Methods, systems, and computer program products for generating GHG emissions estimations associated with logistics contexts using machine learning techniques are provided herein. A computer-implemented method includes obtaining input data related to multiple aspects of at least one logistics context; deriving contextual features from the input data by processing the input data using data profiling techniques; training at least one machine learning model related to energy consumption based on the contextual features; generating at least one energy consumption estimate attributed to at least one logistics implementation by processing data pertaining to the at least one logistics implementation using the at least one trained machine learning model; generating at least one greenhouse gas emissions estimate attributed to the at least one logistics implementation based on the at least one energy consumption estimate; and performing automated actions based on the at least one generated greenhouse gas emissions estimate.

A system is disclosed for managing waste services performed by a service vehicle. The system may have at least one sensor disposed onboard the service vehicle and configured to generate a first signal indicative of a waste service being completed by the service vehicle. The system may also have a computing device in communication with the at least one sensor. The computing device may be configured to determine, based on the second signal, that the waste service has been performed. The computing device may also be configured to selectively generate an electronic response based on performance of the waste service.

A system is disclosed for managing waste services performed by a service vehicle. The system may have at least one sensor disposed onboard the service vehicle and configured to generate a first signal indicative of a waste service being completed by the service vehicle. The system may also have a computing device in communication with the at least one sensor. The computing device may be configured to determine, based on the second signal, that the waste service has been performed. The computing device may also be configured to selectively generate an electronic response based on performance of the waste service.

An environment contribution effect report generation apparatus for generating a report on an environment contribution effect produced by introduction of a facility includes: an environmental load intensity acquisition unit configured to identify and acquire an amount of environmental load caused in generating energy used by the facility per unit amount and evidence information on the amount of environmental load, from a history of energy usage by the facility; and an environment contribution effect report generation unit configured to calculate an environment contribution effect from an environmental load intensity and an amount of energy used by the facility and to generate a report containing the environment contribution effect and the evidence information.

A system for auto defect screening using adaptive machine learning includes an adaptive model controller, a defect/nuisance library and a module for executing data modeling analytics. The adaptive model controller has a feed-forward path for receiving a plurality of defect candidates in wafer inspection, and a feedback path for receiving defects of interest already screened by one or more existing defect screening models after wafer inspection. The adaptive model controller selects data samples from the received data, interfaces with scanning electron microscope (SEM) review/inspection to acquire corresponding SEM results that validate if each data sample is a real defect or nuisance, and compiles model training and validation data. The module of executing data modeling analytics is adaptively controlled by the adaptive model controller to generate and validate one or more updated defect screening models using the model training and validation data according to a target specification.

A system for auto defect screening using adaptive machine learning includes an adaptive model controller, a defect/nuisance library and a module for executing data modeling analytics. The adaptive model controller has a feed-forward path for receiving a plurality of defect candidates in wafer inspection, and a feedback path for receiving defects of interest already screened by one or more existing defect screening models after wafer inspection. The adaptive model controller selects data samples from the received data, interfaces with scanning electron microscope (SEM) review/inspection to acquire corresponding SEM results that validate if each data sample is a real defect or nuisance, and compiles model training and validation data. The module of executing data modeling analytics is adaptively controlled by the adaptive model controller to generate and validate one or more updated defect screening models using the model training and validation data according to a target specification.

A system for auto defect screening using adaptive machine learning includes an adaptive model controller, a defect/nuisance library and a module for executing data modeling analytics. The adaptive model controller has a feed-forward path for receiving a plurality of defect candidates in wafer inspection, and a feedback path for receiving defects of interest already screened by one or more existing defect screening models after wafer inspection. The adaptive model controller selects data samples from the received data, interfaces with scanning electron microscope (SEM) review/inspection to acquire corresponding SEM results that validate if each data sample is a real defect or nuisance, and compiles model training and validation data. The module of executing data modeling analytics is adaptively controlled by the adaptive model controller to generate and validate one or more updated defect screening models using the model training and validation data according to a target specification.

A system for auto defect screening using adaptive machine learning includes an adaptive model controller, a defect/nuisance library and a module for executing data modeling analytics. The adaptive model controller has a feed-forward path for receiving a plurality of defect candidates in wafer inspection, and a feedback path for receiving defects of interest already screened by one or more existing defect screening models after wafer inspection. The adaptive model controller selects data samples from the received data, interfaces with scanning electron microscope (SEM) review/inspection to acquire corresponding SEM results that validate if each data sample is a real defect or nuisance, and compiles model training and validation data. The module of executing data modeling analytics is adaptively controlled by the adaptive model controller to generate and validate one or more updated defect screening models using the model training and validation data according to a target specification.