تحليل آماري و ارزيابي ارتباط بين مصارف انرژي و توليد پسماند در خانوار شهري (مطالعه موردي: شهر اصفهان)

Waste generation forecasting , Household waste generation , Per capita waste generation , Regression analysis , Energy consumption , Energy an‎d resource consumption , Statistical analysis , Urban households

Statistical Analysis an‎d eva‎luation of the Relationship Between Energy Consumption an‎d Waste Generation in Urban Households (Case Study: Isfahan City)

مهندسي عمران

Population growth, urbanization, industrialization, an‎d changes in lifestyle have led to a significant increase in urban solid waste, turning its management into a serious environmental an‎d health challenge. Household waste generation, as a major component of urban waste, is influenced by socio-economic an‎d demographic factors, including household income. Due to limited access to accurate income data in developing countries, using alternative indicators such as energy consumption as a proxy for income level an‎d consumption patterns is considered an efficient approach. Regression models, due to their simplicity, are a suitable tool for estimating waste an‎d identifying influencing factors. This research investigates the relationship between water, electricity, an‎d gas consumption an‎d the amount of waste generated by urban households in Isfahan. The data used comes from field studies of 175 households in different urban areas during hot an‎d cold seasons, along with other complementary socio-economic an‎d demographic variables including education level, number of household members, age, gender, an‎d settlement characteristics. Statistical analyses an‎d regression models were used in SPSS software for analysis. The research results showed that as the seasons change from hot to cold, the per capita total waste generation decreased from 369 to 313 grams per day per person, a decrease that was statistically significant at the 95% confidence level. Dry waste, however, showed relatively stable behavior throughout the year. Over the entire study period, the average per capita total household waste generation was estimated at approximately 358 grams per day per person, of which about 82% was organic waste an‎d 18% was separated dry waste. Correlation analysis results indicated a direct an‎d significant correlation between water an‎d electricity consumption an‎d per capita organic an‎d total waste generation. The intensity of these relationships increased at the household level, meaning that household waste generation increased with household energy consumption. The results of multiple linear regression analysis showed that models based on per capita water an‎d electricity consumption, total household water an‎d electricity consumption, an‎d normalized per capita water an‎d electricity consumption, individually an‎d in combination, were able to predict household waste generation within a range of approximately 4% to 50% for the overall data, hot season, an‎d apartment groups, all of which were statistically significant at the 95% confidence level. In the estimated regression models, water consumption an‎d electricity consumption, respectively, had the greatest share in predicting household waste generation in various models, an‎d the estimated coefficients for both variables were positive. Furthermore, the results indicated that variables related to gas consumption were not significant in the final model. In the regression results for waste generation an‎d energy consumption, the best models for predicting total household waste generation, based on the adjusted coefficient of determination percentage, were 48.6%, 46.1%, an‎d 45.5%, respectively, all of which were statistically significant at the 95% confidence level. The results showed that the performance of dry waste in the regression models was very weak. Additionally, the inclusion of social an‎d physical variables such as the proportion of men in the household, the built-up area, an‎d the per capita built-up area in some models led to a limited improvement in prediction accuracy. In the regression results for waste generation an‎d the complete set of independent variables, the best models for predicting total household waste generation, based on the adjusted coefficient of determination percentage, were 49.5%, 47.5%, an‎d 46.1%, respectively, all of which were statistically significant at the 95% confidence level.

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