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Alignment of words and actions? Government environmental attention and enterprise digital transformation

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2025年 210卷

作者： Shi, Peihao Huang, Qinghua Wuhan Univ Sch Econ & Management Wuhan 430072 Hubei Peoples R China Southwest Univ Sch Econ & Management Chongqing 400715 Peoples R China

This study employs a unique dataset of A-share listed companies from 2007 to 2021 to investigate the impact of government environmental attention on enterprise digital transformation (EDT) and its underlying mechanisms. Empirical findings document that in response to upholding organizational legitimacy, government environmental attention remarkably favors EDT. This core conclusion remains robust after addressing endogeneity concerns and alternative robustness tests. Mechanism analysis unveils that intensified government environmental attention propels companies to expedite green technology innovation, alleviate financing constraints, and enhance human capital quality, all accelerating EDT. Subsequent investigations indicate that heightened government environmental attention notably impacts EDT more for larger plants and highly polluting industries. In regions with lower financial development, government environmental attention serves as a reliable signaling mechanism, motivating EDT. These findings guide plants in accelerating EDT for enhanced sustainable development while shedding light on the evolving mechanisms of government attention allocation and EDT, which offers insights for future research on the correlation between government actions and corporate environmental governance.

关键词： Environmental attention Digital economy generalized additive model Organizational legitimacy

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Classification using global and local Mahalanobis distances

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JOURNAL OF MULTIVARIATE ANALYSIS 2025年 207卷

作者： Ghosh, Annesha Ghosh, Anil K. Saharay, Rita Sarkar, Soham Indian Stat Inst Theoret Stat & Math Unit 203 BT Rd Kolkata 700108 India Univ Calif Santa Barbara Dept Stat & Appl Probabil Santa Barbara CA 93106 USA Indian Stat Inst Interdisciplinary Stat Res Unit 203 BT Rd Kolkata 700108 India

We propose a novel semiparametric classifier based on Mahalanobis distances of an observation from the competing classes. Our tool is a generalized additive model with the logistic link function that uses these distances as features to estimate the posterior probabilities of different classes. While popular parametric classifiers like linear and quadratic discriminant analyses are mainly motivated by the normality of the underlying distributions, the proposed classifier is more flexible and free from such parametric modeling assumptions. Since the densities of elliptic distributions are functions of Mahalanobis distances, this classifier works well when the competing classes are (nearly) elliptic. In such cases, it often outperforms popular nonparametric classifiers, especially when the sample size is small compared to the dimension of the data. To cope with non-elliptic and possibly multimodal distributions, we propose a local version of the Mahalanobis distance. Subsequently, we propose another classifier based on a generalized additive model that uses the local Mahalanobis distances as features. This nonparametric classifier usually performs like the Mahalanobis distance based semiparametric classifier when the underlying distributions are elliptic, but outperforms it for several non- elliptic and multimodal distributions. We also investigate the behavior of these two classifiers in high dimension, low sample size situations. A thorough numerical study involving several simulated and real datasets demonstrate the usefulness of the proposed classifiers in comparison to many state-of-the-art methods.

关键词： generalized additive model Bootstrap HDLSS asymptotics Nonparametric classifier Semiparametric classifier

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Assessing the influencing factors of dengue fever in Chinese mainland based on causal analysis

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SCIENTIFIC REPORTS 2025年第1期15卷 1-14页

作者： Yu, Xingyuan Wang, Xia Tang, Sanyi Shaanxi Normal Univ Sch Math & Stat Xian 710119 Peoples R China Shanxi Univ Sch Math Sci Taiyuan 030006 Peoples R China

Previous studies have identified various factors affecting dengue fever, but most focus on correlations within specific regions, not establishing causality. This study uses Convergent Cross Mapping (CCM) to explore the causal relationships between nine meteorological factors and reported dengue fever cases in 14 Chinese provinces with the highest incidence. Results show that temperature and pressure have causal links with case numbers in more provinces. In Guangdong, which has the most reported cases, Partial Cross Mapping (PCM) reveals a direct causal relationship only between GDP and reported dengue fever cases, while meteorological factors influence dengue fever via their impact on mosquito populations. Principal Component Analysis (PCA) from 30 provinces further confirms the importance of temperature and pressure. Given the significant negative correlation between temperature and pressure, separate models were developed for each province using the Distributed Lag Nonlinear model (DLNM) combined with the generalized additive model (GAM), with GDP as a covariate. The results indicate that the Relative Risk (RR) increases significantly under high temperatures and low pressure within a shorter lag period. GDP significantly promotes case numbers in all provinces.

关键词： Dengue Convergent cross mapping Partial cross mapping generalized additive model Lag Distributed lag nonlinear model

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generalized additive models for nowcasting cloud shading

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SOLAR ENERGY 2014年 101卷 272-282页

作者： Brabec, Marek Paulescu, Marius Badescu, Viorel Acad Sci Czech Republ Inst Comp Sci Dept Nonlinear Modeling Prague 18207 8 Czech Republic West Univ Timisoara Dept Phys Timisoara 300223 Romania Univ Politehn Bucuresti Candida Oancea Inst Bucharest 060042 Romania Romanian Acad Bucharest Romania

The response time of a photovoltaic plant is very short and its output power follows the abrupt change in solar irradiance level due to alternate shadow by clouds. Presence of shading may be quantified by a binary variable: sunshine number (SSN). Various logistic Markovian models of SSN dynamics are introduced and discussed in this paper, going from simple to more complicated model structures. Radiometric data measured at 15 s lag during 2010 in Timisoara (Romania) are used to illustrate their real-world performance. The models are useful for short term forecasting related e.g. to photovoltaic conversion control, including more complicated events like ramp-like switches between overcast and clear regimes. Importantly, presented models are not black-box forecasting tools. They contain physically interpretable structure which is advantageous both in developing, checking and improvement of the model itself, but also as a tool for characterizing various systematic properties of cloud shading. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Sunshine number Nowcasting generalized additive model Markov chain

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generalized additive models with principal component analysis: an application to time series of respiratory disease and air pollution data

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2018年第2期67卷 453-480页

作者： de Souza, Juliana B. Reisen, Valderio A. Franco, Glaura C. Ispany, Marton Bondon, Pascal Santos, Jane Meri Univ Fed Espirito Santo Vitoria Spain Cent Supelec Gif Sur Yvette France Univ Fed Minas Gerais Belo Horizonte MG Brazil Univ Debrecen Debrecen Hungary Ctr Natl Rech Sci Gif Sur Yvette France Univ Paris Saclay Paris France

Environmental epidemiological studies of the health effects of air pollution frequently utilize the generalized additive model (GAM) as the standard statistical methodology, considering the ambient air pollutants as explanatory covariates. Although exposure to air pollutants is multi-dimensional, the majority of these studies consider only a single pollutant as a covariate in the GAM model. This model restriction may be because the pollutant variables do not only have serial dependence but also interdependence between themselves. In an attempt to convey a more realistic model, we propose here the hybrid generalized additive model-principal component analysis-vector auto-regressive (GAM-PCA-VAR) model, which is a combination of PCA and GAMs along with a VAR process. The PCA is used to eliminate the multicollinearity between the pollutants whereas the VAR model is used to handle the serial correlation of the data to produce white noise processes as covariates in the GAM. Some theoretical and simulation results of the methodology proposed are discussed, with special attention to the effect of time correlation of the covariates on the PCA and, consequently, on the estimates of the parameters in the GAM and on the relative risk, which is a commonly used statistical quantity to measure the effect of the covariates, especially the pollutants, on population health. As a main motivation to the methodology, a real data set is analysed with the aim of quantifying the association between respiratory disease and air pollution concentrations, especially particulate matter PM10, sulphur dioxide, nitrogen dioxide, carbon monoxide and ozone. The empirical results show that the GAM-PCA-VAR model can remove the auto-correlations from the principal components. In addition, this method produces estimates of the relative risk, for each pollutant, which are not affected by the serial correlation in the data. This, in general, leads to more pronounced values of the estimated risk compared

关键词： generalized additive model Multicollinearity Principal component analysis Relative risk Serial correlation Vector auto-regressive model

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Revealing the contribution of urban green spaces to improving the thermal environment under realistic stressors and their interactions

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SUSTAINABLE CITIES AND SOCIETY 2025年 126卷

作者： Song, Junze Przybysz, Arkadiusz Zhu, Chunyang Huazhong Agr Univ Coll Hort & Forest 1 Shizishan St Wuhan 430070 Hubei Peoples R China Warsaw Univ Life Sci SGGW Ctr Climate Res SGGW PL-02787 Warsaw Poland

Extreme temperatures in cities have become even more dramatic, making urban heat island (UHI) mitigation strategies difficult to implement. To understand the contribution of urban green spaces (UGS) to improving the thermal environment under realistic stressor interactions, six stressors-road density (RD), building density (BD), building height (BH), percentage of impervious surface (P_IS), percentage of green space (P_GS) and sky view factor (SVF)-were selected in this study to identify their impact on the cooling intensity of UGS (DLST) in summer using a generalized additive model (GAM) method. The results demonstrated that: (1) individual stressors RD, BD, BH, P_IS, and P_GS had a significant impact on DLST and showed threshold effect on DLST at grid scale;(2) stressor interactions BD x SVF, BD x P_IS, BD x BH and SVF x P_IS had significant impacts on DLST;and (3) stressor interactions contributed 54.07 % to DLST in GAM model, the individual stressors contributed 39.81 % to DLST, while in between BD x BH showed the dominant interactive impact on the cooling effect of UGS. These findings will assist with regulating urban stressors to enhance the cooling effects of UGS, thereby improving the urban thermal environment.

关键词： Urban green space Multiple urban stressors Urban thermal environment Stressors interactions generalized additive model

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The Association of Age and Liver Transplant Outcomes in Patients With Acute-On-Chronic Liver Failure

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JOURNAL OF SURGICAL RESEARCH 2025年 309卷 199-211页

作者： Zhou, Jie Zhang, Siyao Liu, Jingxia Ding, Jiawei Ren, Shenli Zhang, Tao Ye, Danni Xu, Fangshen Chen, Zheng Zheng, Huilin Zhang, Lei Zhang, Yu Hu, Zhenhua Zhejiang Univ Sch Med Affiliated Hosp 1 Div Hepatobiliary & Pancreat SurgDept Surg Hangzhou Zhejiang Peoples R China Zhejiang Univ Affiliated Hosp 4 Sch Med Div Hepatobiliary & Pancreat SurgDept Surg Yiwu Zhejiang Peoples R China Zhejiang Univ Sci & Technol Zhejiang Prov Collaborat Innovat Ctr Agr Biol Reso Sch Biol & Chem Engn Hangzhou Zhejiang Peoples R China

Introduction: To explore the role of age at listing on outcomes for patients with acute-on-chronic liver failure (ACLF) awaiting liver transplantation (LT). Materials and methods: We assessed adult candidates listed for LT in the Scientific Registry of Transplant Recipients database from January 1, 2007 to June 30, 2018. Patients were divided into four groups based on age at listing: I (<= 34), II (35-49), III (50-64), and IV (>= 65). The ACLF grade was estimated (est-ACLF), and intent-to-treat survival, overall survival (OS), as well as potential predictors for OS was evaluated. Results: Est-ACLF-3 was associated with a higher 30-d cumulative dropout rate and inferior intent-to-treat survival across age groups. No difference was observed in OS among ACLF grades in group I, but significantly inferior OS was observed in higher ACLF grades in groups II-IV. Multivariate analysis showed age (P < 0.001, ref. group I;hazard ratio 0.995 for group II, P = 0.936;1.196 for group III, P = 0.002;1.651 for group IV, P < 0.001) and est-ACLF grade (P < 0.001, ref. est-ACLF-0;1.214 for est-ACLF-1, P < 0.001;1.246 for est-ACLF-2, P < 0.001;1.578 for est-ACLF-3, P < 0.001) were independent predictors for OS. generalized additive model showed different association between age and OS among different ACLF grades. Conclusions: Young patients with high ACLF grades could achieve similar OS compared with those with low grades. Elderly patients with higher ACLF grades were associated with inferior outcomes after LT. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI

关键词： Acute-on-chronic liver failure Candidate age generalized additive model Intent-to-treat survival Liver transplantation Overall survival Waiting list

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generalized additive modelling of sample extremes

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2005年第1期54卷 207-222页

作者： Chavez-Demoulin, V Davison, AC Swiss Fed Inst Technol Sch Basic Sci Inst Math CH-1015 Lausanne Switzerland ETH Zurich Switzerland

We describe smooth non-stationary generalized additive modelling for sample extremes, in which spline smoothers are incorporated into models for exceedances over high thresholds. Fitting is by maximum penalized likelihood estimation, with uncertainty assessed by using differences of deviances and bootstrap simulation. The approach is illustrated by using data on extreme winter temperatures in the Swiss Alps, analysis of which shows strong influence of the north Atlantic oscillation. Benefits of the new approach are flexible and appropriate modelling of extremes, more realistic assessment of estimation uncertainty and the accommodation of complex dependence patterns.

关键词： bootstrap generalized additive model generalized pareto distribution natural cubic spline North Atlantic oscillation parameter orthogonality peaks over threshold penalized likelihood statistics of extremes temperature data

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Real Estate Insights: The impact of amenities on residential rents - exploring the urban rent premium in Berlin's vibrant community hubs

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JOURNAL OF PROPERTY INVESTMENT & FINANCE 2025年第1期43卷 111-115页

作者： Cajias, Marcelo Wins, Anett Restle, Rebecca PATRIZIASE Dept Investment Strategy & Res Pimasens Germany

PurposeIntegrating amenity scoring into real estate investment strategies represents an innovative approach. The case study focuses on the impact of amenities on residential rents, with Berlin serving as an example of a tight rental market and a dynamic city. The scoring and modeling approach offers a scalable framework for identifying high-potential investment ***/methodology/approachThe PATRIZIA Amenities Basic-Living-Need Scores (c) (BLNs) were developed to measure a location's attractiveness based on the availability and quality of amenities. An AI-driven scoring algorithm takes the quality and quantity of all available amenities into account. The higher the scores, the better and more diversified is a location supplied with amenities for residential living. A generalized additive model is fitted to assess the strength and direction of impact of the BLNs on residential rents while controlling for other factors such as property and locational *** results indicate that amenities are a fundamental determinant of rental values and tenant satisfaction, providing investors with actionable insights to maximize returns in competitive urban markets. Focusing on areas with high BLN scores can lead to better investment outcomes due to higher rental premiums associated with these locations. The study also highlights the need to run a detailed location analysis to avoid the pitfall of amenities *** implicationsThe results indicate that amenities are a fundamental determinant of rental values and tenant satisfaction, providing investors with actionable insights to maximize returns in competitive urban markets. By identifying and accounting for regional peculiarities, investors can make data-driven decisions that align with local tenant preferences and market ***/valueThe PATRIZIA Amenities Basic-Living-Need Scores (c) are calculated via an AI-driven, scalable algorithm and were developed to measure a

关键词： Berlin Residential real estate Amenities pricing Basic-living-needs generalized additive model Urban rent premium

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generalized additive modeling of Building Inertia Thermal Energy Storage for Integration Into Smart Grid Control

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IEEE ACCESS 2021年 9卷 71699-71711页

作者： Voss, Marcus Heinekamp, Jan F. Krutzsch, Sabine Sick, Friedrich Albayrak, Sahin Strunz, Kai Tech Univ Berlin Distributed Artificial Intelligence Lab DAI Lab D-10587 Berlin Germany Tech Univ Berlin Chair Sustainable Elect Networks & Sources Energy D-10587 Berlin Germany Hsch Tech & Wirtschaft HTW Berlin Sch Engn Energy & Informat D-12459 Berlin Germany

The structural mass of a building provides inherent thermal storage capability. Through sector coupling, the building mass can provide additional flexibility to the electric power system, using, for instance, combined heat and power plants or power-to-heat. In this work, a mathematical model of building inertia thermal energy storage (BITES) for integration into optimized smart grid control is introduced. It is shown how necessary model parameters can be obtained using generalized additive modeling (GAM) based on measurable building data. For this purpose, it is demonstrated that the ceiling surface temperature can serve as a proxy for the current state of energy. This allows for real-world implementation using only temperature sensors as additionally required hardware. Compared with linear modeling, GAM enable improved modeling of the nonlinear characteristics and interactions of external factors influencing the storage operation. Two case studies demonstrate the potential of using building storage as part of a virtual power plant (VPP) for optimized smart grid control. In the first case study, BITES is compared with conventionally used hot water tanks, revealing economic benefits for both the VPP and building operator. The second case study investigates the potential for savings in CO2 emission and grid connection capacity. It shows similar benefits when using BITES compared to using battery storage, without the need for hardware investment. Given the ubiquity of buildings and the recent advances in building control systems, BITES offers great potential as an additional source of flexibility to the low-carbon energy systems of the future.

关键词： Buildings Thermal energy Cogeneration Mathematical model Power generation additives Smart grids Building inertia thermal energy storage energy management generalized additive model mixed-integer linear programming sector coupling smart grid virtual power plant

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