# On the Use of General Equilibrium Model to Assess the Impact of Climate Policy in Latvia

Olegs Krasnopjorovs, Daniels Jukna, Konstantins Kovalovs

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Olegs Krasnopjorovs, Daniels Jukna, Konstantins Kovalovs. On the Use of General Equilibrium Model to Assess the Impact of Climate Policy in Latvia. New Challenges in Economic and Business Development – 2022: Responsible Growth. Conference Proceedings., University of Latvia, May 2022, Riga, Latvia. pp.154-159. [https://hal-03861139](https://hal-03861139)

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# ON THE USE OF GENERAL EQUILIBRIUM MODEL TO ASSESS THE IMPACT OF CLIMATE POLICY IN LATVIA

**Abstract.** Ambitious climate policy targets by developed countries towards carbon neutrality created a necessity to model the impact of climate policy on macroeconomic and socioeconomic indicators. Academic literature suggests that Computable General Equilibrium (CGE) model representing the entire economy, linked with a TIMES model reflecting the energy sector in detail, proved to be an effective tool for such modelling. This article contributes to the starting point for creating such a modelling system in the University of Latvia (CGE model) and the Institute of Physical Energetics (TIMES model), focusing on the CGE model. First, it reviews the literature on the use of the CGE model and its effective link with the TIMES model to assess the economic impact of climate policy. Second, it reviews the features of the current Latvian CGE model owned by the University of Latvia, compared to its ORANI-G model prototype. Third, as a list of recommendations, it proposes a roadmap for the future development of the Latvian CGE model to be effectively linked with the TIMES model to assess the impact of climate policy on macroeconomic and socioeconomic indicators.

**Keywords:** Computable General Equilibrium model, climate policy modelling, soft-link.

**JEL code:** C68, Q54, Q58.

## Introduction

Reducing greenhouse gas (GHG) emissions is a key factor in curbing adverse climate change. Increasing the carbon tax and introducing and improving the emissions trading system are key research topics in climate change mitigation policy. Wider use of renewable energy and improvements in energy efficiency have been identified as key drivers for the transition to low carbon emissions (Babatunde, et al., 2017).

Strict GHG emission limits need to be put in place to avoid catastrophic climate risks, as the welfare loss from global warming is likely to be underestimated: excess deaths from floods, droughts, malnutrition, malaria and heat problems also should be considered (Weitzman, 2012).

Although global warming is likely to negatively impact Gross Domestic Product (GDP), its consequences for the European region, including Latvia, are assessed to be moderate (Kompas, et al., 2018). However, these estimates do not include the consequences and costs of natural disasters or extreme weather events, which can be significant. It also excludes utility loss from air pollution, biodiversity loss, the spread of invasive species, changes in the energy mix and high migration costs. Therefore, the actual impact of global warming on the economy and society is likely to be more significant. In addition, lower GDP means lower tax revenues, which might put fiscal stress on governments. Moreover, if global warming is associated with often extreme weather events and natural disasters leading to high exceptional costs, the public finances might face even more immense pressures.

Although some articles have focused on the analysis of only one sector for GHG reduction (Giraudet, et al., 2012; Wong & Alavalapati, 2003), the mainstream view is to consider the inclusion of all significant energy-related sectors, such as agriculture, industry, forestry, construction, transport and waste management, given their prominent role in the economy and their high GHG reduction potential (Babatunde, et al., 2017).

Although climate policy is generally believed to have a negative impact on GDP and is likely to increase the price level, the double dividend hypothesis states that a cleaner environment might go hand-in-hand with higher prosperity.
The double dividend hypothesis states that the introduction or increase of taxes on environmental pollution provides an opportunity to reduce other (distortionary) taxes, with the latter providing additional benefits to society (Jaeger, 2013).

Energy policy is designed to achieve three main objectives: ensuring (1) secure supply of energy; (2) economic growth and job creation; (3) sustainable environment and climate (Jordan, et al., 2012). Although it would be preferable to use a single model to evaluate all energy policy issues, rather than combining several models, such a “super-model” has not yet been developed (Frei, et al., 2003). Thus, academic literature considers linking a Computable General Equilibrium (CGE) model representing the entire economy with a TIMES model reflecting the energy sector in detail, an effective tool for modelling the impact of climate policy.

This article represents the starting point for creating an energy-economy-environment modelling system in the University of Latvia (CGE model) and the Institute of Physical Energetics (TIMES model, and its initial version called MARKAL), with the focus on the CGE model. It aims to develop a roadmap for the future development of the Latvian CGE model to be effectively linked with the TIMES model to assess the impact of climate policy on macroeconomic and socioeconomic indicators. First, it reviews the literature on using the CGE model and its effective link with the TIMES model. Second, it reviews the features of the current Latvian CGE model owned by the University of Latvia, compared to its ORANI-G model prototype. Then, as a list of recommendations, it proposes a roadmap for the future development of the Latvian CGE model. Therefore, this article might be viewed as a cornerstone of the future development of energy-economy-environment modelling in Latvia. However, it should be noted that this article will not present any modelling results. Assessing the impact of climate policy tools on the economy and the respective scenario analysis is, thus, left for further research.

## Literature Review

There are two main types of models for linking the energy sector and the economy: (1) Bottom-Up models and (2) Top-Down models (Herbst, et al., 2012; Prina, et al., 2020). Bottom-Up models (such as TIMES) describe the energy sector in detail but do not include non-energy sector impacts. In turn, Top-Down models (such as CGE) assess climate policy's macroeconomic and socioeconomic impact but ignore energy sector details.

TIMES is one of the Bottom-Up models implemented in several EU countries. The TIMES model is based on maximising prosperity by reducing overall energy system costs. The types of input variables used in the modelling are (1) energy demand and supply curves (i.e., the number of key energy technologies and imported energy goods available at a given price); (2) policy (the impact of legislation, such as taxes and subsidies, on specific technologies or fuel prices); and (3) technical and economic parameters for each technology (i.e., investment costs and interest rates; Balyk, et al., 2019).

Top-Down modelling could be implemented with several approaches such as input-output models, econometric models, CGE models and system dynamics (Herbst, et al., 2012).

The CGE model is a computer simulation that uses a system of equations that characterises the interaction of all sectors of the economy. The simulation of the model usually starts with a baseline scenario with a calculated general equilibrium state. After introducing a shock, such as an increase in a carbon tax, a new equilibrium state is calculated (Babatunde, et al., 2017). A unique feature of the CGE model is its ability to assess the impact of climate policies on non-energy sectors. However, the inability of CGE models to cope with the detailed structure of the energy sector (i.e., it cannot reflect energy supply and technology) is often cited as the most significant shortcoming of Top-Down models.
CGE models often cannot solve the following three aspects of the energy system: (1) changes in energy intensity due to the introduction of new technologies; (2) changes in energy mix following changes in energy demand; and (3) changes in electricity and heating prices due to limited competition in the energy sector (Krook-Riekkola, et al., 2017).

The CGE model mainly consists of a model structure (a system of equations that takes into account economic interrelationships) and a database (expenditure and income flow in the economy, and parameter values) (Babatunde, et al., 2017). The researcher itself calibrates parameter values during a model run. All industries and subsectors are directly or indirectly related to the demand and supply of goods and services and are characterised by expenditure and income flows (in the form of input-output tables; Meng & Siriwardana, 2017).

There are three CGE model types: (1) static model, (2) recursive dynamic model, and (3) forward-looking dynamic model. The static CGE model provides a comparison between the baseline scenario and the equilibrium that occurs after a shock, reflecting who are the ultimate losers or winners of the shock. A distinction is made between short-term and long-term simulation, with the main difference being an assumption of physical capital accumulation: in the short-term simulation, capital is exogenous (i.e., fixed and given outside the model), while in the long-term simulation, capital is allowed to adapt and respond to external conditions (Kompas, et al., 2018). However, the static model does not reveal the costs and benefits of a transition phase from the initial to the new equilibrium; also, the impact over time cannot be assessed (Babatunde, et al., 2017). It may be necessary for policymakers to find out when the economy will be in equilibrium again and compare the behaviour of different macroeconomic variables over time. For this reason, the use of dynamic CGE modelling is recommended (Devarajan & Go, 1998).

A recursive dynamic model is a dynamic alignment of the static model, in which economic agents have adaptive expectations. The solution is obtained from the base year equilibrium, calculating the result for each consecutive year, with economic agents reacting only to changes in the current year (or previous years) and ignoring other known future changes. In turn, economic agents are assumed to have perfect foresight in a forward-looking dynamic model, which makes the model much more complex. Therefore, forward-looking dynamic models have difficulty achieving convergence of a solution (Kompas, et al., 2018). In addition, a forward-looking dynamic model often does not take into account regional or sectoral breakdown, as this would further increase computing time (Babatunde, et al., 2017). With many regional and sectoral dimensions, the size of CGE models is growing exponentially, posing a severe challenge to the most commonly used software packages such as GEMPACK and GAMS (Ha & Kompas, 2016).

The CGE includes three categories of taxes: consumption taxes (paid by consumers and producers for input), production taxes (paid by producers for output and capital), and labour taxes. The introduction of a carbon tax in CGE could be done by increasing the amount of consumption taxes for at least some consumers. Initially, the amount of tax is proportional to the carbon emissions of the respective product. With the introduction of a carbon tax, the input costs for carbon-intensive goods become higher than for other goods, so CGE will try to shift its energy technologies to more environmentally-friendly ones. The TIMES model works similarly to the demand for technology, with lower demand for carbon-dependent goods and higher demand for more environmentally-friendly goods (Wiebe, et al., 2017).

Linking CGE and TIMES models can be a solution to reduce the shortcomings of each model. It allows benefiting both from the consistency of the CGE model and the high level of energy sector detailing in TIMES. Linking the two models would help to close the gap between model differences and obtain more consistent modelling results (Vuuren, et al., 2009) (Dai, et al., 2016).

The first step is to explore linking the two models to consider how changes in the economy may affect performance in the energy system and vice versa. Initially, it would be necessary to understand what types of policy shocks each of
these models can best address and how they can help analyse broader non-energy policy goals, such as economic growth and income distribution (Katris, et al., 2017). The two models could be linked as the following: (1) two independently developed Top-Down and Bottom-Up models (Krook-Riekkola, et al., 2017); (2) simplification or “reduced form” of a model (Bosetti, et al., 2006); or (3) full integration of both models (Böhringer & Rutherford, 2008).

Independently developed CGE and TIMES models can be linked in three ways: (1) soft-linking; (2) hard-linking; (3) development of a hybrid model (Böhringer & Rutherford, 2009; Helgesen, 2013). In the first two cases, models operate independently. In the case of soft-linking, the processing and transfer of information between models are controlled by the user, while in the case of hard-linking, this is done automatically and usually using computer programs (Helgesen, 2013). In a soft-linking case, the user evaluates the results of the models and decides whether and how the input of each model should be changed to make the two sets of results more consistent with each other, making the model input assumptions and results match each model. In a hard-linking case, one model is usually given control over specific results, while the other model is set to reproduce the same results, usually with a different level of aggregation (Holz, et al., 2016). Soft-linking could be the starting point for linking models, as the initial investment in computer programming is usually low, and modelling results could be got relatively fast. However, for productivity and efficiency reasons, the preferred end-product might be hard-linking (Helgesen, 2013).

Although integration of the two models could also be viewed as the preferred end-product, there are significant benefits to keep the CGE and TIMES models separate and independent from each other – such as model design and maintenance, maintaining the relative strengths of both models, and different levels of spatial and temporal distribution (Holz, et al., 2016). The main aspects why model integration might not be the best end-product are: (1) an integrated model may need to simplify one or both models significantly; (2) CGE and TIMES models might operate at different time scales, with different detailing levels or with different regional breakdown; (3) the models have different data sources, which significantly complicates the model integration (Helgesen, 2013). In general, an integration approach that narrows the detailing level is practical when looking at the global picture; however, with less sectoral or technological details, it is less useful for public policy considerations (Andersen, et al., 2019).

Several studies focus on single-sector linkage in Bottom-Up and Top-Down models (Martinsen, 2011; Schafer & Jacoby, 2005). However, the literature suggests that the end-product should be a “full link” and “full-form” approach. A “full-link” and “full-form” approach combines the technological details of the Bottom-Up models and the macroeconomic structure of the Top-Down models (Andersen, et al., 2019), and has been implemented, for instance, in China (Dai, et al., 2016), Portugal (Fortes, et al., 2014), Sweden (Krook-Riekkola, et al., 2017), Denmark (Andersen, et al., 2019) and Poland (Antosiewicz, et al., 2020). Several examples of linking CGE and TIMES models are summarised in Table 1.

In Norway, three models (TIMES, CGE and EXIOBASE) have been merged. The connection points in the TIMES model are mainly consumption indicators, such as the expected demand of sectors and households, which are derived from the CGE, as they take into account population and labour force growth projections. The connection points in the CGE model is an energy mix from five sectors (agriculture, industry, services, transport, consumer welfare production) derived from TIMES. In the iteration process, indicators are exchanged, and convergence is achieved if the values of the indicators do not change significantly from iteration to iteration (Wiebe, et al., 2017).

In China, the connection points of the TIMES model are the relative changes in industrial output that are transferred from the CGE model and provide relative changes to the corresponding future demand for energy services in the TIMES model. In turn, the connection points of the CGE model are energy consumption, which is derived from the TIMES model and adjusts the parameters of energy efficiency improvement (Dai, et al., 2016).
<caption>Table 1.</caption>

Examples of linking the CGE and TIMES models

<table><thead><tr><td>Article</td><td>Data sent from CGE to TIMES</td><td>Data sent from TIMES to CGE</td><td>Linking</td></tr></thead><tbody><tr><td>Böhringer & Rutherford, 2009</td><td>• Energy demand</td><td>• Net energy output<br>• Inputs of non-energy goods to the energy system</td><td>Hard-linking</td></tr><tr><td>Fortes, et al., 2014</td><td>• Energy demand</td><td>• Energy prices<br>• Energy consumption<br>• Policy monetary values</td><td>Integration</td></tr><tr><td>Dai, et al., 2016</td><td>• Energy demand</td><td>• Energy consumption</td><td>Soft-linking</td></tr><tr><td>Holz, et al., 2016</td><td>• Energy demand</td><td>• Energy mix</td><td>Soft-linking</td></tr><tr><td>Abrell & Rausch, 2016</td><td>• Electricity demand and price<br>• Input prices for fuel, capital, labour, goods and services</td><td>• Electricity dispatch<br>• Input demand for fuel, capital, labour, goods and services</td><td>Hard-linking</td></tr><tr><td>Krook-Riekkola, et al., 2017</td><td>• Energy demand</td><td>• Energy intensity parameter<br>• Energy mix<br>• Energy prices</td><td>Soft-linking</td></tr><tr><td>Wiebe, et al., 2017</td><td>• Energy demand</td><td>• Energy mix</td><td>Hybrid-linking</td></tr><tr><td>Andersen, et al., 2019</td><td>• Energy demand<br>• Fuel prices</td><td>• Energy prices<br>• Energy mix</td><td>Soft-linking</td></tr></tbody></table>

Source: authors’ elaboration based on academic literature.

In the soft-linking process, both models depend on the input of the other model. In the case of Sweden, it is recommended to start with the CGE model, as the TIMES model requires energy demand assumptions, while the CGE model can start without TIMES input. The number of iterations needed for a model solution to achieve convergence may not be determined, but the change in results should be reviewed in each iteration, which is particularly relevant in the case of many connection points. The manual review identifies the necessary improvements to the model and provides more information about the system being studied. The most significant change is usually observed in the first iteration. In subsequent iterations, the result is being slightly adjusted, so it is important not to exaggerate the number of iterations, as convergence may not be achieved in the soft-linking case (Krook-Riekkola, et al., 2017), for instance, if the differences in the structure and methodology of the two models are significant (Böhringer & Rutherford, 2009).

In Denmark, on the other hand, it is recommended to start an iteration with the TIMES model to inform the CGE model about future energy prices, energy service production technologies and fuel taxes. Based on this information, the CGE model determines new energy demand and prices. The TIMES model uses these results in the second iteration to predict the connection points found. The iterative process continues until fuel costs, and fuel tax revenues (by industry, services, and fuel) converge in both models (Andersen, et al., 2019).

There are also several challenges and problems with the soft-linking approach. For instance, in the case of a significant economic restructuring (such as a radical reduction in the use of fossil fuels), investment flows could also change significantly. The calculated investments from the TIMES model could be unrealistic and inappropriate, as it does not assess the link between investment demand and the rest of the economy. In turn, the CGE model has difficulty in perceiving the spread of new technologies. Some changes in investment flows can be made by adjusting electricity and
heat prices. Also, the soft-linking approach is time-consuming if a manual check and quality control are performed in each iteration (Krook-Riekkola, et al., 2017).

Despite several challenges and problems, soft-linking is important and necessary to bridge the gap between the results of the CGE and TIMES models. The most important thing is to keep in mind and, if necessary, use the strengths and limitations of the two models, as significant differences in at least some results may be detected (Dai, et al., 2016).

## Assessment of the current Latvian CGE model

The current version of the Latvian CGE model owned by the University of Latvia (and developed by the University of Latvia researchers during the previous project) is based on the Australian CGE prototype-model ORANI-G, mainly it is 2013 version "TPMH0110" (further - ORANI-G). There are only some differences of the Latvian CGE model from ORANI-G. Latvian CGE model: (1) has a more detailed sector structure (65 sectors, compared to 37 sectors in the ORANI-G); (2) divides employees by 10 occupational groups (compared to 8 occupational groups in the ORANI-G); (3) excludes regional dimension (it contains one region, compared to 8 regions in the ORANI-G). Also, most of the model parameters in the Latvian CGE model differ from the ORANI-G (see Table 2).

<caption>Table 2.</caption>

### A comparison of model parameters in Latvian CGE and ORANI-G models

<table><thead><tr><td>Nr</td><td>Parameter</td><td>Comparison</td></tr></thead><tbody><tr><td>1</td><td>Armington elasticities</td><td>In the Latvian CGE model, Armington elasticity values are slightly higher than in the ORANI-G model (1.55 and 1.07, respectively), which could reflect the greater openness of the Latvian economy to international trade flows.<br/>The range of elasticities by sectors in the Latvian CGE model is narrower - from 0.10 to 3.00 (for comparison: in the ORANI-G model from 0.00 to 5.02).<br/>In the Latvian CGE model, the values of Armington elasticities differ by industry. However, the elasticities for the industry defined in the three blocks of the model (intermediate consumption, investment, households) are identical. In contrast, in the ORANI-G model, Armington's elasticity values differ by both industry and model blocks.</td></tr><tr><td>2</td><td>Substitution of labour to capital</td><td>In the ORANI-G model, the coefficient for all industries is 0.5. In the Latvian CGE model, it differs by industry (average value 0.74; range: 0.25 - 1.50); it is higher in the agricultural and service sectors and smaller in the electricity and water supply sectors.</td></tr><tr><td>3</td><td>Export demand elasticities</td><td>EXP_ELAST: In the Latvian CGE model, the same elasticity value is set for all sectors (-5.00).<br/>In the ORANI-G model, the average value is -9.49 (range from -1.64 to -10.00); in addition, most sectors (80%) were assigned a value of -10.00.<br/>EXP_ELAST_NT: In Latvian CGE model: -5.00, in ORANI-G model: -10.00.<br/>IsIndivExp: Similar coefficients between sectors were observed in Latvian CGE (0.42) and ORANI-G models (0.38).</td></tr><tr><td>4</td><td>Income elasticity of demand</td><td>The values of elasticities by sector are similar:<br/>In the ORANI-G model: average value 0.93 (range 0.40 - 1.59)<br/>In Latvian CGE model: average value 1.07 (range 0.42 - 1.68).</td></tr></tbody></table>
<table><tr><td>5</td><td>Propensity to export</td><td>In both models: 0.50 in all sectors of the economy.</td></tr><tr><td>6</td><td>Frisch parameter</td><td>In Latvian CGE and ORANI-G model: -1.82. Higher (by module) Frisch parameter is observed in poorer countries. In contrast, for high-income countries, the value of the Frisch parameter approaches zero.</td></tr><tr><td>7</td><td>Labour substitution by type of skills</td><td>Latvian CGE and ORANI-G model: 0.50 in all sectors. It reflects the substitutability of employees by occupation groups.</td></tr></table>

Source: authors' elaboration based on the materials of the ORANI-G model and the project of the national research program "Energy and Climate Modeling Towards Carbon Neutrality".

Latvian CGE model is static and has two simulation options - short-term and long-term. Both simulation scenarios use the standard available ORANI-G model exogenous and endogenous variables (see Table 3).

<caption>Table 3.</caption>

Latvian CGE and ORANI-G models main assumption about short-term and long-term simulations

<table><thead><tr><td>Nr.</td><td>Short-term simulation</td><td>Long-term simulation</td></tr></thead><tbody><tr><td>1</td><td>Capital and investment have given outside the model (exogenous)</td><td>Capital and investment are determined within the model (endogenous)</td></tr><tr><td>2</td><td>Wages are exogenous; employment is endogenous.</td><td>Wages are endogenous; employment - exogenous.</td></tr><tr><td>3</td><td>Private and government consumption are exogenous.</td><td>Private and government consumption are endogenous.</td></tr><tr><td>4</td><td>The new equilibrium corresponds to the state of the economy after 1 - 3 years.</td><td>The new balance corresponds to the state of the economy after 10 - 20 years.</td></tr></tbody></table>

Source: authors' elaboration based on the materials of the ORANI-G model and the project of the national research program "Energy and Climate Modeling Towards Carbon Neutrality".

The static form of the Latvian CGE model means that it is impossible to observe the dynamics of the variables by periods (years) neither in long-term or short-term simulations.

A cornerstone of the Latvian CGE model database is the Input-Output matrix. Central Statistical Bureau of Latvia produces Input-Output matrices every five years, publishing them with a significant time lag (36 months after the respective year). The most recent publicly available Input-Output matrix for Latvia is for 2015. Therefore, the next Input-Output matrix is for 2020 and will be published at the end of 2023. Currently Latvian CGE model employs the Input-Output matrix for 2017, which was produced by the technical support of the Center of Policy Studies (CoPS), using Input-Output matrix for 2015, Supply-Use tables for 2017 and the GEMPACK program extension, which is not available in the case of a standard license. Thus, the Latvian CGE model is currently based on the data constructed by an external expert, without the possibility of verifying the methodology according to which it was constructed.

One of the other matrices which is included in the Latvian CGE model database is the GHGE matrix. The GHGE matrix is one of the links between the CGE and TIMES models. It provides information on GHG emissions, and it is obtained from the TIMES model. Manual recalculations are required to modify the structure of the sectors displayed in the submitted matrix. The soft link between the two models is not effective due to the lack of harmonisation between sectors. In addition, the principle of introducing a carbon tax in the Latvian CGE model has not been developed yet. It should be noted that an estimate of the increase in production costs due to rising carbon prices currently is, to a large extent, based on expert judgement.
# Conclusions, proposals, recommendations

## Conclusions:

1. CGE model and its soft-linking with the TIMES model is an effective way to assess the impact of climate policy on the economy, as shown by the use of this approach, for instance, in Sweden, Denmark and China. This approach benefits both from the consistency of the CGE model and the high level of detail of the energy sector in TIMES.

2. The current Latvian CGE model is static – it is built based on the ORANI-G Australian prototype. It offers two simulations: short-term and long-term (both using the standard available ORANI-G exogenous and endogenous variables). They compare the initial and final equilibrium of the economy but do not include the costs and benefits of the transition path and do not answer how long (in years) is needed to reach the new equilibrium.

## Proposals and recommendations:

1. The dynamic version of the CGE model should be developed at the University of Latvia. It would allow comparing the behaviour of different macroeconomic variables over time (by years).

2. The principle of introducing a carbon tax in the Latvian CGE model should be developed. An estimate of the increase in production costs due to rising carbon prices should move from expert judgement to a model-based assessment.

3. Calibration of model parameter values for Latvia should be continued. For instance, the Frisch parameter (-1.82) value in the Latvian CGE model is identical to that in the Australian ORANI-G prototype, although income levels differ significantly between countries. Even when the parameter values in the Latvian CGE model differ from the ORANI-G (as in the case of Armington elasticities), their refinement and economic justification might be necessary.

4. Given that the latest Input-Output matrix in Latvia is for 2015, and the Central Statistical Bureau will publish the next Input-Output matrix for 2020 with a significant time lag (at the end of 2023), it is necessary to choose which Input-Output matrix would be the base of the CGE model. There are three alternatives regarding that: a) Input-Output matrix for 2015, which is the latest officially published one; b) Input-Output matrix for 2017, constructed by Center of Policy Studies technical assistance, employing Latvian Supply and Use Tables for 2017, the Input-Output matrix for 2015 and the GEMPACK extension (which is not available in the case of a standard license) and whose methodology cannot be verified; c) Input-Output matrix for 2018, which is not available, but which can theoretically be constructed from the Supply and Use 2018 tables (which will be published by the Central Statistical Bureau at the end of December 2021). The relevance of the third option should be considered taking into account opportunities provided by the software packages GEMPACK and GAMS.

5. An effective soft-link between the Latvian CGE and TIMES models should be established by the University of Latvia in cooperation with the Institute of Physical Energetics. One of the links between the CGE and TIMES models is the GHGE matrix, which structure by sectors should be better harmonised between the two models.

## Acknowledgement

The research was supported by the research project "Modelling and analysis of the economic impact of climate goals" (an agreement to ensure the fulfilment of the task of public administration Nr.5.2-17.2/2021/4).
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