Climate change and income inequality are among the great challenges of our time. Current climate policy debates focus mainly on reducing emissions, while social policy is concerned first and foremost with distributive justice. Rarely are the two considered together — yet a sustainable transformation will only succeed if both goals are pursued simultaneously. Policy must therefore recognize how social and ecological dynamics interact: how climate mitigation can create new inequalities, or how social reforms can influence energy consumption. These relationships also offer political room to generate positive effects. To make use of that potential, we first have to understand these interdependencies.

How we measure sustainability shapes what policy we recommend. At present, two approaches dominate: either social and ecological metrics are analyzed separately, or all sustainability indicators are aggregated into composite indices. While the latter make complex realities comparable and easy to communicate, they obscure the dependencies between dimensions of sustainability. Such simplification risks mischaracterizing the problem and thereby producing ineffective or misguided policy. For shaping a sustainable future, we need metrics that capture social and ecological objectives jointly, alongside statistical models that make their interaction visible.

Multidimensional Targets for Sustainability

Even though sustainability has long been a guiding concept, it remains vague without clear, measurable boundaries. Only when we know where social and ecological thresholds lie can we assess whether a society is living within a sustainable space and design appropriate policy measures.

For ecological thresholds — for example, CO₂ limits — established scientific frameworks now exist. The picture is different on the social side. Income inequality, for instance, can serve as a central benchmark for social sustainability because it correlates with democratic stability, public health, and social cohesion. Yet there is no empirically grounded threshold for when inequality becomes socially unacceptable. To date, inequality thresholds rest mostly on informed estimates rather than robust data.

Based on an international dataset, we therefore define a Gini threshold of 25.7%. This figure is derived from the lower quantile of Gini values among countries with the highest level of democracy. It is thus realistic and linked to political stability. But our measure remains a first empirical approximation — an indicator of the need for further rigorous studies. In combination with the maximum CO₂ emissions compatible with the 1.5°C target, this produces a zone in which ecological viability and social justice are potentially aligned.

Defining a sustainable space via two parameters outlines a two-dimensional target that requires no index reduction. By analyzing their joint distribution — that is, how CO₂ emissions and inequality vary statistically together — their relationship becomes directly observable. Each metric remains intact rather than being collapsed into a single composite number. Methodologically, such relationships can currently be modeled most robustly between two dimensions — an important improvement over one-dimensional analysis, and a foundation for future models that handle even more complex interactions.

The results show that while some countries achieve sustainability in one dimension, none is currently located within the potentially sustainable zone. In most cases one threshold is exceeded — often both. Using this two-dimensional target, we can identify transformation paths showing how countries might move toward a socially and ecologically sustainable balance.

The limits of averages and the value of distributional models

Existing research offers a wide range of explanations for the relationship between inequality and emissions. Some studies find positive correlations — that is, synergies in which lower inequality also means lower emissions, perhaps due to reduced elite capture or mimetic consumption effects. Others find negative correlations (trade-offs), where greater equality is associated with higher emissions — for example, when redistribution increases the purchasing power of low-income households, thereby increasing energy consumption. Still others find decoupled developments, where social and ecological dynamics proceed independently.

Empirically, most research relies on linear regressions asking: “How does an average increase in inequality affect emissions?” Such models have the advantage of being simple to interpret and communicate: they indicate whether more inequality tends to raise or lower emissions. But their weakness is the same as their appeal: focusing on the average smooths over empirical variation. In some countries more equality goes hand in hand with rising emissions; in others lower inequality correlates with reduced emissions. The mean obscures these opposing dynamics and creates the illusion of a linear relationship where, in reality, complex context-dependent patterns exist.

The interaction between inequality and emissions is shaped by multiple factors — income levels, energy structures, economic development, and structural change, among others. These mechanisms can reinforce or weaken each other simultaneously. There is no single causal pathway, but a web of overlapping effects that shifts by context. Seen as a conditional distribution, the picture becomes a network of interactions that evolves alongside these factors.

Copula-based distribution models allow us to map these complex relationships. They capture the full joint distribution of the two target metrics and allow us to analyze their interdependencies across different influencing factors. In doing so, they reveal that emissions and income do not grow proportionally, but often follow nonlinear dynamics. The relationship can show strong synergies in some settings, then flatten or flip into trade-offs as core drivers change.

Results from a global study

Our analysis draws on data from 109 countries, from 1960 till 2019. The results underline the complexity of the picture:

• High-income countries show no clear average relationship between inequality and emissions. Yet this average conceals variation: democracies with a low share of fossil fuels tend to perform better in reducing both inequality and emissions simultaneously.
  
  
• Middle-income countries often show a negative correlation: lower inequality correlates with higher emissions. Here, economic development still relies heavily on fossil energy. Greater social participation still means higher energy consumption — and therefore higher emissions.
  
  
• Low-income countries show a weaker or even reversed relationship. Many households remain excluded from energy-intensive consumption, fundamentally altering the dynamics between income, participation, and emissions.
  
  

These models thus do not produce simple, easily packaged political messages — but a realistic picture of societal development. This realism is precisely their value: they enable nuanced, empirically grounded policy approaches that think social and ecological transformation together. While initially complex, they offer a better representation of social diversity and support the development of context-specific measures — which, in turn, can increase public acceptance.

Context-specific pathways to sustainability

The relationship between inequality and emissions is far more complex than averages suggest. Our work demonstrates that there is no single path to sustainability but multiple routes, with trade-offs and synergies that depend on income levels, economic structures, and political contexts. Even within country groups, the dynamics vary, and shift within countries over time.

Well-worn insights remain valid at a basic level: richer countries emit more; poorer countries emit less; greater participation often increases energy demand. But the analytical contribution lies in differentiating these patterns. Distributional regression allows for deeper insight and thus policy recommendations that reflect this diversity. It shows that the inequality–emissions relationship varies across income groups, political systems, and energy mixes — and also within each of these categories.

For high-income countries, this means harnessing key drivers such as a socially balanced energy transition and structural reforms in the service sector, especially in care, education, and social work. In low-income countries, it means enabling just development pathways that link ecological progress with social participation and access. 

Such context-specific pathways provide a more realistic foundation for political decision-making. Anyone serious about integrating social and ecological goals needs data and methods that make these interactions visible and context-sensitive. Distributional statistics offer precisely this: a move beyond flattening averages toward empirically grounded depictions of complexity. This requires courage and clarity in communication. But it also strengthens political legitimacy by enabling different social groups to see themselves reflected in measures and outcomes.

Only if we learn to measure and understand this diversity can we build an economy that is both ecologically viable and socially just.

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Editorial Note:

This article is produced in collaboration as part of a collaboration between Rethinking Economics International, Makronom and the Economists for Future DE and was originally written in German language. The 2026 contributions engage with ongoing debates on anti-authoritarian and anti-fascist perspectives on economic policy, with particular attention to how social security arrangements can help counter authoritarian and nationalist tendencies. Contributions in this series also explore welfare state design, property relations, pension systems, and institutional reforms with a view to strengthening democratic cohesion, ecological stability, and economic resilience. The views expressed in this article are the author’s own and do not necessarily reflect those of the participating platforms.

About the authors:

Franziska Dorn is a postdoctoral researcher at the Institute for Socioeconomics at the University of Duisburg-Essen. Her research focuses on measuring living standards beyond the median, with an emphasis on time and income poverty, as well as the relationship between social and environmental sustainability.

Simone Maxand is an Assistant Professor of statistics at the European University Viadrina and a faculty member at the Berlin School of Economics. Her research focuses on statistical methods and their application in the fields of climate economics and sustainability.

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