The decoupling of economic growth from environment degradation through more sustainable consumption and production policies has become one of the core pillars of many post-pandemic economic recovery strategies across the globe. Consequently, the circular economy model (CEM) has gained traction as an alternative to the “take, waste, and dispose system” associated with the linear economy, which for centuries has supported imperialist economic activity across the globe and shaped economic activity since the beginning of the Anthropocene.
While there is growing hype that the potential industry-wide applications of artificial intelligence (AI) for various use cases can enhance circularity in Africa, the concept of circularity is not new in Africa, in addition to this, there is extensive literature on its application in indigenous communities. The expectation is that AI-led circularity will result in inclusive and sustainable industrial development, and a more equitable, resilient, and sustainable recovery. Ultimately mitigating the negative environmental outcomes associated with the linear economic model. But will it?
For centuries, technological disruptions have facilitated industrial revolutions and their associated shifts in socioeconomic activity. Simultaneously, these disruptions have disproportionately benefited many wealthier economies, often with little regard for the negative impacts they have for the most vulnerable in these ecosystems. The latest (digital) industrial revolution has the potential to facilitate more sustainable growth trajectories, but also presents systemic risks that could emerge as the result of ubiquitous data-based digital diffusion.
While there is increased discourse on AI-based “green new deals” , and excitement about how CEMs coupled with AI can facilitate the “twin transition”; leveraging AI to enhance circularity and promote environmental sustainability will succeed only if it can facilitate a just transition for all who participate in our interdependent ecosystems.
In Africa, we need to consider the following:
Limited evidence that AI can support inclusive circularity in Africa
With a few exceptions, most countries well positioned to build AI-based circular innovations that can support sustainable and equitable structural transformation are based in the global North. These countries have ecosystems where communities, governments, and private actors have the requisite economic endowments and enablers that facilitate technological-enabled responses to negative climate and ecological change. They are able to draw on their infrastructure, institutions, and resources to develop and implement public policies, that facilitate the design of environmentally friendly productive activity, waste reduction, and efficient consumption across the entire supply chain network in key industries.
A matter of concern is that although advocacy for a circular-based economy and its implementations through AI is increasing globally, most of the research supporting this advocacy has been conducted in high-income countries. Little is known about the conditions necessary to advance the use of AI to support the CEM in African contexts, nor the barriers to scale industry-wide implementation. Over and above an enabling policy and regulatory environment, other critical issues for consideration in Africa include inadequate infrastructure, limited technical capacity, low digital capabilities, weak financing partnerships, as well as the lack of reliable disaggregated public data in key areas such as consumption and waste in priority industries, to name a few.
Given these dynamics, it is crucial to have practical use-case examples and evidence based research of how an AI-led CEM can be applied in socio-economic realities, that are in stark contrast to those found in wealthier nations.
AI- related risks
While the deployment of AI-based technologies could be a game-changer for climate change mitigation efforts, there is limited attention to the possible systemic risks that AI presents to the advancement of just and equitable sustainable development.
Beyond critical considerations of the paradox associated with the negative environmental impacts of deploying AI systems, AI deployments need to be implemented in a manner that acknowledges the historical, cultural, political, economic, and technological contexts of the society and environments where AI-led CEM models will be used. If AI- based solutions are not deployed in a responsible manner, there could be unintended consequences which could worsen inequities and external disruptions.
The true impact of AI in creating a sustainable economic recovery can only be realized if the potential systemic risks, negative environmental impacts associated with AI, and the structural conditions/endowments to support more just and sustainable digital economies and futures are considered. To date, this has not been the focus of many mainstream AI ethics and governance tools, particularly in Africa.
Uneven trans-geographical technological, environmental, social and governance (TESG) factors
While there are pockets of robust, localised production in developing regions, we cannot ignore the uneven technological, environmental, social, and governance (TESG) power dynamics, such as: geo-political influence, global division of labour, uneven digital economy endowments, and control of global value chains, that exist, and spill-over to our increasingly data-driven global digital economy.
Furthermore, the challenge of closing global materials loops and regenerating natural assets through using technology is a daunting task with many dimensions to consider. The linear or “take-make-dispose” economy which has shaped industrial evolution in many global North countries has long relied on structures that ensure cheap and available factors of production (labour, land,capital). These were needed to create conditions for industrialisation-led economic growth, and ancillary phenomena.
Global capitalism–based institutions have also facilitated industrialisation, value chains, and technological innovations in favour of wealthier countries and were central to creating global competitive advantages that spurred economic growth and development for many of those who manage and control today’s global linear supply chains and production systems.
In addition, the interconnected global risks of resource competition, commodity price volatility, and changing consumer demands, amongst others, add further complexity which highlight Africa’s vulnerability in these systems.
Diversity in environmental stewardship and creating just knowledge production
Industrialisation-based environmental destruction, systemic oppression, and the climate crisis are interrelated and are a perpetuation of colonial legacies that are inherent in the global structure of multilateral institutions and systems that shape the global economy. Also, historical legacies still impact the discourse on environmental stewardship. For example, ownership of resources is still framed by global conservation institutions and policies that despite several civil society initiatives, still largely exclude and discriminate against indigenous and rural communities.
Diversity in environmental stewardship is needed to strengthen and improve existing narratives about ecological conservation. We need to acknowledge that equity and inclusion in knowledge production broadens fields of knowledge by including traditionally excluded perspectives in the global governance and policy agenda. Consequently, the normative idea of “green new deals” has raised complex questions regarding who has the expertise to frame alternatives in a diverse world and the implications of these alternatives for marginalised people in different socioeconomic contexts. Just knowledge production is needed to frame development paradigms that draw out the methods and conceptual frameworks that consider the realities of underrepresented actors in the discourse.
Diverse perspectives are also needed in order to initiate a truly equal dialogue where we explore emerging AI risks, identify critical questions, and discuss the limitations of current AI ethics and governance mechanisms. This is necessary to examine under what conditions an AI-based CEM will be able to reduce global inequalities and promote human development in the context of the SDGs in Africa. Also, the inextricable link between gender, racial, ethnic, social, and climate injustices needs to be considered in these processes. Collecting sex-disaggregated data, could spur the initial efforts to understand the multidimensional impacts of both climate change and AI. This can support the development of transversal, evidence based, fit-for-purpose public policies that mitigate intersectional inequalities.
Key Points
Leveraging AI to enhance circularity can provide possible solutions to cross-cutting socio-economic development issues Africa faces , but these innovations may not always result in a positive outcome. They depend on a myriad of factors and multilayered processes. Many developing economies need to balance trade-offs between productive efficiency, industrial innovations, economic justice, and ecological resilience to effectively realise sustainable structural economic transformation.
Although global interdependencies are acknowledged, developed countries continue to pressure developing countries to adopt so-called “global” standards, ethics, policies, and institutional arrangements, with utopian visions for leveraging data-driven technologies to mitigate climate change and environmental degradation that are inadequate to address the everyday realities and struggles of the global Majority. These visions often ignore whether the data-driven technological interventions proposed by wealthier countries are appropriate for developing countries.
We need to critically assess current narratives, multidimensional power dynamics, and emerging pathways of AI and circularity in the global North and the global Majority, and understand how these are similar, differ, or possibly even conflict with each other. This includes assessing the framework in which AI is used, the existing ecosystems where it is deployed, who are the “experts” that frame discourse and governance, and potential impacts of its deployment. Funding and opportunities to encourage African led research and practical exploration beyond academia is crucial to understand if and how beneficial AI can support a just transition to the circular economy and overall sustainable digitalisation.
This blog has been revised and was first published here in March 2022
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