The integrated and indivisible nature of the SDGs is facing implementation challenges due to the silo approaches. We present the three interconnected foci (SDG interactions, modeling, and

tools) at the science-policy interface to address these challenges. Accounting for them will support accelerated SDG progress, operationalizing the integration and indivisibility principles.

The 2024 Summit of the Future aimed to accelerate efforts to meet existing international commitments. The 2030 Agenda for Sustainable Development is the pre-eminent international commitment

to be achieved by 2030, comprising 17 Sustainable Development Goals (SDGs) with the underpinning principles of integration, indivisibility, and universality. However, these principles have

interactions, modeling, and tools) to support accelerated SDG progress and operationalize integration and indivisibility principles. SDG INTERACTIONS SDG interactions refer to the complex

and dynamic relationships between SDGs. They can be unidirectional or bidirectional, and their strength in each direction might vary. Actions and policies to pursue one goal can have

synergies or trade-offs for achieving the others (Box 1)3. Thus, accounting for SDG interactions and aiming to strengthen synergies and mitigate trade-offs in policymaking is crucial. Doing

so can guide toward more systemic, coherent, and effective SDG implementation. Accelerating sustainable development efforts requires shifting focus from achieving specific SDGs in the short

term to a more holistic system-wide approach. Current studies identify more synergies than trade-offs among SDGs, which are dynamic and context-specific4. While trade-offs may be fewer, they

are important considerations from a policy perspective (Box 1), e.g., based on just transition approaches. SDG interactions are currently well understood at global and national scales but

not at subnational scales due to limited data and research. Knowledge of their temporal and spatial dynamics is also restricted. Further, leaving no one behind requires a better

understanding of SDG interactions for underrepresented groups. Current SDG interaction studies often rely on anecdotal evidence, statistical analysis, or models. Thus, understanding the

mechanisms behind underlying SDG interactions is crucial to identifying levers for accelerating systemic changes and SDG progress. Knowledge of interactions is unevenly distributed across

SDGs due to limited data and coherent methodology (Box 1). The consistent SDG data protocols and methods can address this issue5. Another gap is understanding the interaction between SDGs

and other intergovernmental frameworks (e.g., the Paris Agreement and the Global Biodiversity Framework). Coupling the 2030 Agenda with other intergovernmental processes can leverage

co-benefits and minimize conflicts, accelerating progress toward a more sustainable future. The current scientific insights of SDG interactions are highly policy-relevant and sufficient to

inform systemic prioritization of actions to accelerate SDG progress. However, a stronger focus on actionable guidance could strengthen their policy use. There is enough knowledge about

changes needed to achieve SDGs but not how, particularly in different political and social contexts. A way forward is to bring together different research communities working on specific

subfields and to combine quantitative and qualitative approaches6 for identifying interventions for decision-makers. A deeper integration of various approaches is needed to better represent

different dynamics. Providing sound recommendations requires contextualized SDG interaction analyses, accounting for their spatial and temporal characteristics, including opportunities for

policy interventions, sound governance, and stakeholders’ actions. Policymakers’ priorities are shaped by the dynamics and urgency of their problems. Thus, policy-oriented SDG interaction

assessments can improve the choice and design of policies to respond to these challenges. These assessments’ findings and their practical interpretation and communication can inform

policymakers and make them aware of incentives and barriers related to different policies. They can use this knowledge to embed SDG interactions in policies based on adequate interventions.

BOX 1 EXAMPLES OF INTERACTIONS BETWEEN SUSTAINABLE DEVELOPMENT GOALS (SDGS) AND GAPS _SDG interactions depend on actions and policies_: Achieving climate action (SDG13) through large-scale

land-based mitigation can negatively impact food security (SDG 2), water (SDG 6), or biodiversity (SDGs 14 and 15). However, reducing energy demand and enhancing sustainable agricultural

practices can also ensure food security with positive impacts on water, soil, land, and biodiversity. SDG interactions can also be indirect or occur through complex feedback chains.

_Importance of addressing SDG trade-offs_: Pursuing a fossil fuel-based development pathway negatively impacts environmental goals, which can accumulate and escalate over time. Instead, a

shift towards renewable energy may result in short-term trade-offs, such as missed opportunities for economic growth through resource extraction or the loss of livelihoods of dependent

communities. However, this pathway could also unlock investments, green economies, and employment opportunities over the longer term. _Uneven understanding of SDG interactions_: There are

considerable knowledge gaps concerning peace, political institutions, diversity, age-structure changes, and gender-related issues, whereas climate and health (e.g., COVID) have been given

more attention. This gap also holds for the empirical understanding of SDG interactions at different spatial scales, including transboundary spillover effects. SDG MODELING SDG modeling

involves mathematical, statistical, or computational approaches to analyze and project the impact of policies and actions related to SDGs7. For this, scenarios offer plausible future

narratives and can present different pathways and outcomes. Scenarios are projected with quantitative models to study the effects of various measures, their timing and regionality, and

associated synergies, trade-offs, and enabling conditions. The resultant quantitative pathways highlight that SDGs cannot be achieved by 2030 under business as usual, and additional policies

and measures are needed8. The lessons from model-based pathways could support choosing better strategies and policies for accelerating SDG progress. Many models have been developed to

support SDG policymaking. These models range from sectoral to integrated assessment models (IAMs)8, covering various methodologies (Box 2). While IAMs historically focused on climate change,

they are increasingly investigating other relevant SDG aspects9. Doing so contributes to more holistic policy debates in the climate sphere and synergistic policy interventions10. However,

current scenario modeling needs to reflect the complexity of SDGs. Recent modeling studies at global8 and national11 scales have advanced by partially covering all 17 SDGs. Underrepresenting

SDGs in models may also lead to biased outcomes (Box 2). Further, most IAMs provide highly aggregated results at a world-region scale, often lacking short-term actionable strategies for

policymakers and specific actors at (sub)national scales. Also, normative considerations in scenario modeling fail to represent specific actors’ divergent interests and viewpoints, offering

limited actionable insights. Addressing these limitations, the Nature Futures Framework explains how different normative considerations might be systematized12. Moreover, scenario modeling

also needs to link quantitative and qualitative methods, combining social science insights on the societal and political dynamics with quantitative modeling of techno-economic ones11.

Filling the above-highlighted gaps requires models representing all SDGs and developing a broader range of scenarios. However, doing so will be technically challenging because of

difficulties in quantifying some SDGs13 and modeling dilemmas (Box 2). To resolve this, a way forward is a transdisciplinary collaboration with stakeholders and Indigenous communities and

codeveloping inter-comparable Sustainable Development Pathways representing all SDGs (Box 2). Such pathways and quantitative results also provide narratives of societal transformation, which

social sciences and other disciplines can analyze. Besides, these pathways must clearly explain the effort required to meet SDGs, including investment needs and implications of different

financial schemes, emphasizing urgency and solution options. The current scenario modeling provides sufficient (but only partial) knowledge of the actions required to accelerate SDG

progress. More efforts are needed to effectively translate and communicate model results to policy-makers in a way that enables them to design effective interventions. Furthermore,

co-producing models and scenarios that consider policymakers’ needs would enhance the uptake of their results during policymaking. BOX 2 MODELING METHODOLOGIES FOR SUSTAINABLE DEVELOPMENT

GOALS (SDGS) AND THEIR LIMITATIONS _SDG modeling methodologies:_ A wide range of methods for modeling SDGs includes (complex adaptive) system dynamics, social simulations (agent-based

models), network analysis, and economic input-output and computational general equilibrium models, each model type answering specific questions. _Underrepresented SDGs in models_: Gender

equality (SDG 5), Life below water (SDG 14), Peace, justice and strong institutions (SDG 16), and partnerships for the goals (SDG 17) are some critical SDGs underrepresented in the current

models. Also, qualitative scenarios, governance, and social dimensions are lacking, and crucial factors, e.g., population dynamics and gender considerations, are overlooked, presenting a

fragmented view. _Modeling dilemmas_: There is also an inherent decision model practitioners need to make concerning whether (or not) to focus modeling exercises on a limited set (or nexus)

of SDGs, which they represent in an integrated manner. Limiting the focus of models would risk missing the bigger picture of synergies and trade-offs across other SDGs. However, focusing on

a specific set of SDGs could provide in-depth knowledge and better solution options. Thus, both methods are valuable and provide complementary insights. Depending on the study purpose,

covering all SDGs and focusing on a limited set of SDGs are not mutually exclusive if the interlinkages between SDGs, including the study’s boundaries and limitations, are well-communicated.

_SDG model intercomparison_: Making different SDG modeling comparable requires adequate community standards (e.g., data sovereignty, data and modeling protocols, structure, definitions,

metadata, and informed consent) to facilitate data exchange across models and scales (i.e., global vs. local and top-down vs. bottom-up). Furthermore, next-generation models could combine

existing quantitative and qualitative methods to model different sectors to develop a simple but detailed enough model to represent all SDGs. SDG TOOLS SDG tools translate findings from SDG

interactions and models into easily accessible information to support more integrated systemic policy-making for achieving SDGs. Different tools have been developed for targeted users,

including SDG monitoring, SDG interaction analysis and visualization14, and SDG assessment. Many sectoral tools can also be applied to specific SDGs. An overview of available SDG tools and

their purposes would be valuable for users to choose suitable tools for their needs15. Currently, SDG tools are available to track SDG progress, raise educational awareness, inform

policymakers on SDG interactions, and promote public communication. A limited set of tools are developed to guide integrated SDG planning and decision-making. Still, their function in

providing pathways for SDG achievement needs to be improved. A major challenge for existing SDG tools is timely reflecting state-of-the-art scientific knowledge through periodic updates.

Also, SDG tools should interpret the results, including stakeholder-specific actions for making SDG progress. Co-desing and demand-driven tool development can address these gaps. New SDG

tools are needed to address areas and target users yet to be covered. A ‘suite’ of tools would be most appropriate to effectively cater to all stakeholders’ needs and purposes and reflect

SDGs’ complexity. Understanding how SDG tools are used in practice, codeveloping them with the users, and incorporating citizen science and Indigenous knowledge can help address the

abovementioned challenges. In terms of usability and accessibility, a way forward is to enrich the interpretability, reliability, and robustness of the results, ensure transparency and

traceability of data, and enhance applicability to context and scale. Further, awareness of available SDG tools needs to be raised among the users. Tools should be actionable, collaborative,

simple, inclusive, diverse, user-friendly, and transparent to be policy-relevant. However, all these aspects may not go together, and there needs to be a balance. Thus, a ‘suite’ of tools

might be more effective when it consists of tailored tools to specific needs, providing insights into smaller, more manageable chunks of information. LOOKING TOWARDS THE FUTURE Under current

trends, many SDGs cannot be achieved by 2030. Therefore, a transformative systemic approach that accounts for SDG’s underpinning principles is needed. Our three interconnected foci

operationalize SDG’s integration and indivisibility principles, supporting integrated decision-making. Future research should combine these three foci to enhance scientific methods and

understanding and the communication and dissemination of science-based policy. Further, calls for system-based solutions and integrative policy-making and planning should be one key

consideration for the post-2030 agenda on sustainable development2, building on the advancements made since 2015. However, decision-making also depends on values, power, institutions,

incumbency, and political economy. Thus, accelerating SDGs also requires comprehensive and integrative ways of thinking and more coherent policy frameworks across scales, engaging with new

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131–138 (2021). Article  Google Scholar  Download references ACKNOWLEDGEMENTS We developed this comment based on the workshop funded by the European Research Council (ERC) for the BeyondSDG

project (Project number 101077492) and the Rudolf Agricola School for Sustainable Development. We thank A. Geis, A. Jegorenkova, A. Goossens, A. Bonini, A. Bakker, L. Talapessy, L. Tucker,

S. Pellegrom, and S. Rajbhandari for contributing to the workshop. V.D., G.A., F.A., and D.v.V. acknowledge the funding from the European Research Council (ERC) for the PICASSO project under

the H2020-EU.1.1 Excellent Science Programme under Grant No. 819566. M.C. acknowledges the key project of the sustainable development international cooperation program by the National

Natural Science Foundation of China (NSFC) under Grant No. 42361144883. R.W. acknowledges the funding from the European Union’ Horizon 2020 under Grant Agreement No. 861932. X.Z.

acknowledges the funding from the 2024 Strategic Research Fund of the Institute for Global Environmental Strategies under ISC-QA/SRF (SDG-BD). F.C. and Q.X. acknowledge the funding from the

National Key R&D Program of the Ministry of Science and Technology under Grant No. 2022YFC3800700. The funders had no role in study design, data collection and analysis, publication

decisions, or manuscript preparation. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Integrated Research on Energy, Environment, and Society (IREES), Energy and Sustainability Research

Institute Groningen (ESRIG), University of Groningen, Groningen, The Netherlands Prajal Pradhan, Klaus Hubacek, Utkarsh Ashok Khot, Teun Kluck, Jing Li, Anne Warchold & Jin Yan * Potsdam

Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany Prajal Pradhan, Gabriel M. Abrahão, Chaohui Li, Hermann Lotze-Campen & Bjoern Soergel *

Stockholm Environment Institute, Stockholm, Sweden Nina Weitz, Therese Bennich & Henrik Carlsen * Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The

Netherlands Vassilis Daioglou, Geanderson Ambrósio, Frederike Arp, Frank Biermann, Shridhar Kulkarni, Detlef van Vuuren & Eartha Weber * PBL Netherlands Environmental Assessment Agency,

The Hague, The Netherlands Vassilis Daioglou & Detlef van Vuuren * Monash Sustainable Development Institute, Monash University, Melbourne, Australia Cameron Allen * Centre for Blue

Governance, Aalborg University, Aalborg, Denmark Furqan Asif * Chatham House, London, UK Tim G. Benton * Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC),

Nanjing Normal University, Nanjing, China Min Cao, Min Chen & Zifeng Yuan * International Research Center of Big Data for Sustainable Development Goals, Beijing, China Fang Chen & 

Qiang Xing * Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China Fang Chen & Qiang Xing * University of Chinese

Academy of Sciences, Beijing, China Fang Chen & Chaoyang Wu * Department of Economic Geography, Faculty of Spatial Sciences, University of Groningen, Groningen, The Netherlands Michiel

N. Daams & Frans J. Sijtsma * Rudolf Agricola School for Sustainable Development, University of Groningen, Groningen, The Netherlands Michiel N. Daams & Frans J. Sijtsma * Department

of Mathematical Sciences, University of Bath, Bath, UK Jonathan H. P. Dawes * Department of Energy and Climate Change, Asian Institute of Technology, Pathum Thani, Thailand Shobhakar Dhakal

* Carleton University, Civil and Environmental Engineering, Ottawa, ON, Canada Elisabeth Gilmore * Group of Energy, Economy, and Systems Dynamics. Department of System Engineering and

Automation, School of Industrial Engineering, University of Valladolid, Valladolid, Spain Luis J. Miguel & Gonzalo Parrado-Hernando * School of Resource and Environmental Sciences, Wuhan

University, Wuhan, China Yuanchao Hu * Groningen Center for Social Complexity Studies, University of Groningen, Groningen, The Netherlands Wander Jager * International Institute for Applied

Systems Analysis (IIASA), Laxenburg, Austria Samir KC & Caroline Zimm * Population and Stastistics Research Hub, Lalitpur, Nepal Samir KC * Centre for Development and Environment,

University of Bern, Bern, Switzerland Norman M. Kearney * German Institute of Development and Sustainability (IDOS), Bonn, Germany Julia Leininger & Christopher Wingens * Laboratory of

Systems Ecology and Sustainability Science, College of Engineering, Peking University, Beijing, China Chaohui Li * Humboldt-Universität zu Berlin, Department of Agricultural Economics,

Berlin, Germany Hermann Lotze-Campen * Millennium Institute, Washington, DC, USA Matteo Pedercini & Nathalie Spittler * National Planning Commission, Government of Nepal, Kathmandu,

Nepal Ram Kumar Phuyal * Faculty of Spatial Sciences, University of Groningen, Groningen, NL, The Netherlands Christina Prell * Department of Operations, Faculty of Economics and Business,

University of Groningen, Groningen, The Netherlands Arpan Rijal * Department of Knowledge Integration, University of Waterloo, Waterloo, ON, Canada Vanessa Schweizer * BOKU University,

University of Natural Resources and Life Sciences, Vienna, Austria Nathalie Spittler * Department of Environmental Science, Radboud Institute of Biological and Environmental Sciences,

Radboud University, Nijmegen, The Netherlands Birka Wicke * Faculty of Science, IVM, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands Oscar Widerberg * Institute for Food and

Resource Economics, University of Bonn, Bonn, Germany Rienne Wilts * The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources

Research, Chinese Academy of Sciences, Beijing, China Chaoyang Wu * Institute for Global Environmental Strategies, Hayama, Kanagawa, Japan Xin Zhou Authors * Prajal Pradhan View author

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author inPubMed Google Scholar CONTRIBUTIONS P.P., N.W., V.D., F.J.S, and K.H. conceived the study. P.P., N.W., V.D., T.B., H.C., and A. W. developed the workshop methodology. P.P. wrote the

main commentary based on the inputs during the SDG workshop at the University of Groningen. P.P., N.W., V.D., G.M.A., C.A., G.A., F.A., F.A., T.B., T.G.B., F.B., M.C., H.C., F.C., M.C.,

M.N.D., J.H.P.D., S.D., E.G., L.J.M., K.H., Y.H., W.J., S.K., N.M.K., U.A.K., T.K., S.K., J.L., C.L., J.L., H.L.-C., G.P.-H., M.P., R.K.P., C.P., A.R., V.S., F.J.S., B.S., N.S., D.V., A.W.,

E.W., B.W., O.W., R.W., C.W., C.W., Q.X., J.Y., Z.Y., X.Z., and C.Z. edited and reviewed the commentary. CORRESPONDING AUTHOR Correspondence to Prajal Pradhan. ETHICS DECLARATIONS COMPETING

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ARTICLE Pradhan, P., Weitz, N., Daioglou, V. _et al._ Three foci at the science-policy interface for systemic Sustainable Development Goal acceleration. _Nat Commun_ 15, 8600 (2024).

https://doi.org/10.1038/s41467-024-52926-x Download citation * Received: 07 July 2024 * Accepted: 12 September 2024 * Published: 10 October 2024 * DOI:

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