Early systems change necessary for catalyzing long-term sustainability in a post-2030 agenda

Eker, Sibel; Gao, Lei; Hadjikakou, Michalis; Liu, Qi; Kwakkel, Jan H.; Reed, Patrick M.; Obersteiner, Michael; Guo, Zhaoxia; Bryan, Brett A.

doi:10.1016/j.oneear.2022.06.003

Cited by 27 publications

(36 citation statements)

References 92 publications

(148 reference statements)

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“…This discrepancy has been described in Peng et al (2021), and is consistent with the analysis of integrated assessment models performed by van Soest et al (2019). Different types of models have been constructed with more emphasis on the social and economic factors for SDG analysis (Moallemi et al, 2022;Randers et al, 2019). Still, even within the environmental dimensions, several aspects of the N surplus were estimated in post-processing, that could be endogenous in the future.…”

Section: Future Research Directionssupporting

confidence: 65%

“…These studies have made extensive efforts to quantitatively represent SDGs or a subset thereof, using indicators in integrated assessment models. Some have established numerical “target” values for these indicators to delineate levels of success for SDGs (e.g., Moallemi et al., 2022; Randers et al., 2019; Soergel et al., 2021; Van Vuuren et al., 2022). However, few studies have specifically targeted the agriculture sector and systematically assessed the sustainability of agricultural production.…”

Section: Introductionmentioning

confidence: 99%

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Assessing Multi‐Dimensional Impacts of Achieving Sustainability Goals by Projecting the Sustainable Agriculture Matrix Into the Future

Kyle,

Ollenburger,

Zhang

et al. 2023

Earth's Future

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The concept of sustainability inherently spans multiple spatial scales, sectors, variables, and time horizons. This study links a recently developed method of assessing present‐day agricultural sustainability across environmental, economic, and social dimensions with a process‐based integrated assessment model, in order to allow forward‐looking analysis of sustainability by region and scenario. The sustainable agriculture matrix estimates present‐day agricultural sustainability at the national level using 18 indicator variables, of which this study estimates nine to the year 2100, using an enhanced version of the Global Change Analysis Model. Scenarios include a reference scenario, and scenarios that apply the following measures, both individually and in combination, that are thought to improve sustainability: yield intensification, transition toward more plant‐based (“flexitarian”) diets, and economy‐wide greenhouse gas emissions mitigation. The scenarios illustrate considerable complexity and tradeoffs inherent to efforts to improve agricultural sustainability in all regions globally. For example, yield intensification typically increases nitrogen pollution, flexitarian diets can reduce agricultural output, and greenhouse gas mitigation efforts may either increase deforestation or crowd out crop and livestock production due to consequent bioenergy demands. However, there is considerable inter‐regional heterogeneity in the responses, and the importance of such secondary responses also differs by region. The analysis and post‐processing methods developed in this study allow quantification and visualization of the absolute and relative magnitude of the tradeoffs between agricultural sustainability indicator variables across regions, time periods, and scenarios.

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Section: Future Research Directionssupporting

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Assessing Multi‐Dimensional Impacts of Achieving Sustainability Goals by Projecting the Sustainable Agriculture Matrix Into the Future

Kyle,

Ollenburger,

Zhang

et al. 2023

Earth's Future

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“…However, land‐use futures are characterized by substantial complexity and uncertainty driven by the large potential range in input data, model parameter specifications, and the scenario assumptions involved in land‐use modeling, and the sporadic and incomplete consideration of these interacting uncertainties (Moallemi, Kwakkel, et al., 2020; Srikrishnan et al., 2022). There is a need for the comprehensive exploration of this uncertainty space to better account for the diversity of plausible futures and identify pathways to achieving the SDGs (Moallemi, Eker, et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Safe Operating Space for Land‐System SDG Achievement via Machine Learning and Scenario Discovery

et al. 2023

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A sustainable land sector is key to achieving several of the 17 Sustainable Development Goals (SDGs) and 169 targets (UN, 2015) representing the shared environmental, social, and economic aspirations of all UN member nations (Herrero et al., 2021). However, the feasibility of achieving multiple SDG targets in the land sector has been questioned due to the inherent trade-offs involved and calls have been made to redouble efforts in the search for sustainable future pathways (Gao & Bryan, 2017). Multiple land-use models have been developed both as stand-alone models and as a part of multi-sectoral integrated assessment models to understand the complex relationships and dynamics underpinning land-system sustainability (

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“…Past studies have used various quantitative and qualitative methods for SDG interaction analysis, among them consensus‐based expert elicitation (Nilsson et al., 2016; van Soest et al., 2019), literature‐based content analysis (Bandari et al., 2021; McCollum et al., 2018), indicator‐based pairwise statistical correlation (Kroll et al., 2019; Pradhan et al., 2017), statistical modeling of interactions (Anderson et al., 2021), remote sensing tracking (Singha et al., 2021), network analysis (Le Blanc, 2015), and integrated assessment modeling (Gao & Bryan, 2017; Moallemi et al., 2022; Obersteiner et al., 2016; Pedercini et al., 2019; Soergel, Kriegler, Weindl, et al., 2021). Some of these studies have focused only on sector‐specific interactions (e.g., energy [Fuso Nerini et al., 2018], health [Schmidt et al., 2015], food [Herrero et al., 2021]), while some others have had a more overarching approach and included many of the SDGs simultaneously (Allen et al., 2019; Kroll et al., 2019; Pradhan et al., 2017; Soergel, Kriegler, Weindl, et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Eight Archetypes of Sustainable Development Goal (SDG) Synergies and Trade‐Offs

Hosseini¹,

Bertone

Gao

et al. 2022

Earth's Future

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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 examples 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.

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Early systems change necessary for catalyzing long-term sustainability in a post-2030 agenda

Cited by 27 publications

References 92 publications

Assessing Multi‐Dimensional Impacts of Achieving Sustainability Goals by Projecting the Sustainable Agriculture Matrix Into the Future

Assessing Multi‐Dimensional Impacts of Achieving Sustainability Goals by Projecting the Sustainable Agriculture Matrix Into the Future

Quantifying the Safe Operating Space for Land‐System SDG Achievement via Machine Learning and Scenario Discovery

Eight Archetypes of Sustainable Development Goal (SDG) Synergies and Trade‐Offs

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