Center for Global Trade Analysis Department of Agricultural Economics, Purdue University 403 West State Street, West Lafayette, IN 47907-2056 USA [email protected]http://www.gtap.agecon.purdue.edu Global Trade Analysis Project Economy-wide (general equilibrium) analysis of Philippines’ mitigation potential Erwin Corong Center for Global Trade Analysis, Purdue University Crowne Plaza Hotel, Manila 11 January 2016
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Center for Global Trade Analysis
Department of Agricultural Economics, Purdue University
403 West State Street, West Lafayette, IN 47907-2056 USA
Economy-wide (general equilibrium) analysis of Philippines’ mitigation
potential Erwin Corong
Center for Global Trade Analysis, Purdue University
Crowne Plaza Hotel, Manila 11 January 2016
• Introduction and motivation
• Methodology
• Business as Usual (BaU) or Baseline scenario
• Simulation results
• Insights
Presentation outline
2
• With energy use (and population) on the rise, increases in carbon emissions are almost inevitable…
• Increasing use of coal to generate electricity
• Rising demand for transport fuel
• Investigate the potential economic and distributional impact of low carbon growth strategies in the Philippines
• Analysis with particular focus on Philippine policy options
Motivation
3
• Partial equilibrium model (e.g. EFFECT) • More detailed power/energy sector • Impacts limited to one or a few sectors of the economy • Cannot account for economy-wide (feedback) effects or trade-offs
• General equilibrium (CGE) model • Encompass the entire economy (disaggregated database with many industries/sectors,
labor and households types) • Impacts in one sector affects all other sectors of the economy • Accounts for country-specific (supply and demand) constraints (i.e., factors are not
limitless) • Typically less power sector detail
• Combined use of partial and national models is preferable
• Macro-micro framework • Computable general equilibrium (CGE) model with energy detail
• CGE model linked to household survey-based micro simulation module for distributional (poverty) analysis
• Impacts analyzed from macro (GDP, trade) to micro (industries, labor market, household income and poverty)
Modeling approach
5
• Economy-wide: Ideal for tracing detailed impacts and identifying transmission channels
• Policies to reduce carbon footprint will lead to changes in relative energy price
• …then result in changes in the relative prices of goods and services, thus altering the production and economic structure.
• In turn, the changes in relative prices coupled with changes in economic structure will alter household incomes and consumptions patterns.
• Downside: Not a bottom-up energy model
Why use a CGE?
6
• Philippine General Equilibrium model - Energy • SAM-based dynamic recursive CGE model of the Philippine economy • Extension of PHILGEM model (Corong and Horridge 2012) • Facilitates analyses of the possible short- and long-term impacts of policy
responses to low carbon growth policies
• PHILGEM-RD-E explicitly • tracks carbon emissions • allows for energy substitution among non-energy industries • distinguishes electricity generation by technology • allows the electricity sector to substitute away from carbon intensive electricity
generation technologies towards less carbon-intensive and carbon-free electricity generation technologies
PHILGEM-E model
7
Production structure
8 Non-energy industries Electricity and energy industries
Sectors
9
Commodity Description Elements of Set COM Industry Description
1 Paddy rice Paddy 1 Paddy rice
2 Corn Corn 2 Corn
3 Fruits and vegetables FruitsVege 3 Fruits and vegetables
4 Other crops OtherCrops 4 Other crops
5 Livestock and poultry LvstkPoultry 5 Livestock and poultry
6 Other agriculture OtherAgric 6 Other agriculture
7 Mining Mining 7 Mining
8 Coal Coal (Carbon) 8 Coal
9 Crude oil Crude (Carbon) 9 Crude oil and natural gas
• 2010 Social Accounting Matrix • Input-Output table updated using 2010 national accounts
• 2009/2010 Family Income and Expenditure Survey
• The 35 industries are classified into: • 6 agriculture and 3 mining-related industries
• 3 processed food and beverage industries; 5 manufacturing industries that include petroleum refining
• 7 electricity industries composed of 6 types of electricity generation technology and an electricity distribution • 8 services industries that includes government services. In total, these industries produce 40 commodities.
• 2 our of 35 industries are multi-product industries • CrudeNatGas extraction produces both natural gas and crude oil
• Petroleum refining industry produces 5 fuel-related commodities such as gasoline, diesel oil, fuel oil, liquefied petroleum gas, and other petroleum products.
• Households • 160 representative household groups classified by the gender of the household head (female and male), by income
deciles (deciles 1 to 10), and main source of income (wage earners, entrepreneur, transfer-reliant, and diversified) • 160 RHGs mapped to 38400 households in the FIES
• Power generation and carbon emissions data from Department of Energy (DoE)
Model’s database
10
Share in CO2 emissions (74.5 million tons, year 2010)
11
Coal 37%
Crude oil 1%
Natural gas 10%
Gasoline 11%
Diesel 24%
Fuel oil 11%
LPG 4%
Other petrol 2%
Industry 16%
Electricity 43%
Transport 32%
Energy 1%
Others 8%
Source: Department of Energy
Electricity generation, by input type (2010)
12
Oil 7,101.0
Hydro 7,803.4
Geothermal 9,929.2
Coal 23,301.1
Natural gas 19,517.9
Renewable 90.2
Oil 10.5%
Hydro 11.5%
Geothermal 14.7%
Coal 34.4%
Natural gas 28.8%
Renewable 0.1%
Generation (in GWh) Shares (in %)
Source: Department of Energy
• Actual 2010 to 2014 GDP growth rates
• 6% GDP forecast from 2015 to 2050 (shown in Table 3)
• 1.8% yearly population growth rate
• 5% yearly depreciation rate
• Actual electricity generated (in GWh) from 2011 to 2014; and
• 2015 to 2050 electricity projections (in GWh) from the EFFECT model
Coal Crude oil refining Natural gas Gasoline Diesel Fuel oil LPG Other petrol
• REP Scenario • Medium term (relative to 2010)
• Geothermal: 75% increase (i.e., 16,675 GWh) by 2030 • Hydro: 117% increase (i.e., 17,940 GWh) by 2030 • Renewable: 100% increase (i.e., 6645) by 2030
• In the absence of long term policy, assume by 2050 (relative to 2010) • Geothermal: 75% and 150% increase (equivalent to 16,675 and 25,770 GWh) • Hydro: 117% and 234% increase (equivalent to 17940 and 30517 GWh) • Renewable: 100% and 200% increase (equivalent to 6645 and 1095 GWh)
• REP-EFFECT Scenario: Relative to 2016, by 2050 • Geothermal: 170% increase (equivalent to 27,863 GWh) • Hydro: 765% increase (equivalent to 79,042 GWh) • Renewable: 2,502% increase in (equivalent to 9,497 GWh)