Demographic efficiency drivers in the Chinese energy production chain: A hybrid neural multi-activity network data envelopment analysis

YesFor meeting the external requirements of the Paris Agreement and reducing energy consumption per gross domestic product, China needs to improve its energy efficiency. Although the existing studies have attempted to investigate energy efficiency from different perspectives, little effort has yet been made to consider the collaboration among different stages in the production chain to produce energy outputs. In addition, various studies have also examined the determinants of energy efficiency, however, they mainly focused on technology and economic factors, no study has yet proposed and considered the influence of geographical factors on energy efficiency. In this article, we fill in the gap and make theoretical and empirical contributions to the literature. In this study, a two-stage analysis method is used to analyse energy efficiency and the influencing factors in China between 2009 and 2021. More specifically, from the theoretical/methodological perspective, a multi-activity network data envelopment analysis model is used to measure energy efficiency of different processes in the energy production chain. From the empirical perspective, we attempt to investigate the influence of geographical factors on energy efficiency through a neural network analysis. Meanwhile, the comparisons among different provinces are made. The result shows that the overall energy efficiency is low in China, and China relies more on the traditional energy industry than the clean energy industry. The efficiency level experiences a level of volatility over the examined period. Finally, we find that raw fuel pre-process and industry have a significant and positive impact on energy efficiency in China

TEA-IS: A hybrid DEA-TOPSIS approach for assessing performance and synergy in Chinese health care

YesThis paper presents an assessment of the Chinese healthcare system in 31 provinces for a 10-year period in light of relevant physical and human resource variables. First, a novel TEA-IS (Trigonometric Envelopment Analysis for Ideal Solutions) model is developed to assess healthcare efficiency at the province level. Machine learning methods are also employed to predict high-low performance and the synergistic Chinese healthcare province in terms of contextual variables. The results indicate that synergy has played a pivotal role in the Chinese healthcare systems, not only by triggering higher performance levels due to the progressive adoption of best practices over the course of time, but also by being closely related to different socioeconomic and demographic variables, such as the illiteracy rate. It is possible to claim that healthcare performance has remained stable in China over the past two decades, performance and synergy at the province level are still heterogeneous

A new perspective on the U.S. energy efficiency: The political context

YesThis paper offers a new perspective on the energy efficiency literature by bringing evidence of political contextual factors as the predictors of energy efficiency. Specifically, we posit that the Democrat administration is more energy-efficient considering the reduction of environmental impact, in contrast, the Republican administration is more efficient considering only financial expenditures leading to the production of economic growth. In addition, we predict that political administration tenure is negatively correlated with green energy efficiency and that political distancing moderates the relationship between political party administration and energy efficiency. This study sheds light on these matters by performing an efficiency analysis of fifty North American states through a bootstrap DEA non-parametric model, followed by Tobit regressions to evaluate our hypotheses concerning the effect of the contextual factors on the calculated efficiency scores.This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The proposal and application of a 2-Dimensional Fuzzy Monte Carlo Frontier analysis for estimating Islamic bank efficiency

YesThe current study proposes a novel 2-Dimensional Fuzzy Monte-Carlo Frontier Analysis to estimate and compare the level of efficiency for a sample of 49 Islamic Banks across 25 countries worldwide over the period 2013-2021. Additionally, in the second stage, we propose a bootstrapped robust regression approach to comprehensively examine the determinants of efficiency. Our results show that there is heterogeneity in the level of efficiency within the Islamic banking sector. Furthermore, we find that the Islamic banks in the sample experienced an improvement in efficiency over the examined period. Finally, we find that bank size, bank liquidity (measured by the ratio between net loans and gross loans), and bank risk (proxied by the ratio between loan loss reserves and gross loans) have a significant and positive impact on Islamic bank efficiency. Policy implications based on our findings are provided.The full-text of this article will be released for public view at the end of the publisher embargo - 12 months after publication

An Original Information Entropy-Based Quantitative Evaluation Model for Low-Carbon Operations in an Emerging Market

Drawing on the mixed results provided by the existing literature on low-carbon operations management practices, this paper proposes an original evaluation model for CO2 emission reduction practices in Brazil, based on the concept of information entropy. We model the information entropy of different low-carbon operations management practices, such as logistics, manufacturing processes and new product development. Then, in light of the role of stakeholder pressures, motivations and barriers, we take a novel approach to assessing the relative importance of elements of the model by using information entropy to develop probabilistically distinctive weightings for low-carbon managerial practices, computed using a variety of models. These models include (a) the Fuzzy Rasch model, which combines Item Response Theory (IRT) and fuzzy set theory; (b) the Fuzzy AHP (Analytic Hierarch Process) model; and (c) the crisp AHP model, based on eight different judgment scales concerning the relative evolution of each criterion/construct. Our results, both expected and unexpected, suggest that: (i) there is heterogeneity in the ways that different companies perceive the issue of low-carbon practices; (ii) while the firms studied are motivated to reduce CO2 emissions and such reduction is required by various stakeholders, the reduction is implemented solely through low-carbon logistics. Unexpectedly, we find that companies are not adopting a full-range of low-carbon operations practices, which may damage their overall performance. Implications for end-users and policy makers are highlighted. \textcopyright 2021 Elsevier B.V

Differences in outsourcing strategies between firms in emerging and in developed markets

Contains fulltext : 112037.pdf (publisher's version ) (Closed access)26 p

Electroweak measurements in electron–positron collisions at w-boson-pair energies at lep

Contains fulltext : 121524.pdf (preprint version ) (Open Access