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Type :article
Subject :G Geography (General)
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 City’s monthly CE have been estimated 55,536,435 kg from residential, 14,612,942 kg from commercial and 2,606,823 kg from industrial land use. From residential landuse, each household has an average CF of 355.51 kg CO2/ month. The restaurants and roadside hotels have the highest contribution from commercial landuse, average CF of 1231.64 kg and 4150.82 kg CO2/ month, respectively. Meanwhile, the jute and food industries are responsible for 38,016 kg and 14,036 kg 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 study’s results may help policymakers understand CE patterns from each sector to generate future urban growth and planning decisions.

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