Effects of investment on energy intensity:evidence from China

This article explores the effects of investment upon energy intensity by applying a unique panel data of China's 27 provinces between 2004 and 2013.In addition,it also particularly stuthes other factors,such as energy price,economic structure,and urbanization.The results,based on four econometric regression model results,suggest that in general,the indigenous investment on research and development is a more powerful tool to decrease China's energy intensity regardless of region disparity.The foreign direct investment(FDI) has a prominent but not persistent effect on energy intensity.However,the outward direct investment has not shown its significant impact on energy intensity.At the level of an aggregate economy and China's eastern region,the results demonstrate that FDI improves energy efficiency significantly.For the central and western provinces,FDI does not support the similar conclusion.Based on these analyses,we present the corresponding regional policies for policymakers.

Prediction of primary energy demand in China based on AGAEDE optimal model

In this article,we present an application of Adaptive Genetic Algorithm Energy Demand Estimation(AGAEDE) optimal model to improve the efficiency of energy demand prediction.The coefficients of the two forms of the model(both linear and quadratic) are optimized by AGA using factors,such as GDP,population,urbanization rate,and R&D inputs together with energy consumption structure,that affect demand.Since the spurious regression phenomenon occurs for a wide range of time series analysis in econometrics,we also discuss this problem for the current artificial intelligence model.The simulation results show that the proposed model is more accurate and reliable compared with other existing methods and the China's energy demand will be 5.23 billion TCE in 2020 according to the average results of the AGAEDE optimal model.Further discussion illustrates that there will be great pressure for China to fulfill the planned goal of controlling energy demand set in the National Energy Demand Project(2014—2020).

A review of optimization modeling and solution methods in renewable energy systems

The advancement of renewable energy(RE)represents a pivotal strategy in mitigating climate change and advancing energy transition efforts.A current of research pertains to strategies for fostering RE growth.Among the frequently proposed approaches,employing optimization models to facilitate decision-making stands out prominently.Drawing from an extensive dataset comprising 32806 literature entries encompassing the optimization of renewable energy systems(RES)from 1990 to 2023 within the Web of Science database,this study reviews the decision-making optimization problems,models,and solution methods thereof throughout the renewable energy development and utilization chain(REDUC)process.This review also endeavors to structure and assess the contextual landscape of RES optimization modeling research.As evidenced by the literature review,optimization modeling effectively resolves decisionmaking predicaments spanning RE investment,construction,operation and maintenance,and scheduling.Predominantly,a hybrid model that combines prediction,optimization,simulation,and assessment methodologies emerges as the favored approach for optimizing RES-related decisions.The primary framework prevalent in extant research solutions entails the dissection and linearization of established models,in combination with hybrid analytical strategies and artificial intelligence algorithms.Noteworthy advancements within modeling encompass domains such as uncertainty,multienergy carrier considerations,and the refinement of spatiotemporal resolution.In the realm of algorithmic solutions for RES optimization models,a pronounced focus is anticipated on the convergence of analytical techniques with artificial intelligence-driven optimization.Furthermore,this study serves to facilitate a comprehensive understanding of research trajectories and existing gaps,expediting the identification of pertinent optimization models conducive to enhancing the efficiency of REDUC development endeavors.

Feasibility analysis of GNSS-based ionospheric observation on a fast-moving train platform(GIFT)

The ionospheric effect plays a crucial role in the radio communications. For ionospheric observing and monitoring, the Global Navigation Satellite System (GNSS) has been widely utilized. The ionospheric condition can be characterized by the Total Electron Contents (TEC) and TEC Rate (TECR) calculated from the GNSS measurements. Currently, GNSS-based ionospheric observing and monitoring largely depend on a global fiducial network of GNSS receivers such as the International GNSS Service (IGS) network. We propose a new approach to observe the ionosphere by deploying a GNSS receiver on a Hong Kong Mass Transit Railway (MTR) train. We assessed the TECR derived from the MTR-based GNSS receiver by comparing it with the TECR derived from a static GNSS receiver. The results show that the Root-Mean-Squares (RMS) errors of the TECR derived from the MTR-based GNSS receiver is consistently approxi-mately 23% higher than that derived from the static GNSS receiver. Despite the increased error, the findings suggest that the GNSS observation on a fast-moving platform is a feasible approach to observe the ionosphere over a large region in a rapid and cost-effective way.