Green Market Revolution and Generative AI - Report on Emerging technologies

Green Market Revolution and Generative AI

By Shivam Raj

Strategic Design Management Student

Atlas SkillTech University

Abstract: 

With global temperatures rising, it is more crucial than ever to address climate change. Considering that global temperatures are rising by 1.5 degrees Celsius, it is obvious that the green market must embrace a comprehensive and innovative strategy for sustainable practises. This study looks at how generative AI could lead to a revolution in the green market and offers a novel approach to using cutting-edge technology to mitigate the harmful consequences of climate change.

This report's main idea is to use generative AI to collect and examine historical data for certain places. Generative artificial intelligence (AI) gains significant strength in providing production units in the green market with customised solutions by using past climatic data, heat generation patterns, and other relevant information. Businesses are able to make well-informed decisions on sustainable practises by taking into account both the economic viability and the environmental impact thanks to this data-driven approach.

Beyond traditional environmental management techniques, the suggested system goes. Instead, it gives companies the flexibility to set up their manufacturing facilities in accordance with environmentally friendly principles that improve long-term profitability in addition to making the world a better place. By acting as a virtual environmental consultant and offering suggestions in real time based on changing environmental conditions, generative AI ensures resilience and adaptation in the face of a changing climate.

This paper explores the possibilities of integrating generative AI to transform the green market. This technology has the potential to redefine sustainability standards and propel a paradigm shift towards eco-conscious practises by providing a transformative approach to decision-making within the industry. Using generative AI in the green market becomes not only a strategic need but also a moral duty as we stand at the crossroads of the climate crisis, protecting our planet for future generations.

Introduction:

Generative AI which is a type of artificial intelligence can be used to generate/produce images, text, audios and data. Recently, Generative AI is highly being used to produce high resolution images, videos, texts, graphics and data in a matter of seconds. Statistical models in generative AI works on mathematical equations and variables. For generative AI, models are used to identify patterns and generate relevant or similar data. (Peck, 2023)

This technology was initially introduced in chatbots in 1960s which indicates that it is not a new technology. But it was not until 2014 that Generative AI could be used productively  with the introduction of generative adversarial networks, or GANs -- a type of machine learning algorithm. It helped in generating convincingly authentic and real images of people. (Lawton, n.d.)

Generative AI became popular only in 2022 with introduction of Chat GPT which is capable of interacting and giving answers with human like interactions. This technology is being involved in the lives of people just as a necessity similar to electricity or food. From businesses to schools, generative AI is being used to create images, drafts and for research purposes and getting relevant solutions. (Generative AI: What Is It, Tools, Models, Applications and Use Cases, 2023)

Generative AI is currently catering different industries and is involved in different functionalities for different sectors. Where banking sectors use this technology for risk management and fraud detection, the Education sector uses it for teaching, course planning, generating personalized presentations, and much more. The legal sector uses it to create contracts according to their requirements, whereas production companies use the technology for supply chain management and demand forecasting. Traveling sectors use Generative AI for online identity verification and finding out travel destinations. The fashion industry has started using this technology to generate different designs and to get data on upcoming trends. Recent developments in the healthcare sector to inculcate AI has led to development in genetic research and improving medicines. (Kamble, 2023)

Utilization of generative AI in machinery and different processes of trade can help in accumulating data on where pollution is created the highest and how different practices within the Green Market Revolution can be used to correct these problems. Green Market Revolution which is a movement towards a sustainable environment by shifting from the traditional methods of trade will be enhanced by the involvement of Generative AI. However, there is a humor that Generative AI, which itself creates digital pollution by leaving digital footprints behind, will be needed to collect data and help in the sustainability process.

Industry experts and production units owners were interviewed for this report in order to understand their perspectives and insights for the following research. Questions related to problems faced in the industry by both the ends were asked and also some feedback was taken to put additional inputs form the experts. 

This paper discusses how Generative AI can help in revolutionizing the Greem market revolution by assisting in analyzing pollution emissions by different production houses, different precautions that can be used and thought of to reduce these emissions, solutions to create fair trades among people, and different bodies that will be involved in the process.

Problem Statement:

Given that global temperatures are rising at an alarming rate—they are on track to surpass the 1.5 degree Celsius threshold—it is now imperative that the green market undergo a significant transformation. The absence of a dynamic and adaptive system that can take advantage of the abundance of historical data available to help businesses make environmentally conscious decisions, despite increased awareness and efforts to adopt sustainable practises, is a major challenge facing the industry.

Static models and traditional approaches that are inadequate in tackling the intricacies of climate change characterise the current status of the green market. The large and dynamic datasets pertaining to temperature fluctuations, patterns of heat generation, and other critical environmental factors are frequently difficult for traditional methods to absorb and analyse. This restriction hinders the industry's capacity to adapt proactively to the problems caused by climate change and prevents the incorporation of sustainable practises into regular business operations.

Furthermore, companies operating in the green market are lacking a comprehensive tool that can collect and evaluate historical data as well as deliver insights that are specific to a given location. Implementing sustainable solutions that are both economically feasible for production units and environmentally responsible is hampered by the lack of a strong, data-driven framework for decision-making.

By incorporating generative AI into the green market revolution, the challenge at hand is to close this gap. Relentlessly adopting this transformative technology, the industry has been slow to embrace generative AI, despite its obvious potential to identify patterns, analyse historical data dynamically, and provide real-time recommendations. Given that the incorporation of generative AI has the potential to revolutionise the green market and lead it towards a more robust and sustainable future, it is imperative that we overcome this inertia.

This problem statement emphasizes the urgency of addressing the current deficiencies in the green market's approach to climate change. By outlining the limitations of existing methodologies and technology adoption rates, it sets the stage for exploring how the integration of generative AI can revolutionize the industry, providing a solution that is not only innovative but essential for the well-being of our planet.Solution:

As global temperatures rise and approach the crucial 1.5-degree Celsius mark, the green market demands a swift and revolutionary fix. In order to meet this challenge, generative AI shows up as a game-changer, providing customised sustainability solutions based on specific locations.

1. Location based AI:

The ability of generative AI to collect and handle location-specific data is critical to its effectiveness. Users can give the AI access to a vast database of historical climate data, patterns of heat generation, and other pertinent environmental factors unique to their area by providing their location.

2. Dynamic Data analysis and Pattern Recognition:

Advanced machine learning algorithms are used by generative AI to analyse large datasets dynamically. Using historical climate data, it finds complex patterns and correlations that provide a detailed understanding of the environmental dynamics of a particular place.

3. Real Time Recommendations for carbon footprint reduction:

Generic artificial intelligence (AI) analyses location-specific data and then generates recommendations in real time that are specific to each location's environmental challenges. Energy-efficient technologies, waste reduction tactics, and sustainable resource management are just a few of the eco-friendly practises that are included in these recommendations.

4. User Friendly Interface and Accessibility:

By asking users to enter their location using a straightforward system, the solution provides an intuitive user interface. Presented in an easy-to-understand manner, the recommendations enable businesses to make well-informed decisions by providing complex environmental data.

5. Adaptibility to changing environmental conditions: 

Real-time analysis by generative AI guarantees flexibility in response to shifting environmental circumstances. The AI constantly modifies its recommendations in response to changing climate patterns, allowing businesses to remain ahead of environmental challenges.

6. Economic Viability and Business Integration:

Generative AI takes economic viability and environmental impact into account. The solution makes sure that sustainability practises are easily incorporated into the main functions of the green market by matching recommendations with financial capacities and business goals.

7. Overcoming challenges and ethical conditions:

Acknowledging potential challenges, the solution addresses issues such as data privacy, ethical AI usage, and the need for transparent decision-making processes to ensure responsible implementation in the green market.

ETAC

Trigger: 

Generative AI was created to interact with humans and generate text on the basis of the input it received. It was intended to synthetise human conversations.(White, n.d.)

With increased environmental concerns due to rapid rise in temperature around the globe, Green market revolution was introduced by the UN as an initiative to reduce carbon emission in the environment.

Players:

Open AI, Google, Hugging face and microsoft are some of the main players in the industry leading in Generative AI tools. (Hiter & Maguire, 2023)

Market leaders in green market revolution include companies like Patagonia, Johnson & Johnson, IKEA and Hersheys (J.W., 2023)

Drivers:

Corporate Social Responsibility: As a responsible business, it is the duty of any business to keep its environment and the nearby resources safe and continuously do something for the society. (Emeritus, 2022)

Government regulations: With a rise in the temperature and continuous global warming, strict actions are being taken by the government to make usage of Green sources of energy compulsory.

Cost saving: Green energy sources help in reducing the costs with very high margins. There are exemptions on taxes as well for the companies that use green sources of energy. (Emeritus, 2022)

Market demand: There is high demand in the market for opting green products but a shortage to complete the task on time. 

Data accuracy: With high advancements in Generative AI technology, we will be able to extract accurate data that is relevant.

Macro Impact - 

Disruptees:

Traditional Monitoring Systems: There are many manufacturing units that still use the olders methods of keeping records, ie. using books. It is difficult to implement modern methods of records keeping and any other technological change in these units as they don’t want to change their own methods and are not easily adaptable to changes.

Unsustainable Businesses: There are some businesses that are completely unsustainable and cannot apply sustainable methods of energy as they require high energy levels which cannot be fulfilled by the green energy reserves.

Network effects and Ineractions:
Data Sharing: Using AI to gather data and understand the problems in different manners will require a lot of data from each manufacturing units which will further lead to sharing of relevant data with in industries.

Global Reach: Generative AI will help in access to the data worldwide which will help in worldwide reach for all units that are being involved in the system.

Micro Impact:

Competitive Advantage: Having updated and new technologies installed in business will give a head start from the others being the first ones to implement the requirements and establishment in the industry.

Market Reputation and Customer Acquisition: Having a sustainable business helps in creating a good impression in the market and has an upper hand in acquiring new customers as many people are interested in buying sustainable products from the market. This will lead to opening to a wider customer segment.

Financial Benifits:

Cost savings: Applying green energy sources in business helps in huge cost savings on electricity. After a period of almost 5 years, negative electricity bills are produced which help in the long term impacts of the businesses.   

Tax Benefits: There are tax exemptions by the government of India if a business implies green energy sources in their businesses. Also, heavy taxes are levied on huge manufacturing units if their green credits are low and the carbon footprint of their business is high.

Technical merit: 

Data Acuracy: Using Generative AI will give accuracy as there will be no human errors in any calculations and there will accurate solutions to required problems.

 

Scalability: AI will not only help in getting solutions but also will help in scalability as AI will help in telling the best solutions for optimum utilization of resources.

Real time monitoring: Since, the AI technology works on data, it is easy to have real time monitoring of the required data for accuracy.

Tools, Ecosystems and skills: 

Environmental expertise: It will be important to have environmental expertise for the AI to produce accurate required data. It plays the most important role as the algorithms are based on these calculations.

AI intergration, Algorithms and Skills: It is important to have proper skills AI integration and writing accurate algorithms for obtaining best possible result.

Friction:

Lack of Data: It is difficult to have access to all required data because of legal issues from the government to share confidential data. TO process data of decades will be a huge task at a time which leads to usage of lesser data. 

Mistrust on AI: Since AI is a new term in the Indian market, there is a lack of trust among the businessmen to trust the data produced by AI. 

Primary Research:

Brief Introduction: The primary research was done with an objective to understand the point of view of the industry experts. It often happens that the solutions and research cannot be practically be implemented in the real life. Thus, a primary research regarding the topic was conducted.

Methodology: In-person interviews were conducted with the industry professionals and production unit owners because people are able to speak more and put in their opinions as well during an interview. It is difficult to understand the emotions while conducting a survey.

Method of Approach: The interviews were set up by finalising on call. All the interviewees were onboarded via personal contacts.

Insights: 

1. Businesses have a proper supply of energy source from the government which they don’t want to change.

2. Until it will be compulsory for a business to change their power source, they will not change it. It seems extra expense to them.

3. Getting services for free or at lesser costs is preferable to all the businesses.

4. Most of the businesses are aware of the increasing climatic changes but they are not willing to change as it affects their profit.

Analysis: 

1. Most of the production unit owners have their power supplies from the government which has been there for a long time. There have been no problem in the power supply ever which makes them less interested in changing their power source. 

2. It would make the work a lot more easier for the consultants of green energy sources and green certificate providers to analyse and come up with solutions by using generative AI to provide the best solution for businesses.

3. None of the business will change their practice until and unless they are forced to do so.

Conclusion:

In conclusion, the application of generative AI in the green market signifies an important change in environmentally friendly procedures. Utilising dynamic data analysis, location-based intelligence, and real-time recommendations, this solution gives businesses the ability to make well-informed decisions that not only help them reduce their carbon footprint but also help achieve the larger objective of mitigating climate change. The case studies that are being presented show how versatile and effective generative AI can be in a variety of environmental contexts, underscoring its potential to transform the green market and open the door to a more resilient and sustainable future.

References

., J. W. (2023, January 12). 9 Green Marketing Examples To Inspire You. ContentWriters. Retrieved October 10, 2023, from https://contentwriters.com/blog/brands-doing-green-marketing-right/

Emeritus. (2022, December 9). Green Marketing: What it is and Why Customers Love it. Emeritus. Retrieved December 8, 2023, from https://emeritus.org/blog/sales-and-marketing-green-marketing/

Hiter, S., & Maguire, J. (2023, April 17). Top 12 Generative AI Companies. eWEEK. Retrieved October 10, 2023, from https://www.eweek.com/artificial-intelligence/generative-ai-companies/

White, M. (n.d.). A Brief History of Generative AI. How did we get to where we are today in… | by Matt White | Medium. Matt White. Retrieved October 10, 2023, from https://matthewdwhite.medium.com/a-brief-history-of-generative-ai-cb1837e67106

World of Change: Global Temperatures. (n.d.). NASA Earth Observatory. Retrieved October 9, 2023, from https://earthobservatory.nasa.gov/world-of-change/global-temperatures