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This paper investigates the impact of AI penetration rate on the degree of corporate greenwashing and aims to assess the potential of AI in enhancing firms' environmental performance and reducing false disclosures.

This study employs a year and firm fixed-effects model to analyze data from Chinese listed firms from 2012 to 2022. We use the low-carbon city pilot as a quasi-natural experiment to address endogeneity concerns and conduct a series of robustness tests, including adding control variables and transforming the model.

The results of this paper show that the application of AI can inhibit firms' greenwashing behavior, with green innovation activities further enhancing this inhibitory effect. In state-owned firms and those with Party Organizations, the inhibitory effect of AI on corporate greenwashing is more significant. This reduction in greenwashing is more likely to be observed in firms that are heavily influenced by Confucian culture, receive higher public attention regarding their environmental impact, face less market competition, suffer from more serious pollution and face less financial constraints.

We propose a new research perspective that offers novel insights into promoting the green development of firms by revealing the potential of AI in reducing their greenwashing behavior. Corporate boards can explore specific strategies for applying AI to monitor, prevent and correct greenwashing, thereby enhancing corporate environmental performance and social responsibility.

This study aims to investigate the micro mechanism of macro rheological characteristics for composite modified asphalt.Grey relational analysis (GRA) was used to analyze the correlation between macro rheological indexes and micro infrared spectroscopy indexes.

First, a dynamic shear rheometer and a bending beam rheometer were used to obtain the evaluation indexes of high- and low-temperature rheological characteristics for asphalt (virgin, SBS/styrene butadiene rubber [SBR], SBS/rubber and SBR/rubber) respectively, and its variation rules were analyzed. Subsequently, the infrared spectroscopy test was used to obtain the micro rheological characteristics of asphalt, which were qualitatively and quantitatively analyzed, and its variation rules were analyzed. Finally, with the help of GRA, the macro-micro evaluation indexes were correlated, and the improvement efficiency of composite modifiers on asphalt was explored from rheological characteristics.

It was found that the deformation resistance and aging resistance of SBS/rubber composite modified asphalt are relatively good, and the modification effect of composite modifier and virgin asphalt is realized through physical combination, and the rheological characteristics change with the accumulation of functional groups. The correlation between macro rutting factor and micro functional group index is high, and the relationship between macro Burgers model parameters and micro functional group index is also close.

Results reveal the basic principle of inherent-improved synergistic effect for composite modifiers on asphalt and provide a theoretical basis for improving the composite modified asphalt.

This research was carried out in response to the need to find new alternative ways of maintaining public lighting with high pressure sodium vapor lamps. The objective was to develop a public lighting maintenance management system based on fuzzy logic that guarantees maximum energy efficiency and is economically feasible.

A preliminary study was carried out on the complaints due to failures of public lighting for three years in the municipality of Camagüey, Cuba, determining the failure rate of each control and the time between failures, a statistical evaluation of the time between failures was carried out identifying that this variable responds to a Weibull distribution, the membership functions of the proposed four linguistic variables and the rule base for their fuzzy sets were created, obtaining as output linguistic variable the mass replacement and cleaning time.

The fuzzy logic maintenance model developed is effective in making better use of the useful life of high-pressure sodium vapor lamps, increasing the time between maintenance operations of mass lamp replacement and mass cleaning of luminaires up to 8 and 9 years, without compromising the required lighting levels and energy efficiency.

The literature contains very few references to the use of condition-based maintenance in this type of system, so a novel approach by a robust heuristic model of street lighting condition-based maintenance management driven by data is proposed, the model integrates through fuzzy logic all the factors that influence the progressive deterioration of these installations and maintenance actions that guarantee compliance with the established service quality standards, with the maximum energy efficiency that is economically justified.