Carbon dioxide is one of the seven harmful greenhouse gases. It solely makes up 85% of these harmful gases and all the other emissions are expressed in equivalents of carbon dioxide or CO2e. According to the Global Status Report for Building and Construction 2020, the building operations accounted for 28% of global emissions, while the construction-related industries, like cement, glass etc. added an extra 10% to it. Similarly, according to McKinsey report, the mining sector is responsible for 4-7% of greenhouse gas (GHG) emission and the oil and gas operations generate around 5,200 million tones of CO2, says another report. Thus, companies are now having caps on the amount of CO2 emissions as it is very pertinent to control the amount of CO2 emissions and work towards climate change.
This blog is an attempt at explaining what carbon credits are and how AI helps in carbon credit auditing in construction and other industries. So let’s start with the blog!
What are Carbon Credits?
Carbon credits came up as a market-oriented method to reduce the emission of greenhouse gases (GHGs). A carbon credit may be defined as “a permit that allows the holding company to emit a certain amount of carbon dioxide or other greenhouse gases”.
The idea of carbon credit is: those companies that pollute are awarded credits – allowing them to continue polluting up to a specific level after which the limit reduces periodically. In the meantime, the companies can sell any unused credits to any other company who is in need of them. Thus, the companies are doubly incentivized to reduce greenhouse emissions – firstly, they will be fined if they exceed the cap; and secondly, they can make money by saving and reselling some of their emission allowances.
Energy and carbon credits can thus, help in reducing the variable operating expenses, increase the net operating incomes, drop down the rates of capitalization, hike internal return rates and mitigate risks. The property owners and investors who set independent and verifiable audit baselines can generate added income and value from reduction emission via conservation and renewable energy efforts. These dynamics reflect the changing scenario of socially responsible investment models and at the same time create a sustainable competitive edge.
How does AI help in Carbon Credit Auditing?
The following are the 10 applications of AI in Carbon Credit Auditing:
1. AI for monitoring carbon footprints
In simplest terms, carbon footprint can be defined as “the total of greenhouse gases (including carbon and methane) that are generated by any action”. In the present times, with the continuous increase in carbon emissions, it has become pertinent for the companies to reduce their carbon footprint. However, calculating and monitoring carbon footprints manually is not a smart way to do, as it includes elements of inaccuracy, inefficiency and errors. However, the emerging AIoT-powered devices can help companies to track and monitor emissions throughout their carbon footprint. Such AIoT devices can be beneficial for the companies, starting from collecting to arranging data from their activities and operations; and from every part of their value chain, including materials.
2. AI for tracking material embodied carbon emission
Embodied carbon refers to “the carbon dioxide emissions that are associated with the materials and processes throughout the lifecycle of a building or any infrastructure”. It includes any CO2 generated during the manufacturing of materials, starting from material extraction, transport to manufacturer, to manufacturing process; and transport of those materials to the jobsites. However, measuring embodied carbon is very complicated as it requires tracking materials through elaborate manufacturing supply chains. As of 2018, 11% of the global energy and process-related emissions were generated by the materials production for the building. This is where AI can come into play. AI can help in calculating the overall material embodied carbon emission, which otherwise becomes difficult to be tracked for large working sites.
3. AI for tracking fuel carbon emission
The next application of AI can be for tracking fuel carbon emissions. Fuel carbon emission reflects the carbon emissions caused due to combustion of different types of fuels. AI along with other technologies like IoT can be trained to track carbon emissions from different sources in the jobsites. This can help companies to find out the high-emitting and low-emitting fuels, and thus accordingly set targets, make decisions on their use and reduce emissions. For instance, machinery running on CNG is often shown to emit less carbon as compared to a vehicle running on diesel. In such a case, detection of the emissions could be done through IoT devices; while an AI integrated record keeping dashboard can give a comprehensive view about the carbon emission status. This eases fuel usage planning for managing carbon credits.
4. AI for optimizing machinery usage
Machineries are one of the major sources of GHG emissions and other harmful substances such as carbon monoxide, nitrogen oxide and particulate matter emissions. Thus, to regulate this, proper fleet management becomes very important. As the huge number of machineries are used in the jobsites, manual fleet management becomes very inefficient since it is out of human reach to monitor each-and-every machinery at all the times. This is where AI and computer vision technologies can play a crucial role. These technologies can be used to monitor the operating hours, fuel usage or instances of unnecessary machinery usage round-the-clock without any miss and thus help in optimizing machinery usage.
5. AI for monitoring carbon offset
Carbon offset can be defined as “the actions that are intended to compensate the emission of carbon dioxide into the atmosphere as a result of industrial or any other human activity”. Carbon offset monitoring requires proper and elaborate tracking of all the different types of activities undertaken by a company to compensate for carbon emission. It involves extensive calculations, analysis, comparison and contrast between rates of carbon emissions and carbon offset, which definitely cannot be done manually. However, technologies such as AI, computer vision, cloud computing and the like can help industries to automatically record and perform various data analytics that too with minimal human interference.
6. AI for monitoring air quality
Another application of AI in carbon credits auditing is for monitoring the air quality. AI can be used to improve air quality by leveraging its potentiality to detect minute particles in the air. Construction, mining, oil & natural gas and such other industries being a major source of air pollution can reduce it using AI that can help the industries to measure and forecast air quality and levels of pollution as well as track and predict the growth and reduction of air pollution in the jobsites. AI can also help in modeling the chemical reactions between pollutions. For example, algorithms like the Atmospheric Transport Modelling System (ATMoS) can help understand PM 2.5 concentrations and thus manage air quality.
7. AI for C&D waste management
Statistics show that 35% of the world's industrial waste is solely generated by the construction industry. Much of the carbon is emitted into the atmosphere through the accumulated C&D waste dumped in the dumping areas. Proper management and on-site segregation of these C&D wastes thus becomes important. Here, technologies such as AI, computer vision and machine learning can be of great utility as these can be trained to ensure on-site segregation and illegal dumping of these wastes and in turn help in tackling carbon emission and environmental pollution at large.
8. AI for managing carbon credits
As discussed earlier, carbon credit is a tradable permit or certificate that provides the credit holder the right to emit a specific amount of CO2 or any other GHG. Since the credit holder company needs to comply with these limits, exceeding which might bring in compensations to them, it is very important to keep a close track of the carbon credits. Manual management of carbon credits is cumbersome and inefficient. But technologies such as AI, IoT, and cloud computing can together play a very important role by keeping automatic track of the carbon credits of the companies.
9. AI for predicting carbon emissions
Predictive AI can help companies in forecasting future emissions across the company’s carbon footprint, relative to present efforts at reduction, implementation of new carbon reduction methodologies and future demand. This helps them to set, adjust and achieve reduction targets with greater accuracy.
10. AI for empowering overall carbon credit trading
The carbon credits can be traded on both the private and public markets. Any company that has excess or unused carbon credit(s) can sell it to any other company who needs it. This entire process of give-and-take is very complicated and technology such as AI and predictive analytics can help companies to conduct their carbon credit trading in a hassle-free manner and thus empower the entire carbon credit trading.
Which industries can be benefitted?
Construction & Real-estate
Oil & Natural Gas
Mining
How does viAct help construction and other industries in their carbon credit auditing?
The above discussion shows how modern technologies such as AI, machine learning, deep learning, cloud computing and the like can help industries in reducing their carbon emissions. In this regards viAct’s scenario-based AI can play a significant role in measuring, monitoring, tracking, predicting and reducing carbon emissions. Further, its solutions like fleet management and its AI modules such as Air Quality Detection, C&D Waste Classification, and Illegal Dumping Detection can help the construction, and other industries, like real-estate, oil & natural gas, mining, etc. to optimize their machinery usage; monitor air quality and C&D wastes and detect illegal dumping of these wastes, respectively. In addition to this, the auto-documentation and analytics capabilities of viAct’s AI monitoring platform – viHUB can be very helpful to the stakeholders in managing their carbon credits and well as empowering their carbon credit trading in a holistic manner.
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