Kridha
Kridha's demand-driven, analytics based platform aligns with United Nations Sustainable Development Goal (UN-SDG) No. 12 by seeking to ensure sustainable consumption and production patterns.
The fashion industry is the 2nd largest Global Carbon polluter, trailing only behind the energy sector. For instance, in 2020 alone, the fashion industry was responsible for approximately 2.1 billion tonnes of greenhouse gas emissions. Despite its significant environmental impact, the fashion and apparel industry often fall under the radar and fail to attract substantial investments for sustainable change. As of 2021, only a fraction of global investment was aimed at sustainable fashion compared to sectors like renewable energy or electric vehicles. Historically, the fashion industry has been dominated by a supply-side economic model, resulting in overproduction. Roughly 150 billion items are produced each year, a figure that has been steadily increasing for the past decade. This excessive production leads to enormous waste, with a significant proportion of produced items discarded quickly. It is currently estimated that 3 out of 5 items end up in landfills within a year of production. Additionally, the fashion industry heavily relies on materials like polyester, which are derived from fossil fuels. Polyester makes up about 52% of the global textile market as of 2021, a share that has been steadily rising over the years. The production of these materials directly contributes to carbon emissions.
Kridha seeks to revolutionize the fashion industry's economic model, shifting from a supply-side focus to a demand-driven approach. By harnessing data analytics to gain a detailed understanding of consumer needs, Kridha aims to promote meaningful production cycles, aligning supply more closely with actual demand. Through these changes, Kridha aims to reduce waste, minimize carbon emissions, and contribute to the fight against climate change. This approach, if widely adopted, could help the fashion industry transition to a sustainable model, significantly reducing its carbon footprint.
Team Members
Priyanka Ladha (MSx, GSB), Prof. Ilya Strebulaev (PI, GSB)