Linking Distributed Optimization Models for Food, Water, and Energy Security Nexus Management

Ermoliev, Yuri; Zagorodny, Anatolij G.; Bogdanov, Vjacheslav L.; Ermolieva, Tatiana; Havlik, Petr; Rovenskaya, Elena; Komendantova, Nadejda; Obersteiner, Michael

doi:10.3390/su14031255

Cited by 11 publications

(7 citation statements)

References 41 publications

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“…However, an ambivalent impact is observed on SDG 7.3.1 dedicated to energy intensity (primary energy) which merits further analysis since the related net effect remains unclear. AI for land management can support efficient use of resources leading to lower energy consumption and intensity of the economy [174,175]. However, potential rebound effects [176] may arise along with growing energy demand from the DL algorithms [177,178], which might outweigh the benefits.…”

Section: Group 3: Ai As a DImentioning

confidence: 99%

Operationalizing Digitainability: Encouraging mindfulness to harness the power of digitalization for sustainable development

Gupta¹,

Zeballos²,

Castro³

et al. 2022

Preprint

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Digitalization is globally transforming the world with profound implications. It has enormous potential to foster progress toward sustainability. However, in its current form, digitalization also continues to enable and encourage practices with numerous unsustainable impacts affecting our environment, ingraining inequality, and degrading quality of life. There is an urgent need to identify such multifaceted impacts holistically. Impact assessment of digital interventions (DIs) leading to digitalization is important specifically for Sustainable Development Goals(SDGs). Action is required to understand the pursuit of short-term gains toward achieving long-term value-driven sustainable development. We need to understand the impact of DIs on various actors and in diverse contexts. A holistic understanding of the impact it creates will help us align it with visions of sustainable development and identify potential measures to mitigate negative short and long-term impacts. The recently developed Digitainability Assessment Framework (DAF) unveils the impact of DIs with an in-depth context-aware assessment and offers an evidence-based impact profile of SDGs at the indicator level. We performed the impact assessment of diverse technologies using DAF. This paper summarizes the insights from the Digitainable Spring School 2022 on "Sustainability with Digitalization and Artificial Intelligence," one of whose goals was to operationalize the DAF as a tool in the action learning process with diverse professionals in the field of digitalization and sustainability. The DAF guides a holistic context-aware process formulation for a given DI. An evidence-based evaluation within the DAF protocol benchmarks a specific DI’s impact against the SDG indicators framework. The operationalization of the DAF was carried out by looking at four different DIs: smart home technologies (SHT) for energy efficiency, blockchain for food security, artificial intelligence for land use cover and changes (LUCC), and big data for international law. Each of the four studies addresses different DIs for digitainability assessment using different techniques for a diverse group of indicators, demonstrating the potential of the DAF but also outlining the existing data gaps that limit a comprehensive analysis.

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Section: Group 3: Ai As a DImentioning

confidence: 99%

Operationalizing Digitainability: Encouraging mindfulness to harness the power of digitalization for sustainable development

Gupta¹,

Zeballos²,

Castro³

et al. 2022

Preprint

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“…Here matrix 𝐷 (𝑘) defines the marginal sectorial/regional resources use with a vector of resources 𝑑, 𝑑 ≥ 0. The problem of models' linkage under full information can be formulated as a total net profit maximization ∑ ⟨𝑐 (𝑘) , 𝑥 (𝑘) ⟩ 𝐾 𝑘=1 → max (6) s. t. to constraints (2)-( 5), 𝑘 = 1,2, . .…”

Section: Linking Optimization Models Under Asimentioning

confidence: 99%

“…. , 𝑦 (𝐾) ) let us denote by 𝐹(𝑦) the optimal value of function (6) under constraints ( 2)-( 4). Therefore,…”

Section: Nonsmooth Modelmentioning

confidence: 99%

“…The required linkage algorithm is defined as a subgradient procedure maximizing function 𝐹(𝑦) s.t. the joint constraints (5). These constraints define the feasible set of the algorithm, which can be denoted also as 𝑌.…”

Section: Nonsmooth Modelmentioning

confidence: 99%

“…Among the methodological aims of the project is the development of "Models' Linkage" algorithms which are in the core of the IDSS as they enable distributed models' linkage and data integration into one system on a platform [5][6][7][8]. The linkage algorithms solve the problem of linking distributed models, e.g., sectorial and/or regional, into an inter-sectorial inter-regional integrated models.…”

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confidence: 99%

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Artificial Intelligence, Machine Learning, and Intelligent Decision Support Systems: Iterative “Learning” SQGbased procedures for Distributed Models’ Linkage

Ermolieva¹,

Ermoliev²,

Zagorodny³

et al. 2022

Stuc.intelekt

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In this paper we discuss the on-going joint work contributing to the IIASA (International Institute for Applied Systems Analysis, Laxenburg, Austria) and National Academy of Science of Ukraine projects on “Modeling and management of dynamic stochastic interdependent systems for food-water-energy-health security nexus” (see [1-2] and references therein). The project develops methodological and modeling tools aiming to create Intelligent multimodel Decision Support System (IDSS) and Platform (IDSP), which can integrate national Food, Water, Energy, Social models with the models operating at the global scale (e.g., IIASA GLOBIOM and MESSAGE), in some cases ‘downscaling’ the results of the latter to a national level. Data harmonization procedures rely on new type non-smooth stochastic optimization and stochastic quasigradient (SQG) [3-4] methods for robust of-line and on-line decisions involving large-scale machine learning and Artificial Intelligence (AI) problems in particular, Deep Learning (DL) including deep neural learning or deep artificial neural network (ANN). Among the methodological aims of the project is the development of “Models’ Linkage” algorithms which are in the core of the IDSS as they enable distributed models’ linkage and data integration into one system on a platform [5-8]. The linkage algorithms solve the problem of linking distributed models, e.g., sectorial and/or regional, into an inter-sectorial inter-regional integrated models. The linkage problem can be viewed as a general endogenous reinforced learning problem of how software agents (models) take decisions in order to maximize the “cumulative reward". Based on novel ideas of systems’ linkage under asymmetric information and other uncertainties, nested strategic-operational and local-global models are being developed and used in combination with, in general, non-Bayesian probabilistic downscaling procedures. In this paper we illustrate the importance of the iterative “learning” solution algorithms based on stochastic quasigradient (SQG) procedures for robust of-line and on-line decisions involving large-scale Machine Learning, Big Data analysis, Distributed Models Linkage, and robust decision-making problems. Advanced robust statistical analysis and machine learning models of, in general, nonstationary stochastic optimization allow to account for potential distributional shifts, heavy tails, and nonstationarities in data streams that can mislead traditional statistical and machine learning models, in particular, deep neural learning or deep artificial neural network (ANN). Proposed models and methods rely on probabilistic and non-probabilistic (explicitly given or simulated) distributions combining measures of chances, experts’ beliefs and similarity measures (for example, compressed form of the kernel estimators). For highly nonconvex models such as the deep ANN network, the SQGs allow to avoid local solutions. In cases of nonstationary data, the SQGs allow for sequential revisions and adaptation of parameters to the changing environment, possibly, based on of-line adaptive simulations. The non-smooth STO approaches and SQG-based iterative solution procedures are illustrated with examples of robust estimation, models’ linkage, machine learning, adaptive Monte Carlo optimization for cat risks (floods, earthquakes, etc.) modeling and management.

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Thermal Power Plants’ Coal Stock Short Term Projection Method for Ensuring National Energy Security

Shulzhenko

Kostyukovskyi

Malyarenko

et al. 2023

Studies in Systems, Decision and Control

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Linking Distributed Optimization Models for Food, Water, and Energy Security Nexus Management

Cited by 11 publications

References 41 publications

Operationalizing Digitainability: Encouraging mindfulness to harness the power of digitalization for sustainable development

Operationalizing Digitainability: Encouraging mindfulness to harness the power of digitalization for sustainable development

Artificial Intelligence, Machine Learning, and Intelligent Decision Support Systems: Iterative “Learning” SQGbased procedures for Distributed Models’ Linkage

Thermal Power Plants’ Coal Stock Short Term Projection Method for Ensuring National Energy Security

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