Modelling six sustainable development transformations and their accelerators, impediments, enablers, and interlinkages

doi:10.21203/rs.3.rs-2437723/v1

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Modelling six sustainable development transformations and their accelerators, impediments, enablers, and interlinkages

https://doi.org/10.21203/rs.3.rs-2437723/v1

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published 18 Jan, 2024

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There is an urgent need to accelerate progress on the Sustainable Development Goals (SDGs) and recent research has identified six critical transformations. However, studies are yet to demonstrate how these transformations could be practically accelerated in a national context and what their combined effects would be. Here we deploy integrated systems modelling with transition storylines to elaborate and project six transformation pathways to the SDGs in Australia. By combining quantitative accelerators in the form of decisive policies and investments with storylines that diagnose common impediments and identify enabling conditions for systems change, our study advances knowledge on how the six transformations could be unlocked and accelerated. We find that conditions for transformation are emerging due to recent cascading crises, that feasible and affordable interventions and solutions are readily available that could trigger ‘S-shaped’ acceleration by 2030, and that continued long-term investment in climate action and resilience could stabilize progress towards sustainable wellbeing targets by 2050.

Earth and environmental sciences/Environmental social sciences/Sustainability

Earth and environmental sciences/Environmental social sciences/Socioeconomic scenarios

Earth and environmental sciences/Environmental social sciences/Climate-change policy

Scientific community and society/Social sciences/Interdisciplinary studies

Scientific community and society/Social sciences/Government

Sustainable Development Goals (SDGs)

transformation

transition

modelling

pathways

systems

Since 2015, progress towards the global Sustainable Development Goals (SDGs) has been too slow and uneven, and some early advancements have been all but derailed by COVID-19 ^[^1-3^]. As we approach the mid-point of the 2030 Agenda, the urgent need to accelerate transformations to the SDGs is widely acknowledged ^[^4-7^]. A key theme for the second global SDG Summit in 2023 is for political guidance for transformative and accelerated actions leading up to 2030. Yet in the post-COVID recovery context, government budgets are strained, and financial, human, and technical resources need to be strategically harnessed. While the SDGs provide a blueprint for countries to ‘build back better’ ^[³^,⁸^,⁹^], deeper transformations will be needed over the longer term to achieve sustainable wellbeing^[¹⁰^]. Large gaps in knowledge remain as to how the necessary transformations can be practically accelerated as well as their potential beneficial effects and unintended consequences for the SDGs.

In recent years, research has articulated six entry points^{^[1]} ^[¹¹^] or six transformations ^[¹²^] to achieve the SDGs, providing an integrated organising framework that helps to simplify the 17 goals and focus on their interconnections ^[¹³^]. These require rapid and coherent transitions to new kinds of energy, mobility, housing, and food systems while also advancing human wellbeing and increasing resilience to future shocks ^[¹⁴^]. Integrated modelling provides a critical tool for exploring these transformations and their effects^[^15-18^], however modelling studies are yet to attempt the ambitious task of modelling all six transformations in a single analysis. Projecting such complex, interlinked and composite systems into the future is a key methodological frontier ^[¹⁹^] and multi-system interactions remain poorly understood ^[²⁰^]. Further, common critiques of existing modelling studies are that they give inadequate attention to social, political and behavioural barriers to transformation and the role of different societal actors ^[¹⁴^,²¹^]. Emerging methods aim to bridge these gaps, including through transition storylines ^[²²^,²³^] and pathways ^[²⁴^] which identify key bottlenecks and explain the enabling conditions needed for techno-economic interventions to succeed.

Here we make significant advancements on previous national modelling studies to address these key research gaps. Our focus is on improving understanding of the national acceleration of six transformations to the SDGs, including important accelerators, impediments, enabling conditions, system interactions and long-term effects. We combine systems modelling of alternative COVID-19 recovery trajectories for Australia with transition storylines that articulate the conditions for system change (Figure 1). A ‘Build Back the Same’ (BBS) trajectory is used as a baseline and signals a return to the pre-COVID status quo. Contrasting this, our ‘Sustainable Wellbeing Transformation’ (SWT) trajectory involves the acceleration of six transformation pathways (TPs) (Methods). Each TP combines a set of quantitative policy levers (or ‘accelerators’) used in the model projections with a qualitative storyline which diagnoses key impediments and describes how various actors improve critical enabling conditions for acceleration. We use an upgraded national integrated system dynamics model (iSDG-Australia v2.0) to project and evaluate progress made by each trajectory and TP towards all 17 SDGs by 2030 as well as a broad set of ‘transformation targets’ by 2050 (Methods). We show how the six transformations can be accelerated through deliberate and coordinated action from governments and all societal actors with a firm commitment to structural reforms that not only build back better but shift Australia onto a trajectory towards long-term sustainable wellbeing.

[1] 1. Human wellbeing and capabilities; 2. Sustainable and just economies; 3. Sustainable food systems and healthy nutrition; 4. Energy decarbonisation with universal access; 5. Urban and peri-urban development; 6. Global environmental commons.

COVID-19 and cascading crises

In Australia, the COVID-19 pandemic arrived on the heels of the 2019 catastrophic bushfires and was succeeded by unprecedented flooding. Together the crises had a profound impact, ending 30 years of economic growth, temporarily halting net migration, disrupting supply chains, and exacerbating weaknesses in Australia’s development model ^[25]. In response to the pandemic, the Australian government implemented emergency economic stimulus of around 15% of gross domestic product (GDP) ^{[26, 27]}. A significant share was provided as social transfers directly to individuals, households, and businesses. New infrastructure investment targeted transport, construction, and the oil and gas sector, with green stimulus making up less than one percent in 2020 ^[28]. The socio-economic effects of COVID-19 ^{[29, 30]} and the emergency stimulus measures are incorporated into our modelling projections, showing a V-shaped economic recovery, an A-shaped peak in annual unemployment, and a V-shaped dip in total greenhouse gas (GHG) emissions, among other effects (Supplementary Figs. 1(a)-(d)).

Moving ahead, Australia faces a range of possibilities for its medium-term recovery. At the end of 2020, the national Government’s announcement of a ‘gas-led recovery’ signalled a return to the status quo which forms the basis for our ‘Build Back the Same’ (BBS) narrative and trajectory (Supplementary Tables 1 and 2). The long-term outlook for BBS is one of stagnation and slow decline in progress towards sustainable development (Fig. 2(a)). Following a brief jump in SDG progress in 2020 resulting from the stimulus expenditure, progress subsequently slows with only modest gains on the SDGs by 2030 (59% average progress across all goals and targets). The best performing goals are education (SDG 4), health (SDG 3) and water (SDG 6), while progress lags behind on environmental goals (SDGs 14 and 15). However, these gains are not sustained and over the longer-term to 2050 progress on the SDGs tapers off and then declines to approximately 55% (Fig. 2 (b)), largely due to worsening climate change impacts and a lack of investment in adaptation and resilience.

A Critical Juncture: Trajectories Diverge

A return to the status quo is not a given. Crisis events can lead to a critical juncture and provide opportunities for systems transformation ^{[31, 32]} (Fig. 1). Our ‘Sustainable Wellbeing Transformation’ (SWT) trajectory is premised on such a juncture occurring in Australia from 2022, triggered by compounding crises (Supplementary Table 1). On this trajectory, government acts decisively to accelerate six transformations to achieve the SDGs by 2030. The SWT trajectory incorporates a range of additional policy ‘accelerators’ which are packaged into six transformation pathways (TPs) (Fig. 1; Supplementary Table 2). These cover a broad mix of measures, including tax reform, additional spending on social services and transfers, and scaled up investment in sustainable energy, industry, transport, agriculture, and infrastructure. Annual government expenditure increases by 7.5% on average or approximately AUD87 billion per annum which is offset through additional revenue measures (Supplementary Fig. 1(d)). This sees a continued but scaled down stimulus following the immediate response to COVID-19 which is largely wound back in 2030, with longer-term policy assumptions to 2050 focused on addressing climate change.

The medium-term outlook for the SWT trajectory is one of acceleration reaching 82% progress on all SDGs by 2030 (Fig. 2). Strong performance is evident across all goals, with education (SDG 4), sustainable energy (SDG 7), sustainable cities (SDG 11), climate change (SDG 13), marine biodiversity (SDG 14) and partnerships (SDG 17) achieving over 90% progress. The additional investment to 2030 and continued measures to achieve net zero have long-term positive effects, with performance on the SDGs continuing to rise to reach 89% average progress on all goals and targets by 2050 (Fig. 2(b)). Results from the sensitivity analysis (Methods) show that these results are relatively robust to significant changes in the global outlook, with 95% of (6,000) simulations ranging between 75% and 91% average progress (Fig. 2(b); see also Supplementary Fig. 2(a)).

The S-shaped Acceleration Dynamics Of The Six Transformations

The additional progress on the SDGs in the SWT trajectory results from the combined effects of policy settings contained within the six TPs, with progress accelerating rapidly over the period to 2030. Here we measure the progress made on each of the individual transformations using a unique set of transformation targets associated with each TP. These targets provide a consistent long-term framework to measure and evaluate the success of each transformation based on 2050 target values, and a means to explore multi-system interactions between the TPs (Methods; Supplementary Table 4).

Successful transformations often go through phases of emergence, acceleration and stabilization, taking the shape of an S-curve ^{[5, 19, 33–35]}. Acceleration occurs when a tipping point is crossed, shifting from incremental to rapid non-linear progress ^[36]. These dynamics are evident in the projections for the six TPs in the SWT trajectory (Fig. 3) as well as model projections associated with the adoption of sustainable technologies and practices such as renewables, electric vehicles, and sustainable agriculture (Supplementary Fig. 3). These acceleration dynamics are driven by the mix of policy levers as well as tipping points (e.g. price parity) for critical technologies^[37]. However, success is far from guaranteed and different pathways are possible including stabilisation, lock-in, breakdown or collapse ^{[38, 39]}.

For the six TPs, the S-curves in Fig. 3a project the average progress made by the SWT and BBS trajectories towards the 2050 transformation targets associated with each TP (Supplementary Table 4). In the SWT trajectory, progress on each of the TPs accelerates (Fig. 3(a)), closing in on 100% achievement of transformation targets in most cases, but with more limited progress for TP2 (economy, 79%) and TP3 (food, 87%). For the BBS trajectory, projected progress is particularly limited in transforming the economy (TP2), food systems (TP3) and the environmental commons (TP6), and the projections show different pathways of early lock-in, stagnation or decline. Overall, the projections for the SWT trajectory are consistently ahead of BBS across each TP (Fig. 3(b)). Total average progress towards all six TPs reaches over 92% for the SWT trajectory, well ahead of BBS which locks-in by 2030 at under 62% (Fig. 3(c)). These results are robust to significant changes in the global outlook (sensitivity analysis range of 85–93%) (Fig. 3(c); see also Supplementary Fig. 2(b)).

The modelling results are consistent with empirical research on transitions which suggests that acceleration is contingent upon decisive government action ^{[5, 7, 40, 41]}. Such coordinated, government-led development is also a common scenario archetype ^{[42, 43]}. However, a frequent criticism^{[44, 45]} is that governments are unlikely to act decisively unless the right conditions are in place ^[7] to overcome impediments such as political feasibility, vested interests, social acceptance, and barriers to technology diffusion ^{[40, 46, 47]}. These issues are addressed in our TP storylines (Supplementary Table 3) which describe how transformative change could unfold across key systems (Methods). The storylines diagnose key impediments and transition bottlenecks for each transformation and explain how societal actors could strengthen enabling conditions for systems change and the decisive government action needed for acceleration. We further explore these broader issues here, focusing on TPs 2 (economy), 3 (food) and 4 (energy) as examples.

A Sustainable And Just Economy As A Central Pillar (Tp2)

Our projections suggest that progress towards a sustainable and just economy (TP2) (Fig. 3a) can be accelerated by reforming Australia’s social protection and tax systems and incentivizing sustainable production and consumption systems (Supplementary Table 2). These systems are in a state of inertia due to a range of impediments (Supplementary Table 3). Divisions in Australia’s economic debate are sharp with unresolved conflicts between economic growth and wider policy goals, the profits of businesses and the wages of workers, public ownership versus privatisation, and wealthy inner cities versus struggling outer suburbs and regions ^{[25, 48]}. Attempts at tax reform have met with fierce resistance from businesses, workers, property owners, retirees, and shareholders ^[49]. Large sunk investments and policy support for incumbent export-oriented industries crowds out green investment and new business models ^{[50, 51]}.

However, conditions for systems change may be taking shape in Australia, with positive signs of new solutions, coalitions, and political momentum for reforms (Supplementary Table 3). Several proposals for ‘wellbeing budgets’ are moving forward ^{[48, 52]}, building on experience in New Zealand. Most large businesses are now reporting on the SDGs, Australia’s $3.3 trillion superannuation sector is aligning investments to the goals ^{[53, 54]}, and powerful actors are investing in emerging green technologies and mega projects ^{[55, 56]}. There is growing momentum for much-needed tax reform ^{[49, 57, 58]}. In our TP2 storyline (Supplementary Table 3), these green shoots accelerate as actors orient towards shared goals of halving poverty, reducing inequality, tackling debt, and a green economy.

By 2050, SWT projections for transformation targets associated with TP2 (economy) approach an average of 79% progress (Fig. 3(a)), well above the baseline of 57% under the BBS however less than other TPs. Poverty rates decline by more than half, income inequality declines by 30% (Gini coefficient from 0.33 to 0.23), green investment boosts manufacturing output by 90%, along with reductions in domestic material consumption (-33%) and material footprint per unit of output (-22%) (Supplementary Figs. 4(a)-(e)). Accelerating TP2 also provides a critical foundation for all other TPs by ensuring sustainable finance, the lack of which is a key impediment to accelerating transformations ^{[59, 60]}. Additional revenue from tax reforms in TP2 funds a broad suite of government investments including an increase in social transfers and redistribution to address poverty and inequality (TP2), increased expenditure on health, education and resilience (TP1), investments to transition to sustainable food systems (TP3), energy decarbonization (TP4), and sustainable cities (TP5), as well as for increased biodiversity protection and reforestation (TP6). Tax reform is therefore a key accelerator for transformation.

Strong Synergies From A Food System Transformation (Tp3)

In the wake of multiple crises, our storyline for TP3 (food) focuses on enabling conditions that unlock the transition towards a healthy, regenerative, and equitable food system. Barriers to change include the unsustainable growth imperative which drives the need to maximise output at the lowest cost ^{[51, 61, 62]} and which incentivises industrial farming, ultra-processed foods, a concentrated retail sector, and large volumes of packaging and food waste ^{[63, 64]}. Sunk investments create vested interests that resist change ^{[61, 65]}, modern lifestyles rely on fast, cheap, and convenient foods ^[65], and sustainable indigenous agricultural practices have been largely erased ^[65]. Current policy settings provide little support or incentives for farmers to adopt sustainable practices.

In our TP3 storyline (Supplementary Table 3), systems change comes in response to cascading crises. Enabling conditions include new shared goals, shifting narratives and preferences for healthy diets and lifestyles, new business models supporting local supply chains, disruptive emerging technologies, and a growing regenerative agriculture movement that builds into a coordinated coalition for change ^{[61, 65–68]}. With these conditions in place governments act decisively, providing new incentives, extension services and financing options to support farmers to adopt regenerative practices and integrate indigenous knowledge. Rebuilding a robust social welfare system (TP2) and investments in reforestation and carbon farming (TP6) also help farmers to maintain their livelihoods while transitioning to regenerative practices. The food system reorients towards sustainable targets, and over the long-term new technologies such as meat alternatives and feed substitutes are scaled up to reduce GHG emissions.

By 2050, SWT projections for eight transformation targets associated with TP3 reach an average of 87% progress, well above the baseline of 56% for BBS (Fig. 3(a)). The accelerators (Supplementary Table 2) include an increase in investment in sustainable agriculture as well as ambitious reductions in emissions from livestock (-83%) and cropping (-27%) by 2050 ^[68], largely resulting from technological advancements. Important gains are made in the share of harvested area sustainably managed along with reductions in fertilizer consumption and population below the food poverty line (Supplementary Figs. 5(a)-(d)).

An Accelerating Energy Transition Takes Off (Tp4)

Australia’s energy transition (TP4) is already accelerating (with renewables at 29% of electricity) ^[69] despite almost 30 years of inertia, resistance and national policy uncertainty ^{[50, 70]}. Australia’s abundant fossil fuel resources support a powerful sector which has succeeded in weakening, delaying or shaping policy responses to climate change ^[50]. Incumbents have worked to ensure that electricity market rules favour large, centralised fossil fuel generators, making market entry harder for decentralised renewable sources ^{[50, 71]}. High fossil fuel subsidies contribute to an uneven playing field, and Australian lifestyles are structured around the consumption of readily available, reliable and affordable energy ^[25].

In our TP4 storyline (Supplementary Table 4), the decarbonisation of the energy system takes off, with key drivers including community concerns about climate change, an aging coal generation fleet, and mature and competitive alternative technologies ^{[37, 68, 71]}. The strong coalition of political actors, business, unions and the community gathers momentum. All national and state governments legislate ambitious reduction targets for 2030 and net zero by 2050. Detailed technical roadmaps by research institutions provide clear decarbonisation pathways which governments and stakeholders put into action ^{[68, 72, 73]}. Important triggers for acceleration include price-parity tipping points for renewables and other technologies which provide solutions that governments can push. Early policy levers include subsidies, incentives and public investment that improve economic competitiveness and provide complementary infrastructure, while later measures include the accelerated phase out of unsustainable technologies and practices.

The SWT projections for TP4 reach full achievement of associated transformation targets by 2050 (Fig. 3(a)) due to increased investment in renewables and energy efficiency, fuel switching, and an accelerated phase out of fossil fuel generation (Supplementary Table 2). By 2050, the share of renewables in electricity and in total final energy consumption reach 100% and 70%, respectively (Supplementary Figs. 6(a) and (b)). After 2030, emphasis shifts to harder-to-abate sectors in industry and agriculture in line with a deep decarbonisation pathway ^[68].

Multi-system Interactions Deliver Net Zero By 2050

The transition of the energy system (TP4) paves the way for important gains across other TPs, including decarbonising Australia’s built environment and transport sectors (TP5), enabling green manufacturing (TP2), and reducing energy emissions in the agriculture sector (TP3). For TP5 (urban), governments incentivize the electrification of buildings, mandate timber buildings and resource recovery targets, provide electric vehicle subsidies and tax rebates, invest in charging infrastructure, and improve waste management through circular economy initiatives. The combined effect of these transformations results in a 72% reduction in total GHG emissions between 2016 and 2050 (Fig. 4). Combining these with large investments in reforestation (TP6) and climate change adaptation (TP1) places a net zero outcome within reach by 2050 (Fig. 4). The results highlight that accelerating progress on TP4 can create a ‘pull effect’ with many positive spill-overs for other transformations, demonstrating how important shifts in single systems can propagate across systems, fundamentally changing the directionality for all systems and resulting in a ‘deep transition’ ^{[74, 75]}.

Complex Synergies And Trade-offs Among The Tps

The modelling results reveal that TP4 (energy) has the greatest measurable synergistic policy effects on other TPs, enabling a green transition in TP2 (economy), TP3 (food), TP5 (urban) and TP6 (environment). The matrix heatmap in Fig. 5(a) presents the modelled effects of the policy settings in each TP (modelled individually) on progress towards the transformation targets for each TP (Methods). For example, reading from left (rows) to right (columns) (Fig. 5(a)), the projection for TP1 (by itself) results in synergies with all other TPs, particularly for TP5 (+ 8.4% through more resilient urban systems) and TP3 (+ 6.6% through more resilient food systems). The total additional synergies from TP1 amount to + 24.6% progress (final column). Moving down (Fig. 5(a)), TP2 shows more limited quantifiable synergies with other TPs (+ 4.3%) while TP3 projects strong synergies with TP6 on biodiversity (+ 14.7%), largely due to improved land management. Overall, the largest synergies can be seen between TP4 on energy decarbonisation on other TPs (+ 30.8%).

Some trade-offs are also evident in these results (negative values), for example between TP3 (food) and TP5 (urban). The largely positive values suggests that synergies generally compensate for trade-offs. However, if we sum the individual gains for all TPs (Fig. 5(b), + 36% above BBS) and compare them against the aggregate results for the SWT trajectory (+ 30% above BBS), there is a discrepancy of around 6% progress which is ‘lost’ when the TPs are layered upon one another. This indicates areas of conflict or duplication in the complex multi-system interactions which we unpack in Fig. 5(c). In each stacked bar (Methods), the filled component presents the synergies received by each TP from all other TPs in the SWT trajectory, which are greatest for TP6 (environment) and TP3 (food). The transparent component (Fig. 5(c)) subtracts the synergies received by each TP from the individual TP simulations so that negative values represent aggregation losses (trade-offs) and positive values represent multiplier effects.

These losses more clearly identify potential trade-offs associated with each TP and are particularly large in the case of TP2 (economy) due to interactions with TP4 (energy) where combining their interventions results in conflicts and dampening effects. For example, by simultaneously increasing and decreasing productivity, industrial output, and material consumption as a result of interventions in different sectors. The net positive result in TP3 (food) reveals a multiplier effect where aggregate gains for SWT are greater than the sum of individual gains (Fig. 5(c)). This boost in progress is unlocked when multiple interventions such as social protection (TP2) and investment in sustainable agriculture (TP3) leverage greater gains in food security and TP3 targets.

The Contribution Of The Tps To Accelerating Sdg Progress

Finally, Fig. 6 presents the simulated contribution of each TP towards achieving the 17 SDGs in 2050 (Methods), which reveals the complex interactions between the TPs and SDGs. We choose to evaluate these effects in 2050 (as opposed to 2030) to capture the long-term implications of the transformations. Overall, TP1 (wellbeing) makes the greatest contribution, particularly for goals relating to poverty (SDG 1), food and nutrition (SDG2), economy (SDG8), cities (SDG11), and climate action (SDG13). This reveals the positive effect of increasing resilience on the SDGs, particularly over the long-term. TP6 (environment) also contributes strongly to SDGs progress, however these gains are largely associated with land and biodiversity (SDG15) and oceans (SDG14) which accelerate from a very low baseline. TP3 (food) mainly accelerates performance on food and nutrition (SDG2) and biodiversity (SDG15), with positive effects on many other goals. TP4 (energy) makes a strong contribution to climate action (SDG13) as well as energy (SDG7), economy (SDG8), industry (SDG9), and cities (SDG11). For TP5 (urban) the biggest contributions are towards sustainable cities (SDG11), energy (SDG7) and climate action (SDG13). Finally, TP2 (economy) results in moderate gains across a broad range of goals, primarily on poverty (SDG1), gender equity (SDG5), economy (SDG8), income inequality (SDG10), and government revenue (SDG17).

As we reach the mid-way point of the 2030 Agenda there is a growing emphasis among governments and international organizations on accelerating transformations to the SDGs. Through our transformation pathways approach, we find that the acceleration of six transformations is both technically feasible and affordable but will need to be supported by enabling conditions to overcome common lock-ins and barriers that have hampered progress to date. While our research has focused on Australia and each country will follow its own pathway, the approach of combining systems modelling with transition storylines is highly adaptable and transferrable to other country contexts, and our findings are broadly relevant for national and international researchers and practitioners seeking to accelerate progress towards the SDGs and longer-term outcomes of net zero and sustainable wellbeing. We highlight here some of our key findings and place these in the context of emerging global research and practice, as well as study limitations and areas for further research.

Advancements In Sdgs-related Modelling

Our study builds on an expanding SDGs-oriented modelling literature at both the global ^{[15, 16]} and national levels ^{[18, 76, 77]}. Global scenario projections for the Shared Socioeconomic Pathways (SSPs) have shown that SDG targets would not be achieved by 2030 or even 2050 without ambitious policies ^[78]. Recent global studies have concluded that early systems change is important ^[16] and that more ambitious policies could deliver better outcomes for the SDGs^[15] but may take decades to manifest. Previous national modelling in Australia^[18] similarly revealed that achieving greater gains on the SDGs by 2030 was possible, however even the most optimistic sustainability transitions scenario fell well short of achieving the SDGs. Critical gaps in the existing modelling literature relate to the limited treatment of the six key transformations needed to achieve the SDGs, how these transformations can be accelerated, as well as the socio-political impediments to acceleration that have hampered progress to date.

In this context, our study contributes several significant methodological and conceptual advancements. Firstly, we move beyond recent studies by modelling all six transformations to the SDGs, which enables a more complete and holistic picture of the combined effects and complex interactions of these multi-system transformations. The development and integration of our six transformation pathways module into a national-scale system dynamics model thus advances an important methodological frontier ^[19] by simulating complex, interlinked and composite systems into the long-term future. Secondly, our focus on identifying key ‘accelerators’ that can trigger positive tipping or ‘S-curve’ dynamics provides new insights to support decision makers seeking to move through the emergence phase and advance more rapidly towards the SDGs by 2030. The results also demonstrate how to ensure that any gains made are also resilient and stabilize over the longer-term to 2050. Thirdly, our transformation pathways create a bridge between the quantitative model projections and the qualitative transition storylines which are informed by empirical transitions research and explain the enabling conditions needed for techno-economic interventions to succeed. This addresses not only the ‘what’ but also the ‘how’ of transformation and advance knowledge on important acceleration dynamics. A priority area for further research would be to explore how systems modelling could assist with identifying and quantifying important reinforcing feedbacks and positive tipping points that can trigger acceleration.^{[36, 79]}

A Well-designed And Sequenced Package Of Accelerators

While the 2030 deadline is rapidly approaching, our results reveal a broad mix of measures that, if implemented over the next decade, could accelerate progress on the SDGs. These accelerators are largely government policies and investments, a finding that is supported by the expert literature that shows that decisive government action is critical for accelerating transitions ^{[5, 7, 40, 41]}. While in a post-COVID recovery context government budgets are strained, we find that feasible and affordable solutions are available through a mix of targets, policy reforms, large-scale investments, subsidies, and regulations. Additional annual investment requirements are modest (at around + 7.5% annual expenditure to 2030) and can be financed through tax reforms. However, the mix and sequencing of interventions is also critically important ^{[80, 81]}. For example, early interventions incentivize investment, build complementary infrastructure, and bring forward price-parity tipping points through subsidies and large-scale public investments, while later measures deliberately phase out unsustainable configurations, such as the regulated phase out of coal and gas generators and internal combustion vehicles. These policy mixes not only accelerate the transformations but also help to stabilise the new configurations over time.

Our results also highlight other considerations for governments in developing a well-designed and sequenced package of accelerators. Tax reform is a prerequisite for financing the six transformations and provides much-needed revenue for public investments. The adoption of wellbeing budgets paves the way for greater investments in health and education and social protection. These investments are important as they can ameliorate concerns around the negative impacts of rapid transitions, increase social acceptance for change, and improve the political feasibility for more ambitious reforms ^{[82, 83]}. The rapid transition to renewable electricity enables investments in green manufacturing and new zero carbon industries, and can also create a ‘pull effect’ with many positive spill-overs for other transformations including urban and transport systems. Deliberately incentivizing healthy lifestyles can shift values and behaviours in urban populations and drive changes in dietary preferences and practices which support sustainable food systems, reduce material consumption, and diminish or remove key pressures on the natural environment. Over the long-term, investments in climate change resilience are critical to ensure that all transformations stabilize and are resilient to inevitable future disruptions.

Barriers And Enabling Conditions For Acceleration

While governments are important, a range of societal actors have critical roles to play in accelerating transformations. This is because a common characteristic of systems is their inertia and path dependence and the right conditions need to be in place to overcome techno-economic, social-cognitive, and institutional-political lock-ins and compel governments to take decisive action ^{[7, 22, 40, 46, 47, 65]}. By diagnosing these lock-ins for each transformation pathway we can better inform societal actors on the enabling conditions that could lead to systems change. Common impediments include resistance from powerful vested interests and lobbies, a lack of public support, highly consumptive lifestyles, and other market, policy and structural barriers to new technologies and practices. In our transition storylines, important actors and enabling conditions for change include coalitions of business, unions, governments and community groups that build political momentum for change, powerful actors and policy entrepreneurs that shift narratives in favour of change, actors in innovation and research who develop solutions that policymakers can push, and a sustainable finance sector that scales up investment in promising technologies and solutions. The recent culmination of cascading crises has also destabilised existing systems and created a window of opportunity for systems change^[31]. However, a limitation of our study is that while we diagnose and describe these barriers and conditions, we do not as yet endogenize and quantify these effects. This represents an emerging and priority area for further research.

The Complexity Of Multi-system Interactions

Our findings show that all six transformations together deliver the strongest gains on the SDGs as well as a net zero outcome by 2050, and can be coherently designed to be mutually reinforcing. While synergies appear to dominate, the national scale of the analysis potentially gleans over localised trade-offs that occur from structural change in the economy which will create winners and losers in different industries and local regions. These could be further explored by spatially downscaling the modelling framework, presenting an opportunity for future research.

The more limited progress made on the sustainable and just economy transformation (TP2) and its apparent trade-offs with energy decarbonization (TP4) also suggest priority areas for future analysis. In particular, there appear to be internal conflicts within TP2 regarding the ‘sustainable’ versus ‘just’ objectives, where efforts to deliver a green and resource efficient economy reduce industry output in some sectors with implications for income and employment. Complex feedbacks between TP2 and TP4 also result in apparent trade-offs. For example, material efficiency targets and additional taxes (TP2) slow economic production, industrial output, and emissions, while at the same time measures to decarbonise the energy system (TP4) both reduce material intensity, productivity and emissions while also stimulating growth in green industrial output and material consumption. When all TPs are combined, their overall aggregate effects are complementary, however the results may also gloss over these underlying tensions which warrant further analysis. Despite these conflicts, energy decarbonization (TP4) had the strongest measurable synergies with all other TPs, while the environment (TP6) was the largest beneficiary of synergies. Investing in resilience and wellbeing (TP1) has the biggest impact on the SDGs and is crucial for stabilizing transformations over the long-term. Dual investments in maintaining livelihoods and a sustainable food transformation unlock multiplier effects of a just transition.

Overall, our study sheds new insights on important accelerators, barriers, enabling conditions and multi-system interactions that could help countries unlock the necessary transformations to accelerate progress on the SDGs by 2030 and place their countries on a path to net zero and long-term sustainable wellbeing.

The methodological framework for our analysis (Fig. 1) comprises several novel components which were informed by the social sciences literature on sustainability transformations as well as the literature on integrated scenario modelling. To date, these two important tracks have largely evolved in parallel^[84]. Models comprehensively and systematically represent technology, economy, environment, and policy, whereas social sciences unpack the behaviour of various actors, common impediments and enablers, transformation dynamics over time, and heterogeneity within and across societies ^[85]. Both fields are important in the study of transformations to the SDGs and our study methods combine these approaches by integrating a metanarrative and six TP storylines with quantitative model projections and analysis using a national-scale integrated system dynamics model for Australia (iSDG-Australia v2.0).

Conventional approaches for integrating qualitative narratives with quantitative projections often generate scenarios based on a (2 x 2) matrix which represents forces and drivers with high importance and uncertainty ^[86], as advocated in early IPCC reports ^[87] and used in our previous scenario modelling research ^{[17, 18]}. Broader societal developments are generally assumed as exogenous by means of high-level narratives that inform model assumptions, for example the widely used Shared Socio-Economic Pathways ^[88]. This can end up biasing policy recommendations toward easier quantifiable technical and economic pathways ^[85].

Emerging approaches that begin to address these limitations include creating a ‘dialogue’ between models and detailed socio-technical storylines or transition pathways to compare and contrast insights ^{[22, 23, 89]}. This broadens the scope from not only techno-economic considerations addressed by models but also key bottlenecks, lock-ins and branching points in transitions which require changes in both policies and actor strategies to improve social acceptance and political feasibility of transitions ^[22]. This addresses the ‘how’ of achieving planned and actionable change ^{[24, 90]}. These pathways help to explain the processes that create favourable conditions for the policies and interventions needed to accelerate progress towards desired goals. This aligns with the approach taken in this study which combines quantitative model projections with qualitative storylines that address impediments and enablers into six transformation pathways.

Critical Juncture And Trajectory Metanarratives

Advancing on our previous research^[18] which used a conventional scenario matrix approach nested within the global SSPs, this study starts by describing two alternative future trajectories which are separated by a critical juncture resulting from compounding crises. Such junctures are a well-documented feature in transformations, and are often associated with major crises or shocks which destabilize the existing regime and open windows-of-opportunity for change ^{[31, 32]}. An important premise for our analysis is that several cascading crises create the potential for a critical juncture or bifurcation, with two emerging trajectories. These trajectories are equally plausible and are guided by metanarratives that broadly align with ‘business-as-usual’ and ‘sustainability transition’ scenarios for Australia ^[18]. They also draw exogenous global assumptions where relevant from SSP2 (Middle of the Road) and SSP1 (Sustainability).^[91]

Firstly, the ‘Build Back the Same’ (BBS) trajectory assumes that during Australia’s medium-term recovery from 2022 to 2030, policy settings and ambition return to pre-COVID levels with no additional investment beyond the emergency stimulus measures (Supplementary Tables 1 and 2). This places Australia largely on a business-as-usual recovery pathway which sees a continuation of pre-COVID trends. Exogenous global assumptions are based on SSP2 ^[88] and the BAU scenario from Allen, Metternicht ^[18].

Contrasting this, the ‘Sustainable Wellbeing Transformation’ (SWT) trajectory (Supplementary Tables 1 and 2) assumes that a pervasive narrative emerges in Australia around the need for structural change and to build back better using the SDGs as a roadmap. This gains support from powerful actors and coalitions which legitimizes stronger policy action. Central to this action is an extension of economic stimulus over the period from 2022 to 2030 which places Australia on an accelerated trajectory towards the SDGs and longer-term transformations. Exogenous global assumptions draw from SSP1^[88] and the Sustainability Transition scenario from Allen, Metternicht ^[18].

Both trajectories incorporate the measurable effects of COVID-19 and other shocks as well as the government’s emergency response and stimulus measures taken largely in 2020/21 (Supplementary Fig. 1). Following the critical juncture from 2021/2, the trajectories diverge considerably with alternative medium-term recovery trajectories which place Australia on considerably different pathways.

Transformation Pathway Storylines

Nested within the broad metanarrative for the SWT trajectory we develop six transformation pathways (TPs) encompassing the full complement of systems to be transformed to achieve the SDGs, as articulated in global studies ^{[11, 12]}. To create a bridge between the quantitative projections and the more qualitative actor strategies to address transition lock-ins or ‘bottlenecks’, each pathway comprises a qualitative storyline as well as a set of quantitative settings and assumptions which are used to parameterise the model.

To construct each storyline, we first developed a standard template informed by the expert literature. A large and growing body of research on sustainability transitions and transformations explores common barriers and enabling conditions for accelerating transformations ^{[5, 7, 19, 32, 40, 47]}. Decisive government action is seen as critical, including by setting targets, launching missions, shaping markets, investing in infrastructure, and regulating businesses ^{[5, 7, 12, 40, 41]}. A more active and interventionist role by governments is central to SWT.

However, governments are unlikely to act unless the right conditions are in place ^[7]. Common barriers include techno-economic, social-cognitive, institutional-political, and social-ecological lock-ins which lead to system inertia and path dependence ^{[22, 40, 46, 47, 65]}. These can result from large sunk investments which create vested interests, economies of scale which challenge new market entrants, lifestyles which become organised around unsustainable practices, and policy settings and networks which favour incumbents or stifle innovation. Policymakers can become captured by vested interests, tied up by lobby groups, or lack the capacity, resources and incentives to act ^{[5, 92]}. Important conditions for overcoming inertia result from a range of sources and societal actors. These include changing external pressures from the maturation of innovations which provide solutions that policymakers can push, shifts in public opinion and pervasive narratives, coalitions that organise actors towards new goals, and support from powerful actors and policy entrepreneurs ^{[7, 40, 93, 94]}. Shocks, crises and slow-moving trends can generate instability in existing systems, creating windows of opportunity for systems change ^{[31, 32, 34]}.

Informed by this research, each TP storyline adopts a similar template which includes a high-level analysis of progress and challenges, key transformation bottlenecks (techno-economic, social-cognitive, political-institutional, and social-ecological), positive signs and emerging seeds of change, and a narrative storyline which explains the processes that create favourable contexts and conditions for ambitious new policies and interventions which accelerate each transformation (Supplementary Table 3). A range of context-specific source material was reviewed to construct the TP storylines, including empirical research on sustainability transitions in Australia, national data and existing scenario modelling and foresight studies, assessments of Australia’s progress on the SDGs, government strategies and reports, and analyses and technical reports from think tanks, the private sector and civil society organisations (Supplementary Tables 2 and 3). This literature also informed the quantitative policy settings and assumptions developed for each TP to complement and align with the qualitative TP storylines so that they could be parameterised in the model. Given the very broad scope of the analysis covering a comprehensive suite of transformations, the intention was to build on existing research to develop a mix of key interventions needed to accelerate progress along with explanatory storylines addressing the enabling conditions and context needed to support the quantitative pathways.

Elaboration Of Quantitative Settings And Assumptions

Each of the six TPs included a range of different quantitative interventions including government expenditure, taxes, subsidies, policies as well as assumptions regarding technology diffusion and behavioural changes. Tax, subsidy and expenditure settings for the BBS trajectory remained in line with recent time series data (as a proportion of GDP), while other assumptions were informed by timeseries trends and existing research (Supplementary Table 3). In the SWT trajectory, policy settings and assumptions for each TP were parameterised in line with the TP storyline, and based on official time series datasets, available costings studies and reports, other modelling studies and research for Australia, global benchmarking (e.g. against OECD countries), and iterative analysis of model projections (Supplementary Table 3).

The model projections also relied upon several key exogenous assumptions which varied in some cases between the two trajectories. These relate to exogenous parameters associated with population (net migration), governance, global temperature changes and impacts, climate change adaptation costs, demand for commodities, interest rates on debt, and average technology improvements. For each trajectory, these were parameterised using global projections from the SSPs and Regional Concentration Pathways (RCPs) ^[91], as well as modelling from other international organisations and research institutes, scientific literature, and national studies in Australia (Supplementary Table 3). Given the uncertainty associated with these exogenous assumptions, they were also subjected to sensitivity analysis.

Model Description, Calibration And Validation

The study applied an integrated, macroeconomic system dynamics model (iSDG-Australia 2.0), the original version of which is detailed in Allen, Metternicht ^[18]. The iSDG family of models ^[95] are built in a stock and flow structure and formulated as a set of differential equations encompassing 3000 + variables organised across 30 economic, social and environmental sectoral modules. A description of each of the sectoral modules along with key assumptions and source literature is available in the model documentation ^[95].

The latest version of the iSDG-Australia model (v2.0) incorporates an expanded set of 37 modules which include recent advancements for Australia’s built environment ^[96] and transport sectors ^{[97, 98]}, amongst others. For this study, we further develop the model to incorporate a COVID-19 shock in key economic and population sectors and the associated government emergency stimulus response over the period 2020-21. A new and novel ‘transformations module’ was also developed and structured around the six transformations to achieve the SDGs. This included both a set of interventions for accelerating each transformation as well as a suite of ‘transformation targets’ for measuring progress on each transformation over the period to 2050. Enhancements were also made to a range of sectoral modules to increase the suite of policy measures and assumptions available for each transformation, including for fuel switching, green manufacturing, accelerated phase out of technologies, sustainable agriculture, and waste management.

Drawing on good practice model verification procedures ^[99–103], model validation included both structural and behavioural validation. The model is calibrated on an extensive database of 25–30 years of historic time series data commencing in 1990 and sourced from official and verified national government sources (Australian Bureau of Statistics and government administrative databases), as well as official data from international databases (Supplementary Tables 3 and 4). In each sector, parameters were calibrated using multi-parametrical optimisation and reference parameter ranges. Behaviour reproduction tests were used to evaluate the goodness-of-fit of simulated and actual data using plotted graphs and error statistics (R²), mean percentage error (MPE), and mean absolute percentage error (MAPE) ^[101]. Goodness-of-fit statistics calculated for a selection of critical variables for the baseline BBS simulation are included in Supplementary Table 5.

Model Projections

Following final calibration of the iSDG-Australia 2.0 model, the baseline BBS scenario was projected through to 2050 based on a continuation of current policy and expenditure settings. Parameterisation for each transformation pathway for the alternative SWT trajectory was based on the settings in Supplementary Table 3. The simulation period was set to include the implementation period for the SDGs as well as net zero (2021–2050), with alternative policy assumptions introduced from 2021 onwards and with most ending in 2030. In some cases, the model uses time delays which result in more gradual effects from interventions.

To explore individual and aggregate effects of the transformation pathways, each pathway was projected individually before projecting all pathways simultaneously as an aggregated SWT trajectory. This enabled an assessment of interactions and potential spill-over effects between the different transformations, for example the positive effects that a transformation towards a sustainable and just economy (TP2) might have on transforming wellbeing and capabilities (TP1). It also enabled an evaluation of the impacts of each transformation on the full suite of SDG targets and indicators, as well as a comparison of individual and aggregate results.

Targets For 2030 And 2050 And Method For Assessing Progress

The performance of the BBS and SWT trajectories as well as each of the six TPs were evaluated against a set of 52 SDG targets and 80 indicators covering all 17 goals in 2030, as well as a set of 67 transformation targets (TTs) covering each of the six transformations in 2050 (Supplementary Table 4). The selection and prioritisation of SDG targets and indicators was based on the official SDG indicator framework ^[104] as well as recent baseline assessments of Australia’s progress on the SDGs ^{[25, 105]}. The selection of TTs for 2050 drew upon the set of SDG indicators and was also informed by global studies which provide guidance on 2050 targets ^[78] as well as on linking SDG indicators to the six transformations ^[106] (Supplementary Table 4).

Target values for each indicator for 2030 and 2050 were formulated drawing on a range of sources, including the SDG targets themselves, additional targets used in Australia’s SDG baseline assessments and previous modelling ^{[18, 25, 105]}, threshold values taken from the global SDG Index ^[2] and other global studies and benchmarks (Supplementary Table 4). The aim was to formulate ambitious but credible targets for Australia to reach by 2030 and 2050, however in most cases they are not official Australian government targets.

Progress towards each target is simulated in the model over the period from 2016 to 2030 (for 2030 targets), and 2031 to 2050 (for 2050 targets). The projections reveal Australia’s proportional achievement of a target (from 0 to 100%). A normalised scale (0-100) was used, whereby the reference value in 2015 was considered the ‘zero point’ and the target values for 2030 and 2050 were considered the final points (reflecting % progress). For the 2050 targets, the projected performance by 2030 was used as the starting point for measuring additional progress to 2050.

Methods For Assessing Interlinkages

Interlinkages between the different TPs as well as between the TPs and the SDGs are explored in both a qualitative sense (drawing upon important interlinkages highlighted in the storylines) as well as quantitatively through the modelling results. The very broad scope of the system dynamics model combined with the six TPs approach supports a complex quantitative analysis of feedbacks and interlinkages across different systems and targets. Each TP has a unique set of TTs which we use to evaluate progress on each transformation by 2050. In our SWT trajectory, we simulate all six TPs simultaneously and as such the projections represent their aggregate effects. While each TP is designed to accelerate progress towards its own unique set of TTs, it also has implications (synergies and trade-offs) for the achievement of TTs associated with the other transformations.

We explore these interactions by simulating each TP individually and evaluating effects on the total set of TTs as well as the TTs associated with each TP. Synergies (trade-offs) occur when the simulation of a TP results in improved (worsened) performance on TTs associated with other TPs when compared against the BBS baseline. We then sum these individual results and subtract them from the results from the SWT trajectory when all TPs are simulated simultaneously to highlight any discrepancies. Where these values are negative, they suggest ‘aggregation losses’ involving potential areas of duplication or hidden trade-offs when TPs are combined. Where they are positive, they suggest multiplier effects where additional gains are only made when TPs are combined. We interpret synergies in a broad sense, including positive spill-overs between the TPs as well as stronger multiplier effects.

In a similar way we also explore the effects of each TP on the achievement of each of the 17 SDGs. As we are interested in the long-term effects of each TP, the analysis is done using the projected results for 2050. We calculate the additional percentage contribution that each individual TP makes towards the achievement of each SDG in 2050 when compared against BBS baseline. To present these results in Fig. 6, we convert these values to a proportional contribution by dividing the individual values by the sum of additional percentages from all TPs on each SDG. We then multiply these proportional contributions by the results for the SWT trajectory (i.e. by the projected additional gains on each SDG made by the SWT trajectory compared against the BBS trajectory). This enables us to decompose the results for the SWT trajectory and allocate a contribution for each TP towards additional progress made on the SDGs.

Sensitivity Analysis

Sensitivity analysis was used to complement the model calibration process and to test key model assumptions. For very large system dynamics models such as the iSDG tool, good practice is to focus on those relationships and parameters that are both highly uncertain and likely to be influential ^[101]. Previous sensitivity analysis for the iSDG model has found that sensitivity of the simulated SDG performance results to varying global assumptions is low to moderate ^{[18, 96]}.

The national scope of the model does not enable the inclusion of endogenous structure for global drivers such as trade, interest rates, action on climate change, or migration. We tested the sensitivity of model outputs to ten key exogenous global assumptions (Supplementary Table 6). The output variables of interest included the progress across each of the six TPs, performance scores for each of the 17 goals, aggregate TP and goal performance, and population, real GDP and GHG emissions.

We followed the general workflow described in Pianosi, Beven ^[107], running Monte Carlo simulations in which model parameters were randomly adjusted within a predetermined range (min/max) using a normal distribution. We adopted the all-at-a-time (6000 simulations) random Latin Hypercube sampling method. Input ranges for each sensitivity variable were informed by available literature and previous modelling studies (Supplementary Table 6).

Data availability statement

Additional materials and data are available in the Supplementary Information. All datasets collected and analysed during the current study are available from the corresponding author on request.

Code availability statement

The iSDG simulation model can be made available from the Millennium Institute on reasonable request.

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Modelling six sustainable development transformations and their accelerators, impediments, enablers, and interlinkages

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Abstract

Figures

Introduction

Results

COVID-19 and cascading crises

A Critical Juncture: Trajectories Diverge

The S-shaped Acceleration Dynamics Of The Six Transformations

A Sustainable And Just Economy As A Central Pillar (Tp2)

Strong Synergies From A Food System Transformation (Tp3)

An Accelerating Energy Transition Takes Off (Tp4)

Multi-system Interactions Deliver Net Zero By 2050

Complex Synergies And Trade-offs Among The Tps

The Contribution Of The Tps To Accelerating Sdg Progress

Discussion

Advancements In Sdgs-related Modelling

A Well-designed And Sequenced Package Of Accelerators

Barriers And Enabling Conditions For Acceleration

The Complexity Of Multi-system Interactions

Methods

Critical Juncture And Trajectory Metanarratives

Transformation Pathway Storylines

Elaboration Of Quantitative Settings And Assumptions

Model Description, Calibration And Validation

Model Projections

Targets For 2030 And 2050 And Method For Assessing Progress

Methods For Assessing Interlinkages

Sensitivity Analysis

Declarations

References

Additional Declarations

Supplementary Files

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