DICE-MAT
General Scope and Connection with Climate Mitigation
Introduction
Increasing global population and wealth have led to a rapid rise in material consumption, causing resource depletion and waste generation, as well as high levels of energy use and related greenhouse gas (GHG) emissions from the production and use of materials. The circular economy (CE) has been proposed as an alternative to the linear economic model and aims to reduce primary material consumption and material wastes by narrowing loops, slowing loops, and closing loops. The potential of CE and material efficiency strategies for GHG emission reductions (related to SDG 13) has been highlighted in several scientific publications. It is believed that reducing material consumption through CE strategies could play an essential role in meeting climate targets. While cost-benefit integrated assessment models (IAMs), such as DICE, are used to assess climate policy strategies by determining cost-optimal GHG emission reduction pathways, they do not represent material stocks and flows nor the impact or potential of CE strategies for climate change mitigation pathways.
In this project, we address this gap by further developing MIMOSA, a simple and transparent cost-benefit IAM covering relevant technological and socio-economic (yet no material or CE) dynamics similar to DICE, towards a global meta-model that describes the key dynamics in which CE strategies impact climate change mitigation efforts. MIMOSA has been developed and used in existing research to examine the interaction of the most significant factors, such as socio-economic developments, climate system uncertainty, damage estimates, mitigation costs and discount rates. We build on and aim at extending the existing model by integrating a novel ‘stylized’ energy, material and CE modelling component. To achieve this goal, we intend to leverage the global climate and resource databases of IPCC and IRP, as well as other material/CE modelling data from existing research. Depending on data availability, we aim to decompose the existing model's mitigation component to include energy and material intensities. Material stocks and CE measures will be represented in a global generic material model. Furthermore, these dynamics will be assessed across developed, emerging and developing world regions and many broad sectors (such as industry, transport and buildings). Implementing these dynamics in climate mitigation models will allow policymakers to build upon scenarios and insights into optimal pathways to meet Paris Agreement targets, distinguishing newly introduced CE measures from more traditional measures for climate mitigation.
Model Scope
Overall Objective and Approach
The MIMOSA model is a highly abstract, DICE-like growth model designed to analyze the effects of climate change mitigation measures on greenhouse gas emissions reductions and their socioeconomic implications. The primary objective of the MIMOSA model is to understand the temporal socio-economic-climatic dynamics resulting from emissions and emissions mitigation actions. MIMOSA does not generate new emissions or mitigation-related outcomes. Instead, it is calibrated against existing global emissions and mitigation data, enabling the exploration of interesting scenarios and dynamics related to traditional mitigation measures and, through envisioned extensions, also CE measures.
Optimization Model
MIMOSA is an optimization model that maximizes the net present value (NPV) utility of consumption by optimizing yearly emissions and carbon prices. This optimization process is achieved using a calibrated marginal abatement cost (MAC) curve, which links emissions reductions to the corresponding carbon prices.
Existing model structure
The MIMOSA model has a global focus, providing insights into the macroeconomic dynamics and emissions reductions at a global scale. Further, extensions to incorporate regional and country-specific analyses have been developed in the existing version. The time horizon of the MIMOSA model extends until 2100, allowing for long-term projections and policy evaluations. The existing version considers the economy as a whole without distinguishing any sectors.
The existing model structure is highly generic, with macroeconomic developments driving emissions, which, in turn, cause damages to the economy. The model considers non-CE emissions mitigation interventions dependent on a variable carbon price derived from the MAC curve, calibrated against AR6 data.
Extensions for Circular Economy Dynamics
To enhance the model's analytical capabilities, efforts are being made to introduce CE dynamics and potentially also dynamics related to energy, high-level sectors, and materials across global regions. We are following an iterative approach in extending the model's scope, and extensions are dependent on the availability of data. Starting from a global perspective, we are examining the feasibility of incorporating energy and CE components.
The energy component aims to dissect the emissions mitigation actions of the existing model by distinguishing between two traditional factors that determine emissions: energy use and energy carbon intensity. By introducing this breakdown, the original MAC curve would be replaced with two MAC curves, one for each factor. We could introduce CE also without energy, but it will provide more interesting dynamics and might allow for more detailed CE and material modelling.
As for the CE component, we have outlined a spectrum of potential approaches to model CE measures and their impact on emissions mitigation, which we are currently evaluating. On the simpler side, we might build on the existing MAC curve and introduce direct impacts of industry CE actions on industry emissions. A more advanced approach would be to develop a simple material model, where we link newly introduced material activity factors to emissions or energy and introduce high-level CE measures. Starting with, and potentially limited to, industry, primary and secondary material production would serve as an activity factor. We would introduce a generic material (e.g., calibrated to steel for simplicity), model primary/secondary production and in-use lifetime for materials, and have three high-level CE measures (reduce, prolong, recycle). To build a dynamic optimization model, the cost of implementing CE measures are also required, besides emissions reduction impacts. Depending on data availability, we introduce scenario analysis of a cost factor (time-dependent preferably) or better price-dependent as a cost curve.
Iterative Development
The development of the MIMOSA model is an iterative process that depends on the availability of energy modelling data and circular economy data. The feasibility of various approaches will be assessed, and the model will be refined accordingly.
Model Development
- Status: in progress
- Environment: Python
- Documentation: in progress
- Source code: in progress
Circular Economy Features
R Words coverage and implemented in the model
Being a global and highly abstract model, the CE extensions currently discussed for implementation are of a broader perspective as well. A simpler option of the solution space would not distinguish any value retention strategies (R strategies) but estimations of direct impacts from industry circularity/material efficiency measures on emissions mitigation. If model development allows for more advanced solutions, we would aim for up to three high-level CE measures: reduce, prolong, recycle (prolong might implicitly contain repair, reuse and refurbish strategies).
CE strategies and connection with climate change mitigation.
The extended MIMOSA model will either consider direct emissions mitigation impacts from CE (in the industry sector) or calculate emissions impacts through energy and/or material activity factors that might be introduced. The latter might follow the below logic:
Emissions = Emissions/Energy * Energy/Activity * Activity
Insights for Analytical Framework
The idea of the extended MIMOSA model is to illustrate key dynamics related to energy (separating energy use as an intermediary step in emissions calculations), CE (modelling impact of CE measures, potentially distinguishing demand reduction, lifetime prolongation, and recycling), material (building a simple material model linked to energy and/or emissions), sectors (modelling of industry sector, potentially more) and geographies (global regions, e.g., OECD, China, developing economies).
Refinement, Integration, Future Development
Refinement process
- Iterative evaluation of feasibility and development towards envisioned extensions and dynamics, depending on model complexity and data availability
- Results from bottom-up models might also be used to refine the data inputs for the model (if there is limited global data available in the literature, we might build model extensions based on sensitivity analyses of input ranges – which can then be refined by new data points)
