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The provision of high-quality e-Government services requires efficient and collaborative sharing of data across varied types of government agencies. However, interagency government data sharing (IDS) is not always spontaneous, active and unconditional. Adopting a stickiness theory, this paper reports on a research study, which explores the causes of data stickiness in IDS.

This study employed an inductive case study approach. Twenty-three officials from the government of City M in Hubei Province, Central China, were approached and interviewed using a semi-structured question script.

The analysis of the interview data pointed to 27 causes of data stickiness in five main themes: data sharing willingness; data sharing ability; data articulatability; data residence; and data absorptive capacity. The analysis revealed that interagency tensions and lack of preparedness of individual agencies are the main causes of data stickiness in IDS.

The case setting is based on China's Government, but the findings offer useful insights and indications that can be shared across international borders.

Centrifugal model tests can accelerate the characterization of landslides and demonstrate the form of slope failure, which is an important measure to research its instability mechanisms. Simply observing the slope landslide before and after a centrifugal model test cannot reveal the processes involved in real-time deformation. Electromagnetic sensors have severed as an existing method for real-time measurement, however, this approach has significant challenges, including poor signal quality, interference, and complex implementation and wiring schemes. This paper aims to overcome the shortcomings of the existing measurement methods.

This work uses the advantages of fiber Bragg grating (FBG) sensors with their small form-factor and potential for series multiplexing in a single fiber to demonstrate a monitoring strategy for model centrifugal tests. A slope surface deformation displacement sensor, FBG anchor sensor and FBG anti-slide piling sensor have been designed. These sensors are installed in the slope models, while centrifugal acceleration tests under 100 g are carried out.

FBG sensors obtain three types of deformation information, demonstrating the feasibility and validity of this measurement strategy.

The experimental results provide important details about instability mechanisms of a slope, which has great significance in research on slope model monitoring techniques and slope stability.

This study aims to investigate the relationship between climate policy uncertainty (CPU) and corporate environmental, social and governance (ESG) performance. We attempt to uncover the underlying rationale of how CPU influences corporate ESG performance and provides empirical evidence for companies’ strategic enhancement of ESG performance with risk reduction objectives.

We conduct a regression analysis using panel data from 4,490 Chinese listed companies spanning the period from 2011 to 2022. In addition, we use propensity score matching analysis (PSM), two-stage least squares (2SLS), system generalized method of moments (sys-GMM) and difference-in-differences (DID) methods to analyze the enterprise systematic risk.

The empirical findings reveal a positive correlation between CPU and corporate ESG performance, with a stronger effect observed in non-state-owned enterprises, heavy-polluting industries and those facing fierce market competition and strict environmental regulation. Mechanism analysis suggests that as CPU increases, companies with higher systemic risk tend to improve ESG performance more significantly, highlighting risk mitigation as a primary motive. Robustness tests further validate the consistency of our conclusions. Additionally, we find that enhancing ESG performance helps mitigate the risks and improve total factor productivity arising from the increased CPU.

This study examines the impact of CPU on the ESG performance of Chinese listed companies and its underlying logic. The conclusions of this paper provide important policy references for coordinated development and security, as well as for effectively mitigating the adverse impact of CPU. We hope to offer insights for companies to identify potential risk factors, thereby enhancing their level of sustainable development and sense of environmental responsibility.