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Type :article
ISSN :2509-9426
Main Author :Zullyadini A. Rahaman
Title :Modeling the Impacts of Residential, Commercial, and Industrial Land use on Carbon Footprints Using Kernel Density Function in an Urban Setting
Place of Production :Tanjung Malim
Publisher :Fakulti Sains Kemanusiaan
Year of Publication :2023
Notes :Earth Systems and Environment
Corporate Name :Universiti Pendidikan Sultan Idris
HTTP Link :Click to view web link

Abstract : Universiti Pendidikan Sultan Idris
Rapid urbanization accelerates the rate of carbon emissions (CE), resulting in unfavorable fluctuating in climatic conditions of urban settings. One of the prominent reasons behind these climatic anomalies is the direct result of CO2 emission and heat-trapping gasses from industries and urban built-up areas. This study addresses the ongoing CE issues in Khulna City by analyzing CE from residential, commercial, and industrial land use. Moreover, the study explores diverse sources of CE from intense energy consumption using electricity, gas, fuel, and wood. Consequently, the carbon footprint (CF) has been estimated using specific CE coefficients for each landuse category. Specific concentration of CE for each landuse category is demonstrated on maps using geospatial and kernel density applications. Results suggested Khulna Citys monthly CE have been estimated 55,536,435kg from residential, 14,612,942kg from commercial and 2,606,823kg from industrial land use. From residential landuse, each household has an average CF of 355.51kg CO2/month. The restaurants and roadside hotels have the highest contribution from commercial landuse, average CF of 1231.64kg and 4150.82kg CO2/month, respectively. Meanwhile, the jute and food industries are responsible for 38,016kg and 14,036kg CO2/month, respectively. Compared to the sources of CE, industrial and residential landuse have a consumption pattern of 99% and 83% of electricity, respectively, rather than other sources. This studys results may help policymakers understand CE patterns from each sector to generate future urban growth and planning decisions. 2022, King Abdulaziz University and Springer Nature Switzerland AG.
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