Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda

Nishant, Rohit; Kennedy, Mike; Corbett, Jacqueline

doi:10.1016/j.ijinfomgt.2020.102104

Cited by 538 publications

(225 citation statements)

References 153 publications

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“…t) and in this case, new tasks raise aggregate output and are immediately produced with labor; • For n > _ n (ρ) , we have w(t) > R(t) , as a result, automated tasks raise aggregate output and are immediately produced with capital; and21 The functions w N (n) and w I (n) depicted in this figure are introduced and explained below.…”

mentioning

confidence: 99%

The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment

Acemoğlu

Restrepo

2018

American Economic Review

1,544

498

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We examine the concerns that new technologies will render labor redundant in a framework in which tasks previously performed by labor can be automated and new versions of existing tasks, in which labor has a comparative advantage, can be created. In a static version where capital is fixed and technology is exogenous, automation reduces employment and the labor share, and may even reduce wages, while the creation of new tasks has the opposite effects. Our full model endogenizes capital accumulation and the direction of research toward automation and the creation of new tasks. If the long-run rental rate of capital relative to the wage is sufficiently low, the long-run equilibrium involves automation of all tasks. Otherwise, there exists a stable balanced growth path in which the two types of innovations go hand-in-hand. Stability is a consequence of the fact that automation reduces the cost of producing using labor, and thus discourages further automation and encourages the creation of new tasks. In an extension with heterogeneous skills, we show that inequality increases during transitions driven both by faster automation and the introduction of new tasks, and characterize the conditions under which inequality stabilizes in the long run. (JEL D63, E22, E23, E24, J24, O33, O41)

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mentioning

confidence: 99%

The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment

Acemoğlu

Restrepo

2018

American Economic Review

1,544

498

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“…Any decision support in forest management requires understanding the type, scale, and depth of available information and knowledge about forest systems (Stock and Rauscher, 1996). Moreover, given large uncertainty in human behavior (Nishant et al, 2020), it is important to understand how people affected by a particular forest management application will react. In the absence of cognitive support, forest managers largely make decisions relying on subjective values, individual preferences, perceptions, and expectations.…”

Section: Forest System Complexity and Algorithmic Decision-makingmentioning

confidence: 99%

Trustworthy Predictive Algorithms for Complex Forest System Decision-Making

Rana

Varshney

2021

Front. For. Glob. Change

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Advances in predictive algorithms are revolutionizing how we understand and design effective decision support systems in many sectors. The expanding role of predictive algorithms is part of a broader movement toward using data-driven machine learning (ML) for modalities including images, natural language, speech. This article reviews whether and to what extent predictive algorithms can assist decision-making in forest conservation and management. Although state-of-the-art ML algorithms provide new opportunities, adoption has been slow in forest decision-making. This review shows how domain-specific characteristics, such as system complexity, impose limits on using predictive algorithms in forest conservation and management. We conclude with possible directions for developing new predictive tools and approaches to support meaningful forest decisions through easily interpretable and explainable recommendations.

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“…Di Vaio et al [8] explore the influences of AI on business models in the context of the SDGs identifying a research gap for SGD 12 "sustainable consumption and production patterns". Nishant et al [9] see the potential benefit of AI in enabling effective and efficient environmental governance with a focus on developing resilient sustainable systems. Their comprehensive literature review summarizes the applications of AI in societal matters and aspects of national economics and the design of energy systems.…”

Section: Introductionmentioning

confidence: 99%

“…Their comprehensive literature review summarizes the applications of AI in societal matters and aspects of national economics and the design of energy systems. In conclusion, they mention the improvement in "industrial environmental performance" as one out of four promising practical applications of AI [9]. Without a detailed description or specific examples, the optimization of resource consumption in industrial processes is assessed as being beneficial for operations at every scale [9].…”

Section: Introductionmentioning

confidence: 99%

“…In conclusion, they mention the improvement in "industrial environmental performance" as one out of four promising practical applications of AI [9]. Without a detailed description or specific examples, the optimization of resource consumption in industrial processes is assessed as being beneficial for operations at every scale [9]. The present article addresses these issues by identifying the utilization of AI in this regard by a thorough literature review.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

et al. 2021

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Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of thecurrent AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency.

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Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda

Cited by 538 publications

References 153 publications

The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment

The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment

Trustworthy Predictive Algorithms for Complex Forest System Decision-Making

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

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