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This article features various ways AI can be applied to audit carbon credits

The total quantity of carbon dioxide (CO2) and other greenhouse gases (GHG) emitted in the lifecycle of the product or service, or in any specific financial year, is referred to as a carbon footprint. The measurement is commonly represented in kilos of CO2 equivalents, accounting for the impacts of various greenhouse gases on global warming.

A carbon credit is a marketable permit or certification that entitles the holder to emit one tonne of carbon dioxide or the equivalent of some other greenhouse gas it is effectively a carbon offset for greenhouse gas producers. The primary purpose of carbon credits is to help reduce greenhouse gas emissions from industrial activity in order to mitigate the impacts of global warming. They can also sell excess carbon credits.

Companies are thus motivated to cut greenhouse emissions on two levels: first, they will be penalized if they exceed the quota, and second, they may profit by preserving and reselling part of their emission permits.

a. A carbon offset that is traded in the voluntary market for credits is known as a voluntary emissions reduction (VER).

b. Emission units (or credits) produced within a legal framework with the goal of offsetting a projects emissions are known as certified emissions reductions (CERs).

Emerging IoT-powered devices can assist businesses in tracking and monitoring emissions throughout their whole carbon footprint. These IoT devices may help businesses gather and organise data regarding their activities and operations, as well as from every component of their supply chain, including materials.

Embodied carbon measurement is difficult because it necessitates tracing materials via complex manufacturing supply networks. AI can aid in the calculation of overall materials embodied carbon emissions, which can be difficult to track for big work sites.

Carbon offset monitoring necessitates meticulous documentation of all the many sorts of operations carried out by a corporation to offset carbon emissions. AI, object recognition, cloud computing, and other technologies can assist businesses in automatically recording and analysing data with minimum human intervention.

Artificial intelligence (AI) can assist companies in measuring and forecasting air quality and pollution levels, as well as tracking and predicting the increase and decrease of air pollution on job sites.

AI can learn to enforce on-site sorting and prevent illegal disposal of the wastes, which will aid in the reduction of carbon emissions and pollution in general.

AI & predictive analytics can assist businesses in conducting hassle-free carbon credit trading, therefore empowering the whole carbon credit trading industry.

Manual asset management becomes inefficient when the number of machines employed on job sites grows, as it is impossible for humans to monitor each and every machine at all times. AI technology may be utilised to continuously monitor operation hours, fuel use, and instances of wasteful equipment utilisation without missing a beat, assisting in the optimization of machinery usage.

AI, IoT, & cloud computing can all work together to maintain track of a companys carbon credits in an automated manner.

Predictive AI can assist businesses in estimating future emissions throughout their carbon footprint, taking into account current efforts, new carbon reduction strategies, and future demand.

AI, as well as other technologies such as IoT, may be used to track carbon pollution from various sources on job sites. This can assist businesses in identifying high-emitting & low-emitting fuels and, as a result, setting objectives, making decisions about their use, and reducing emissions.

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Applications of Artificial Intelligence in Carbon Credit Auditing - Analytics Insight