Safeguarding China's long-term sustainability against systemic disruptors

With the 2030 deadline for achieving the UN Sustainable Development Goals approaching, China has made notable progress (e.g., eradication of extreme poverty and large-scale conservation efforts) but faces future uncertainties—termed disruptors—that threaten SDG attainment. These include global disruptors (pandemic disease, deglobalization, climate change) and domestic disruptors (ageing and shrinking population, biodiversity loss). Prior work on China has largely been retrospective or focused on a subset of SDGs, leaving a gap in forward-looking assessments of systemic disruptors and robust response strategies. This study aims to quantify the individual and compound impacts of five major disruptors on China’s SDG performance for 2030 and 2050, and to evaluate policy portfolios that could safeguard long-term sustainability.

The paper situates its contribution against studies that examined individual disruptors in isolation (e.g., COVID-19’s economic and social impacts; shocks to energy, water, and food systems from the Russo–Ukrainian War) and against national/local SDG assessments in China that explored synergies, trade-offs, and policy effects but were backward-looking or covered limited SDGs. The authors highlight the need for a systemic, forward-looking analysis capturing interconnected disruptors converging into polycrisis, and their implications for SDG attainment.

The study develops and applies the iSDG-China system dynamics model (customized from the Millennium Institute’s iSDG platform) to simulate integrated economic, social, and environmental dynamics for China through 2050. The iSDG model comprises over 3,600 variables across 30 interlinked modules (10 social, 10 economic, 10 environmental), capturing key feedback loops and nonlinearities. Calibration involved structural validation (expert-informed module structures, equations, parameter ranges) and behavioral validation using historical data (2000–most recent) with goodness-of-fit metrics (R2, MAE, MSE, RMSE, MAPE) from official Chinese yearbooks and international databases.

Scenario framework: 3,001 states of the world (SOWs) and 243 policy portfolios form 729,243 scenarios. SOWs include: one baseline (no disruptors), 2,500 SOWs with a single disruptor (500 Monte Carlo samples per disruptor), and 500 SOWs with all five disruptors active concurrently. Disruptors: pandemic disease, ageing and shrinking population, deglobalization, climate change, biodiversity loss. Parameter ranges were drawn from literature and official datasets (Supplementary Table 1). Monte Carlo sampling used the all-at-a-time method with Latin hypercube sampling from uniform distributions.

Policy portfolios: five policy clusters (education, health, energy/climate, water, land) each with three ambition levels: no response (baseline), moderate, ambitious. Combinatorics yield 243 portfolios, including single-policy portfolios (one sector upgraded, others baseline) and integrated portfolios (multiple sectors upgraded) (Supplementary Table 2).

SDG assessment: 87 indicators mapped to 17 SDGs are rescaled to 0–100 using decision-tree bounds: upper bounds via absolute thresholds, universal access principles, literature, top-performer averages, or proportional improvements from 2015; lower bounds via SDG Index thresholds, bottom-2.5th percentile, or worst historical/simulated values. Overall and pillar-specific (economic, social, environmental) SDG scores are computed as means of relevant indicators (Supplementary Table 4).

Main effects estimation: To quantify each disruptor’s average impact considering uncertainties in others, 3,000 random scenarios were generated where each disruptor had a 50% chance of being active; active disruptors received random severity within predefined ranges, inactive took baseline values. Main effects equal the difference in average SDG performance between simulations with a given disruptor active vs inactive (no additional policies).

• Baseline trajectory (no disruptors, no new policies): Overall SDG score rises from 68.3 (2022) to 71.0 (2030) and 72.5 (2050), driven by SDGs 2, 4, 5, 6, 8, but remains far from full attainment.
• Individual disruptors reduce SDG performance; the two most influential by 2050 are ageing and shrinking population and climate change (main effects: −1.6 and −1.4 overall SDG points on average, respectively).
• Ageing and shrinking population scenarios: average overall SDG reductions of −0.3 (2030) and −1.5 (2050) points vs baseline. By 2050: working-age population −~53 million; GDP per capita growth averages 3.6%/yr (vs 5.1% baseline); government revenue −39.7%; water consumption −15.4%; GHG emissions −39.8%. Economic slowdown depresses SDG 8 and SDG 17, with knock-on declines in SDGs 3–4 and 14–15 due to constrained public spending.
• Climate change scenarios: average overall SDG reductions of −0.4 (2030) and −1.4 (2050). By 2050: GHG emissions +19.5% (SDG 13 worse), water consumption +23.2% (SDG 6 worse), harming SDG 15; slower efficiency gains amplify environmental pressures.
• Compound (all disruptors) effects: average overall SDG reductions −1.6 (2030) and −4.8 (2050); worst-case declines up to −2.1 (2030) and −7.0 (2050). By 2050: working-age population −7.1%; exports −34.2%; government revenue −52.3%; fiscal deficit more than doubles (SDG 17). Environmental outcomes are mixed: SDG 13 improves relative to baseline (+1.8 in 2030; +13.0 in 2050), but SDG 14 and SDG 15 drop sharply (−12.5 and −19.4 by 2050). Large socio-economic losses accumulate over time (e.g., SDG 1 −11.1, SDG 3 −10.6, SDG 8 −22.9, SDG 17 −15.1 by 2050).
• Policy effectiveness: All policy portfolios improve overall SDG scores vs baseline portfolio, but gains vary. Under all disruptors, improvements range 0–2.2 (2030) and 0.1–5.0 (2050) points; without disruptors, 0–2.6 (2030) and 0.2–7.8 (2050).
• Single-policy portfolios: Environmental policies are most effective within their domains. Below 2°C and below 1.5°C policies markedly raise SDG 13 (especially under climate change). Bio/Bio+ significantly lift SDGs 14–15 under biodiversity loss. However, Below 1.5°C introduces trade-offs (higher system costs → higher deficits, reduced funding for SDGs 3–4) yielding a worse overall SDG score than Below 2°C; revenue recycling (e.g., carbon tax with dividends) can mitigate trade-offs.
• Best integrated portfolio: Edu+, UHC+, Below 2°C, Bio+, Water+ outperforms others, achieving average overall SDG scores of ~80 (no disruptors) and ~73 (all disruptors) by 2050, and still delivers a +5-point gain vs baseline even under compound disruptors. It boosts social and environmental pillars but cannot fully offset economic stress (e.g., debt/SDG 17).

The analysis demonstrates that systemic disruptors, especially ageing and shrinking population and climate change, threaten China’s SDG trajectory via intertwined demographic, fiscal, and environmental feedbacks. Concurrent disruptors amplify losses beyond the sum of individual effects, revealing structural vulnerabilities and the risk of polycrisis-driven setbacks across many SDGs. Nevertheless, integrated, cross-sector policy portfolios—linking education, healthcare, clean energy transition compatible with a Below 2°C pathway, water-use efficiency, and ecological conservation/restoration—can bolster resilience and materially improve SDG outcomes even under compounded shocks. Aligning climate action with broader sustainable development is essential to mitigate trade-offs (e.g., fiscal pressures from ambitious mitigation) and to leverage synergies, particularly in lagging environmental SDGs. Effective implementation requires coordinated governance, stakeholder engagement, cross-boundary information sharing, and balancing short-term responses with long-term objectives to avoid undermining SDG progress.

This study provides a forward-looking, system-wide quantification of how five systemic disruptors could affect China’s SDG attainment and shows that ageing and shrinking population and climate change are the most consequential threats by 2050. It demonstrates that compound disruptors can cause substantial socio-economic and environmental setbacks, yet well-designed integrated policy portfolios can significantly improve resilience and overall SDG performance—even under polycrisis conditions. The results underscore the importance of coordinated, cross-sector investments in education, health, energy transition consistent with a Below 2°C pathway, water efficiency, and biodiversity conservation. Future research should enhance macroeconomic detail (e.g., finance, property markets, subnational debt) and evaluate more specific policy instruments (e.g., carbon pricing with revenue recycling, retirement reform, dietary shifts) within integrated modeling frameworks to refine robust pathways to long-term sustainability.

Key limitations include: (1) scope constraints of the iSDG model—omission of domestic macroeconomic challenges (e.g., property market stresses, local government debts) and global issues (e.g., developing-country debt crises, global slowdowns) that could influence China’s trajectory and amplify deglobalization impacts; (2) exclusion of more specific policies (e.g., fertilizer use, dietary changes, carbon pricing designs, postponed retirement, monetary/fiscal stimulus) due to model granularity; and (3) the need for enhanced economic modeling precision to integrate such policies. Future model improvements could enable more detailed policy analyses and interactions affecting sustainability.