With the development of sustainable cities, densely populated higher education institutions increasingly emphasize the sustainability of campuses and their impact on the environment. However, there is a lack of means to quantify emission reduction measures. This study aims to propose an evaluation framework that can quantify energy conservation and emission reduction measures and incentive policies. To this end, this study adopts a mixed methods approach, using questionnaires to assess the effectiveness of management and communication interventions and the impact of incentives on residents’ willingness to participate in emission reduction efforts. The survey results show that although the support for the intervention measures is slightly higher than the average, specific measures such as adjusting dormitory lights-out time and providing sports equipment show superior emission reduction potential. Universities could reduce carbon emissions by about 560 tons per year without incentives and just using interventions. However, when incentives and interventions are combined, the university’s annual emissions reductions are expected to increase to 800 to 1045 tons. Research also highlights the importance of understanding the relationship between occupant behavior, energy consumption, and building carbon emissions. By quantifying the impact of carbon reduction measures and incentives on the daily behaviors of residents, universities can more effectively implement sustainable campus strategies.

Changes of Remote Sensing Data in Urban Buildings Based on Neural Network Algorithm: Remote sensing data refer to remote sensing data sets with typical big data characteristics obtained by various remote sensing technologies. Based on this understanding, massive remote sensing datasets are the main method, and auxiliary data from other sources are integrated. The DN value (digital number) is the brightness value of the remote sensing image pixel and the gray value of the recorded ground objects. Unitless is an integer value. The value is related to the sensors radiation resolution, ground object emissivity, atmospheric transmittance, and scattering rate. This study aims to study the neural network algorithm for remote sensing data changes in urban buildings. This study deliberately goes to Tianjin Binhai New Area, taking this as an example to analyze and study the change of construction land. It uses satellite remote sensing data as a basis to analyze the trend of its building land. This study proposes which factors lead to the decline of urban construction land in Tianjin Binhai New Area. Finally, through experimental research and analysis, it is found that from 1996 to 2016, the registered population of Tianjin Binhai New Area has increased a lot, accounting for 26.49 of the total population. The number of urban employees increased by 956,500. This is also the leading factor in the direction of the city for the building.