Food Waste and the Circular Economy: Comparison
Please note this is a comparison between Version 2 by Jason Zhu and Version 1 by Helen Onyeaka.

Food waste is a global issue with significant economic, social, and environmental impacts. Addressing this problem requires a multifaceted approach; one promising avenue is using artificial intelligence (AI) technologies.

  • food waste
  • circular economy
  • artificial intelligence
  • resource efficiency

1. Introduction

Food waste remains a major problem globally, with estimates suggesting that one-third of all food produced is lost or wasted. Moreover, it costs the global economy USD 936 billion annually [17][1]. This has high economic and social costs and contributes to environmental problems such as greenhouse gas emissions and resource depletion [18][2].
An important area of effort in the food system for promoting sustainable development, in addition to dietary and production pattern improvements, is lowering food waste [19][3]. Adverse ecological effects of the food system can be addressed by minimizing food waste, increasing the safety of food and water [20][4], and greatly lowering both the immediate and long-term implications of food waste on the economy, society, and environment [4][5].
An approach that values resources and emphasises effectiveness and efficiency in resource use and waste minimisation is the circular economy (CE) [21][6]. The persistent pursuit of waste reduction is one of the objectives of the circular economy (CE) [21][6]. That is, the goal is to reduce resource consumption and increase product usefulness while maintaining the value of goods and materials for a long time [22][7].
According to the Ellen MacArthur Foundation (EMF), cited in [21][6], the CE is a comprehensive strategy for economic growth, crafted for the benefit of society, industry, and the environment, as opposed to the “produce-consume-eliminate” linear paradigm. It is intended to eventually uncouple development from the use of limited resources since it is regenerative by design [23][8]. Korhonen et al. propose that the CE is developed using mechanisms for production and consumption to the greatest extent possible to optimise the service provided by the linear flow of energy and nature, by utilizing cyclical material flows, renewable energy sources, and cascade energy flow types [24][9].
The circular economy can be applied to different stages in the food system: production, consumption, waste, and surplus management [25][10]. The current state of food waste and the circular economy is complex, with progress in some areas and persistent challenges in others.
The circular economy approaches used to address food waste face various barriers, including cultural issues, business and business finance, regulatory and governmental, technological, and supply-chain management shortfalls [25][10]. One of the main issues in emerging economies, such as India, Bangladesh, and Pakistan, is the lack of government policy surrounding the use of a circular economy [26][11].
On the one hand, there is growing awareness and commitment to addressing the issue of food waste, with many governments, businesses, and individuals taking action to reduce waste and promote more sustainable food systems. On the other hand, the continuous and growing literature supports implementing a circular economy to stem the problem of food waste [27][12].
Ultimately, the successful implementation of a circular economy for food waste will depend on continued commitment and collaboration from all actors involved in the food system and ongoing innovation and adaptation in response to changing circumstances and emerging challenges [28][13].
Furthermore, a report released by the United Nations Environment Programme has brought attention to the alarming scale of global food waste [5][14]. According to the 2021 Food Waste Index [5][14], an estimated 931 million tonnes of food are discarded annually, with an average per capita food waste of 74 kg per household.
Approximately 569 million tonnes of this waste falls under the category of household waste. However, it is not solely households that contribute to this issue. Supermarkets and other businesses are also major culprits, disposing of significant quantities of food, which collectively amounts to hundreds of millions of tonnes yearly. The report reveals that food service establishments are responsible for wasting approximately 244 million tonnes annually, while the retail sector discards around 118 million tonnes.
These findings underscore the pressing need for concerted efforts and innovative solutions to combat global food waste. Addressing this issue is crucial, as it poses substantial economic, social, and environmental challenges on a global scale.

2. The Circular Economy Concept and Its Potential for Reducing Waste and Increasing Resource Efficiency

The circular economy (CE) has gained traction over the past few centuries and gathered momentum recently as a model that supports more ethical production and consumption patterns. However, natural resources have been overused due to the increased growth in global goods consumption. By establishing a system that emphasises material reduction, reuse, recycling, and recovery across manufacturing, distribution, and consumption, the CE responds to the need to separate environmental pressure from economic growth [29][15].
The circular economy (CE) development paradigm is unlike the conventional linear economy, which follows a model of producing, consuming, and discarding goods. The circular economy emphasises the principles of reducing, reusing, and recycling (the ‘3 Rs’) to minimise the negative impacts of human activities [30][16]. Its goal is to control the flow of clean energy resources, manage and regulate limited stockpiles, protect and enhance natural capital, and ensure that all goods, systems, and materials are maximally useful and valuable [31][17]. Even though the circular economy (CE) is a concept that has grown in prominence in recent years and is viewed as an overarching idea aiming to lower the amount of material used and the amount of trash produced [32][18], some scholars believe that there are variances in the definition of the concept [33][19], traits and features [34][20], how its goals are defined [35][21], how they are carried out, and the metrics used to measure its performance and effectiveness [36][22]. Only a recent systematic assessment examined the issue, describing the CE as a “developing concept that still requires development to consolidate its definition, boundaries, principles, and associated behaviours” [37][23].
In the words of Moraga et al. [32][18], CE aims to cut down on material inputs and waste production. The validity of this claim shows its direct influence on reducing the use of organic materials and promoting a shift towards recycling waste as secondary raw materials. Also, the CE aims to boost items’ chances of being resold and lengthen their valuable lives. In a nutshell, the CE gives priority to actions that have apparent adverse effects on the environment, such as using recyclable packaging, promoting eco-friendly products, lowering emissions and waste, evaluating renewable and alternative energies, conserving energy, utilising low-impact consumer goods, eco-designing, recovering waste, and dematerialising [36][22].

3. The Role of AI in Addressing Food Waste and Supporting the Circular Economy

Artificial intelligence (AI) is the mimicking of human intellect using computers. Artificial intelligence (AI) is regarded as an evolving paradigm that impacts governments and the scientific community, as well as traditional political and economic means to address these issues [38][24]. The latest advancements in AI technologies, including deep learning, image identification, machine learning, and natural language processing, make it clear that these technologies will continue to influence daily life [39][25].
An economic model known as “take, make, and dispose of” powers the world economy, which depends on taking enormous amounts of finite resources and fossil fuels from the ground and burning them for energy.
The group of technologies known as artificial intelligence (AI), which simulates cognitive processes like learning and reasoning in humans, has the potential to alter food systems and change how food is produced and distributed throughout the world [40][26]. However, simply giving computers intelligence or consciousness does not automatically qualify as artificial intelligence (AI) in the same way as humans. It simply means a computer can solve a particular problem or related issues. On the other hand, harnessing the power of AI to transition the food system from a linear to a circular model is one of the era’s most significant technological breakthroughs. According to the Ellen MacArthur Foundation [31][17] and Magnin [40][26], this possibility remains largely unexplored.
In addition to protecting and regenerating biological systems, AI can produce value rather than extract it. Furthermore, a study by the Ellen MacArthur Foundation identified three areas where artificial intelligence (AI) could have the most influence on the shift to a circular food system: obtaining food farmed sustainably and locally when applicable, designing out avoidable food waste, and developing and marketing healthier food items [31][17].
The majority of crops are being farmed in a way that depletes soils, agrobiodiversity, and waterways while taking more from natural systems than it gives back. AI can assist in the replacement of traditional agricultural techniques like monoculture, the widespread use of synthetic chemical fertilizers, and intense land usage with more regenerative ones. Also, AI can assist farmers from the start by minimising food waste and building systems that minimise wasteful food use [41][27].
A study by the McKinsey Global Institute [41][27], discovered that by applying these strategies to minimise food waste, AI might potentially provide a chance to increase top-line revenue by much to USD 127 billion per year by 2030, as well as anticipated that by 2030, AI may increase global economic activity by an additional USD 13 trillion.
Food can now be processed cyclically as demand for quick, easy-to-prepare meals rises. In addition, food innovators and designers can make it simpler for people to obtain healthy food items by using AI to help them obtain components from regeneratively grown plants, swap out animal proteins for plant-based ones, reduce processing waste, and avoid dangerous additives [31][17]. Integrating AI into the food industry holds immense potential for addressing food waste, supporting the circular economy, and enhancing sustainability in food production.

4. Using AI to Support Circular Economy Initiatives

4.1. Use of AI to Identify Opportunities for Waste Reduction and Recycling

Reduce, reuse, and recycle are the three major driving forces behind the circular economy, which has the dual goals of minimising the use of virgin resources and achieving sustainable development [52][28]. A circular economy reduces the carbon footprint involved with the production of new materials by promoting recycling and reuse. Additionally, using recycled materials significantly lowers the carbon footprint in an economical and environmentally responsible way. Additionally, a circular economy greatly reduces waste production, thus reducing the carbon footprint. The achievement of environmental, social, and fiscal sustainability is thus seen as being driven by it.
The public’s wish for recycling and reusing recycled materials made research on food recycling networks possible. AI technologies can spur business innovation in solid waste management (SWM) when used correctly. In almost every industry, the effectiveness, security, and calibre of production processes can all be enhanced with artificial intelligence (AI). AI is presently used to deal with complex problems in SWM, social security, safety, health, climate, energy, facilities, transport, and other areas. AI automation boosts efficiency and business processes to new heights of consistency, speed, and scalability while lowering costs. As a result, the application of AI algorithms for SWM improvement has grown significantly globally [53][29].
Artificial intelligence can analyse new data from various sources and produce results in almost real-time, adapting as necessary. However, for governments, the degree of precision is essential [54,55][30][31].
Globally, the use of AI algorithms for SWM optimisation keeps growing [53][29]. Second, AI applications can analyse new data from numerous sources and instantly generate results, adapting as required. Governments highly value this level of precision.
A circular economy includes a variety of industries, including those in the economy, metallurgy, chemical, biotechnology, and information technology and communications (ITC), and ITC can help to govern and advance a circular economy [56][32]. Regarding manufacturing, circular economy principles can be translated into various approaches, including remanufacturing, recycling, industrial symbiosis, etc. This concurrent approach is called circular manufacturing (CM) [57][33].
Artificial intelligence (AI) can be used to encourage data collection for sustainable goals, particularly concerning the manufacturing industry [58][34].

4.2. Applications of Artificial Intelligence (AI) in Waste Management and Recycling

AI assists in identifying the most suitable and affordable disposal options for the various returned products if regenerative processes cannot be implemented [59][35]. AI can be used to create decision-support tools that assess a product’s quality, the need for reprocessing, and whether regenerative methods can be used.
In recent years, the recovery of resources, including reusing, recycling, and obtaining energy from refuse, has received increased attention using cutting-edge techniques like artificial intelligence [53,60][29][36].
With the ultimate aim of reusing waste as a resource, artificial intelligence is used to support decision-making for biowaste treatments and develop bioenergy by depending on social, environmental, and economic criteria [61][37]. Implementing AI technologies to improve sustainable trash management will aid in reducing the overall amount of natural resources consumed by recycling, reusing, or recovering materials before they reach the end of their usefulness [62][38].
The amount of natural resources consumed can be reduced by reusing, recycling, or recovering some materials before they end their useful lives. AI technologies can be used to improve sustainable waste management [62][38]. AI is used for tracking an item to help monitor its condition and assess its suitability for reuse, as well as observe its environment to determine recycling possibilities.
AI technologies constantly improve the processes for collecting, transporting, sorting, and recycling various wastes [63][39].
AI allows for the proper use of data collected from industrial systems because it can watch and monitor data related to processes and products [64,65][40][41].

4.3. Potential Benefits of an AI-Supported Circular Economy Initiative

Recent years have seen a significant increase in interest in resource recovery from refuse [66][42].
Artificial intelligence can support the use of circular manufacturing strategies, improve energy efficiency, and extend the usable life of products and components by extracting as much value as possible from resources [67][43].
Artificial intelligence supports the decision-making process at the factory level (i.e., for goods and processes), accelerating an increase in circular economy values by tracking and monitoring products in real-time to determine their residual value [68][44].
Artificial intelligence helps with the challenges of transformation by incorporating information into operational aspects in a secure manner [69][45] and using rapid methods that increase system adaptability [70][46].


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