The Most Effective AI Techniques for Lowering CO2 Emissions in Food and Automotive

by Sergej Lugovic

The aim of this mental exercise is to assess the CO2 footprint of food and cars, focusing on both production and usage aspects. To facilitate analysis, we have segmented the study into four key areas: food production, food waste, car production, and car usage. By analyzing data from these sectors, we aim to identify opportunities for reducing CO2 emissions and formulate strategic objectives to address environmental challenges. Furthermore, we will explore the potential role of AI in addressing these issues. Through this exercise, we seek to gain insights that can inform sustainable practices and guide efforts to mitigate carbon emissions effectively. 

Disclaimer: The data presented in this text are based on available references and estimates. The aim of the text is not to provide precise numerical values but rather to use these estimates to support an exploration of potential improvements to the system. The focus is on identifying strategies for reducing CO2 emissions and fostering sustainable practices. This disclaimer emphasizes the exploratory nature of the analysis and clarifies the purpose of using estimated numbers.

Food numbers crunching

Global food production, as evidenced by FAO data, amounted to approximately 9.7 billion metric tons in 2019, contributing an average of 1.63 tons of CO2 equivalent emissions per ton of food produced, as estimated from an official EU publication.. Additionally, recent estimates indicate that global food waste generates approximately 4.4 gigatonnes of CO2 equivalent emissions annually, with each ton of wasted food contributing approximately 3.38 tons of CO2 equivalent emissions. Addressing food waste is crucial to reducing its substantial carbon footprint and mitigating environmental impacts.

Food Production: 

Based on FAO data, global food production in 2019 totaled about 9.7 billion metric tons. From an official EU publication, estimated total global food-system emissions stood at 15.8 gigatonnes of CO2 equivalent. This indicates an average emission of roughly 1.63 tons of CO2 equivalent per ton of food produced.

Food Waste

According to recent estimates, global food waste emissions amount to approximately 4.4 gigatonnes of CO2 equivalent (GtCO2e) annually. Comparatively, global food waste is estimated to reach around 1.3 billion tons annually. Calculations reveal that, on average, each ton of wasted food contributes approximately 3.38 tons of CO2 equivalent emissions. 

Cars number crunching

The automotive sector accounts for approximately 7.66 gigatons of CO2 emissions annually, with 5.6 gigatons attributed to car usage and 0.42 gigatons to car production. These numbers underscore the significant environmental impact of the automotive industry

Car Production: Based on an annual production of approximately 74.8 million cars globally, with an average CO2 footprint of 5.6 tonnes per car, the total CO2 emissions from car production amount to approximately 0.42 gigatons annually. (with roughly three-quarters of that amount being attributed to the steel in the vehicle frame) 

Car Usage: The total CO2 emissions from all cars globally per year are estimated to be approximately 6.44 gigatons. This calculation is based on an estimated global fleet of 1.4 billion cars, each emitting about 4.6 metric tons of CO2 annually (USA EPA). These emissions highlight the significant environmental impact of transportation and emphasize the urgent need for sustainable mobility solutions to mitigate climate change.

Table 1

Cars Production vs Food Production 
Annual Emissions (GtCO2)
CO2 Emissions per Ton
5.6 tonnes per car
1.63 tonnes per ton of food produced


Table 2

Cars Usage vs Food Waste 
Cars Usage
Food Waste
Annual Emissions (GtCO2)
CO2 Emissions per Ton
4.6 tonnes per car
3.38 tonnes per ton of food wasted


From this small analysis, we identify few key issues that could be addressed by using AI intervention, each with the potential for significant positive environmental impact.

Firstly, we observed a contrast between CO2 emissions from car production and food production, with food production emissions nearly 40 times higher despite cars relying on potentially recyclable materials for about three-quarters of their production. Exploring how AI, particularly various computer science disciplines related to reducing food production CO2 emissions are:

Natural Language Processing (NLP):  a) NLP algorithms can analyze vast textual data sources, including research papers and industry reports, to identify climate-smart farming practices that reduce carbon emissions; b) These insights can inform farmers about techniques like conservation tillage and crop rotation, promoting organic farming practices with lower carbon footprints.

Computer Vision: a) Computer vision technology can detect crop diseases early, minimizing the need for chemical treatments and reducing associated emissions.b) By optimizing irrigation management and improving food processing efficiency, computer vision systems help reduce food waste and its carbon footprint.

Forecasting: a) AI-powered forecasting models predict climate-related risks, enabling farmers to implement adaptive strategies that mitigate crop losses and emissions. b) Accurate yield forecasts and market demand predictions help optimize production schedules, reducing food waste and emissions throughout the supply chain.

Personalization: a) Personalized farming plans optimize inputs like fertilizers and water, minimizing excess usage and associated emissions. b) Tailored supply chain management reduces food miles and promotes sustainable eating habits, lowering the overall carbon footprint of food consumption.

Fraud Detection: a) AI-powered traceability systems ensure the integrity of organic certifications, allowing consumers to support environmentally friendly practices. b) Fraud detection algorithms prevent the distribution of contaminated products, reducing healthcare-related carbon emissions.

Secondly, Co2 footprint from producing car is quite low and with three-quarters of that amount being attributed to the steel in the vehicle frame which is recyclable 

Car and steel production, as a major contributor to CO2 emissions, can benefit from AI interventions, particularly in recycling, which accounts for three-quarters of emissions due to the recyclable nature of steel frames in cars: By strategically applying AI, the steel and automotive industries can collaboratively address CO2 emissions challenges, advancing toward a sustainable circular economy.

Natural Language Processing (NLP): a) NLP algorithms can analyze a vast array of textual data sources, including research papers, industry reports, and regulatory documents, to identify advancements and best practices in steel production and recycling techniques. b) By extracting insights from textual data, NLP can inform decision-making processes within the steel industry, automotive manufacturers, and policymakers, facilitating the adoption of more sustainable steel production methods and recycling practices.

Computer Vision: a) Computer vision technology can be deployed in steel production facilities to optimize manufacturing processes, enhance quality control, and automate sorting of scrap metal for recycling. b) By monitoring production lines and recycling operations, computer vision systems can detect defects, anomalies, and opportunities for process optimization, leading to improvements in resource efficiency and waste reduction.c) Additionally, computer vision-enabled sorting systems can accurately identify and segregate different types of scrap metal, including steel, enabling efficient recycling and reuse in automotive manufacturing, thereby reducing the environmental impact of raw material extraction and processing.

Forecasting: a) AI-driven forecasting models can predict future trends in steel demand, market dynamics, and regulatory requirements, enabling steel producers and automotive manufacturers to anticipate shifts in materials sourcing and sustainability standards. b) By forecasting the availability of recycled steel and other sustainable materials, automotive companies can proactively integrate circular economy principles into their supply chain strategies, ensuring a reliable and environmentally responsible supply of materials for vehicle production.

Personalization: a) Personalized sustainability dashboards and performance metrics can provide steel producers and automotive manufacturers with real-time insights into their carbon footprint, energy consumption, and resource utilization. b) By empowering employees with personalized feedback and incentives for improving recycling rates and reducing emissions, companies can foster a culture of sustainability and innovation within their workforce, driving continuous improvement in environmental performance.

Fraud Detection: a) AI-powered monitoring systems can detect instances of fraud, misrepresentation, and greenwashing in the steel and automotive industries, ensuring transparency and accountability in sustainability reporting and claims. b) By analyzing data from sustainability reports, emissions inventories, and supply chain audits, fraud detection algorithms can identify discrepancies and inconsistencies in environmental disclosures, enabling stakeholders to hold companies accountable for their environmental impact and sustainability commitments.

Car usage is 6,4 giga tons of CO2 equivalent , let's explore how AI can address the challenges associated with CO2 emissions from car usage:

Natural Language Processing (NLP): a) NLP algorithms can analyze social media conversations, customer feedback, and online reviews to understand public sentiments, attitudes, and preferences towards sustainable transportation options.b) By identifying key drivers and barriers to the adoption of eco-friendly modes of transportation, NLP-powered sentiment analysis can inform targeted communication strategies and awareness campaigns to promote the use of electric vehicles (EVs), public transit, carpooling, and other low-carbon alternatives. c) Additionally, NLP-driven chatbots and virtual assistants can provide personalized travel recommendations, route optimizations, and real-time updates on traffic conditions and public transit schedules, empowering users to make informed decisions that minimize their carbon footprint.

Computer Vision: a) Computer vision technology can be integrated into traffic management systems, smart infrastructure, and autonomous vehicles to optimize traffic flow, reduce congestion, and minimize idling times at intersections and traffic chokepoints. b) By analyzing traffic patterns, vehicle speeds, and driver behavior, computer vision systems can identify opportunities for improved route planning, traffic signal synchronization, and dynamic lane management to enhance the efficiency of urban mobility networks and reduce emissions from vehicular congestion. c) Furthermore, computer vision-enabled vehicle monitoring systems can assess vehicle performance, detect malfunctions, and provide predictive maintenance alerts to optimize fuel efficiency and reduce emissions from individual vehicles over their lifecycle.

Forecasting: a) AI-driven predictive analytics can forecast future trends in transportation demand, population growth, urban development, and energy consumption to inform long-term infrastructure planning and investment decisions. b) By anticipating shifts in travel behavior, commuting patterns, and modal preferences, forecasting models enable policymakers and urban planners to design more sustainable, accessible, and resilient transportation systems that prioritize walking, cycling, and public transit while reducing reliance on single-occupancy vehicles. c) Moreover, forecasting tools can support the deployment of EV charging infrastructure, hydrogen fueling stations, and other alternative fueling technologies in locations where they are most needed, facilitating the transition to cleaner and more sustainable modes of transportation.

Personalization: a) Personalized mobility apps and platforms powered by AI algorithms can analyze user preferences, travel habits, and environmental values to recommend personalized transportation options that align with individual preferences and sustainability goals. b) By offering tailored incentives, rewards, and discounts for eco-friendly travel behaviors such as carpooling, ridesharing, and active transportation, personalized mobility solutions encourage users to make greener choices and reduce their carbon footprint.c) Personalized commuting plans that integrate multiple modes of transportation, including walking, cycling, public transit, and shared mobility services, can optimize travel efficiency and reduce emissions by minimizing reliance on private vehicles for daily travel needs.

Fraud Detection: a) AI-powered vehicle emissions monitoring systems can detect instances of emissions tampering, defeat devices, and fraudulent emissions testing practices that may lead to excessive air pollution and non-compliance with emissions regulations. b) By analyzing real-time emissions data from vehicle sensors, onboard diagnostics, and remote monitoring devices, fraud detection algorithms can identify anomalies, deviations, and discrepancies in emission levels and flag vehicles for further inspection or enforcement action. c) Moreover, AI-enabled regulatory compliance platforms can streamline emissions reporting, verification, and enforcement processes, ensuring transparency, accountability, and integrity in the monitoring and regulation of vehicle emissions to mitigate their environmental impact.

Given that food waste contributes 4.4 gigatons of CO2 equivalent emissions, AI can play a crucial role in addressing this challenge:

Natural Language Processing (NLP): a) NLP algorithms can analyze consumer feedback, social media discussions, and market trends to identify behavioral patterns and preferences related to food consumption and waste generation. b) By understanding consumer attitudes towards food waste and exploring the underlying reasons for over-purchasing or discarding food, NLP-powered sentiment analysis can inform targeted interventions and educational campaigns to promote mindful consumption practices. c) Additionally, NLP-based chatbots and virtual assistants can provide personalized tips and recipes for meal planning, portion control, and creative ways to use leftovers, helping consumers reduce food waste at home.

Computer Vision: a) Computer vision technology can be deployed in smart refrigerators and pantry systems to monitor food inventory, track expiration dates, and identify spoilage or decay in real-time. b) By visually analyzing food items and their condition, computer vision systems can alert users to impending spoilage and suggest recipes or meal ideas to use up perishable ingredients before they go to waste. c) Furthermore, computer vision-enabled food sorting and composting systems can facilitate the separation of organic waste from recyclables and landfill-bound materials, optimizing waste management processes and reducing emissions from landfill decomposition.

Forecasting: a) AI-driven demand forecasting models can predict consumer purchasing patterns, seasonal fluctuations, and market demand for different food products, enabling retailers and suppliers to optimize inventory management and minimize overstocking. b) By aligning production and distribution with anticipated demand, forecasting algorithms help prevent food surpluses that may lead to waste, thereby reducing emissions associated with transportation, storage, and disposal. c) Forecasting tools can also support dynamic pricing strategies that incentivize consumers to purchase surplus or perishable food items nearing their expiration dates, reducing food waste while maximizing revenue for retailers.

Personalization: a) Personalized meal planning apps powered by AI algorithms can take into account individual dietary preferences, nutritional needs, and cooking skills to create customized shopping lists and weekly menus that minimize food waste. b) Tailored recommendations for portion sizes and meal compositions can help users optimize their food consumption habits, reducing over-purchasing and the likelihood of leftovers being discarded. c) Personalized incentives and rewards programs can encourage consumers to adopt sustainable behaviors such as donating surplus food to food banks or participating in community composting initiatives, fostering a sense of social responsibility and collective action.

Fraud Detection: a) AI-enabled supply chain traceability platforms can ensure the integrity and authenticity of food products from farm to fork, reducing the risk of food fraud and contamination that may lead to waste. b) By providing transparent and verifiable information about the origins, handling practices, and quality of food products, traceability systems empower consumers to make informed choices and minimize the likelihood of purchasing items that may end up being discarded. c) Fraud detection algorithms can also identify instances of mislabeling or deceptive marketing practices that contribute to consumer confusion and food waste, enabling regulatory authorities to enforce compliance and hold accountable those responsible for misleading claims.

In the food sector, we observed significant CO2 emissions associated with both production and waste, highlighting the urgent need for sustainable practices throughout the supply chain. AI interventions, including Natural Language Processing (NLP), Computer Vision, Forecasting, Personalization, and Fraud Detection, offer promising solutions to reduce food waste, optimize resource utilization, and promote sustainable consumption habits. By harnessing the power of AI, stakeholders can work towards mitigating the environmental impact of food production and waste generation, contributing to a more sustainable food system.

Similarly, in the automotive industry, CO2 emissions from car production and usage pose significant environmental challenges. While car production accounts for a relatively small portion of total emissions, the recyclable nature of materials, particularly steel, presents opportunities for reducing the carbon footprint of vehicle manufacturing. AI-driven approaches, such as NLP, Computer Vision, Forecasting, Personalization, and Fraud Detection, can enhance sustainability practices in the automotive sector by optimizing production processes, promoting the adoption of electric vehicles, and improving resource efficiency.

Overall, AI has the potential to play a transformative role in addressing CO2 emissions across various industries. By integrating AI technologies into decision-making processes, policymakers, businesses, and consumers can collectively work towards achieving carbon neutrality and building a more resilient and sustainable future. Through continued research, innovation, and collaboration, we can leverage AI to create tangible solutions that mitigate climate change and preserve our planet for future generations.