Build Regression Models Research Articles

Reporting quality of clinical trial protocols: a repeated cross-sectional study about the Adherence to SPIrit Recommendations in Switzerland, CAnada and GErmany (ASPIRE-SCAGE)

ObjectivesComprehensive protocols are key for the planning and conduct of randomised clinical trials (RCTs). Evidence of low reporting quality of RCT protocols led to the publication of the Standard Protocol...

167 Effect of Reducing Dietary Crude Protein on Growth Performance of Fattening Pigs: A Meta-Analysis

Abstract Reducing dietary crude protein (CP) is a nutritional strategy implemented to improve the sustainability of pig production. Its effect on growth performance has been broadly studied in fattening pigs but the literature is lacking a quantitative summary of the knowledge acquired. A meta-analysis was performed to assess the effects of reducing dietary CP on pig growth performance. Articles included in the meta-analysis studied effects of CP reduction, with at least 3 CP levels, on the growth performance of fattening pigs (20-115 kg) and were published from 1990 to 2019. The database contained 42 articles, which correspond to 67 trials. Diet composition was recalculated with INRA-AFZ (2004) tables. Trials using iso-digestible lysine and iso-net energy diets with constant or above requirements levels of essential amino acids (EAA, METEX NØØVISTAGO recommendations) were selected for the final analysis, with a 5% acceptance limit. The final selection contained 11 trials and 44 treatments. The general linear model procedure of MINITAB (2019) was used to build regression models, including a fixed trial effect. Dietary CP reduction tested ranged between 1.6 and 7.5 percentage points (%pt) and was performed by reducing soybean meal and increasing cereals inclusion in all trials except one using rapeseed meal. Reducing dietary CP did not significantly impacted feed intake, average daily gain or feed conversion ratio when EAA were adequately supplied. Nitrogen efficiency was improved by 2.7 %pt per %pt of CP reduction (P < 0.001). No statistical analysis could be performed on carcass composition data as there were only 6 trials remaining after selection for these parameters, but fatter carcasses were observed in half of the trials. This study highlighted that few CP reduction studies have been performed while controlling energy and EAA levels. There was no effect of reducing dietary CP on growth performance when those parameters were controlled but more research is needed on very low CP diets for fattening pigs.

A New Method for Generating the SMOPS Blended Satellite Soil Moisture Data Product without Relying on a Model Climatology

Soil moisture operational product system (SMOPS) is developed by National Oceanic and Atmospheric Administration (NOAA) to provide the real-time blended soil moisture (SM) for numeric weather prediction and national water model applications. However, all individual satellite SM data ingested into the current operational SMOPS are scaled to global land data assimilation system (GLDAS) 0–10 cm SM climatology before the combination. As a result, the useful information from the original microwave SM retrievals could be lost, and the GLDAS model errors could be brought into the final SMOPS blended product. In this paper, we propose to scale the individual SM retrievals to the soil moisture active passive (SMAP) data through building regression models. The rescaled individual SM data and the SMAP observations then have similar climatology and dynamics, which allows producing the SMOPScdr (distinguishing with the current operational SMOPSopr) data using an equal-weight averaging approach. With respect to the in situ SM measurements, the developed SMOPScdr is more successful tracking the surface SM status than the individual satellite SM products with significantly decreased errors. The proposed method also preserves the climatology of the reference SMAP data for the period when SMAP is not available, allowing us to produce a long-term SMOPScdr data product.

Association Rule Mining Meets Regression Analysis: An Automated Approach to Unveil Systematic Biases in Decision-Making Processes

Decisional processes are at the basis of most businesses in several application domains. However, they are often not fully transparent and can be affected by human or algorithmic biases that may lead to systematically incorrect or unfair outcomes. In this work, we propose an approach for unveiling biases in decisional processes, which leverages association rule mining for systematic hypothesis generation and regression analysis for model selection and recommendation extraction. In particular, we use rule mining to elicit candidate hypotheses of bias from the observational data of the process. From these hypotheses, we build regression models to determine the impact of variables on the process outcome. We show how the coefficient of the (selected) model can be used to extract recommendation, upon which the decision maker can operate. We evaluated our approach using both synthetic and real-life datasets in the context of discrimination discovery. The results show that our approach provides more reliable evidence compared to the one obtained using rule mining alone, and how the obtained recommendations can be used to guide analysts in the investigation of biases affecting the decisional process at hand.

Оценка изменения линейных приростов сосны обыкновенной Печоро-Илычского заповедника на основе климатического прогноза температуры приземного воздуха в российской Арк­тике

For the Arctic region of the European territory of Russia, the authors validated the global climate models of the CMIP6 project with the ERA5 reanalysis data. They identified the most accurate model, based on which made the forecast of surface temperature for the months of July-September under the conditions of two scenarios: high SSP8.5 emissions and low SSP2.6. The results give an idea of the average warming in the next 29 years for these months by 1.8—2.0 (°C). The researchers investigated the response of the increments of the Scots pine (Pinus sylvestris L.) in the Pechora-Ilychsky Reserve to the predicted warming. In addition, they revealed a significant effect of precipitation amounts on the growth parameters of forest stands. The built regression model reflects the total contribution of these climatic factors to the formation of pine increments.

Health Risks of Using Headphones with Mobile Electronic Devices for Young People

Introduction: Urban population is exposed to a combined effect of physical risk factors. A large number of young people in­ crease their noise exposure by listening to audio files using headphones. Objective: To study health effects of using headphones in schoolchildren and students. Materials and methods: In 2019–2020, a questionnaire-based survey of 800 secondary school children and students (345 boys and 455 girls aged 13–20 years) was conducted in the cities of Moscow, Voronezh, and Izhevsk. The inclusion criteria were young age (a pupil/student), a signed informed consent, and no experience (for controls) or a year or more of experience in using headphones with portable electronic devices (for cases). The questionnaire data were analyzed using the statistical analysis software Statistica 10.0 to estimate contingency coefficients and relative risks and to build regression models. Results: We established that listening to audio files at full volume, using headphones in public transport, and having no headphones-free day a week posed significant health risks for young people: the relative risk of hearing impairment attributed to an every-day use of headphones at maximum volume was 3.20 (95 % confidence interval, 2.40–5.21). Conclusions: We established risk factors affecting wellbeing and hearing loss in young people. Since these factors manageable, they should be considered in health education of schoolchildren and students.

Ultra‐Wideband, Polarization‐Independent, Wide‐Angle Multilayer Swastika‐Shaped Metamaterial Solar Energy Absorber with Absorption Prediction using Machine Learning

AbstractThis paper proposes a double layer of gold multipattern swastika (DLMP) resonator based on SiO2 substrate. The average absorption of 95% is achieved for the DLMP metasurface‐based solar absorber in the spectrum (0.1–3 μm) covering the ultraviolet, visible, near‐infrared (NIR), and some range of mid‐infrared regions which makes proposed solar energy absorber ultra‐wideband. The absorptance rate of more than 90% is achieved for the bandwidth of 2516 nm, in absorptance spectrum of 0.314 to 2.830 μm. Shape analysis is also carried out for proposed structure with simulations of five variations and comparative analysis in terms of absorptance response under solar radiation is also presented to check the effect of shape variation on absorption. Furthermore, the influence of several structural parameters on absorptance spectra is also investigated. It is also observed that the absorptance spectrum of proposed solar absorber is angle insensitive for the range of 0° to 70° and is also polarization insensitive. General regression neural network is used to build regression models which can learn and predict the behavior of absorbers in assorted conditions. Experimental results prove that these models can predict the absorber behavior with high accuracy and can reduce the simulation time, resource requirements by 80%.

Correlation-based feature extraction from computer-aided design, case study on curtain airbags design

Many high-level technical products are associated with changing requirements, drastic design changes, lack of design information, and uncertainties in input variables which makes their design process iterative and simulation-driven. Regression models have been proven to be useful tools during design, altering the resource-intensive finite element simulation models. However, building regression models from computer-aided design (CAD) parameters is associated with challenges such as dealing with too many parameters and their low or coupled impact on studied outputs which ultimately requires a large training dataset. As a solution, extraction of hidden features from CAD is presented on the application of volume simulation of curtain airbags concerning geometric changes in design loops. After creating a prototype that covers all aspects of a real curtain airbag, its CAD parameters have been analyzed to find out the correlation between design parameters and volume as output. Next, using the design of the experiment latin hypercube sampling method, 100 design samples are generated and the corresponding volume for each design sample was assessed. It was shown that selected CAD parameters are not highly correlated with the volume which consequently lowers the accuracy of prediction models. Various geometric entities, such as the medial axis, are used to extract several hidden features (referred to as sleeping parameters). The correlation of the new features and their performance and precision through two regression analyses are studied. The result shows that choosing sleeping parameters as input reduces dimensionality and the need to use advanced regression algorithms, allowing designers to have more accurate predictions (in this case approximately 95%) with a reasonable number of samples. Furthermore, it was concluded that using sleeping parameters in regression-based tools creates real-time prediction ability in the early development stage of the design process which could contribute to lower development lead time by eliminating design iterations.

Modelling the Toughness of Nanostructured Polyhedral Oligomeric Silsesquioxane Composites Fabricated by Stereolithography 3D Printing: A Response Surface Methodology and Artificial Neural Network Approach

In this study, stereolithography (SLA) 3D printing was used to prepare toughened composites by facile blending of chemically compatible nanoscale polyhedral oligomeric silsesquioxanes (POSS) to commercial photoreactive resin. Due to the complex nature of 3D printing, the mechanical performance of the final parts cannot be simply determined or even estimated until they are manufactured and tested. Thus, response surface methodology (RSM) and artificial neural network (ANN) were used to build regression models for determining the toughness of fabricated composites as function of toughener (POSS) amount and printing conditions (layer thickness and annealing temperature). The influence of the mentioned process parameters on toughness were investigated through a 17-run three-factor three-level Box-Behnken RSM design (BBD). The same experimental design was also used to acquire a data set for ANN. Finally, both the modeling methodologies were compared by coefficient of determination (R2) and residual distribution values. Results reveal that ANN possesses a better data fitting and predictive power as compared to RSM.

Machine Learning-based Soft Computing Regression Analysis Approach for Crime Data Prediction

The crime rate in India is considerably increasing day by day. Consequently, the data associated with crime is also increasing, opening doors for data-driven approaches to these data to extract insightful knowledge, which can help police and other law enforcement organizations of the country in crime control and prevention. Crime prediction using machine learning algorithms on crime data can predict region-wise crime counts. In this paper, a machine learning-based soft computing regression analysis approach for Indian Crime Data Analysis (ICDA) is proposed. Different regression algorithms, namely, Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Support Vector Regression (SVR), and Random Forest Regression (RFR) are uses to build regression models. These regression models can predict a total number of Indian Penal Code (IPC) crime counts and crime counts of different types of crime (murder, rape, kidnapping and abduction, riots, to name a few) region-wise and state-wise and all over the country for a given year. Adjusted R squared value and Mean Absolute Percentage Error (MAPE) is used to evaluate and compare proposed regression models. In the proposed approach for ICDA, district-wise spatial-temporal crime data of years 2001 to 2012 is used, collected from the official website of NCRB. For the chosen data, it is concluded that the region-wise total IPC crime prediction RFR model fits the best with an adjusted R squared value of 0.9631551 and an error of 0.2027437. Whereas for region-wise thefts crime count prediction, the RFR model fits the best with an adjusted R squared value of 0.966604 and an error of 0.16571.

DRY BULK FREIGHT MARKET RESEARCH BASEDON CORRELATIONANDREGRESSIONANALYSIS

Thecorrelationandregressionanalysisappliedtothefreightratesdemonstrates a fairly good results which is confirmed by the relevant estimates of the models.However, the impact of each determinant on the freight rates is not the same in the different timeperiod. Moreover, it is important to focus on the certain segment of dry bulk freight market moreaccuratelytoobtainthemathematicalmodelreflectingsegmentspecific.The study is aimed to develop the methodological basic for applying the correlation andregressionanalysistothefreightratesontheexampleofBalticPanamaxIndex.Various linear regression model structures for freight rates have been proposed,withappropriate detailing of the underlying market determinants. It has been determined that eachfreightmarketsegment is characterized a certain specificityofdemand,as wellas competitionwith other segments, these should be taken into account when building regression models. Theideas suggested have been applied to Panamax segment. The Panamax bulk carriers serve mostlythe grain, coal and ore; thus, these three types of goods are included in the model as demanddeterminants. The correlation and regression analysis were used to establish and mathematicallydraw the dependence of freight indexes on the set of the major freight market determinants ontheexampleoftheBalticPanamaxIndex.The output shows the obtained regression model can be used to predict the Baltic PanamaxIndex. Based on the forecasting of considered factors -seaborn grain trade, seaborn ore trade,seaborn coal trade, Panamax bulk carriers’ sector of merchant fleet, Panamax bulk carriersshipbuilding,Panamaxbulkcarriersscrapping,thevaluesoftheBalticPanamaxIndexforfollowingtimepointscanbeestimated.Two approaches were considered –the regression analysis for original data and forlogarithmically transformed data. Whether the first approach with the original data or the secondonewiththe logarithmically transformed data is applied,theresultsapprove theregressionanalysis use to establish the relationship between freight indexes (freight rates) and the set of themajor freightmarketdeterminants.Keywords: freight rates, Panamax bulk carriers, model, market determinants, maritimetransportation,forecast.

Reliability of Machine Learning in Eliminating Data Redundancy of Radiomics and Reflecting Pathophysiology in COVID-19 Pneumonia: Impact of CT Reconstruction Kernels on Accuracy

Background: Radiomical data are redundant but they might serve as a tool for lung quantitative assessment reflecting disease severity and actual physiological status of COVID-19 patients. Objective: Test the effectiveness of machine learning in eliminating data redundancy of radiomics and reflecting pathophysiologic changes in patients with COVID-19 pneumonia. Methods: We analyzed 605 cases admitted to Al Ain Hospital from 24 February to 1 July, 2020. They met the following inclusion criteria: age≥18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We categorized cases into 4 classes: mild ≤25% of pulmonary parenchymal involvement, moderate - 25-50%, severe - 50-75%, and critical – over 75%. We used CT scans to build regression models predicting the oxygenation level, respiratory and cardiovascular functioning. Results: Radiomical findings are a reliable source of information to assess the functional status of patients with COVID-19. Machine learning models can predict the oxygenation level, respiratory and cardiovascular functioning from a set of demographics and radiomics data regardless of the settings of reconstruction kernels. The regression models can be used for scoring lung impairment and comparing disease severity in follow up studies. The most accurate prediction we achieved was 6.454±3.715% of mean absolute error/range for all the features and 7.069±4.17% for radiomics. Conclusion: The models may contribute to the proper risk evaluation and disease management especially when the oxygen therapy impacts the actual values of the functional findings. Still, the structural assessment of an acute lung injury reflects the severity of the disease.

SIMULATION OF APPROPRIATE RHEOVASOGRAPHIC INDICATORS OF THE FEMUR IN VOLLEYBALL PLAYERS OF ECTOMORPHIC SOMATOTYPE DEPENDING ON ANTHROPOMETRIC FEATURES

The aim of the study was to build regression models of rheovasographic parameters of the femur in volleyball players of ectomorphic somatotype depending on the features of anthropometric indicators. Materials and methods: 113 volleyball players of a high level of sportsmanship of adolescence (from 16 to 20 years old) underwent somatotypological study according to the calculated modification of the Heath-Carter method. 26 volleyball players of ectomorphic somatotype were selected. They performed tetrapolar rheocardiography on a computer diagnostic complex according to the method of Ronkin and Ivanov to establish the indicators of peripheral hemodynamics and anthropometry according to the method of V.V. Bunak. The mathematical models were built in the package “STATISTICA 5.5” for Windows using direct stepwise regression analysis. Results: Due to the use of multifactor regression analysis, we performed mathematical modeling of rheovasographic parameters of the femur in volleyball players of ectomorphic somatotype, which allows to determine the appropriate values of these indicators taking into account anthropometric and somatotypological features of each athlete. Linear regression equations for 16 indicators of peripheral hemodynamics were constructed from possible rheovasographic parameters of the femur. 4 models were built for time indicators (in which the indicators of the external structure of the body by 59.31 - 78.01% determine the value of the parameters of this group); for amplitude - 5 (coefficient of determination of features from 54.00 to 76.13%); for integral indicators of the rheovasogram of the femur - 7 (coefficient of determination 60.10 - 77.41%). Conclusions: In volleyball players of ectomorphic somatotype up to 16 constructed regression models to determine the appropriate rheovasographic parameters of the femur included 94 dimensions of the external body structure, among them were often the thickness of fat folds, craniometric parameters, girth and anterior-posterior body dimensions, somatotype components.

Target Aggregation Regression based on Random Forests

In the era of big data, data volume is growing explosively, and the proportion of unlabeled data is increasing due to the high cost of label acquisition and privacy protection policy. Therefore, weakly labeled learning has drawn much attention to recognize patterns in the real-world data. Learning with target aggregation is an emerging technique in the last few years. However, to the best of our knowledge, the target aggregation regression problem has not been discussed. In this paper, we make use of random forest to build regression model for data with aggregated targets. The experiment shows the promising result of the proposed model.

Study on the Optimization of Hyperspectral Characteristic Bands Combined with Monitoring and Visualization of Pepper Leaf SPAD Value.

Chlorophyll content is an important indicator of plant photosynthesis, which directly affects the growth and yield of crops. Using hyperspectral imaging technology to quickly and non-destructively estimate the soil plant analysis development (SPAD) value of pepper leaf and its distribution inversion is of great significance for agricultural monitoring and precise fertilization during pepper growth. In this study, 150 samples of pepper leaves with different leaf positions were selected, and the hyperspectral image data and SPAD value were collected for the sampled leaves. The correlation coefficient, stability competitive adaptive reweighted sampling (sCARS), and iteratively retaining informative variables (IRIV) methods were used to screen characteristic bands. These were combined with partial least-squares regression (PLSR), extreme gradient boosting (XGBoost), random forest regression (RFR), and gradient boosting decision tree (GBDT) to build regression models. The developed model was then used to build the inversion map of pepper leaf chlorophyll distribution. The research results show that: (1) The IRIV-XGBoost model demonstrates the most comprehensive performance in the modeling and inversion stages, and its , , and are 0.81, 2.76, and 2.30, respectively; (2) The IRIV-XGBoost model was used to calculate the SPAD value of each pixel of pepper leaves, and to subsequently invert the chlorophyll distribution map of pepper leaves at different leaf positions, which can provide support for the intuitive monitoring of crop growth and lay the foundation for the development of hyperspectral field dynamic monitoring sensors.

Cognitive Models of Loneliness in Women in Early and Middle Adulthood

The problem of loneliness is caused by a disunity and alienation of modern society, dissatisfaction with the basic social needs for acceptance and support. The fact that the subjective feeling of loneliness does not coincide with the real degree of social isolation determines the need to study the cognitive aspects. The aim of this study is to show the role of cognitive structures in loneliness in women. A survey of 144 women aged 26-55 years was conducted. Using the UCLA Loneliness Scale and the Differential Loneliness Experience Questionnaire, loneliness was measured as a global experience, positive and negative attitudes towards it. Cognitive models were studied using the Dysfunctional Attitude Scale, Young Schema Questionnaire, the author’s questionnaire that reveals cultural myths and gender stereotypes about loneliness. A comparative analysis of the groups showed no differences in the perception of loneliness among women with different family situations; it also does not depend on having children and their number. Age was found to be a significant determinant of perceptions of loneliness and family circumstances. Cognitive models influence women’s loneliness to a greater extent than family situation. The publication describes correlations between perceived loneliness and individual dysfunctional attitudes, and builds regression models of predictors of loneliness in different age groups. The study shows the prevalence of cultural stereotypes about the need for marriage and motherhood, and a special female vulnerability in relationships. The construct “tolerance to loneliness” is proposed and described, which reveals the positive potential of this experience for self-knowledge and self-development.

English

The mechanized planting of potato mini-tubers is vital for the development of the potato industry in China. A vibration-based seeding device for mini-tubers was designed and tested. The objectives of this study were to describe the design process of the main components of the seeding device by theoretical analysis, evaluate the reliability of the key components of the seeding device by finite element analysis, assess the seeding device’s working performance, and obtain a better combination of operational parameters by bench tests. According to a theoretical analysis of the seeding device design, the eccentricity of the eccentric wheel (EEW), rotational speed of the drive shaft (RSDS) and linear speed of the conveyor belt (LSCB) were selected as critical factors influencing the seeding device’s working performance. A Box-Behnken design (BBD) was applied to conduct bench tests and build regression models. Based on the optimized results, verification experiments were performed by a bench tester. The results showed that the multiple-seeding index (MUSI) was 3.10%, the miss-seeding index (MISI) was 4.01%, and the qualified-seeding index (QSI) was 92.89% when the EEW, RSDS and LSCB were 4 mm, 821 rpm, and 86 mm s-1, respectively. Verification experiments showed that the results agreed well with the theoretical optimization values

Clinical and Neural Predictors of Treatment Response to Music Listening Intervention after Stroke.

Patients with post-stroke impairments present often significant variation in response to therapeutic interventions. Recent studies have shown that daily music listening can aid post-stroke recovery of language and memory, but reliable predictors of treatment response are unknown. Utilizing data from the music intervention arms of a single-blind randomized controlled trial (RCT) on stroke patients (N = 31), we built regression models to predict the treatment response of a two-month music listening intervention on language skills and verbal memory with baseline demographic, clinical and musical data as well as fMRI data from a music listening task. Clinically, greater improvement in verbal memory and language skills after the music listening intervention were predicted by the severity of the initial deficit and educational level. Neurally, greater baseline fMRI activation during vocal music listening in the left parietal cortical and medial frontal areas predicted greater treatment-induced improvement in language skills and greater baseline engagement of the auditory network during instrumental music listening predicted improvement in both verbal memory and language skills. Our results suggest that clinical, demographic, and neuroimaging data predicts music listening treatment response. This data could be used clinically to target music-based treatments.

Impact of Organizational Support on Faculty Commitment to Academic Accreditation in Higher Education Institutions

The study aimed to identify the degree of perceived organizational support (POS) of faculty members and their commitment to academic accreditation (CTAA). Then, it explores the effect of POS on CTAA, gender, nationality, and administrative positions on them. Participants included a convenient sample of 221 faculty members from Sultan Qaboos University in Oman. Data analysis included measures of central tendency and structural equation modeling to conduct confirmatory factor analysis and build regression models. Results showed moderate degree of faculty POS and high level of CTAA. Significant gender difference is also observed for POS, with male faculty members showing higher POS and female members showing higher CTAA. In addition, non-Omani members reported significantly more POS and those with administrative positions reported significantly higher CTAA. Finally, empirical evidence is validated for the positive influence of POS on CTAA. Keywords: Organizational support, commitment to change, academic accreditation, higher education, Oman

Trends in Russia’s GDP Growth under Environmental Constraints

The paper illustrates an approach to GDP growth in the context of an ecological economy. The subject of the research is the establishment of the interdependence of the state of the ecology of the environment and the results of human activity. The aim of the study is to determine the presence or absence of a relationship between pollutants in each type of environmental pollutants and the level of economic development of the country, represented by GDP per capita indicators. The relevance of the study is due to the ecologically unbalanced growth of GDP, which is accompanied by an increase in disproportions between the volume of pollutants emitted by the extractive, processing, processing, agricultural and infrastructure sectors of the economy, and the conditions of human life due to the deterioration of the “ecological quality” of the environment, which limits the possibilities for further development of human capital. The scientific novelty of the research lies in the development of economic and mathematical models, methods, and numerical algorithms for assessing and analyzing the state of air, water, and environmental pollution under the influence of the country’s economic growth. Methods used: empirical and statistical analysis, building regression models, algorithmic and predictions, building time trends, etc. The authors based the methodology of substantiating the method for assessing the environmental constraints on GDP growth on an empirical study of the state of the environment and the state of the Russian economy in 2000–2018. Results: the authors have determined a set of indicators reflecting the state and trends of changes in environmental pollutants in Russia (carbon dioxide emissions, wastewater, production, and consumption waste) and their interdependence with economic development, which predetermine long-term social, environmental, and energy consequences. An algorithm has been developed to substantiate environmental restrictions on Russia’s GDP growth in the period 2000–2018. The algorithm is based on a modified Kaya equation, through which the relationship between each type of pollutant and indicators of GDP per capita, energy resources, and industrial production is checked. In accordance with GDP growth, the forecast of environmental restrictions was developed according to scenarios of 10–40% and showed the inevitability of implementing a plan to prevent environmental pollution in Russia. It is concluded that Russia must promote environmental and low-carbon policies, reduce emissions, waste, and energy consumption over the next few decades to achieve sustainable development. The country is faced with the task of moving away from a nature-destroying economy, thereby saving natural capital, minimizing the costs of eliminating the negative environmental consequences of technogenic economic development in the future.