| CPC G06Q 30/018 (2013.01) [G06N 20/00 (2019.01); G06Q 10/04 (2013.01)] | 20 Claims |
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1. An apparatus for carbon emission optimization using machine-learning, wherein the apparatus comprises:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive an integrated logistics data collection;
determine at least one projected carbon emission as a function of the integrated logistics data collection, wherein determining the at least one projected carbon emission comprises:
training a carbon emission projection model using carbon emission training data, wherein the carbon emission training data comprises a plurality of logistics datasets as input correlated to a plurality of historical carbon emissions as output, wherein training the carbon emission projection model comprises:
updating the carbon emission training data as a function of the inputs and outputs of a previous iteration of the carbon emission projection model; and
retraining the carbon emission projection model using the updated carbon emission training data; and
determining the at least one projected carbon emission as a function of the integrated logistics data collection using the trained carbon emission projection model;
generate at least one transportation plan as a function of the integrated logistics data collection and the at least one projected carbon emission;
continuously receive a current logistics datum from an external source; and
iteratively modify the at least one transportation plan based on the current logistics datum, wherein iteratively modifying the at least one transportation plan comprises:
identifying a carbon emission outlier as a function of the current logistics datum and the trained carbon emission projection model;
determining at least one carbon emission offset as a function of the carbon emission outlier; and
updating the transportation plan to incorporate the at least one carbon emission offset.
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