The intellectual stakes are considerable. Current fiscal practice across most advanced economies remains anchored in the New Economic Consensus, an offshoot of neoclassical economics that treats budget deficits as inherently inflationary, subordinates fiscal policy to central bank interest rates, and largely ignores the environmental damage generated by economic activity. Sustainability researchers have sharpened their critique of this orthodoxy, arguing that its core assumptions fail to capture ecological limits, nonlinear dynamics, and distributional realities. Dimitrieva’s analysis instead draws on post-Keynesian traditions and Modern Monetary Theory, which hold that currency-issuing governments are not financially constrained in the way households are, and that the true limits on public spending are real resources, productive capacity, and inflation risk.

The six frameworks differ profoundly on the desirability of growth itself. Green Growth, championed by the OECD, the UN Environment Programme and the World Bank, rests on the hope of decoupling GDP expansion from environmental harm through technological innovation, carbon pricing and circular economy practices. Yet critics, most prominently in work questioning whether green growth is possible at all, point to the speculative nature of absolute decoupling and the stubborn problem of rebound effects, where efficiency gains simply fuel more consumption. Degrowth takes the opposite stance, insisting that wealthy nations must democratically and deliberately scale down energy and material throughput without compromising wellbeing. Post-Growth, grounded in Herman Daly’s steady-state economics, envisions the destination: an economy of constant stocks and flows operating within the planet’s regenerative and absorptive capacities, where knowledge and culture may flourish even as material extraction stabilises.

Doughnut Economics, developed by Kate Raworth, supplies the study’s normative compass. Its visual logic is elegant: an inner ring of social foundations, from water and housing to gender equality and political voice, derived from the Sustainable Development Goals, and an outer ring of planetary boundaries drawn from the Earth-system science of Johan Rockström and colleagues, whose recent work shows humanity has already breached six of nine boundaries. The band between the rings is the safe and just space where humanity can thrive. Notably, the analysis highlights a structural weakness of the SDGs themselves: their economic assumptions remain largely neoclassical, and Goal 8’s pursuit of GDP growth can directly conflict with the climate and biodiversity goals, a tension researchers have quantified as a sustainable development oxymoron. The paper argues the Goals need recalibration to incorporate distributive and biophysical insights from the more transformative paradigms.

Modern Monetary Theory plays the operational role in the proposed synthesis. Because monetarily sovereign governments, such as those of the United States, the United Kingdom, Australia and Japan, spend in currencies they issue, they need not wait for tax revenue or bond markets to fund transformative investment. Taxes, in this framing, create demand for the currency, manage inflation, and redistribute income; they are not the financing precondition for spending. This insight, the paper notes, became impossible to ignore after the 2008 financial crisis and the pandemic, when governments ran large deficits without fiscal collapse. MMT therefore unlocks what the author calls fiscal space: the capacity to finance universal public services, renewable infrastructure and a Job Guarantee, an employment buffer stock first proposed by Hyman Minsky that stabilises both prices and livelihoods.

The crucial move is differentiation. The study argues that Green Growth strategies are best suited to low-income countries and sectors naturally amenable to greening, such as renewable energy, transport and tourism, where expanding access to public goods remains essential and clean-technology leapfrogging offers genuine development gains. Degrowth-oriented policies, by contrast, fit high-income, high-consumption economies, where deliberately downscaling throughput is vital for staying within planetary boundaries. These differentiated pathways then converge on a Post-Growth steady-state economy, anchored in the Doughnut’s vision of a safe and just space. The paper’s conceptual pathway diagram traces this progression: MMT-enabled fiscal space at the start, context-specific Green Growth or Degrowth transitions in the middle, and Doughnut-guided steady-state convergence at the end.

The comparative analysis is conducted across five dimensions: vision of the economy, relation to growth, role of the state, role of money, and concrete use of fiscal tools. On money, the frameworks split revealingly. Green Growth treats green finance, green bonds and climate-adjusted central bank operations as instruments within capitalist structures, a critique voiced by degrowth scholars who see financialisation commodifying nature. Degrowth and Doughnut Economics propose more radical redesigns, including full-reserve banking, complementary currencies, and the democratisation of money creation, treating money as a social relationship rather than a neutral medium. MMT reframes money as a sovereign public utility limited only by inflation and real resources. The Sustainable Development Goals camp promotes sovereign green bonds and retooled central bank mandates to close the vast financing gap, particularly the pandemic recovery gap widening between rich and poor nations.

On fiscal instruments themselves, the convergences are striking. Nearly every framework endorses progressive, equity-oriented taxation: shifting the tax base away from labour and toward wealth, resource extraction, rent, inheritance and ecologically harmful consumption. Degrowth scholarship adds minimum and maximum income thresholds, universal basic income, and shorter working weeks, though a systematic review of fifteen years of degrowth research found concrete, empirically grounded proposals remain scarce. Here the author identifies a critical blind spot: mainstream degrowth and post-growth literature often defaults to orthodox fiscal framing, assuming wealth taxes must precede public spending. MMT dissolves that sequencing problem. Eco-social policies can be funded directly through sovereign monetary capacity, with taxation serving afterwards as the instrument for demand management, inflation control and redistribution, tools of governance rather than prerequisites for action.

The paper is candid about limits. Monetary sovereignty is not universal: Eurozone members, low-income countries and states burdened by foreign-currency debt cannot simply spend their way to sustainability, raising urgent questions about how non-sovereign nations can expand fiscal space without deepening vulnerability. The author also acknowledges omitting important perspectives, including the Wellbeing Economy, commons-based approaches, the care economy, and Latin American Indigenous frameworks such as Buen Vivir, and points to the Porter Hypothesis tradition in arguing that well-designed environmental regulation can enhance, rather than erode, competitiveness and innovation.

The implications, if the synthesis gains traction, are significant. Fiscal policy would be repositioned as the central engine of socio-ecological transformation: public investment steered toward renewables, low-carbon infrastructure and social services; subsidies and taxes aligned with throughput limits rather than GDP targets; democratic oversight and international cooperation securing coherence between national development needs and planetary ceilings. The Sustainable Development Goals, recalibrated to shed their internal contradictions, could serve as the institutional scaffold for a global consensus. What emerges is not a single doctrine but a pluralist meta-framework, one that treats Green Growth, Degrowth, Post-Growth, Doughnut Economics, MMT and the SDGs as complementary lenses on a shared problem: how to raise and allocate public resources so that economies serve people and planet alike, within the biophysical limits of a finite Earth.

Subject of Research: A comparative analysis of six sustainability-oriented economic frameworks and their integration into a meta-framework for sustainable fiscal policy

Article Title: Sustainability and fiscal policy: bridging economic frameworks for global equity and ecological balance

Article References: Sustainability and fiscal policy: bridging economic frameworks for global equity and ecological balance. (n.d.). https://doi.org/10.1007/s12232-026-00549-7

Image Credits: AI Generated

DOI: 10.1007/s12232-026-00549-7

Keywords: fiscal policy, green growth, degrowth, post-growth, doughnut economics, modern monetary theory, sustainable development goals, planetary boundaries, steady-state economy, just transition, ecological macroeconomics, monetary sovereignty

The research, conducted by Wonhyuk Cho of Ewha Womans University in Seoul and Danuvas Sagarik of the National Institute of Development Administration in Bangkok, appears in the journal Global Public Policy and Governance. The authors set out to address a gap that has grown as governments around the world, from Thailand with its digital government development plan to agencies across the European Union, experiment with embedding machine learning in administrative decision-making. While a substantial literature has documented the efficiency gains that AI can deliver in public services, far less is known about how bureaucrats themselves respond when algorithms are proposed for budgetary choices, a domain that concerns not merely productivity but the fundamentally distributive question of who receives funding and who does not.

The institutional viability of any algorithm-enabling budgeting reform, the authors argue, hinges on whether the bureaucrats charged with carrying it out view it as preferable to the alternatives. History offers plenty of cautionary tales on this point. Public administration research has repeatedly shown that bureaucratic organizations resist reforms perceived as threatening, whether those reforms involve shared service centers or broader restructuring programs, and that the success or failure of administrative change often depends on securing cooperation from insiders. If civil servants quietly oppose algorithmic budgeting, even the most technically sophisticated systems could stall in implementation.

To measure these preferences, the researchers implemented a three-arm survey experiment involving a large sample of 3,820 public sector personnel. Respondents were randomly assigned to read vignette scenarios describing one of three reform pathways: the adoption of AI-assisted budgeting, an expansion of the government workforce, or the introduction of participatory budgeting, in which citizens help decide spending priorities. Random assignment ensures that any differences in reported support across the three groups can be attributed to the reform scenario itself rather than to pre-existing differences among respondents, the standard logic of experimental design in the social sciences.

The headline finding is striking. In analyses restricted to respondents who correctly recalled their assigned treatment and weighted to account for differential treatment recall, AI-assisted budgeting reforms attracted significantly higher bureaucratic support than workforce expansion. This suggests that, at least among the civil servants surveyed, the prospect of algorithmic assistance in allocating public funds is not met with the resistance that fears of automated job displacement might predict. Instead, bureaucrats appear to view AI as a more attractive reform than hiring additional personnel, perhaps because algorithmic tools promise to augment their capacity without the organizational disruptions, coordination costs, and budgetary competition that come with expanding the payroll.

When it came to participatory budgeting, however, the picture was more nuanced. The robustness analyses found no statistically significant differences in bureaucratic support between the AI-assisted budgeting treatments and the participatory budgeting treatments. In other words, civil servants were roughly equally comfortable with delegating budgetary insight to algorithms and with opening budgetary decisions to citizen participation. This equivalence is notable because the two reforms embody very different theories of legitimacy: one rests on technical optimization and data-driven objectivity, while the other rests on democratic inclusion and deliberation. The finding hints that bureaucrats may judge reforms less by their philosophical underpinnings than by more practical considerations of workload, discretion, and administrative feasibility.

The authors were careful to probe the robustness of their results. Beyond the manipulation-restricted analyses, they estimated intent-to-treat effects, which include all randomized respondents regardless of whether they remembered their assigned scenario, adjusting for covariates and incorporating organizational fixed effects to account for differences across the agencies and institutions in which respondents work. Under this more conservative specification, the estimates did not show statistically significant differences across the outcome dimensions. The divergence between the two analytical strategies underscores a familiar lesson in experimental social science: results can be sensitive to how treatment recall and analytic choices are handled, and conclusions about bureaucratic preferences should therefore be drawn with appropriate caution.

The study sits within a rapidly expanding research landscape on AI in government. Previous work has documented automation bias and selective adherence to algorithmic advice among public sector decision-makers, showing that street-level bureaucrats tend to trust AI recommendations when those recommendations confirm their existing professional judgment. Other studies have mapped the barriers to AI adoption in public organizations, examined how AI is reshaping the role of bureaucrats in different organizational contexts, and explored how public values such as efficiency and equity shape civil servants’ willingness to use AI to reduce administrative burdens. Citizen-facing research has also flourished, with survey experiments revealing when and why the public accepts the use of AI in services such as policing and local government. What distinguishes the new study is its focus on budgeting, the heart of distributive governance, and its head-to-head comparison of AI against rival reform pathways rather than against the status quo.

The implications for policymakers are significant. Governments contemplating algorithmic budgeting often worry about backlash from public employees, whose cooperation is essential for data collection, model validation, and the day-to-day operation of any decision-support system. The evidence suggests that such fears may be overblown, at least in comparative perspective: bureaucrats do not appear to view AI-assisted spending as uniquely threatening. Yet the absence of a significant advantage over participatory budgeting also suggests that algorithmic reform is not a slam dunk. Reformers cannot assume that AI carries inherent legitimacy among the administrative workforce; it competes on roughly equal footing with democratic alternatives. The practical lesson may be that the success of AI in fiscal governance will depend less on winning bureaucratic hearts and minds than on careful system design, transparent safeguards, and clear communication about how algorithmic recommendations relate to human discretion.

As governments worldwide continue to draft national AI strategies and embed machine learning in everything from tax policy optimization to healthcare allocation, understanding the preferences of the people who run the administrative state becomes ever more important. This study provides some of the first experimental evidence that, when given a choice between algorithmic budgeting and simply hiring more staff, bureaucrats lean toward the machines. Whether that preference translates into successful implementation, and whether it holds across countries, sectors, and levels of government, remains an open question that future research will need to answer.

Subject of Research: Bureaucratic support for AI-assisted public budgeting compared with workforce expansion and participatory budgeting

Article Title: Do bureaucrats prefer algorithmic budgeting? Experimental evidence on bureaucratic support for AI-assisted public spending

Article References: Cho, W., & Sagarik, D. (2026). Do bureaucrats prefer algorithmic budgeting? Experimental evidence on bureaucratic support for AI-assisted public spending. Global Public Policy and Governance, 6(2), 157-175. https://doi.org/10.1007/s43508-026-00145-z

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00145-z

Keywords: artificial intelligence, public budgeting, bureaucrats, participatory budgeting, public administration, survey experiment, algorithmic governance, fiscal policy, government reform, public finance, civil service, prefer