Dose institutional quality influences the relationship between urbanization and CO2 emissions?

As a result of rapid economic expansion, increased energy use, and urbanization, global warming and climate change have become serious challenges in recent decades. Institutional quality can be the remedy to impede the harmful effect of factors on environmental quality. This study investigates the impact that urbanization and institutional quality on environmental quality in in the Belt and Road Initiative (BRI) countries from 2002 to 2019. By using two step generalized method of moment, the findings shows that urbanization leads to an increase in carbon dioxide emissions and a decline in environmental quality. On the other hand, the square term of urbanization indicates that an increase in urbanization leads to a reduction in emissions at a later stage after reach a certain level. Education, on the other hand, has the reverse impact of increasing carbon emissions; economic growth, foreign direct investment, and government effectiveness all boost carbon emissions. In a similar vein, the interaction between urbanization and the effectiveness of the government is unfavorable, underscoring the transformative role that the effectiveness of the government plays in leading to environmental sustainability. Finally, the findings of this study have considerable policy implication for the sample countries.

A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction.

Health assessment and remaining useful life prediction are usually seen as separate tasks in industrial systems. Some multitask models use common features to handle these tasks synchronously, but they lack the usage of the representation in different scales and time-frequency domain. A lack of balance also exists among these scales. Therefore, a gated multiscale multitask learning model known as GMM-Net is proposed in this paper. By using the time-frequency representation, GMM-Net can obtain features of different scales via different kernels and compose the features by a gating network. A detailed loss function whose weight can be searched in a smaller scale is designed. The model is tested with different weights in the total loss function, and an optimal weight is found. Using this optimal weight, it is observed that the proposed method converges to a smaller loss and has a smaller model size than long short-term memory (LSTM) and gated recurrent unit (GRU) with less training time. The experiment results demonstrate the effectiveness of the proposed method.

Interval State Estimation in Active Distribution Systems Considering Multiple Uncertainties.

Distribution system state estimation (DSSE) plays a significant role for the system operation management and control. Due to the multiple uncertainties caused by the non-Gaussian measurement noise, inaccurate line parameters, stochastic power outputs of distributed generations (DG), and plug-in electric vehicles (EV) in distribution systems, the existing interval state estimation (ISE) approaches for DSSE provide fairly conservative estimation results. In this paper, a new ISE model is proposed for distribution systems where the multiple uncertainties mentioned above are well considered and accurately established. Moreover, a modified Krawczyk-operator (MKO) in conjunction with interval constraint-propagation (ICP) algorithm is proposed to solve the ISE problem and efficiently provides better estimation results with less conservativeness. Simulation results carried out on the IEEE 33-bus, 69-bus, and 123-bus distribution systems show that the our proposed algorithm can provide tighter upper and lower bounds of state estimation results than the existing approaches such as the ICP, Krawczyk-Moore ICP(KM-ICP), Hansen, and MKO.

Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems.

In this paper, a distributed state estimation method based on moving horizon estimation (MHE) is proposed for the large-scale power system state estimation. The proposed method partitions the power systems into several local areas with non-overlapping states. Unlike the centralized approach where all measurements are sent to a processing center, the proposed method distributes the state estimation task to the local processing centers where local measurements are collected. Inspired by the partitioned moving horizon estimation (PMHE) algorithm, each local area solves a smaller optimization problem to estimate its own local states by using local measurements and estimated results from its neighboring areas. In contrast with PMHE, the error from the process model is ignored in our method. The proposed modified PMHE (mPMHE) approach can also take constraints on states into account during the optimization process such that the influence of the outliers can be further mitigated. Simulation results on the IEEE 14-bus and 118-bus systems verify that our method achieves comparable state estimation accuracy but with a significant reduction in the overall computation load.