectrum similarity among these land types. It should be noted that the features of time series and texture play a key role in the terrace recognition process, rather than terrain factors, which is different from previous studies. The sample purification strategy proposed provides a more reliable regional scale terrace distribution map compared to the existing product, and is potentially transferable to other mountainous areas as a robust approach for rapid identification of terrace.

Local projections in unstable environments

This paper develops a local projection estimator for estimating impulse responses in the presence of time variation. Importantly, we allow local instabilities in both slope coefficients and variances. Monte Carlo simulations illustrate that the method performs well in practice. Using our proposed estimator, we shed new light on the effects of fiscal policy shocks and the size of government spending multipliers. Our analysis uncovers the existence of instabilities that were unaccounted for in previous studies, and links time variation in the multipliers to the size of government debt.

Aboveground Biomass Estimation and Time Series Analyses in Mongolian Grasslands Utilizing PlanetScope Imagery

Mongolia, situated in central Asia and bordered by Russia to the north and China to the south, experiences a semi-arid climate across most of its territory. Grasslands are pivotal in Mongolia’s agricultural sustainability and food security, facing rapid changes in the last two decades that underscore the ongoing need for innovative approaches to assess vegetation conditions. This study aims to evaluate grassland biomass measurement and prediction through the analysis of high-resolution satellite data. By conducting a time series assessment of grazing-induced changes in vegetation dynamics at the long-term monitoring sites of the Botanic Garden and Research Institute, Mongolian Academy of Sciences, we seek to refine our understanding. The investigation covers biomass estimation across various Mongolian grassland landscapes, encompassing desert, steppe, and mountain regions. Spanning the grassland growing season from May 2020 to October 2023, the research leveraged diverse ground data types, including surface reflectance measurements, geographic coordinates for satellite data correction, and aboveground dry biomass. These components were instrumental in developing a biomass estimation model reliant on establishing correlations between the satellite-derived Normalized Difference Vegetation Index and biomass. The predicted biomass facilitated the time series map analysis and dynamic analysis. The PlanetScope surface reflectance correlates strongly at 0.97 with field measurements, indicating robust relations. Biomass and the Normalized Difference Vegetation Index show correlations of 0.82 for dry grassland, 0.80 for mountain grassland, and 0.65 for desert grassland, with a combined correlation coefficient of 0.62, revealing distinct characteristics across these grasslands. Time series dynamic analysis reveals rising biomass differences between grazed and ungrazed areas, suggesting potential grassland degradation. Variations in the slope coefficient of biomass differences among grassland types indicate differing degradation patterns, emphasizing the need for effective grazing management practices to sustain and conserve Mongolian grasslands. This highlights the potential of remote sensing in monitoring and managing grassland ecosystems.

A traction coefficient formula for EHL point contacts operating in the linear isothermal region

Many mechanical systems including rolling/sliding parts, require traction data across a spectrum of operating conditions to predict their motion effectively. Numerous studies have examined the thermal effects and shear-thinning concerning the traction curve, but only a few have focused on the traction coefficient in the linear isothermal regime for low SRR. In this work, we investigate traction coefficient characteristics of EHL point contacts in the linear isothermal regime, over a wide range of operational conditions. To this end, we conduct numerical simulations utilizing a fully-coupled finite element-based model, resulting in a prediction formula for the traction coefficient slope. With this formula, the traction coefficient slope could be predicted for the operating conditions considered.

Can resolving geopolitical tensions help South Asian countries elude the carbon curse of natural resources?

South Asia is regarded as highly vulnerable to climate change-related concerns. Thus, it is of utmost importance for South Asian countries to conceptualize environmental development policies that can be implemented to tackle carbon emission-related environmental hazards. Moreover, due to high reliance on natural resources, almost all South Asian nations find it extremely difficult to reduce their carbon discharges. Therefore, taking note of these concerning issues, this study aims to explore the environmental impacts associated with natural resource dependency, geopolitical tensions, financial development, and urbanization in the context of major South Asian countries including Bangladesh, Pakistan, India, and Sri Lanka. Notably, this study emphasizes whether reducing geopolitical tensions, by resolving external conflicts, can help these nations limit the carbon emission-related problems that are specifically associated with natural resource consumption. In this regard, a composite carbon curse index is calculated using data regarding three carbon emission-based indicators of environmental quality (per capita carbon dioxide emissions, rate of carbon intensity of energy use, and per capita consumption-based carbon footprints). For analytical purposes, several advanced panel data estimation methods that are robust against cross-sectionally dependent data with heterogeneous slope coefficients and endogeneity-related issues, are employed. Overall, the results endorse that more dependence on natural resources for stimulating economic growth worsens the carbon curse faced by the South Asia nations. By contrast, reducing geopolitical tensions through the resolution of external conflicts is observed to help the selected South Asian countries limit the carbon curse associated with natural resource consumption, both directly and indirectly. Therefore, in line with these findings, relevant decarbonization policies are recommended, which the concerned South Asian governments can take note of when designing green growth policies in the future.

Influence of passive deformation in the lift coefficient of a NACA0012 wing model

The extensive use of lightweight materials in aerial vehicle wings involves structural flexibility phenomena that generate non-negligible deformation effects. This influence is not restricted to big aircraft but also plays a role in smaller aeroplanes and Unmanned Aerial Vehicles (UAVs). Here, we conduct wind tunnel experiments to analyze the effect of passive deformation on the wing model lift slopes. To isolate the deformation effect, we compare rigid wings with a NACA0012 airfoil imposing a prescribed spanwise deformation. We study three levels of deformation: non-deformed, around 2% and 4.5% of tip deflection. Also, we consider the effect of the wing length by using three different semi-aspect ratios (1, 2, and 4), so a total of nine rigid wing models have been analyzed for a range of Reynolds number from 80×103 to 160×103. Deformed wing models show an increase in lift coefficient compared to non-deformed wing cases. Both deformation levels exhibit a qualitatively similar lift increment. A correlation to predict lift coefficient slope in a flat plate is adapted for a NACA0012 airfoil and validated using our experimental results and literature data. The adjusted correlation can quantify the deformation effect on the lift slope, which is comparable to using a slightly longer wing model.

Advanced Cd1-xMnxTe:Fe2+ semiconductor crystals for IR applications: The role of the composition and sin-band redshift spectra effect

Doped semiconductor crystals of solid solution Cd1-xMnxTe:Fe2+ were grown by the high-pressure Bridgman method, covering the range of their existence as a zinc blende structure (0 < x < 0.76). The concentration of the Fe2+ impurities was approximately 10−3 wt% in all the studied samples. The structural and optical properties of the crystals were investigated, including the case of high concentrations of x > 0.4. The correlations between the composition of solid solution crystals of Cd1-xMnxTe:Fe2+, the lattice constant, the band gap, and the maxima positions of the Fe2+ active ion absorption and emission spectra were found experimentally and explained physically. A theoretical model based on the principle of compositional additivity for solid solution semiconductor materials was first used for explaining the long-wavelength sin-band linear “redshift” of absorption and emission bands in the spectra of Cd1-xMnxTe:Fe2+ crystals with increasing solid solution concentration. A new effect is discovered for the differentiated redshift of the Jahn-Teller components (bands) of the total absorption spectrum. Longwavelength absorption bands have a stronger redshift than shortwavelength absorption bands. The redshifts in the maxima of the total absorption and emission spectra have shift (slope) coefficients Kab≈1.7 nm/at.% and Kem≈6.1 nm/at.%, respectively. The obtained results can be used to predict and design laser media based on Cd1-xMnxTe:Fe2+ solid solution crystals while controlling the lasing range (for all Mn concentrations).

Stability analysis of loess fill slope supported by frame prestressed anchors considering tensile strength cut-off

In the stability analysis of loess fill slope, the fissure nature of loess is often ignored, which makes the stability calculation of fill slope too conservative. Based on the upper limit theory of plastic limit analysis, the stability analysis model of loess-filled fissured slope supported by frame prestressed anchors was established. Considering the tensile strength cut-off yield property of soil, the stability coefficient of slope was calculated, and the influence of different factors on slope stability was analyzed. The results show that ignoring the fissures in loess will overestimate the stability of the fill slope, and the support structure can significantly improve the stability of the loess-filled fissure slope. The research results of this paper can further enrich the stability analysis theory of loess-filled fissured slope supported by frame prestressed anchors, which is of great significance to guide engineering practice.

Trends in the Growth of Damage Extents in a Steel Conveyor Belt’s Core

The initial set of four measurements (scans) captures the failures occurring in the core of steel cable conveyor belt segments used in a lignite coal mine in Poland. These measurements utilize the DiagBelt+ magnetic system, enabling the analysis of averaged damage sizes. The scanning results allowed the analysis of complete changes in the failure sizes to a maximum of six different time intervals from 1 to 19 months. The cumulative increase in the length and width of the failure (their total extent in both directions) is a linear function of time. However, the inclination of the selected lines (growth speed) depends on the age of the belt segment at the time of its first scan. It is possible to find the best regression function of slope coefficients for the age of the belt segment. The obtained results are crucial for rational belt management, including reliable short-term forecasts of belt needs with the flow of time, indications of the best moments for belt segment replacements, and estimating the budget of the belting department.

Spectral-based Quantitative Ultrasound Imaging Processing Techniques: Comparisons of RF Versus IQ Approaches.

Quantitative ultrasound (QUS) is an imaging technique which includes spectral-based parameterization. Typical spectral-based parameters include the backscatter coefficient (BSC) and attenuation coefficient slope (ACS). Traditionally, spectral-based QUS relies on the radio frequency (RF) signal to calculate the spectral-based parameters. Many clinical and research scanners only provide the in-phase and quadrature (IQ) signal. To acquire the RF data, the common approach is to convert IQ signal back into RF signal via mixing with a carrier frequency. In this study, we hypothesize that the performance, that is, accuracy and precision, of spectral-based parameters calculated directly from IQ data is as good as or better than using converted RF data. To test this hypothesis, estimation of the BSC and ACS using RF and IQ data from software, physical phantoms and in vivo rabbit data were analyzed and compared. The results indicated that there were only small differences in estimates of the BSC between when using the original RF, the IQ derived from the original RF and the RF reconverted from the IQ, that is, root mean square errors (RMSEs) were less than 0.04. Furthermore, the structural similarity index measure (SSIM) was calculated for ACS maps with a value greater than 0.96 for maps created using the original RF, IQ data and reconverted RF. On the other hand, the processing time using the IQ data compared to RF data were substantially less, that is, reduced by more than a factor of two. Therefore, this study confirms two things: (1) there is no need to convert IQ data back to RF data for conducting spectral-based QUS analysis, because the conversion from IQ back into RF data can introduce artifacts. (2) For the implementation of real-time QUS, there is an advantage to convert the original RF data into IQ data to conduct spectral-based QUS analysis because IQ data-based QUS can improve processing speed.

Long-term changes in 90Sr pools of Scots pine biomass in the Chornobyl Red Forest

The trenches of the waste burial site in the Chornobyl Red Forest represent a big reservoir of radionuclides for the artificial plantation of Scots pine established in that area, but the long term dynamics of tree biomass contamination, especially with 90Sr, remains unclear. The present study was conducted between 2005 and 2018 on two groups of trees of the same age. The IN group is represented by trees growing on the trench containing highly radioactive contaminated fertile soil and organic matter, while the OUT group is located outside the trench. Within a little more than one decade, the total aboveground biomass doubled in the trees of the group OUT and increased more than four times in the group IN. In the group OUT, the concentrations of 90Sr have decreased in all biomass compartments compared to 2005, while in the group IN, the concentrations demonstrated a trend to increase. Regression analysis shows that both decrease in the compartment concentrations in the group OUT (slope coefficient 0.55) and increase in the group IN (1.58) were significant. As a result of the changes in the biomass inventories and 90Sr concentrations, in absence of changes in plantation density, the contamination of total aboveground biomass by 90Sr in the group OUT would have increased slightly in 2018 (from approximately 18 GBq ha−1 to 23 GBq ha−1) compared to 2005, while in the group IN it would have increased almost 6-fold, reaching approximately 560 GBq ha−1, or about (19 ± 9) % of the total 90Sr inventory in the trench area. Trenches of the Red Forest were shown to act as long-lasting hot spots of 90Sr bioavailability for forest trees.

Count Data Regression Modelling: An Application to Monkeypox Confirmed Cases

Introduction: With the presence of COVID 19, some countries also faced an increase in number of cases due to Monkeypox virus. The main aim of this research was to investigate whether it is possible to fit count data regression models to predict the daily incidence of Monkeypox confirmed cases.&#x0D; Methods: In this study we have used two types of traditional count regression models like Poisson regression model and Negative binomial regression model using identity and logarithmic link function. Since our data was overdispersed, Negative binomial regression model with logarithmic link function fitted well as compared to other models. The parameters were estimated using SPSS, version 23.0.&#x0D; Results: The Negative Binomial Regression model with logarithm function fits well to the data related to Monkeypox cases. Therefore, the model shows that majority of the countries like Brazil, Canada, France, Germany, Peru, Spain, United Kingdom and United States of America shows significant decrease in number of cases with respect to time. The prediction line was plotted using this model where the line predicts well about the daily Monkeypox cases reported by different countries.&#x0D; Conclusion: From our study, we concluded that the count data regression model can be used widely to predict the incidence of any disease. The countries like Canada and Brazil have largest and smallest slope coefficient which shows maximum and minimum decrease in expected number of cases confirmed each day respectively.

Specification of asymptotic Po&amp;#x0301;lya type estimate for Dirichlet series converging in half - plane

We study the asymptotic behavior of a Dirichlet series with positive exponents, converging in the left half - plane, on an arc of bounded slope ending on the convergence line. In the paper we obtain conditions under which the sum of the Dirichlet series satisfies an asymptotic equality of Po&amp;#x0301;lya type on a set, the upper density of which is equal to one. In 2023 we obtained results related to dual cases. We showed that a Po&amp;#x0301;lya type identity holds on an asymptotic set of positive upper density depending on the slope coefficient (Lipschitz constant) of the arc. In this paper, we prove a common theorem covering both of these cases, and we show that the asymptotic set has an upper density, which is equal to one.

A Linear Model for Measuring the Experimental Resolution, Minimum Detection Limit, and Accuracy of Common Analytical Techniques

Objective: This study aims to establish methods based on linear models to measure the experimental resolution, minimum detection limit, and accuracy of flame atomic absorption spectrometry (FAAS), gas chromatography (GC), and liquid chromatography–mass spectrometry (LC–MS) and explore their applications.Methods: A series of equally diluted samples (copper, benzene, and renin) are measured. A linear regression analysis is performed between the detected concentrations and the expected concentrations. Under the optimal detection state, the minimum dilution gradient corresponding to the linear regression equation is defined as the experimental resolution. Meanwhile, the resolution of the measurement result using zero as the baseline is considered as the minimum detection limit of the detection system. The accuracy is calculated by using the deviations of the coefficient of determination, slope, and intercept. When the fluctuation range is less than 5%, the accuracy is considered acceptable.Results: The experimental resolutions of FAAS, GC, and LC–MS all reach 10% or better, which can be used for quantitative detection. The minimum detection limits of FAAS, GC, and LC–MS are 0.064, 0.001 μg/mL, and 0.088 ng/mL, respectively. The sensitivity of LC–MS is higher than that of the other two instruments according to the present data. When the coefficient of determination is greater than 0.98, the accuracy is further calculated. The accuracy results (relative errors) of FAAS, GC, and LC–MS are 1.015%, 1.430%, and 3.475%, respectively. FAAS is considered to be the most accurate.Conclusions: The method for measuring the experimental resolution, minimum detection limit, and accuracy based on linear models is simple and reasonable. The experimental performance is evaluated from multiple perspectives, which simplifies and improves the process and effect of the evaluation in order to suggest improvement strategies for analysis experiments and related equipment.

Exploring the Criticality of Natural Resources Management and Technological Innovations for Ecological Footprint in the OECD Countries

ABSTRACT: The urgent imperative to confront the complex interplay between managing natural resources and fostering technological innovation, particularly in OECD countries, has become a focal point in global conversations. It is abundantly clear that critical issues such as the rampant overexploitation of our natural resources, insufficient embrace of sustainable practices, and the profound repercussions of technological advancements on our ecological footprint demand meticulous scrutiny and comprehensive action. This scrutiny is vital to guide the formulation of judicious policies and strategies capable of addressing these complex challenges. The challenges faced by OECD countries, distinguished by their advanced industrialization, and elevated standards of living, are further compounded by the depletion of natural resources resulting from extensive economic activities. The extraction and utilization of resources, encompassing water, minerals, and energy, emerge as significant contributors to ecological degradation. The heightened demand for raw materials to sustain technological industries has precipitated the overexploitation and depletion of natural resources, manifesting in activities such as mining, deforestation, and other extraction processes, leading to habitat destruction and biodiversity loss. Furthermore, the manufacturing and utilization of technology, particularly within industries heavily reliant on non-renewable energy sources, have been substantial contributors to pollution and greenhouse gas emissions. The study assesses the dynamic relationship between natural resources, economic growth, and technological innovation with respect to ecological footprints. It analyses the annual data series of a panel of 20 organisation for economic cooperation and development countries using the second-generation cointegration panel and cross-sectional autoregressive distributed lag methodology. The results of the slope coefficient confirm the existence of slope heterogeneity among countries. Furthermore, it affirms the long-term relationship among variables. Overall, the findings highlight the importance of effective utilisation of resources while investing in technological innovation to attain sustainable development goals.