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The purpose of this paper is to investigate the justification of objective assessment of the agricultural sector energetic potential, and the increasing of the accuracy of assessments results of energy resources of plant by-products.

The study of the problems of bioenergetic potential assessment in the study is carried out in the following order: first, the potential is assessed based upon the 2005-2017 year’s observation data; second, the energetic potential is assessed based upon linear and nonlinear optimization model; and finally, the assessment of the bioenergetic potential predicted values is carried out under the condition of the current pace of development of agricultural business by 2035.

The findings show that the solving of optimization tasks enabled us to make a comparison of the real structure of agricultural production and to justify the optimal structure of the cultivated areas under the conditions of agricultural business profit maximization with due allowance for both main and additional energy products. Using the linear trend model the predicted value of the agricultural sector energetic potential by the year 2035 is obtained. However, it is far more likely that the domestic bioenergetics will take a slower pace of development and to satisfy its own energy demands.

Based on the data of the reference interval of 2005-2018, the predicted values of biomass for 2035 were obtained in the amount of 28 million tons of oil equivalent, which taking into account the indices of generation efficiency, is sufficient to produce 104 billion kW-h.

The use of biomass for energy generation can impact the local environment, for example, by affecting air quality, biodiversity, habitats and ecosystems and water quantity and quality and by changing the local use of land. Social impacts also may arise, notably by affecting local community livelihoods (for example, access to and use of land and resources), food security and economic parameters such as employment and poverty.

The paper presents for the first time the results of the empiric analysis of the Ukrainian sector bioenergetic potential formation that showed that even with respect to the losses during the energy generation, the agricultural production energetic potential will be enough to substitute nuclear national power engineering.

A concise introduction to the normalized quadratic expenditure or cost function is provided so that the interested reader will have the necessary information to understand and use this functional form. The normalized quadratic is an attractive functional form for use in empirical applications as correct curvature can be imposed in a parsimonious way without losing the desirable property of flexibility. We believe it is unique in this regard. Topics covered include the problem of cardinalizing utility, the modeling of nonhomothetic preferences, the use of spline functions to achieve greater flexibility, and the use of a “semiflexible” approach to make it feasible to estimate systems of equations with a large number of commodities.

The aim of this chapter is to assess the real exchange rate misalignments. A smooth transition autoregressive model (STAR) is used for Tunisian exchange market. This model allows us to see whether these differences are temporary or persistent over the period 1975–2012. We start by defining the exchange rate’s fundamental determinants to provide the equilibrium exchange rate value. Then, we study the observed exchange rate adjustment toward its equilibrium level. Vector autoregressive model and vector error correction model are applied to characterize the joint dynamics of variables in the long run. The results indicate a long-run relationship between variables. In order to consider the nonlinearity for better results, we will move to nonlinear smooth transition model. We found there is a high degree of exchange rate misalignment. We recognized that this difference decreases in the long run and disappears at the end.

The most simple equivalent frame system with reduced degrees of freedom is proposed for handling multi‐story multi‐bay infilled frames. The system is composed of homogenized continuum for the reinforced concrete members braced with unilateral diagonal struts for each bay, which are only activated in compression. Identification of the equivalent system characteristics and nonlinear material properties are accomplished from the concepts of inverse analysis approach along with statistical tests of hypotheses is employed to establish the appropriate filtering scheme and the proper accuracy tolerance. The suggested system allows for nonlinear finite element static and dynamic analysis of sophisticated infilled reinforced concrete frames. Sensitivity analysis is undertaken to check the suitability of the proposed system to manipulate various structural applications.

The standard approach in measuring demand responses and consumer preferences assumes particular parametric models for the consumer preferences and demand functions, and subsequently fits these models to observed data. In principle, the estimated demand models can then be used (i) to test consistency of the data with the theory of consumer behavior, (ii) to infer consumer preferences, and (iii) to predict the consumer's response to, say, new prices following a policy reform. This chapter focuses on an alternative, nonparametric approach. More specifically, we review methods that tackle the earlier issues by merely starting from a minimal set of so-called revealed preference axioms. In contrast to the standard approach, this revealed preference approach avoids the use of parametric models for preferences or demand. The structure of the chapter is as follows. First, we introduce the basic concepts of the revealed preference approach to model consumer demand. Next, we consider issues like goodness-of-fit, power, and measurement error, which are important in the context of empirical applications. Finally, we review a number of interesting extensions of the revealed preference approach, which deal with characteristics models, habit-formation, and the collective model.

This manuscript reports evidence on how debt-taking decisions of top management in a multi-country setting do affect credit rating scores assigned by credit rating agencies (CRAs) as global monitors of creditworthiness of borrowers. This aspect has been long ignored by researchers in the literature. The purpose of this paper is twofold. A test model is specified first using theories to connect debt-taking behavior to credit rating scores. Once that model helps to identify a number of statistically significant factors, the next step helps to identify threshold values at which the variables driving debt-taking behavior would worsen the credit rating scores as turning points of the thresholds.

The study identifies factors driving creditworthiness scores due to debt-taking behavior of countries and develops a correct research design to identify a model that explains (1) credit rating scores and the factors driving the scores and runs (2) panel-type regressions to test model fit. Having found factors driving debt-taking behavior by observed units, the next step identifies threshold values of factors at which point further debt-taking is likely to worsen credit rating risk of the observed units. This is a robustness test of the methodology used. The observed units are 20 countries with data series across 14 years.

First, new findings suggest there are about six major factors associated with debt-taking behavior and credit rating changes. Second, the model developed in this study is able to account for substantial variability while the identified factors are statistically significant within the normal p-values for acceptance of hypotheses. Finally, the threshold values of factors identified are likely to be useful for managerial decisions to judge the levels at which further debt-taking would worsen the credit rating scores of the observed units.

The observed units are from 20 countries over 14 years of annual data available on credit rating scores (privately obtained from Standard and Poor [S&P]). The sample represents major economies but did not include emerging countries. In that regard, it will be worthwhile to explore the debt-taking behavior of emerging economies in a future study using the methodology verified in this study.

The findings help add few useful guidelines for top management decisions. (1) There are actually factors that are associated with debt-taking behavior, so the authors now know these factors as guides for managerial actions. (2) The authors are free to state that the credit rating changes occur on objective changes in the factors found as significantly related to the debt-taking behavior. (3) The threshold values of key factors are known, so top management could use these threshold values of named factors to monitor if a debt-taking decision is going to push the credit rating to a worse score.

There are society-wide implications. Knowing that the world's debt level is high at US$2.2 for each gross domestic product (GDP) dollar across almost 200 countries, any knowledge on what factors help drive creditworthiness scores, thus credit riskiness, is revealed in this paper. Knowing those factors and also knowing the turning points of the factors – the threshold values – likely to worsen creditworthiness scores is a powerful tool for controlling excessive debt-taking by an observation unit included in this study (The dataset in this research can also be used to see inter-temporal movement on debt-taking in a future study).

In the authors' view, there are many studies on debt-taking behavior. But none has connected debt-taking on how (1) named factors are observable to management that affect credit rating changes and (2) if a factor affects creditworthiness, at which point of the factor value, the creditworthiness will flip to worsen the score. These aspects are seldom found in the literature. Hence, the paper is original with practical value at the global level.

The purpose of this paper is to evaluate the rate of discrepancy between the group of expected project results and the group of corresponding results realized preliminarily at the design stage.

The authors formulate and solve the problem to determine the values of two assemblies of independent variables guaranteeing the project with the best (X→^*) and as well this one with the worst (X→^**) tolerance field of the expected Z_x realization. The problem is formulated as an optimization one in nonlinear mathematical programming and it is solved by applying the flexible tolerance method.

By the use of basic relations in statistical science an expression of standard deviation σ_Zx of Z_x is found and proposed. The limits in which are expected to be change in the deviations between the main project results and the corresponding ones of the implemented design, mathematically defined previous to its realization.

The accent is put on the cases when the device characteristics are presented as polynomials, obtained on the bases of the design of experiments (DOE) and the response surface methodology. An arbitrary technical characteristic Z_x=Z(x→) is presented.

A researcher could confine numerically the tolerance field between project results and expected realized ones in the whole investigated region.

The paper is characterized by an original approach and a new expression of standard deviation is proposed which will be of interest to designers.

This study reexamines fiscal deficit sustainability in South Africa.

The study applies three cointegration testing approaches, namely testing for multiple structural changes in a cointegrated regression model, time-varying cointegration test and asymmetric cointegration test.

The results point to the existence of a level relationship between government revenue and spending. In addition, the long-run equilibrium relationship between government revenue and spending in South Africa is found to be characterized by breaks. As such, assuming a constant cointegrating slope may be misleading. Results from time-varying cointegration and an estimation of a cointegrated two-break model indicate that cointegrating coefficient has been time-varying but has remained less than 1 for the entire study period, indicating that fiscal deficits have been weakly sustainable. This finding is also confirmed by the results from an estimated asymmetric error correction model.

In view of the findings, authorities should put in place policies to improve the fiscal budgetary stance and reinforce the sustainability of the fiscal deficits in South Africa. Among other things, South Africa could undertake reforms to state-owned companies to reduce their reliance on public funds, slow down the pace of the public sector wage growth and devise effective economic measures to boost long-term growth. In addition, tax compliance and other revenue collection measures should be enhanced for additional tax revenue.

The contribution of this study is twofold; first, the study uses a long series of annual data spanning over a century, from 1913 to 2020. Indeed, cointegration is better modeled using long spans of time series data. Second, to examine the existence of a level relationship between spending and revenue, the study uses cointegration tests which allow capturing time-variation in the cointegrating slope coefficient, and accounting for asymmetries in the relationship between government spending and revenue. It is important to allow for time-variation in the cointegrating slope coefficient, especially when it has been hardly treated in the empirical literature on fiscal deficit sustainability. Allowing for time-variation in the cointegrating slope coefficient helps us to analyze fiscal deficit sustainability by periods of time. Indeed, the degree of fiscal sustainability can change from one time period to another.