Name: IRENCGE-DF (Italian Regional and Environmental Computable General Equilibrium of Department of Finance)
Type: CGE
Institutions: Italian Ministry of Economy and Finance; World Bank
Documentation: IRENCGE-DF Documentation
Description: A macroeconomic tool for analyzing the impact of climate-related tax policies on GDP, production, employment, greenhouse gas emissions reductions, and distributional effects.
Questions to be answered/variables considered:
- A given policy’s indirect and economy-wide effects must be estimated to assess multiple policy scenarios against effectiveness, efficiency, and equity principles.
- The key outputs are on the main macroeconomic variables (e.g., GDP, production, employment), the distributional effects across income deciles, the effectiveness in reducing greenhouse gas emissions, the revenue-raising potential, and the impact of revenue-recycling scenarios.
Strengths:
- A detailed database—the social accounting matrix (SAM)—is used for sectoral analysis and measuring distributional impacts. This database distinguishes between 74 activities, 68 commodities, 10 household groups, and 10 tax categories.
- The tool contains an environment module that includes energy as a production input alongside labor and capital, paired with inter-fuel substitution of eight energy types and a multi-input and multi-output production structure.
- It also contains a climate change damage module that considers impacts on total factor productivity, labor productivity, tourism export demand, sea level rise (reduction in land productivity), energy demand, and flood damages. This also includes adaptation to endogenously reduce damages.
- The model is calibrated to tax policy analysis through the inclusion of other microsimulation modes that analyze specific categories of taxes (e.g., corporate income tax, personal income tax, and VAT).
- It has the ability to assess distributional impacts.
Limitations:
- It has simplified economy functions.
- It uses fixed key parameters (e.g., elasticity of substitution).
- There is an absence of endogenous technological change. • It disregards the role of money supply and demand in the financial sector.
Assumptions:
- The model makes standard neoclassical assumptions (e.g., perfectly competitive markets and capital accumulation deriving from savings).
- Economic agents make myopic decisions about production, consumption, and investment (i.e., their expectations are made only based on information available in the period of the decision, not on what will happen in the future).
Use: It is most suitable in the policy design phase for comparative analysis of policy scenarios, particularly for understanding distributional impacts, revenue impacts, revenue recycling scenarios, and contributions to the reduction of greenhouse gas emissions.
Development/lessons/challenges:
- Developing this CGE model demanded substantial financial investment, diverse technical expertise, and time commitment (particularly for the development of the SAM). This was effectively managed by leveraging existing expertise from international organizations and collaborating with other countries.
- To expand climate modeling capabilities, approaches include developing in-house simplified static models (though this may overlook economic interactions), applying user-friendly toolkits from organizations such as the IMF (which may not account for country-specific details), or building an in-house general equilibrium model with technical support from international organizations or countries with existing models.
- A collaboration with the Italian Energy Agency to develop a link with its TIMES model aims to incorporate greater energy system detail into the model and to support the OECD’s Inclusive Forum on Carbon Mitigation Approaches (IFCMA). Challenges to this include aligning the different models’ scope, resolution, assumptions, and sector definitions. Their integration could enhance both models by incorporating technological change and behavioral realism, offering insights into Italy’s path to net zero by 2050.
- The European Commission’s new Technical Support Instrument (TSI) could support the development of a supplementary model and enhance existing models through utilization of the GreenREFORM model developed by the Danish Research Institute for Economic Analysis and Modelling (DREAM). This multi-country project will facilitate collaboration, knowledge transfer, and capacity building among experts from diverse backgrounds.
Name: GEEM (General Equilibrium Environmental Model)
Type: DSGE
Institutions: Italian Ministry of Economy and Finance; University of Rome
Documentation: Paper with documentation
Description: A macroeconomic tool for assessing the effectiveness of climate-energy policies, their economic impact, and (in some cases) households’ distributional impacts.
Questions to be answered/variables considered:
- The tool allows analysis of the macroeconomic impact of climate and energy policies designed to reduce emissions or induce utilization of clean energy sources, and it disentangles the effects of different shocks and the performance of policy interventions independent of climate and energy instruments.
- Policy interventions include technological changes, a reduction in markups/increase in market competition, and fiscal reforms.
- Macroeconomic factors include GDP, employment, investment, and sector-specific emissions.
Strengths:
- The model allows the comprehensive integration of environmental and macroeconomic policies and it can simulate the interplay between environmental regulation and economic outcomes.
- The presence of real and nominal rigidities allows the capture of the slow adjustments of structural change dynamics consistently with short-term economic frictions.
- Consistency with the main DGE models used by the European Commission ensures that the results align and are comparable with those produced by the Commission’s leading models.
- Incorporation of cap-and-trade subject sectors according to the EU Emissions Trading System (e.g., the electricity sector and part of the manufacturing sector embodied through intermediate goods), and the transportation sector.
Limitations:
- The model has high computing needs (relative to number of incorporated sectors, frictions, and agents).
- It is highly reliant on accurate parameterization.
- It overlooks heterogeneity among economic agents.
- It can fail to capture nonlinear dynamics and the impact of large shocks.
- It gives limited consideration to distributional effects.
Assumptions:
- Economic actors have perfect foresight.
- It uses a representative agent (i.e., it assumes all agents are identical or can be aggregated into a single representative entity).
Use: It can be used for research purposes, and to assist policymakers in developing balanced and long-term economic strategies.
Development/lessons/challenges:
- Properly calibrating the model’s microfoundations required the complex integration of multiple economic and environmental datasets and implementation of intertemporal dynamics programming.
- Key lessons include the importance of flexible model designs, data updates, and continuous benchmarking to ensure the robustness and adaptability of policy analysis. Additionally, inter-university collaboration facilitates knowledge transfer and leveraging of diverse expertise in economic modeling and environmental policy.
- Future advancements will focus on estimating key parameters to improve reliability and accuracy. This may involve incorporating Bayesian estimation techniques for continuous data updating and collaborating with international institutions to improve the model’s ability to simulate the long-term economic impacts of climate policies by incorporating more detailed data on the behavior of economic agents.
- The introduction of heterogeneity factors, inspired by recent advances in Heterogeneous Agent New Keynesian models, can better capture distribution consequences and the diverse behaviors and interactions of economic agents.
Keywords
macroeconomic indicatorsmodelspolicy simulationtaxestransition