ORCID Profile
0000-0001-8346-4043
Current Organisation
CSIRO
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: IOP Publishing
Date: 11-2021
Abstract: The 17 Sustainable Development Goals (SDGs) represent a holistic and ambitious agenda for transforming the world towards societal well-being, economic prosperity, and environmental protection. Achieving the SDGs is, however, challenged by the performance of interconnected sectors and the complexity of their interactions which drive non-linear system responses, tipping points, and spillover effects. Systems modelling, as an integrated way of thinking about and modelling multisectoral dynamics, can help explain how feedback interactions within and among different sectors can lead to broader system transformation and progress towards the SDGs. Here, we review how system dynamics, as a prominent systems modelling approach, can inform and contribute to sustainability research and implementation, framed by the SDGs. We systematically analyse 357 system dynamics studies undertaken at the local scale where the most important SDG impacts and their initiators are often located, published between 2015 (i.e. SDGs’ inception) and 2020. We analyse the studies to illuminate strengths and limitations in four key areas: ersity of scope interdisciplinarity of the approaches the role of stakeholder participation and the analysis of SDG interactions. Our review highlights opportunities for a better consideration of societal aspects of sustainable development (e.g. poverty, inequality) in modelling efforts integrating with new interdisciplinary methods to leverage system dynamics modelling capabilities improving genuine stakeholder engagement for credibility and impacts on the ground and a more in-depth analysis of SDG interactions (i.e. synergies and trade-offs) with the feedback-rich structure of system dynamics models.
Publisher: Wiley
Date: 10-2018
DOI: 10.1002/INST.12209
Publisher: Elsevier BV
Date: 09-2019
Publisher: Elsevier BV
Date: 11-2017
Publisher: Elsevier BV
Date: 10-2011
Publisher: California Digital Library (CDL)
Date: 31-01-2022
DOI: 10.31223/X54W5H
Abstract: The 17 Sustainable Development Goals (SDGs) represent a holistic and ambitious agenda for transforming the world towards societal well-being, economic prosperity, and environmental protection. Achieving the SDGs is, however, challenged by the performance of interconnected sectors and the complexity of their interactions which drive non-linear system responses, tipping points, and spillover effects. Systems modelling, as an integrated way of thinking about and modelling multisectoral dynamics, can help explain how feedback interactions within and among different sectors can lead to broader system transformation and progress towards the SDGs. Here, we review how system dynamics, as a prominent systems modelling approach, can inform and contribute to sustainability research and implementation, framed by the SDGs. We systematically analyse 357 system dynamics studies undertaken at the local scale where the most important SDG impacts and their initiators are often located, published between 2015 (i.e., SDGs’ inception) and 2020. We analyse the studies to illuminate strengths and limitations in four key areas: ersity of scope interdisciplinarity of the approaches the role of stakeholder participation and the analysis of SDG interactions. Our review highlights opportunities for a better consideration of societal aspects of sustainable development (e.g., poverty, inequality) in modelling efforts integrating with new interdisciplinary methods to leverage system dynamics modelling capabilities improving genuine stakeholder engagement for credibility and impacts on the ground and a more in-depth analysis of SDG interactions (i.e., synergies and trade-offs) with the feedback-rich structure of system dynamics models.
Publisher: Elsevier BV
Date: 09-2017
Publisher: Elsevier BV
Date: 03-2017
Publisher: IEEE
Date: 12-2018
Publisher: Routledge
Date: 28-11-2019
Publisher: Elsevier BV
Date: 12-2018
Publisher: Elsevier BV
Date: 11-2019
Publisher: IEEE
Date: 10-2018
Publisher: California Digital Library (CDL)
Date: 16-12-2022
DOI: 10.31223/X5JP9P
Abstract: Decision-making under uncertainty is important for managing human-natural systems in a changing world. A major source of uncertainty that challenges decisions is rooted in their multi-actor settings, i.e., the poorly understood societal actors with erse values, complex relationships, and conflicting management approaches. Despite general agreement across disciplines on co-producing knowledge for viable and inclusive outcomes in multi-actor settings, there is still limited conceptual clarity and no systematic understanding on what co-production means in decision-making under uncertainty and how it can be achieved. Here, we use content analysis and clustering to systematically analyse 50 decision-making cases with multiple time and spatial scales across 26 countries and in 9 different sectors in the last decade to serve two aims. The first is to synthesise the key recurring approaches that underpin high quality decision co-production across many cases of erse features. The second is to identify important deficits and opportunities to leverage existing approaches towards flourishing co-production in supporting decision-making. We find that four general approaches emerge centred around: promoting innovation for robust and equitable decisions broadening the span of co-production across interacting systems fostering social learning and inclusive participation and improving pathways to impact. Additionally, five key areas that should be addressed to improve decision co-production are identified in relation to: participation ersity social learning power relationships governance inclusivity and transformative change. Characterising the emergent approaches and their key areas for improvement can help guide future works towards more pluralistic and integrated science and practice.
Publisher: Cambridge University Press (CUP)
Date: 2022
DOI: 10.1017/SUS.2022.7
Abstract: Models are increasingly used to inform the transformation of human–Earth systems towards a sustainable future, aligned with the sustainable development goals (SDGs). We argue that a greater ersity of models ought to be used for sustainability analysis to better address complexity and uncertainty. We articulate the steps to model global change socioeconomic and climatic scenarios with new models. Through these steps, we generate new scenario projections using a human–Earth system dynamics model. Our modelling brings new insights about the sensitivity of sustainability trends to future uncertainty and their alignment with or ergence from previous model-based scenario projections. The future uncertainty and complexity of alternative socioeconomic and climatic scenarios challenge the model-based analysis of sustainable development. Obtaining robust insights requires a systematic processing of uncertainty and complexity not only in input assumptions, but also in the ersity of model structures that simulates the multisectoral dynamics of human and Earth system interactions. Here, we implement the global change scenarios, that is, the shared socioeconomic pathways and the representative concentration pathways, in a feedback-rich, integrated assessment model (IAM) of human–Earth system dynamics, called FeliX, to serve two aims: (1) to provide modellers with well-defined steps for the adoption of established scenarios in new IAMs and (2) to explore the impacts of model uncertainty and its structural complexity on the projection of these scenarios for sustainable development. Our modelling shows internally consistent scenario storylines across sectors, yet with quantitatively different realisations of these scenarios compared to other IAMs due to the new model's structural complexity. The results highlight the importance of enumerating global change scenarios and their uncertainty exploration with a ersity of models of different input assumptions and structures to capture a wider variety of future possibilities and sustainability indicators. New study highlights the importance of global change scenario analysis with new, SDG-focused IAMs.
Publisher: Elsevier BV
Date: 03-6000
Publisher: Elsevier BV
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 11-11-2019
Publisher: Wiley
Date: 07-2019
Publisher: Journal of Artificial Societies and Social Simulation
Date: 2018
DOI: 10.18564/JASSS.3629
Publisher: American Geophysical Union (AGU)
Date: 09-2023
DOI: 10.1029/2022EF003326
Publisher: Elsevier BV
Date: 04-2023
Publisher: California Digital Library (CDL)
Date: 07-04-2023
DOI: 10.31223/X50H2B
Abstract: Transforming the global food system is necessary to avoid exceeding planetary boundaries. A robust evidence base is crucial to assess the scale and combination of interventions required for a sustainable transformation. We developed a risk assessment framework, underpinned by a meta-regression of 60 global food system modeling studies, to quantify the potential of in idual and combined interventions to mitigate the risk of exceeding the boundaries for land-system change, freshwater use, climate change, and biogeochemical flows by 2050. Limiting the risk of exceedance across four key planetary boundaries requires a high but plausible level of ambition in all demand-side (diet, population, waste) and most supply-side interventions. Attaining the required level of ambition for all interventions relies on embracing synergistic actions across the food system.
Publisher: Resilience Alliance, Inc.
Date: 2021
Publisher: American Geophysical Union (AGU)
Date: 09-2022
DOI: 10.1029/2022EF002873
Abstract: Achieving the Sustainable Development Goals (SDGs) is contingent on managing complex interactions that create synergies and trade‐offs between different goals. It is, therefore, important to improve our understanding of them, their underlying causal drivers, future behaviors, and policy implications. Prominent methods of interaction analysis that focus on modeling or data‐driven statistical correlation are often insufficient for giving an integrated view of interaction drivers and their complexity. These methods are also usually too technically complex and heavily data‐driven to provide decision‐makers with simple practical tools and easily actionable and understandable results. Here, we introduce a flexible and practical systemic approach, termed archetype analysis, that generalizes a number of recurring interaction patterns among the SDGs with unique drivers, behaviors, and policy implications. We review eight interaction archetypes as thinking aids to analyze some of the important synergies and trade‐offs, supported by several empirical ex les related to the SDGs (e.g., poverty, food, well‐being, water, energy, housing, climate, and land use) to demonstrate how they can be operationalized in practice. The interaction archetypes are aimed to help researchers and policymakers as a diagnostic tool to identify fundamental mechanisms of barriers or policy resistance to SDG progress, a comparative tool to enhance knowledge transfer between different cases with similar drivers, and a prospective tool to design synergistic policies for sustainable development.
Publisher: Elsevier BV
Date: 09-2020
Publisher: Wiley
Date: 22-03-2022
Publisher: Wiley
Date: 30-08-2021
Publisher: American Geophysical Union (AGU)
Date: 03-2021
DOI: 10.1029/2020EF001843
Abstract: The achievement of global sustainability agendas, such as the Sustainable Development Goals, relies on transformational change across society, economy, and environment that are co‐created in a transdisciplinary exercise by all stakeholders. Within this context, environmental and societal change is increasingly understood and represented via participatory modeling for genuine engagement with multiple collaborators in the modeling process. Despite the ersity of participatory modeling methods to promote engagement and co‐creation, it remains uncertain what the extent and modes of participation are in different contexts, and how to select the suitable methods to use in a given situation. Based on a review of available methods and specification of potential contextual requirements, we propose a unifying framework to guide how collaborators of different backgrounds can work together and evaluate the suitability of participatory modeling methods for co‐creating sustainability pathways. The evaluation of method suitability promises the integration of concepts and approaches necessary to address the complexities of problems at hand while ensuring robust methodologies based on well‐tested evidence and negotiated among participants. Using two illustrative case studies, we demonstrate how to explore and evaluate the choice of methods for participatory modeling in varying contexts. The insights gained can inform creative participatory approaches to pathway development through tailored combinations of methods that best serve the specific sustainability context of particular case studies.
Publisher: American Geophysical Union (AGU)
Date: 03-2023
DOI: 10.1029/2022EF003083
Abstract: We developed a machine learning based surrogate model to identify sustainability pathways through rapid scenario generation and defined the safe operating space for achieving them via scenario discovery. We trained a surrogate model to replicate the Land‐Use Trade‐Offs integrated model of the Australian land system. Latin hypercube s ling was used to create many scenarios exploring the impact of uncertainties in key drivers including future socio‐economic development, climate change mitigation, and agricultural productivity at a granular level. Economic and environmental impacts were evaluated against nationally downscaled SDG targets. Scenario discovery revealed new pathways to achieving five SDG targets for 2050 which required crop yield increases above 1.78 times, a carbon price above 100 AU$ tCO 2 −1 , a % bio ersity levy on carbon plantings, and carefully regulated land‐use policy. Machine learning based surrogate modeling teamed with scenario discovery revealed the policy and scenario settings required for a more sustainable future for the Australian land sector.
Publisher: Elsevier BV
Date: 11-2020
Publisher: Springer Science and Business Media LLC
Date: 14-03-2021
Publisher: IEEE
Date: 12-2018
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 03-2017
Publisher: Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc.
Date: 03-12-2017
Publisher: Elsevier BV
Date: 05-2014
Publisher: Elsevier BV
Date: 2020
Publisher: Elsevier BV
Date: 07-2022
Publisher: Authorea, Inc.
Date: 11-07-2023
DOI: 10.22541/AU.168908143.31581770/V1
Abstract: The 2030 Agenda offers a list of global environmental, social, and economic objectives to attain sustainable development. However, achieving the Sustainable Development Goals (SDGs) is challenging given the complex interactions between different SDGs and their spillover effects. System dynamics models have the capacity to integrate multisectoral dynamics of SDG interactions. We developed a system dynamics model-the Local Environmental and Socio-Economic Model (LESEM)-to analyse and quantify context-based SDG interactions at the local scale using a participatory model co-design process with local stakeholders. The LESEM was developed for a case study in the Goulburn-Murray Irrigation District in northern Victoria, Australia. We present an illustrative application of the model that quantifies SDG interactions across four high-priority SDGs, namely clean water and sanitation (SDG 6), agricultural activities (SDG 2), economic growth (SDG 8), and life on land (SDG 15). Our results suggest that agricultural land area may shrink by 62,522 ha due to the decline in water resource availability (SDG 6) under a business-as-usual (BAU) scenario from 2022 to 2050. However, the results also highlight that agri-food production (SDG 2) is likely to increase due to intensification to meet future agri-food demand, and higher values of farm output may improve local prosperity. The projections also suggest that environmental pressures may increase due to increasing agricultural intensification and reduced water availability. The LESEM facilitates integrated and strategic decision-making and helps local policymakers identify and quantify potential trade-offs and synergies that benefit multiple SDGs, which eventually leads local communities toward sustainability.
Publisher: Elsevier BV
Date: 05-2018
Location: Australia
Location: Iran (Islamic Republic of)
No related grants have been discovered for Enayat A. Moallemi.