CERES

Political Economy for Inclusive Wealth Governance and Sustainability
 
CERES

To maintain the basis of human life on Earth, the global commons such as the atmosphere, the oceans and the biosphere must be managed so that planetary boundaries are not exceeded. This requires a paradigm shift in the definition of wealth, which should explicitly include natural capital in addition to physical and human capital. The FutureLab CERES – Ceres, the Roman goddess of agriculture, is also described as a legislator – conducts research at the interface between natural resources and state capacity, and explicitly addresses the interactions between them. Against the backdrop of climate change and the overexploitation of natural resources, the research programme aims at changing the understanding of wealth that incorporates natural capital. This also incorporates the analysis of suitable policy instruments to enable inclusive wealth governance.

The research focusses on countries that play a key role in the protection of natural resources, such as Brazil, Indonesia, Colombia or the Congo. They are severely threatened by climate damage, have a high level of biodiversity and accrue high economic profits from fossil resources, rare earths and/or deforestation.

The central question of the FutureLab is how states can contribute to a fair and sustainable management of global commons. To this end, the FutureLab's research is divided into four work packages:

1. Political Economy realities and barriers to transformation

2. Machine Learning-based ex-post policy evaluation

3. Interaction of state capacity and inclusive wealth

4. Political Economy approaches to international cooperation

1. Political Economy realities and barriers to transformation

This work package aims to take stock of the political economy realities in select countries: Which actors influence the formulation of policy instruments for the governance of inclusive wealth and in what way? What are political economy barriers to transformation? Effective policy measures need to take these contexts into account and integrate them into their design.

2. Machine Learning-based ex-post policy evaluation

Policymakers have to choose between a variety of possible policy instruments that promise sustainable resource use and emission reduction. However, up to now, researchers have only evaluated individual measures , whereas in reality measures are almost always implemented in policy packages. The aim of this research stream is therefore to identify effective packages of measures with the help of Machine Learning methods. Thus, these work package makes an important contribution to attribution research of effective policies.

3. Interaction of state capacity and inclusive wealth

So-called developing countries and emerging economies often lack the state capacity to implement policy measures that support sustainability, to moderate their negative effects by use of compensatory measures and thus to increase their acceptance in society. At the same time, it is precisely these countries that are often most affected by climate change, for example through the increase in extreme weather events and in their impact on economic growth. This work package examines firstly how the development of prosperity and different forms of governance are linked, with a special focus on the financial consequences of climate change. Secondly, research looks at how regulations can be socially accepted and effectively implemented in countries with weaker state structures and thirdly, building on this, which steps would be suitable to increase the local acceptance of state measures to protect the global commons.

4. Political Economy approaches to international cooperation

This work package examines which policy instruments exist at the international level to promote ambitious climate policies in the selected countries, and which incentives can be created for cooperative action, for example transfer payments linked to effective climate policies. Methodologically, the focus is on the mathematical modelling of game-theoretical approaches, as well as their empirical-economic validation.

Methods

CERES uses the following methods to analyse the preconditions for the governance of inclusive wealth:

  • Case studies
  • Machine Learning-based ex-post policy evaluation
  • Ex-ante analysis and theory building
  • Agent-based and game-theoretic approaches to political economy

Evidence-based policy advice

The FutureLab's distinctly interdisciplinary cooperation between political scientists, economists and specialists in Artificial Intelligence and Machine Learning aims to set new standards for decision-relevant research. The results are intended to enable decision-makers to identify key levers in the multi-level system of climate governance in an informed and evidence-based manner and to shape them sustainably in accordance with policy recommendations.

The FutureLab CERES is funded by the Werner Siemens Foundation.