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This study presents how to develop the information used during the decision-making process regarding the endowment of the Romanian air fleet with fighter aircraft, where after the analysis, we have as a result the appropriate model for the requirements and criteria for modernization and reorganization of the fleet. This analysis is performed by mathematical methods under certainty by the moments method or the Deutch Martin method. The first step of the method is to normalize the consequence matrix by linear transformations. It is taken into account that the four chosen criteria are of maximum. The moments method optimally generates clear results regarding the purchase of fighter aircraft that contribute to the development of the fleet.

Many economic and causal parameters depend on nonparametric or high dimensional first steps. We give a general construction of locally robust/orthogonal moment functions for GMM, where first steps have no effect, locally, on average moment functions. Using these orthogonal moments reduces model selection and regularization bias, as is important in many applications, especially for machine learning first steps. Also, associated standard errors are robust to misspecification when there is the same number of moment functions as parameters of interest. We use these orthogonal moments and cross-fitting to construct debiased machine learning estimators of functions of high dimensional conditional quantiles and of dynamic discrete choice parameters with high dimensional state variables. We show that additional first steps needed for the orthogonal moment functions have no effect, globally, on average orthogonal moment functions. We give a general approach to estimating those additional first steps. We characterize double robustness and give a variety of new doubly robust moment functions. We give general and simple regularity conditions for asymptotic theory.

The Bartik instrument is formed by interacting local industry shares and national industry growth rates. We show that the typical use of a Bartik instrument assumes a pooled exposure research design, where the shares measure differential exposure to common shocks, and identification is based on exogeneity of the shares. Next, we show how the Bartik instrument weights each of the exposure designs. Finally, we discuss how to assess the plausibility of the research design. We illustrate our results through two applications: estimating the elasticity of labor supply, and estimating the elasticity of substitution between immigrants and natives.

Image classification need two main components, i.e., features and classifier. The feature commonly used for classification of images with different scale is invariant moment; its value is invariant against the spatial transformation dealing with translation, scale and rotation. The classifier that is widely used for classification is LVQ NN. It is shallow network containing only two layers, the initial value of its weight is more fixed so that its output is more stable and its algorithm is relatively simple thus both training and testing process are run fast. Based on these facts, therefore, this research proposed a combination method of invariant moment and LVQ NN (IM-LVQ). The ability of the proposed method would be compared with two other methods. Firstly, the combination method of invariant moment and Euclidean distance (IM-ED). Secondly, the combination of invariant moment and principal component analysis (IM-PCA). The performance of the three methods was evaluated quantitatively with several metrics, viz.: Confusion Matrix, Accuracy, Precision, True Positive Rate, False Positive Rate, ROC graph and training time. The evaluation of the metrics was based upon the changing (reduction) of the scale/size of training image. The results showed that IM-LVQ method outperformed the other two methods in aforementioned metrics.

This study aimed to examine the role of institutional quality on environment and energy consumption for 66 developing countries by using data from 1991 to 2017. Different environmental indicators such as CO 2 emissions, CH 4 emissions, forest area, organic water pollutants, and energy consumption. The paper constructs institutional quality index by covering three main aspects: political stability, administrative capacity, and democratic accountability. System generalized method of moments results reveal that institutional quality has a positive impact on most of the environmental indicators such as CO 2 emissions, CH 4 emissions, and forest area. Institutional quality was having a positive impact on energy consumption based on oil and fossil fuel resources. Furthermore, it results in a signal that economic globalization has not increased environmental quality over time in developing countries.

The study examines the moderating role of institutional quality and technological innovation on the empirical relationship between FDI inflows and four indicator variables of CO 2 emissions in 40 Asian countries for the period 1996–2016, by using generalized method of moment (GMM) estimation. First, from non-interactive regression, FDI inflows have positive impacts on CO 2 emissions; over all, from our empirical results, we conclude that the moderating role of institutional quality and technological innovation is crucial in the nexus between FDI and carbon CO 2 emissions and the interaction between institutional quality indicators and FDI inflows significantly reduce the level of CO 2  emissions. Furthermore, the significant moderating effect of technological innovation is observed on the association between FDI and CO 2 emissions. The results are important for policy makers in setting up long- and short-term policy to protect environmental quality.

Parameter estimation is a critical problem in the wide applications of uncertain differential equations. The method of moments is employed for the first time as an approach for estimating the parameters in uncertain differential equations. Based on the difference form of an uncertain differential equation, a function of the parameters is proved to follow a standard normal uncertainty distribution. Setting the empirical moments of the functions of the parameters and the observed data equal to the moments of the standard normal uncertainty distribution, a system of equations about the parameters is obtained whose solutions are the estimates of the parameters. Analytic examples and numerical examples are given to illustrate the proposed method of moments.

The Belt and Road Initiative (BRI) is closely linked to the ecological sustainability of the infrastructure ventures that intrinsically include the aspects of climate change and pollution. Though there exists literature on the environmental Kuznets curve (EKC) and pollution haven hypothesis (PHH), very few explore the scope in the light of Belt and Road host countries (B&RCs). Therefore, the study examines the income-induced EKC and Chinese outward foreign direct investment (FDI)-based PHH in the multivariate framework of people’s connectivity and technology innovation in B&RCs from 2003 to 2018. The outcome of the study reveals that the observed relationship is quantile-dependent, which may disclose misleading results in previous studies using traditional methodologies that address the averages. Utilizing the novel “Method of Moments Quantile Regression (MMQR)” of Machado and Silva (J Econom 213:145–173, 2019), the findings confirm an inverted U-shape association between economic growth and CO 2 emissions only at lower to medium emission countries, thus validating the EKC hypothesis. The Chinese outward FDI flows increase carbon emissions at medium to high emission countries, thereby confirming PHH. The findings also indicate that people’s connectivity contributes to increasing emissions while innovation mitigates carbon emissions at lower to medium polluted countries. Moreover, the outcomes of Granger causality confirm one-way causality between economic growth and CO 2 emissions, between FDI and CO 2 emissions, between people’s connectivity and CO 2 emissions, and between innovation and CO 2 emissions. The results offer valuable insight for legislators to counteract CO 2 emissions in B&RCs through innovation-led energy conservation in infrastructure projects while adopting green and sustainable financing mechanisms to materialize mega construction projects under the BRI.

Energy innovations are critical to combating global warming and climate change. In this context, we focus on the impact of energy research–development (R&D) expenditures, which are the input of energy innovations, on CO 2 emissions. For this purpose, we investigate the effect of disaggregated energy R&D expenditures on CO 2 emission in 19 high-income OECD countries over the period 2003–2015. The dynamic panel data method is followed for empirical analysis. The results of the study show that R&D expenditures for energy efficiency and fossil energy have an increasing effect on CO 2 emissions. Contrary to expectations, there is no significant relationship between renewable energy R&D expenditures and CO 2 emissions. Remarkably, there is strong evidence that the power and storage R&D expenditures have a reducing effect on CO 2 emissions. In light of the empirical findings, policy implications and recommendations to potential readers and authorities are further discussed.

We employ the new Method of Moments Quantile Regression approach to expose the role of natural resources, renewable energy, and globalization in testing Environment Kuznets Curve (EKC) in MINT panel covering the years 1995–2018. The outcome validates the EKC curve between economic progress and carbon emissions from the third quantile to the extreme highest quantile. The result also shows that natural resources increase CO 2 emissions at the lowest quantile and then turn insignificant from the middle to the highest quantiles due to the potential utilization of resources in a sustainable manner. The renewable energy mitigates CO 2 emissions at the lower half quantiles. Still, for upper quantiles, the results are unexpected and imply that the countries’ total energy mix depends heavily on fossil fuels. As far as globalization is concerned, the significant results from medium to upper quantiles reveal that as globalization heightens due to foreign direct investment or trade, energy consumption also expands, leading to the worst environment quality. Thus, the present study’s consequences deliver guidelines for policymakers to utilize natural resources sustainably and opt technologies based on clean energy, which may offset environmental degeneration.