Urban lighting is one of the significant issues that urban designers and architects are facing, and it has received a special attention in recent years. Urban lighting pursues critical goals such as preserving the livability of the city during nighttime, providing a sense of security, maintaining the city"s readability, etc. Use of incompatible patterns for lighting design wastes significant amount of energy annually. A master lighting plan for urban areas should be recommended to achieve these goals and prevent wasteful energy use in lighting. One of the solutions for designing an appropriate plan is to notice the pedestrian traffic pattern in the city by considering space syntax model and integration maps analysis. In this research, one of Tehran"s regions has been chosen and its integration map has been generated using appropriate software. Tehran is selected as case study of this research while the results might be applied in other similar cities especially in developing countries. First, based on the integration degree of the passages which reveals pedestrian traffic pattern and considering recommended illuminance standards, the average required lighting has been determined. Second, comparing the measured lighting intensity and the standard amounts reveals the correlation between the presented urban lighting and energy consumption model. Eventually, different solutions for appropriate urban lighting design based on acceptable energy consumption patterns have been suggested.

Climate change is known as a serious threat to the human species, and its significance should be considered in building design. This study aims to investigate the relationship between energy consumption and CO2 emission in Iran during the years 2018–2019 using artificial neural networks (ANNs) and regression methods. The input data were gathered and optimized by the particle swarm optimization (PSO) algorithm. Lighting, equipment load rate, wall U-value, roof U-value and people density were deliberated as effective parameters. Afterwards, the ANN was created, trained and tested by the radial basis function (RBF) algorithm; also, the data were evaluated based on statistical analysis in SPSS software. The results demonstrated R2 = 0.99 and the 45-degree line for the predicted value. Energy consumption and CO2 were reduced to 35% and 73.21%, respectively. Furthermore, CO2 emissions and energy consumption had an inverse relationship with infiltration rates (−0.201) and (−0.098). Furthermore, CO2 emission and energy consumption had a linear relation in Iran with the equation of y = 1.63x + 0.52. Moreover, based on ANOVA test, R2 linear was 0.985 and R = 0.993, illustrating significant accuracy. Architects and designers could enjoy these findings as guidelines for renovation and designing purposes so as to alleviate the negative environmental impacts of construction.