For studying optimal policies and interventions to inform regional decision makers facing technological, economic and social transitions, we have developed the Dynamic Framework for Regional Socio-Economic Transitions (D-RESET).

Climate change, energy security, local environmental concerns and changing demography are some of the issues driving technological, economic and social transitions in regional (subnational or local) economies. Many regional economies are characterized by the concentration of certain natural resources (fossil fuels, renewable potential or forestry), industrial/educational hubs or their geographic locations (proximity to a port or a city). When the circumstances change, like international and national climate policies calling to phase out fossil fuels or a (gradual or disruptive) change of market prices implied by adaptations of international markets, the burden of transition often falls on the corresponding regional economies. Such transitions involve long-term and dynamic changes in the economy with potential impacts on local industries, government and household incomes, employment, migration and other aspects of regional welfare.

This framework, based on optimal control theory (see e.g., Grass et al., 2008), uses a system of ordinary differential equations (describing the state of the system), that reacts on control variables (e.g., investments into incumbent or transition technologies/sectors) set by the decision maker to optimize an intertemporal objective function (e.g., costs or social welfare).

The D-RESET framework offers several advantages for analyzing dynamic regional transformation processes:

  1. Analysis of optimality conditions (first order conditions; FOC) of control variables: Each control variable defines one FOC that connects the optimal to the current state values and its shadow prices (see ii below). This relation explains the different (mutual) dependencies of policies as well as the current states of the different market sectors, the labor market and their future effects. The analytic approach is not possible in purely numerical studies and is applicable in a steady state and along the transitional path, as well as in a scenario where a (stochastic) shock changes the system disruptively. While our model also allows for the analysis of non-optimized or partially optimized policy scenarios, knowledge of the optimal solution implies a crucial benchmark against which to assess policy scenarios with a view to identifying crucial inefficiencies and scope for improvement. Finally, our framework allows to study optimizing choices from the perspective of different agents (e.g. planner vs. actors in a decentralized economy) with differing objectives (intertemporal social welfare vs. sectoral or private objectives).     
  2. Analysis of dynamic shadow prices (adjoint variables): The shadow prices capture the future (i.e., the dynamic) effect of the current market sectors and the labor market and are useful to strengthen the intuition of the economic (i.e., costs) and social drivers (i.e., social welfare) of the optimal solution. Shadow prices (referred to as adjoint or costate variables) are part of the canonical system of the Maximum Principle and are usually solved along within the numerical solution approach. An analytic consideration (backward integration by using the transversality or limiting transversality conditions, see e.g., Kuhn et al., 2010; Wrzaczek et al., 2010), however, illustrates the overall dynamic effects of the current system by a detailed decomposition into individual effects and their interaction.
  3. Qualitative insight into the transitional path: Analyzing the dynamics of the optimal policies and the shadow prices adds additional intuition on the (optimal) transitional path. It disentangles different (and sometimes opposing) effects that are mixing up in a purely numerical solution consideration and identifies which of them accelerate or decelerate the process. This is especially important for an understanding of the (optimal) timing of different policies, as is crucial in the context of many transitions and transformations.

The proposed analysis is useful to study aggregate systems as well as for selected sectors only. The systematic analysis can complement other studies by giving a deeper insight into optimal process and dynamic decision making and the respective decomposition of the main (short- and long-term effects) effects. The model is open both to theoretical analysis and calibration to specific contexts of study and according to available data. Based on a numerical implementation, the explicit representation of possibly complex policy and sectoral dynamics for specific contexts allows a detailed understanding of (a) the long-run dynamics of policy making and (b) its impacts on heterogeneous sectors or populations that go beyond what typical general equilibrium or sectoral models can do. In contrast to agent-based models, it allows an understanding of the optimal structure of policy choices over time as well as the identification of inefficiency and its causes.

In the following we sketch one application of the D-RESET framework to highlight how it can be used and connected with other models.

Example: Application of D-RESET to synthetic fuel-led energy transitions

Advanced or synthetic fuels, derived from recycled CO2, are seen as an alternative to fossil fuels in difficult-to-decarbonize sectors such as aviation and long-distance freight transport. The European Green Deal and Renewable Energy Directive III (RED III) mandates that by 2030, at least 5.5% of transport fuels must be advanced fuels, including 1% from renewable fuels of non-biological origin (RFNBOs) (Marelli et al., 2025).

However, the transition from fossil to carbon-neutral synthetic fuels would also have a short-to-long term impact on fuel costs for end customers along with other aspects of social welfare like employment and income in the fossil versus synthetic fuel industries. As a result, an integrated welfare and economic assessment focusing on social externalities, heterogenous impacts, and long-term dynamics is required to enable a just transition towards synthetic fuels. To address this gap, we propose to employ a dynamic optimization framework, D-RESET, that evaluates the pathways and welfare implications of establishing a circular economy, focusing on the integration of synthetic fuels in aviation and other relevant industries. The model's objective is to maximize a comprehensive social welfare function defined over household consumption and environmental quality, to provide a holistic assessment of societal well-being that extends beyond simple income metrics.

The socio-economic D-RESET model can also be linked with a technologically explicit techno-economic model (such as IIASA’s BeWhere) which can provide the marginal costs of production for carbon-neutral synthetic fuels and materials. These projected fuel and material prices can then be used as an exogenous input in the socio-economic model. In response, the production sectors and households within the socioeconomic model endogenously determine their optimal consumption, yielding a time-dependent demand profile for these new fuels and materials under various policy scenarios. A core feature of the analysis is the quantification of the associated social costs through an integrated, endogenous labor market model. This model dynamically allocates labor across production sectors, incorporating friction like the costs and time lag of worker retraining and inter-sectoral mobility.

In summary, the integrated modeling framework offers a holistic assessment of the transition towards a synthetic fuel-led circular economy. It is specifically designed to evaluate the comprehensive welfare implications of integrating synthetic fuels into the energy system. The model captures not only the macroeconomic effects of this transition but also the crucial labor market dynamics central to ensuring a just transition towards circular economic systems. Furthermore, treating CO2 as both a pollutant and a valuable feedstock, it provides novel insights into the effectiveness of policy instruments under a circularity paradigm. The result is a robust decision-support tool for designing climate policy that is economically efficient, socially equitable, and technologically coherent.

JUSTCOAL

One application of the D-RESET model can be found in the JUSTCOAL project. The project concerns the modelling of the regional welfare impacts of just coal transitions.

Coal transition

Modelling the regional welfare impacts of just coal transitions using the D-RESET model

Coal, the most carbon-intensive fossil fuel, is a major contributor to anthropogenic carbon emissions and climate change. Coal mining and combustion are also a leading cause for premature mortality due to local air pollution. On the other hand, coal is also central to many regional and local economies that rely on its mining, transportation, energy production and exports. With changing climate and rapidly depleting carbon budgets, the urgency for coal phase-out has become more prominent and many regional economies are under pressure to transition away from coal in a time bound manner.

Publications

For the first working paper on JUSTCOAL using the D-RESET model see the reference below.

Grass, D., Kuhn, M., Patange, O. , Fürnkranz-Prskawetz, A., & Wrzaczek, S. (2025). Out of the black into the green? Modeling the pathways for regional coal transitions. IIASA Working Paper. Laxenburg, Austria: WP-25-002