首页|Advanced optimization algorithms for enhanced urban block-scale carbon accounting: A case study from Beijing, China
Advanced optimization algorithms for enhanced urban block-scale carbon accounting: A case study from Beijing, China
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NETL
NSTL
Elsevier
Urban carbon emissions have garnered significant attention following the establishment and implementation of global carbon neutrality goals. Accurate carbon accounting in urban areas is crucial for formulating effective emission reduction strategies and assessing their effectiveness. However, the mixed-use nature of urban land presents significant challenges to precise carbon accounting. This study adopts the perspective of urban community living circles and leverages optimization algorithms and the inventory method to propose a novel carbon accounting method that addresses these challenges. Furthermore, this method's reliability was validated through an analysis of eight land parcels with varying spatial configurations in the Beijing area, along with comprehensive sensitivity analyses, while its practical applications were also explored. The findings are as follows: (1) The urban block-scale carbon accounting method optimizes population allocation across industries and incorporates land-use policies into objective function constraints, enhancing both accuracy and applicability. (2) In Beijing's functional core area, annual carbon emissions within the 5-15-min standard-scale living circles were 8.43 ktC, 24.95 ktC, and 151.95 ktC, respectively, with an emission intensity of 810.42 tC/ha in the 10-15-min zone. (3) Urban functional and residential land collectively accounted for approximately 97 % of total emissions, with urban functional land exerting a greater impact. This study presents a reliable, robust, and systematic urban block-scale carbon accounting method that integrates carbon management with land-use policies, demonstrating significant practical value.
Urban carbon accountingOptimization algorithmCommunity living circleUrban functional landBeijingEMISSIONS
NETL
NSTL
Elsevier
