Evaluating Corporate Environmental Performance in the Context of Artificial Intelligence Implementation: The Contingent Roles of Ownership Type and External Monitoring

Business Strategy and the Environment, Год журнала: 2025, Номер unknown

Опубликована: Фев. 9, 2025

ABSTRACT Corporate environmental performance (CEP) has emerged as a matter of topmost importance within the business domain, organizations increasingly acknowledge exigency integrating sustainable practices into their core operational frameworks. Drawing from natural resource‐based view, institutional, stakeholder, and legitimacy theories, this study hypothesizes positive relationship for impact artificial intelligence (AI) on Chinese listed firms' CEP, moderated by roles state ownership (SOWN) external stakeholders, i.e., auditor (BIG4), financial analysts (FA), media coverage (MC). The analyzes comprehensive dataset 9033 firm‐year observations A‐share firms Shanghai Shenzhen stock exchanges over period 2010–2019. AI is measured through computer‐assisted textual analysis annual reports, while CEP quantified using responsibility scores an independent rating agency (HEXUN‐RKS). Employing fixed effects panel regression models, empirical findings reveal significant between CEP. results moderation demonstrate that BIG4 FA significantly strengthen AI–CEP relationship, but MC does not. Moreover, study's heterogeneity analyses indicate more pronounced in decision‐effective highly competitive industries. Importantly, main were validated instrumental variables, lag lead two‐step system generalized method moments, alternate proxy contributes new insights AI's impacts emerging economies like China undergoing digital transformation. It highlights how structure heightened oversight compel to leverage enhancing

Язык: Английский

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Multiview angle UAV infrared image simulation with segmented model and object detection for traffic surveillance

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 12, 2025

With the rapid development of infrared (IR) imaging UAV technology, aerial image processing technology has been applied in different fields. But it is not very convenient to obtain real images some cases because flight limitations, acquisition costs and other factors. So, necessary simulate by computer. This paper proposed an improved simulation method based on open source AirSim. By improving original AirSim method, quality via 3-dimensional segmented model processing. The traffic scene with viewing angles are simulated this we constructed dataset (IR-TSS) containing seven types objects. We propose efficient EfficientNCSP-Net net for IR-TSS use popular methods comparative experiments. experimental results show that mAP50 greater than 96% object detection dataset, which better those existing methods. only contributes research simulations scenes, but also referential significance

Язык: Английский

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Integration of Artificial Intelligence with Environmental Biosensors

Environmental earth sciences, Год журнала: 2025, Номер unknown, С. 415 - 423

Опубликована: Янв. 1, 2025

Язык: Английский

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Content analysis of multi-annual time series of flood-related Twitter (X) data

Natural hazards and earth system sciences, Год журнала: 2025, Номер 25(2), С. 879 - 891

Опубликована: Фев. 26, 2025

Abstract. Social media can provide insights into natural hazard events and people's emergency responses. In this study, we present a language processing analytic framework to extract categorize information from 43 287 textual Twitter (X) posts in German since 2014. We implement bidirectional encoder representations transformers combination with unsupervised clustering techniques (BERTopic) automatically social content, addressing transferability issues that arise commonly used bag-of-words representations. analyze the temporal evolution of topic patterns, reflecting behaviors perceptions citizens before, during, after flood events. Topics related low-impact riverine flooding contain descriptive hazard-related while focus shifts catastrophic impacts responsibilities during high-impact Our analytical enables analysis dynamics citizens’ perceptions, which facilitate lessons-learned analyses improve risk communication management.

Язык: Английский

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Integrating AI, machine learning, and nanotechnology: shaping the future of water quality management

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 111 - 134

Опубликована: Янв. 1, 2025

Язык: Английский

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