ABSTRACT
This article seeks to explore the potential of AI-driven interventions in addressing climate-related issues, with a particular focus on fostering critical discourse surrounding the socio-cultural implications of digital innovation across climate streams and human collectives. As a fellow in the social enterprise Karo Sambhav, this article will examine selected AI use cases that promote the development of circular economy, assessing both their potential contributions and perceived risks to future employment prospects and socio-cultural dynamics, particularly in the context of India. Critically, the article will offer a qualitative methodology, through an Environmental, Social and Governance (ESG) analysis of AI-driven waste management.
The main goal of the author is to highlight the ambiguity of digital interventions in the modern era across cultures, communities, and agile weather conditions. The article will conclude with relevant recommendations to foster responsible usage of AI in waste verticals.
INTRODUCTION
The pervasive climate crisis requires a renowned approach to human action and climate resilience. Some scholars posit that the human-centric approach to climate solutions may not sufficiently address the severity of contemporary environmental challenges. In recent years, there has been a notable surge in the adoption of digital interventions within the climate domain, leveraging various emerging technologies to mitigate global environmental impacts. The paper will scrutinize the following research question:
RQ: To what extent can AI-driven interventions enhance the scope and impact of India’s waste value chain?
By delving into the concepts of circular economy and core AI functionalities within waste management solutions, this article will offer a qualitative analysis of the environmental, societal, and governmental dimensions of AI-driven circular economy. Ultimately, the aim is to foster a critical discussion on the intersection of digital innovation, the informal climate sector, and human livelihoods to ensure both planetary and human welfare.
LITERATURE REVIEW
The notion of circular economy represents a comprehensive approach to managing the life cycles of products, acknowledging the scarcity of vital materials and advocating for the repurposing of various components to foster sustainable and responsible resource utilization across generations. This recognition of material scarcity necessitates innovative strategies for creating primary and secondary raw materials, many of which are being tested and implemented through the integration of AI, Internet of Things, blockchain and other emerging technologies. Circular economy principles aim to minimize material waste and pollution, extend the longevity of products, and regenerate natural systems. The potential benefits of embracing circular economy models are substantial, with recent studies suggesting that the European circular economy could generate a net benefit of nearly 2 trillion euros by 2030, fostering job creation, innovation, and environmental conservation efforts. However, the long-term viability of circular economy models necessitates novel digital approaches to ensure their sustainability and resilience in the face of evolving environmental challenges (Ellen MacArthur Foundation, 2019).
This review will provide an overview of AI applications within circular economy frameworks, with a specific focus on smart waste management strategies. The insights gleaned from this review will serve as a foundation for discussions on Environmental, Social, and Governance (ESG) considerations within the Indian context. When evaluating the potential contributions of AI to human livelihoods, it is essential to differentiate between AI’s utility in enhancing human capabilities and its impact on various industry verticals. This article will elucidate AI’s operational significance in the climate domain, particularly in predictive maintenance and analytics within the waste value chain. While a global analysis of AI’s impact on human functionality is valuable, it is the localized application across industry verticals that fully demonstrates AI’s potential and its associated risks.
This article underscores the hybridization of traditional circular economy approaches with emerging technologies to promote planetary welfare and sustainable livelihoods. Against the backdrop of an escalating climate crisis, the role of AI in climate discourse becomes increasingly vital, diversifying mitigation strategies and advancing the achievement of Sustainable Development Goals (SDGs). AI, as a cluster of technologies, merits recognition for its collective potential in addressing climate challenges, enabling the expansion of human capabilities through data analysis, and predictive insights (Rathore & Malawalia, N.A).
AI-based algorithms offer support for various data processes, including trend classification, image recognition, natural language processing, and predictive analysis (Pregowska, Osial, & Urbańska, 2022). The accelerated development of generative AI in recent years holds promise for climate action, facilitating the design of circular products and mitigating human biases in circular processes. For instance, AI can provide designers with informed insights into product design and accelerate testing processes (Cornelius, 2024). Moreover, AI’s predictive capabilities can forecast usage patterns and identify inefficiencies in resource management, as demonstrated by a pilot program in the UK analyzing real-time water quality to mitigate water pollution (Rose, 2023).
Furthermore, AI fosters innovation in business models through dynamic pricing mechanisms, algorithmic matching for sharing secondary raw materials, and predictive analytics for reverse logistics. From a broader perspective, enhanced AI features can establish smart infrastructure for circular cities, facilitating the efficient distribution of secondary raw materials in the global economy (Rathore & Malawalia, N.A). The literature highlights AI’s catalytic role in waste management, particularly in advancing sustainable material practices (Qu, 2021; Abdallah et al, 2020; Fang et al.,2019). Potential AI applications include automated quality assessment of materials, sorting processes, and recommendations for optimized usage (Rathore & Malawalia, N.A). Notably, studies have emphasized the development of classification robots, predictive models, and smart bins for real-time waste monitoring, predictive collection, and overall waste management facility optimization. Various countries, including Austria, Germany, New Zealand, Japan, Singapore, and South Korea, have embraced AI in waste management and recycling solutions to improve resource utilization, efficiency, and recycling opportunities (Soni et al., 2019). Recent pilot studies have demonstrated the efficacy of AI interventions in waste management, enabling real-time supervision and material collection with enhanced safety measures. Scholars have underscored AI’s capacity to enhance operational efficiency, improving waste collection accuracy and timeliness (Pregowska, Osial, & Urbańska, 2022; Gutierrez, Jensen, Henius, & Riaz, 2015). However, there remains a need to address potential challenges and ethical considerations associated with the widespread adoption of AI in waste management.
DISCUSSION
Despite the promising potential of AI-driven interventions in the waste value chain, it is imperative to acknowledge and address the broader implications of their excessive and irresponsible usage. To this end, this paper advocates for a critical examination of the multifaceted impacts of AI within the context of waste management, emphasizing the interdisciplinary nature of digital innovation. While the discourse surrounding AI often revolves around hypothetical narratives of its capabilities, supported by limited case studies, particularly in emerging economies, it is essential to explore and mitigate the potential bottlenecks and challenges associated with AI implementation in climate sectors, such as waste management.
From an environmental perspective, the widespread adoption of AI technologies is hindered by its high energy consumption and data processing requirements. Current trends favor the utilization of large AI models to accommodate diverse user audiences and broader scope targets. However, the proliferation of such models necessitates substantial energy consumption and research and development costs, raising concerns about their environmental impact. As highlighted by GlobalData (Thomas, 2024), evidence suggests that by 2026, the energy consumption of AI’s datacenters is expected to double, reaching levels comparable to those of nation-states. Consequently, the substitution of climate damage caused by human activities with that generated by algorithms contradicts the principles of social innovation, necessitating a reconsideration of AI deployment strategies. Furthermore, societal considerations underscore the need to address the digital divide exacerbated by the accelerated proliferation of AI across various sectors. Access to digital benefits remains unequal due to socio-cultural factors and accessibility barriers, perpetuating disparities among communities. In the context of India, characterized by complex caste and gender dynamics and a significant informal sector, the deployment of AI technologies must be tailored to accommodate diverse audiences and address existing inequalities. Moreover, AI’s inherent biases and alienating nature pose significant challenges, particularly for marginalized communities. The reinforcement of socio-cultural biases by AI algorithms exacerbates existing inequalities and perpetuates discriminatory practices, disproportionately affecting vulnerable populations. In the informal waste sector, marginalized groups, including female and youth waste workers, face heightened risks of exclusion and exploitation. The intersectional nature of AI further amplifies these risks, underscoring the need for proactive measures to safeguard the welfare of vulnerable communities (Sherman, 2024; Saban Ireni & Sherman, 2022). Therefore, the repercussions for the future workforce and the welfare of the informal climate sector should be considered, as the vulnerable communities are more likely to suffer the most.
The potential displacement of informal waste workers by AI-driven technologies presents a philosophical dilemma regarding societal progress and human welfare. While AI algorithms can replicate certain human actions more efficiently, they risk exacerbating social inequalities and diminishing the dignity of labor. Moreover, the erosion of informal waste professions threatens to marginalize vulnerable communities further, highlighting the need for ethical and equitable AI deployment strategies. This scenario suggests the digital delivery of an instrument that gradually leads to the removal of the informal waste profession, containing repetitive and redundant actions. Importantly, these human actions can be easier for the algorithm to mimic but still represent some of the core functionalities of informal waste workers. Therefore, the risk of AI possibly replacing human workers increases in the waste value chain, due to the repetitive nature of human action and possible reduction in costs to the aggregators. Nonetheless, climate action should serve the planet’s citizens, and not its algorithms, bringing into conflict the mere positioning of AI as a suitable alternative to certain functionalities of human workers. From a governance perspective, the regulatory landscape surrounding AI remains fragmented and inadequate, posing challenges for policymakers seeking to address AI-related risks effectively. Recently, the first AI regulation was approved in the EU, addressing the threats of deepfakes and electoral interventions, suggesting risk-based classification for the aptness of AI actions. While the European Union has taken steps to regulate AI, international efforts to establish comprehensive regulatory frameworks remain nascent. The global nature of AI necessitates contextually relevant and diverse regulatory approaches to mitigate potential harms and safeguard human rights across industries and cultures. Crucially, any ESG analysis is more likely to vary across industries, professions, and cultures, and therefore impact the needed policies to harness the AI potential and moderate the aligned harms. Greater scrutiny should be attributed to AI literacy and adaptation of informal waste workers to the new workforce, with an increased focus on females and youth.
Moreover, it is crucial to provide increased social protection and support for those workers to pursue the profession they aspire to, under defined legal thresholds. The question of the regulatory aptness of the informal sector is ambiguous and exceeds the scope of this paper. On the one hand, the regulators are required to set legal thresholds to sustain the formal economy and guarantee equal tax payments, to ensure just conditions for all employers. On the other hand, in such a culturally diverse population as India in rural and urban settings as one, not all can join the formal economy, and are forced to be part of the informal sector to keep their welfare. As the Indian informal sector exceeds 80 percent of the national economy, this question becomes more complex, and requires interim solutions supporting informal workers and providing the right mechanisms to become more digitally fluent and transition into the formal economy.
To conclude, this paper sought to discuss the existing AI use cases in the climate ecosystem, particularly waste management, in a bid to promote sustainable and data-driven circular economy. Despite the great promise of AI-driven tools and interventions, it is crucial to acknowledge the environmental, societal and governmental aspects of such technology in traditional and informal climate practices as waste management.
Consequently, the paper recommends the establishment of supporting mechanisms for the informal waste sector in India, providing them with the right fluency skills to cope with the AI revolution and cater for their personal and collective needs. Due to the ambiguous request from regulators to support informal and non-regulatory practices, which do not align existing waste regulations, there might be a need for separate societal schemes that are supported by the regulation, to strengthen the Indian informal sector and not reduce it drastically. This can be supported by the work of multilateral organizations and civil society, in similarity to the existing work of the Global Partnership on Artificial Intelligence, established by the OECD (GPAI, n.d.).
While AI offers promising solutions for improving waste management efficiency and sustainability, its deployment must be guided by ethical considerations and a commitment to inclusive development. By addressing the environmental, societal, and governmental dimensions of AI deployment, policymakers can harness the potential of AI to advance sustainable development goals while mitigating its associated risks. Ultimately, the responsible integration of AI into waste management practices requires collaborative efforts from governments, industries, civil society, and academia to ensure that no individual or community is left behind in the pursuit of a more sustainable future.
As the frequency of artificial and non-human phenomena increases, there is a tendency to separate the human element from them. Algorithms receive greater scrutiny, due to their potential advancement of humanity and Darwinist evolution. Yet, this article posits that humans are and will always be the main benefactor of any innovation, and no individual should be forgotten. The design of AI-driven interventions should be environmentally, socially, and governmentally modified to accommodate the needs of all human collectives, including marginalized communities. Every person has the right to be seen and acknowledged, and algorithms should not discriminate against individuals due to the formality of their profession.
REFERENCES
Abdallaha, M., Talib, M. A., Feroz, S., Nasir, Q., Abdalla, H., & Mahfood, B. (2020). Artificial intelligence applications in solid waste management: A systematic research review. Waste Management, 109, 231–246.
Cornelius, R. (2024). Building a digital circular economy: Harnessing technology for sustainability. SLR. Retrieved from: Building a digital circular economy: Harnessing technology for sustainability (slrconsulting.com)
Ellen MacArthur Foundation. (2019). Artificial intelligence and the circular economy – AI as a tool to accelerate the transition. Retrieved from: artificial-intelligence-and-the-circular- economy.pdf (mckinsey.com)
Fang, B., Yu, J., Chen, Z., Osman, A. I., Farghali, M., Ihara, I., Hamza, E. H., Rooney, D. W., & Yap, P.-S. (2023, May 9). Artificial intelligence for waste management in smart cities: A review. Review of Environmental Chemistry Letters. Advance online publication. https://doi.org/10.1007/s10311-023-01604-3
Global Partnership on Artificial Intelligence. (N.D). Working Group on the Future of Work. Retrieved from https://gpai.ai/projects/future-of-work/
Gutierrez, J. M., Jensen, M., Henius, M., & Riaz, T. (2015). Smart waste collection system based on location intelligence. Procedia Computer Science, 61, 120–127.
Ireni-Saban, L., & Sherman, M. (2021). Ethical Governance of Artificial Intelligence in the Public Sector (1st ed.). Routledge. DOI: 10.4324/9781003106678
Rathore, A. S., & Malawalia, P. (N.A). Deploying Artificial Intelligence for Circular Economy and its Link with Sustainable Development Goals. International Journal of Policy Sciences and Law, 1(4).
Rose, E. (2022). Big Data has a big water problem – is AI the answer? Diginomica. Retrieved from: Big Data has a big water problem – is AI the answer?
Soni, U., Roy, A., Verma, A., & Jain, V. (2019). Forecasting municipal solid waste generation using artificial intelligence models—A case study in India. SN Applied Sciences, 1, 162.
Pregowska, A., Osial, M., & Urbańska, W. (2022). The Application of Artificial Intelligence in the Effective Battery Life Cycle in the Closed Circular Economy Model—A Perspective. Recycling, 7(6), 81.
Qu, D. (2021). Application of Artificial Intelligence in Waste Classification Management at University. In H. Sharma, V. K. Vyas, R. K. Pandey, & M. Prasad (Eds.), Proceedings in Adaptation, Learning and Optimization, Proceedings of the International Conference on Intelligent Vision and Computing (ICIVC 2021) (pp. 23–25). Qingdao, China: Springer.
Sherman, M. (2024, January 23). Intersectionality of AI-Powered Deepfakes: Lessons from Bollywood. Fidutam. Retrieved from Intersectionality of AI-Powered Deepfakes: Lessons from Bollywood
Thomas, E. (2024, March 14). How “energy-hungry AI” is straining grids and spreading climate disinfo. Power Technology. Retrieved from How “energy-hungry AI” is straining grids and spreading climate disinfo.
Science & Technology Diplomat, IFS
AI Literacy Lead @ GPAI-OECD