CLIMATE, FAST FASHION AND AI : THE “KILLER” COMBINATION

CLIMATE, FAST FASHION AND AI : THE “KILLER” COMBINATION Fast fashion can be defined as the unquenchable thirst for consumption of fashion commodities which is fuelled by the cost effective and pocket friendly garments available in the market. The fashion industry has in recent years been severely criticised for its indifference towards the social and environmental impact of its myopic business policies in nurturing this trend (Niinimaki et al.,2020). The rising environmental exploitation can easily be attributed to the fast-growing consumption of clothing and consequentially textile production. It is interesting how Artificial Intelligence plays into this as prima facie it appears to have no connection with fashion whatsoever. The implementation of predictive analytics and AI in marketing and advertising of fashion brands in recent years have shown tremendous improvement in sales and revenue. Fashion brands are using predictive technologies like growth hacking to enhance customer experience and expand active customer pool thereby accelerating consumption and production (Forbes,2019). The environmental footprint of fast fashion Almost 10% of global CO2 emissions is produced by the fashion industry (BBC, 2020). The industry consumes almost 79 trillion litres of water annually and is responsible for almost 20% of industrial water pollution (Niinimaki et al., 2020). It also accounts for 20% of the plastics produced globally each year. The UNFCCC (2021) estimates that the annual total greenhouse gas emissions from the manufacture of textiles alone were 1.2 gigatonnes. Evidently, this is larger than the combined emissions of 1.1gigatonnes from all foreign aircraft and maritime shipping. The production of garments has almost doubled in the last decade as suggested by reports

Figure 1- Source: CWR, Pulse of the fashion industry 2017 Report, IEA from McKinsey (2016), and the World Economic Forum (2016). An estimated 87% of the processed fibre needed for garment production is not recycled and finds it way to an incinerator or a landfill (Bloomberg, 2022). The fashion industry is quite slow to improve its sustainability. Although some mid to large sized companies have taken strides to increase sustainability in production but more than half of the market is still falling behind on aligning their corporate strategies with sustainable environmental goals (Boston Consulting Group, 2017). Fashion, the world’s second largest polluter will take up a quarter of the world’s carbon budget by 2050 if it does not implement sustainable business strategies on an industrial scale. How AI and marketing analytics amplifies sale of fashion products. Success in the fashion industry up until the middle of the 1980s was based on low-cost mass production of standardised styles that did not change frequently due to the design restrictions of the factories, such as Levi's 501 jeans and a man's white shirt, although there were exceptional cases of quickly changing haute couture (Brooks, 1979). Customers then, it seems, were less concerned with style and fashion and preferred simple clothing. As people started to become more fashion-conscious, the desire for traditional but straightforward clothing decreased as a result (Bailey, 2004). Brand awareness via advertising and marketing is the most essential element that precedes decision making in any form of product marketing (Rossiter, 1987). The world has come a long way from paper-based advertising in the mid-20th century to audio visual mode of advertisement (telemarketing) in the late-20th and first decade of the 21st century. Over the last decade and a half, since the advent of predictive analytics and machine learning models in sales and marketing, there has been a tremendous shift of the global market from print and tele advertising to digital marketing analytics.

Figure 2 - Source: Statista, WARC 2020 According to McKinsey & Co., marketing and sales-related areas have the largest potential benefit of AI due to its effects on marketing activities including next-best offers to clients (Davenport et al., 2011), programmatic buying of digital advertisements (Parekh, 2018), and predictive lead scoring (Harding 2017). For instance, throughout the period 1975-2018, global per-capita textile production climbed from 5.9 kilogramme to 13 kg per year (Peters, 2019). The average cost of clothes and footwear per person in the EU and UK has reduced from 30% in the 1950s to 12% in 2009 and just 5% in 2020, despite an increase in the number of items owned (Sajn, 2019; Jackson, 2008). More affordable clothing also triggers consumption and fuels the fast fashion trend (Anguelov, 2015). Nowadays, the average American consumer buys one piece of clothes every 5.5 days. In a similar vein, it is predicted that by 2030, global garment consumption will increase to 102 million tonnes from its current projection of 62 million tonnes (Global Fashion Agenda, 2017). If these production and consumption trends are closely observed, it can be found in line with the advent and growth period of digital predictive marketing. Analytics based marketing can be attributed a major share of responsibility to accelerate this trend of uncontrolled consumption by personalised advertisements and targeted marketing techniques. It is not too distant past, that Cambridge Analytica utilised data of around 87 million Facebook users and successfully influenced the election results in the US (The Guardian, 2018). The immense potential this technology can manifest in terms of influencing mass psychology and controlling customer behaviour is posing a serious threat to the environment when it comes to the fashion industry. In conclusion Fast fashion is evidently generating a huge stress on the environment and the global climate on an exponential scale. Also, having coupled with Artificial Intelligence technologies for their business strategies, the fashion industry is steadily expanding its customer pool which creates a domino effect on accelerating production and hence enlarging the carbon footprint of the industry. The fact that these huge clothing corporations have outsourced their production to most countries in the global south to procure cheap labour (Kumar, 2008) thereby spending millions of dollars on transportation, is pushing the limits of environmental tolerance. Policies should be formulated to mandate these companies to adhere to the SDG 15 of the UN and transparently monitor their adherence. Carbon offsetting sanctions should be scrutinized and taxed heavily to mitigate the abuse of this leeway by these large fashion brands. Moreover, the failure to arrest this trend by policy and legislation will hold the UN constituent nations from achieving their carbon neutral goals and restrict the global temperature rise and invite unforeseen and potentially menacing consequences. BIBLIOGRAPHY Anguelov, N. (2015), ‘The dirty side of the garment industry: Fast fashion and its negative impact on environment and society’, CRC Press. Boston Consulting Group (2017), ‘Pulse of The Fashion Industry Report 2017’, Global Fashion Agenda. Available at: https://globalfashionagenda.org/product/pulse-of-the-fashion-industry-2017/ (Accessed : Dec 03, 2022) Brooks A., (2019), ‘Clothing poverty: The hidden world of fast fashion and second-hand clothes’, Bloomsbury Publishing. Cadwalladr C.,Harrison E.G (2018), ‘Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach’, The Guardian, 17 March. 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