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)

Influence of different proteolytic strains of Streptococcus thermophilus in co-culture with Lactobacillus delbrueckii subsp. bulgaricus on the metabolite profile of set-yoghurt

Proto-cooperation between Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus is one of the key factors that determine the fermentation process and final quality of yoghurt. In this study, the interaction between different proteolytic strains of S. thermophilus and L. delbrueckii subsp. bulgaricuswas investigated in terms of microbial growth, acidification and changes in the biochemical composition of milk during set-yoghurt fermentation. A complementary metabolomics approach was applied for global characterization of volatile and non-volatile polar metabolite profiles of yoghurt associated with proteolytic activity of the individual strains in the starter cultures. The results demonstrated that only non-proteolytic S. thermophilus (Prt-) strain performed proto-cooperation with L. delbrueckii subsp. bulgaricus. The proto-cooperation resulted in significant higher populations of the two species, faster milk acidification, significant abundance of aroma volatiles and non-volatile metabolites desirable for a good organoleptic quality of yoghurt. Headspace SPME-GC/MS and 1H NMR resulted in the identification of 35 volatiles and 43 non-volatile polar metabolites, respectively. Furthermore, multivariate statistical analysis allows discriminating set-yoghurts fermented by different types of starter cultures according to their metabolite profiles. Our finding underlines that selection of suitable strain combinations in yoghurt starters is important for achieving the best technological performance regarding the quality of product

Influence of different proteolytic strains of Streptococcus thermophilus in co-culture with Lactobacillus delbrueckii subsp. bulgaricus on the metabolite profile of set-yoghurt

Proto-cooperation between Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus is one of the key factors that determine the fermentation process and final quality of yoghurt. In this study, the interaction between different proteolytic strains of S. thermophilus and L. delbrueckii subsp. bulgaricuswas investigated in terms of microbial growth, acidification and changes in the biochemical composition of milk during set-yoghurt fermentation. A complementary metabolomics approach was applied for global characterization of volatile and non-volatile polar metabolite profiles of yoghurt associated with proteolytic activity of the individual strains in the starter cultures. The results demonstrated that only non-proteolytic S. thermophilus (Prt-) strain performed proto-cooperation with L. delbrueckii subsp. bulgaricus. The proto-cooperation resulted in significant higher populations of the two species, faster milk acidification, significant abundance of aroma volatiles and non-volatile metabolites desirable for a good organoleptic quality of yoghurt. Headspace SPME-GC/MS and 1H NMR resulted in the identification of 35 volatiles and 43 non-volatile polar metabolites, respectively. Furthermore, multivariate statistical analysis allows discriminating set-yoghurts fermented by different types of starter cultures according to their metabolite profiles. Our finding underlines that selection of suitable strain combinations in yoghurt starters is important for achieving the best technological performance regarding the quality of product