Page 32 - AI for Good-Innovate for Impact

AI for Good-Innovate for Impact

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Domain Climate Action
Testbeds or https:// greengrowthindex .gggi .org/ ?page _id = 3126
pilot deploy-
ments

3�2� Use case description

3�2�1 Description

The Global Green Growth Institute (GGGI) [1] is actively engaged in addressing the challenge of
accurately quantifying the co-benefits arising from climate mitigation and adaptation measures
within national strategic plans. This challenge is compounded by a limited understanding of
how these measures contribute to achieving specific Sustainable Development Goal (SDG)
targets. To tackle these complexities, GGGI has developed the Green Growth Simulation
(GGSim) Tool [2] that is linked to the Green Growth Index, measuring a country’s performance
in achieving the SDGs [3].

Previous GGSim applications covered SDG indicators such as energy intensity (SDG 7.3.1),
share of renewables in the total final energy consumption (SDG 7.2.1), installed renewable
energy capacity per capita (SDG 7.b.1 and 12.a.1), food loss and waste index (SDG 12.3.1.a and
b), share of forest area to total land area (SDG 15.1.1), above-ground biomass in forest (SDG
15.2.1), water use efficiency (SDG 6.4.1), level of water stress (SDG 6.4.2), treated wastewater
(SDG 6.3.1), CO and non-CO2 emissions ((SDG 13.3.2), etc.
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Tool Overview: The GGSim Tool includes systems dynamics models for energy and
transport, agriculture, forestry, other land use (AFOLU), water, waste, and pollution, aiming
to comprehensively analyze the complex dynamics of climate action initiatives. However,
to enhance its capability further, GGGI is in the process of integrating social inclusion and
gender models into the tool. This scenario-based analysis platform integrates AI-based network
analysis for assessing SDG co-benefits in national strategic plans such as National Adaptation
Plans (NAPs), Nationally Determined Contributions (NDCs), and Low Emissions Development
Strategies (LEDS).

Collaborative Efforts: The collaborative effort between GGGI and Professor Janos Abonyi's team
at the University of Pannonia, Hungary [4], has developed a national green growth simulation
tool for Hungary [5]. This tool facilitates the assessment of SDG co-benefits stemming from
selected policy interventions outlined in the Hungary National Clean Development Strategy
(NCDS). The systems covered by the tool include energy, land, water, and waste sectors. The
collaboration resulted in the pilot application of machine learning to implement and validate
network analysis for water use efficiency (SDG 6.4.1) and level of water stress (SDG 6.4.2).

Building upon this work, the collaboration aims to enrich the GGSim Tool further by integrating
AI-supported network analysis with existing systems dynamics models. This enhancement
will enable a more comprehensive evaluation of SDG co-benefits, particularly in the realm
of social inclusion and gender, which are crucial for promoting equitable and sustainable
development. The SDG indicators include, for example, the proportion of population using
safely managed drinking water services (SDG 6.1.1), proportion of population using (a) safely

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