Enterprise mergers and acquisitions(M&A)are vital strategies for companies worldwide to expand markets,enhance competitiveness,and achieve strategic goals.The Chinese government’s strong support for the new energ...Enterprise mergers and acquisitions(M&A)are vital strategies for companies worldwide to expand markets,enhance competitiveness,and achieve strategic goals.The Chinese government’s strong support for the new energy industry has created a favorable policy environment for the development of the lithium battery sector.Against this backdrop,Tianqi Lithium Corp’s acquisition of Sociedad Química y Minera(SQM)in Chile has garnered widespread attention.This paper takes Tianqi Lithium Corp’s acquisition of SQM as the research subject,conducting a detailed analysis of the motives behind the M&A.Subsequently,financial indicators are employed to conduct a performance analysis from a financial perspective,examining the impact of the M&A.Finally,based on the findings of the case analysis,relevant suggestions are proposed to offer a reference for the development of enterprise mergers and acquisitions.展开更多
In the late 1980s,the prevailing corporate model usually depicted companies as economic entities pursuing shareholders’profit maximisation interests without a thought for the consequences of this behaviour on the loc...In the late 1980s,the prevailing corporate model usually depicted companies as economic entities pursuing shareholders’profit maximisation interests without a thought for the consequences of this behaviour on the local community and environment.However,over the last decades,corporate scandals challenged that corporate model’s validity and paved the way for a sustainable corporate model.The latter emphasises a triple bottom line approach that incorporates social,economic,and environmental objectives.By implementing a sustainable corporate model,companies achieve both economic and social goals in a balanced approach.This research investigates the B Corporation(B Corp)certification system,which helps companies implement a sustainable corporate model voluntarily.B Corp certification is a badge signal that companies’business model adheres to ethical standards and meets socially conscious stakeholders’expectations.Our research aims to provide a deep contextual understanding of the determinants and implications of the B Corp certification’s adoption.We adopt a semantic approach to review and systematise management and accounting literature on Certified B Corporations(B Corps)through institutional theory’s lenses,which help us explain why firms decide voluntarily to become B Corps.展开更多
Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantil...Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.展开更多
文摘Enterprise mergers and acquisitions(M&A)are vital strategies for companies worldwide to expand markets,enhance competitiveness,and achieve strategic goals.The Chinese government’s strong support for the new energy industry has created a favorable policy environment for the development of the lithium battery sector.Against this backdrop,Tianqi Lithium Corp’s acquisition of Sociedad Química y Minera(SQM)in Chile has garnered widespread attention.This paper takes Tianqi Lithium Corp’s acquisition of SQM as the research subject,conducting a detailed analysis of the motives behind the M&A.Subsequently,financial indicators are employed to conduct a performance analysis from a financial perspective,examining the impact of the M&A.Finally,based on the findings of the case analysis,relevant suggestions are proposed to offer a reference for the development of enterprise mergers and acquisitions.
文摘In the late 1980s,the prevailing corporate model usually depicted companies as economic entities pursuing shareholders’profit maximisation interests without a thought for the consequences of this behaviour on the local community and environment.However,over the last decades,corporate scandals challenged that corporate model’s validity and paved the way for a sustainable corporate model.The latter emphasises a triple bottom line approach that incorporates social,economic,and environmental objectives.By implementing a sustainable corporate model,companies achieve both economic and social goals in a balanced approach.This research investigates the B Corporation(B Corp)certification system,which helps companies implement a sustainable corporate model voluntarily.B Corp certification is a badge signal that companies’business model adheres to ethical standards and meets socially conscious stakeholders’expectations.Our research aims to provide a deep contextual understanding of the determinants and implications of the B Corp certification’s adoption.We adopt a semantic approach to review and systematise management and accounting literature on Certified B Corporations(B Corps)through institutional theory’s lenses,which help us explain why firms decide voluntarily to become B Corps.
基金supported by the National Natural Science Foundation of China (Project No.42375192)the China Meteorological Administration Climate Change Special Program (CMA-CCSP+1 种基金Project No.QBZ202315)support by the Vector Stiftung through the Young Investigator Group"Artificial Intelligence for Probabilistic Weather Forecasting."
文摘Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.
