MEG4C: A Computable General Equilibrium Model for Colombia LAMP Second Meeting San José - Costa Rica October 2 4, 2012 Ana María Loboguerrero Sustainable Environmental Development Deputy Directorate National Planning Department
CONTENTS 1. MEG4C overview 2. Economics of Climate Change Study for Colombia (impacts and mitigation) 3. Research with LAMP data
The MEG4C Model The MEG4C is a recursive dynamic CGEM model based on the OECD GREEN model and built for the assessment of the economic consequences of climate change in Colombia, and the various public policies that can be proposed to face this problem. The geographic scope of MEG4C is at the national level, i.e. describing Colombia as a whole country which is interacting with the rest of the world (ROW). Currently a regional version of the model is being developed (department level). In the model there are 15 sectors and 4 agents. Sectors are defined by specific aggregations of national accounts that reflect the level of detail needed for the analysis.
The MEG4C Model The sectors and agents included in MEG4C are: SECTORS Agriculture Livestock Fishery Manufactured foods Forestry Fossil fuels Minerals (Metallic and non-metallic) Energy Water and waste services Industry Machinery Construction Commerce Transport Services INSTITUTIONS Households Firms Government Rest of the world
Production Constant returns to scale. In the labor market, it is assumed an imperfect substitution between 2 types of labor (skilled and unskilled). It is assumed that demand plus unemployment equals supply by an adjustment of prices, neglecting the possibility of changes in unemployment. The ability to substitute or transform domestic goods into exported ones or viceversa is represented by a Constant Elasticity of Transformation (CET) function.
Production Production Leontief Intermediate Consumption Aggregated Value + Energy CES Good 1 Armington Good 15 Armington Labor CES Capital-Energy Bundle CES Domestic Domestic Imported ROW Imported ROW Unskilled Skilled Energy Capital
Household Consumption Homothetic preferences are assumed. Utility function for households is supposed to be: U = i µ i ln( Ci θi ) + µ s ln S P where µ and θ are ELES parameters, and: C Consumption S Savings P Pr ices
External Sector The external sector is modeled assuming imperfect substitutability between foreign and domestic goods (for imports the Armington assumption and for exports the Constant Elasticity of Transformation CET assumption). The decision to produce for the domestic (QD) or the external market (exports, QE) or to demand from the domestic or the external market (imports, QM) is determined by the ratio of the foreign price (PE for exports and PM for imports) to the domestic price (PD). The relevant parameter ruling the substitution possibilities is the elasticity of substitution (ε E, ε M, both greater than cero). If PD increases (for example due to climate change impacts) the domestic producers would prefer to sell in the internal market and therefore will reduce their exports.
Calibration The dynamics of the model are calibrated according to given rates of population and GDP growth, workforce and labor and capital productivities. GDP growth rates Variable Population growth rates Labor productivity Capital productivity Government deficit or surplus and expenditures Current account deficit or surplus Source Data from ECDBC up to 2040 and a growth accounting model from there up to 2100. DANE data up to 2020 and a model of population growth from there up to 2100. Calibration for the construction of the baseline GDP Calibration for the construction of the baseline GDP Medium-term Fiscal Framework up to 2022 and a constant ratio of GDP from there up to 2100. Medium-term Fiscal Framework up to 2022 and a constant ratio of GDP from there up to 2100.
Growth Model Growth Accounting Labor (population, labor force, unemployment) Total Factor Productivity Investment GDP Growth The growth accounting model assumes a Cobb-Douglas production function: Y = A (( ) ) 1 α α 1 u L K where capital accumulation is achieved through an investment structure described by the following equation: K t = ( 1 δ ) K + t 1 I t 1
Growth Model A structural unemployment rate u of 7,5% was considered. Capital share in output is 40% (this percentage has been obtained from national accounts for the last 20 years). The depreciation rate δ is 4,92%. Investment I is fixed at 35% of GDP. The analysis gives as result an average GDP growth rate of nearly 5% up to 2022, after this, calculations with the Cobb-Douglas model give 3,6%. The election of the different parameters was done following Julio (2001) and Arango, Posada and García (2007).
Important Assumptions Assumptions Government saving is modeled exogenously, that is, tax rates adjust endogenously to obtain a specified level of savings. Savings determine investment, that is, given a savings level for the economy, the investment level is automatically established for each period. Current account balance is adjusted through the exchange rate to obtain a fixed value. The elasticities used in the model. These values were obtained from Bourneaux, Nicoletti, & Oliveira-Martins (1992). Baseline dynamics and calibration. Effect on results Insights of the impacts of climate change policies on government revenue are limited. There is no consistent way to modify investment exogenously. Insights of the impacts of climate change policies on trade balance, exports and imports are limited. Results about the substitution possibilities (between inputs, and between domestic and foreign goods) are sensible to the chosen values. Model stability under impacts is sensible to the dynamic parameter specification.
Economics of Climate Change Study using MEG4C MEG4C can be used to assess the economic costs of climate change and of adaptation measures
Results on Climate Change MEG4C is able to provide data about the way climate change impacts spread through out sectors different from the directly impacted ones. For instance, data show that the manufactured food sector suffers a large indirect impact under an A1B scenario. In terms of per capita consumption, it is found that it falls 8.01% with respect to the baseline scenario in 2100.
Mitigation Analysis In the same way, MEG4C gives useful insights about the economic effects of mitigation measures, including taxation policies to curb emissions (green taxes). MITIGATION ANALYSIS MEG4C CO2eq taxes levied on energy goods consump:on Analyzed measures: Demand- side measures, Electricity genera:ons measures (wind park and geothermal plant) and Transport measures (BRT,SITP,SETP and truck scrapping) CEDEC Study Cost- benefit analysis Changes in demand parameters and external demand for forest related goods Analyzed measures: Demand- side measures (efficient bulbs, refrigerators and electric vehicles) and supply- side measure (forest planta:ons).
Marginal Abatement Cost Curve
CO 2 eq Taxes Exercise Green taxes are levied on households, energy and transport sectors to reach the same emission reductions obtained with cost-benefit analysis. It is assumed that tax revenue is used to encourage some behavior in other sectors as would be expected from specific mitigation measures. Since CO 2 eq taxes generate a new revenue source for the government, it is important to analyze different scenarios for the use of this new income (recycling mechanism); the following three were used: NR: no specific destination KCR: the tax revenue subsidizes the capital expenditure LCR: the tax revenue subsidizes the labor expenditure
CO 2 eq Taxes Exercise Cost-benefit analysis change in CO 2 eq emissions Efficient bulbs and refrigerators (households electricity consumption) Electric vehicles households fossil-fuel consumption Transport sector Energy generation sector
CO 2 eq Taxes Exercise RESULTS Percental change in national GDP:
CO 2 eq Taxes Exercise RESULTS Percental change in sectoral GDP in each scenario: It is important to notice the effect that the measures have on the energy sectors. Particularly, although the tax is never imposed to the fossil fuels sector, this is specially affected.
CO 2 eq Taxes Exercise RESULTS The CO 2 eq emissions reduction is related with the behavior of the GDP Mitigation measure Effect on the sector Effect on other sectors Sectoral emissions reduction Indirect emissions change Variable effect on aggregate emissions
Example: Efficient Bulbs Demand-side measures: Efficient bulbs Energy efficiency measures Substitution of incandescent bulbs Substitution of refrigerators Change in the minimum consumption parameter of the electricity goods and the manufacturing goods Expenditure in electricity Expenditure in manufactures Since the goods of the CEDEC study are not the same as those from the MEG4C, it is necessary to calculate factors of proportionality. These are calculated using the data from the CEDEC study (expenditure in bulbs) and the data from the SAM.
Example: Efficient Bulbs Sectoral GDP Households Consumption CO 2 Tons changes GDP Decrease electricity household consumption Decrease electricity GDP Increase disposable income Increase consumption of other goods Increase in GDP
Supply-side Measures Forestry Plantations Forestry Plantations actions CO 2 eq emissions reduction by carbon capture Generate an increase in the forestry sector GDP Change in the foreign demand for forestry goods Foreign demand Sectoral GDP increases 400% in 2040 Part of the new output of the forestry sector is sold domestically replacing illegal plantations products but the major part of it will be exported. Since the model does not contain a land factor market, a ratio of carbon capture to GDP increase is obtained from CEDEC study to calculate the reduction in CO 2 eq emissions.
Supply-side Measures Forestry Plantations Change in GDP Sectoral contribution to the GDP growth Increase in external demand for forestry goods Increase in forestry GDP Increase inputs used in forestry Increase in household income Increase consumption for other goods GDP increases
Research with LAMP Data LAMP database could be an input to MEG4C for: Option Description 1. Trends for exogenous variables. LAMP database results on trade, capital flows, international prices, etc., can be used in MEG4C to further specify implications for Colombia of other models results. 2. Building new energy-related scenarios. LAMP data can support technological assumptions about energy sectors which can be used in MEG4C to investigate its indirect economic effects in Colombia. 3. Comparison of climate change/related policies effects among countries. 4. Information on variables not specifically modeled by MEG4C. Once comparability of data sets is established for models results concerning Colombia, insights of relative impacts between Colombia and similar countries could be obtained. After appropriate downscaling to Colombia, results on variables not included in MEG4C, such as land use, can be used to refine scenarios until future developments are made in the model.
Research with LAMP Data In addition to the possibilities shown before, interaction between MEG4C and the other models within the LAMP effort could open up new areas of research in the medium- and long-run: A wider framework for analyzing specific mitigation measures additional to the ones considered until now (carbon taxes). Perspectives about modeling and implementation of biodiversity, ecosystem services, etc. Exchange of modeling experiences, specific details of implementation, solving and analysis. Economics of the adoption of new/cleaner energy technologies by Latin American countries, and in particular CCS technologies, in response to climate change or proposed related policies.