Combined with the bidirectional long short-term memory network, a temporal prediction model is constructed to characterise the dynamic evolution characteristics of carbon emissions and environmental comfort.On this basis, a multi-objective optimisation framework is established.The non-dominated sorting genetic algorithm II is adopted to solve the optimal Pareto frontier, thus realising the coordinated trade-off and dynamic regulation of energy consumption and comfort.On the premise of maintaining the indoor thermalhumidity environment within the optimal comfort range, the energy consumption of lighting and Heating, Ventilation, and Air Conditioning (HVAC) systems is successfully reduced by 21.4%.The optimisation of environmental quality significantly improves the cognitive status of researchers, with an estimated 11.5% increase in innovative work efficiency.The research findings confirm that reducing the carbon footprint of campuses can effectively empower scientific research and innovative productivity, providing a scientific paradigm for the refined management of green and smart parks.
Li et al. (Thu,) studied this question.