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This study aims to examine the direct effects of Green Human Resource Management (GHRM) practices on employee green behavior (EGB) in the university setting in Bangladesh and to find the indirect effects of how GHRM promotes EGB through sequentially mediating employee environmental knowledge management (EEKM) (environmental knowledge and knowledge sharing) and green self-efficacy (GSE).

For the empirical study, the researcher uses partial least squares structural equation modeling to test the proposed conceptual model built on existing literature for greening workplaces in the university in Bangladesh. The study has collected data from 288 Bangladeshi university employees using convenient sampling.

The findings that GHRM practices positively and significantly promote EGB, which captures the employee's tendencies to exercise green behavior in daily routine activities such as turning off lights, air conditioning, computers and equipment after working hours, printing on both sides, recycling (reducing, repair, reuse), disseminating good green ideas, concepts, digital skills and knowledge to peers and champion green initiatives at work. Moreover, the findings also revealed the sequential mediation of EEKM (environmental knowledge and knowledge sharing) and GSE of employees between the link GHRM and EGB. At last, the findings suggested that HR managers can implement the GHRM practices to promote green behaviors among the academic and non-academic staff of the university.

This study contributes to the field by extending knowledge of Social Cognition Theory and Social Learning Theory for greening workplaces in Bangladesh, particularly universities. Specifically, this empirical study is unique to the best of our knowledge and highlights the role of EEKM and GSE as mediation between GHRM and EGB association.

Properly planned road illumination systems are collectively a public wealth and the commissioning of such systems may require extensive planning, simulation and testing. The purpose of this simulative work is to offer a simple approach to facilitate luminance-based road lighting calculations that can be easier to comprehend and apply to practical designing problems when compared to complex multi-objective algorithms and other convoluted simulative techniques.

Road illumination systems were photometrically simulated with a created model in a validated software platform for specified system design configurations involving high-pressure sodium (HPS) and light-emitting diode (LED) luminaires. Multiple regression analyses were conducted with the simulatively obtained data set to propound a linear model of estimating average luminance, overall uniformity of luminance and energy efficiency of lighting installations, and the simulatively obtained data set was used to explore luminaire power–road surface average luminance characteristics for common geometric design configurations involving HPS and LED luminaires, and four categories of road surfaces.

The six linear equations of the propounded linear model were found to be well-fitted with their corresponding observation sets. Moreover, it was found that the luminaire power–road surface average luminance characteristics were well-fitted with linear trendlines and the increment in road surface average luminance level per watt increment of luminaire power was marginally higher for LEDs.

This neoteric approach of estimating road surface luminance parameters and energy efficiency of lighting installations, and the compendia of luminaire power–road surface average luminance characteristics offer new insights that can prove to be very useful for practical purposes.