Filipino 2040 Energy: Power Security and Competitiveness

Transcription

1 ENERGY POLICY AND DEVELOPMENT PROGRAM (EPDP) UPecon Foundation Working Paper R August 2016 Filipino 2040 Energy: Power Security and Competitiveness By Majah-Leah V. Ravago, Raul V. Fabella, Ruperto P. Alonzo, Rolando A. Danao, Dennis S. Mapa University of the Philippines and Energy Policy and Development Program (EPDP) EPDP Working Papers are preliminary versions disseminated to elicit critical comments. They are protected by Republic Act No and are not for quotation or reprinting without prior approval. This study is made possible by the generous support of the American People through the United States Agency for International Development (USAID) under the Energy Policy and Development Program (EPDP). EPDP is a four-year program implemented by the UPecon Foundation, Inc. The contents or opinions expressed in this paper are the authors sole responsibility and do not necessarily reflect the views of USAID or the United States Government or the UPecon Foundation, Inc. Any errors of commission or omission are the authors and should not be attributed to any of the above.

2 ENERGY: POWER SECURITY AND COMPETITIVENESS 1 As of August 23, 2016 The Filipinos vision for the self by 2040 is for them to enjoy a stable and comfortable lifestyle, having enough for their daily needs and unexpected expenses, so they can plan and prepare for their own and their children s futures. This paper looks at one major commodity that bears heavily on every Filipino consumer s expenses: electricity. By focusing on the generation sector, it presents two possible scenarios for the next 25 years and illustrates how policy reforms on fuel mix can potentially reduce blended generation charges that make up 47% of the total electric bill. This paper also provides an assessment of the power sector s performance and suggests broad key reforms and alternative pathways needed for the sector to contribute to the overall vision of a stronggrowth economy and improved well-being of Filipinos by The Philippines envisions its per capita income to grow to P316,173 ($6,873), at constant 2000 prices 2 ) from where it is in 2015 at P74,453 ($1,618). This will entail an average per capita income growth of 5.96% per year, which is a substantial leap from the approximately 2% growth per year in the last 25 years. This higher growth path would require an average gross domestic product (GDP) growth of 7% per year for the next 25 years something that we attained only in The impressive growth attained during these years was mainly driven by private and government consumption, which was, in turn, partly fueled by overseas Filipinos remittances the growth of which started to slacken in 2015 to about 5% per year in current terms (BSP 2015). The sustainability of the growth during the last four years remains tenuous. Optimism in the economy is dampened by concerns over the perennially high cost of power, as well as resource adequacy and reliability that would support the country s potential growth. Exacerbating these concerns is the large population of over 100 million that continues to grow at 1.9% annually. The challenge for the country is both the sourcing and timing of additional power supply to meet the growing demand in order to avert a recurrence of the power crisis that occurred in the early 1990s and, at the same time, bring the cost of power down. Adequate power supply is also needed to attain sustainable high productivity and capital-driven growth, where the main drivers are private and foreign direct investments. Power cost is where the Philippines loses out in the cost of doing business compared to our neighboring countries. After Singapore, the country has the highest power cost in the 1 Written by Majah-Leah V. Ravago, Raul V. Fabella, Ruperto P. Alonzo, Rolando A. Danao, and Dennis S. Mapa of the University of the Philippines under the Energy Policy and Development Program (EPDP); with inputs from Jeff Ducanes in modeling electricity consumption and assistance from Shirra de Guia, J. Kat Magadia, Miah Pormon, Tim Guanzon, Mico del Mundo; Mari-An Santos and Jean Lau Wang. They also gratefully acknowledge the valuable review and comments of Prof. Francisco Viray that became the basis of their numerical computation. This paper is made possible by the generous support of the American People through the United States Agency for International Development (USAID) under the Energy Policy and Development Program (EPDP). EPDP is a fouryear program implemented by the UPecon Foundation, Inc. The views expressed in this paper are those of the authors and do not necessarily reflect the views and policies of the USAID or the United States Government or the UPecon Foundation, the Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequence of their use. By making any designation of or reference to a particular territory or geographic area, or by using the term country in this document, ADB does not intend to make any judgments as to the legal or other status of any territory or area. 2 For this paper, the currency exchange rate used is P46 = US$1.

3 2 Southeast Asian region. This high cost of power means that we are foregoing large-scale manufacturing investments that eventually find home and create employment in other countries. While there are other contributory factors, the high power cost is, in large part, the reason why the manufacturing sector s share in total GDP has retreated and the industry as a whole has lagged badly behind services in the Philippines a disease we have elsewhere called development progeria (Daway and Fabella 2015), where services forge ahead to developedcountry levels in income-poor countries. This translates into slow growth and slow poverty reduction. Reversing the so-called development progeria is what Filipinos envision by The Electric Power Industry Reform Act (EPIRA) of 2001 has a well-intentioned objective of opening up access to and fostering competition in the retail supply of electricity to lower the price for consumers. However, to date, electricity prices in the Philippines remain among the highest in Asia. Figure 1 shows the trend in electricity tariffs for residential and industrial customers in selected Asian economies. In 2013, the Philippines residential rate was $0.14/kWh, much higher than the rate in Singapore ($0.12/kWh), Thailand ($0.08/kWh), Indonesia ($0.04/kWh), and Malaysia ($0.06/kWh).The same trend is observed for industrial tariff among these countries except for Singapore whose industrial rate ($0.11/kWh) is higher than the Philippines ($0.10/kWh). Figure 1: Electricity Tariffs in Select Asian Economies, 2005 ($ cents/kwh) 20 (a) Residential (b) Industrial Sources of basic price data: Enerdata. n.d. Energy data.

4 3 Why is the Philippines power cost so high despite the fact that 25-29% of our fuel source is from relatively lower-cost hydro and geothermal? There are many suggested reasons for this, viz., fuel mix, taxes and subsidies, low reserves and low generation capacity per capita, average size of generation plants, overall efficiency, volatility, and absence of competition in Power Supply Agreement (PSA) contracting (see more discussion in latter sections). EPIRA mandates all industry participants to unbundle their own operations according to their functions and, consequently, unbundle their rates, charges, and costs. What constitutes the retail price of electricity for residential consumers in the Philippines? For an average household that consumes about 200 kwh of electricity a month in Manila Electric Company s (Meralco) franchise area, the total monthly bill is about P1,888 ($41) using the average price of P9.68/kWh in This household would typically have a refrigerator, electric fan, flat iron, TV set, and radio. Figure 2 shows that generation charges make up 47.37% of the bill, followed by distribution charges that include supply and metering at 27.32%. Transmission charges make up about 8.98%. Note that the regulated monopoly subsector (transmission and distribution) constitutes 36.3% of the bill. Figure 2: Manila Electric Company s (Meralco) Breakdown of Tariff System Loss 5.00% Transmission 8.98% FIT-Allowance 0.40% Taxes/UC/Subsi dies 10.93% Generation 47.37% Distribution 27.32% Source of basic data: Meralco, Average Rates by Customer Class and Billing Component In this paper, we focus on the generation sector. We present two possible scenarios for the Philippines in 2040 and illustrate how policy reforms with regard to fuel mix can potentially lower power rates. A significant reduction in the blended generation charges that make up 47.37% of the total bill will clearly improve the economic well-being of Filipino consumers. The simple numerical exercise illustrates that the optimal fuel mix is not constant over time but should exploit the opportunities opened up by less costly resources while taking environmental

5 4 (including health) costs into account to bring the price of power down. We also provide an assessment of the power sector s performance and suggest broad key reforms and alternative pathways needed for the sector to contribute to the overall vision of a strong-growth economy in I. THE PHILIPPINES IN 2040: STRONG VS. WEAK GROWTH While developing countries like the Philippines are often beset by problems that require immediate action, looking ahead over the span of a full generation affords decision makers a perspective that can guide the choice of present responses. Such a visioning exercise helps identify courses of action that must be taken now and over the medium term, especially when processes are cumulative and activities require a long setup time to produce results or have enduring and even irreversible effects. Two scenarios for the Philippine economy up to 2040 are identified in Figure 3. The first scenario, the strong-growth scenario, assumes an average GDP growth rate of about 7% annually from 2016 to With low population growth that ranges from 1.46% to 0.61% for (see Table 1), the per capita GDP growth is expected to average at 5.96% during the same period. With this economic growth momentum, the average per capita income of the country is expected to reach P316,173 ($6,873) by 2040, giving the country a high-income status. Figure 3: Strong and Weak Growth Scenarios of GDP per Capita, (P) Author s calculations. Note: GDP per capita (constant 2000 prices) from 1990 to 2015 (actual) and 2016 to 2050 (projected) This high economic growth trajectory will be achieved through the demographic dividend. A demographic dividend is an improvement in economic productivity due to increased number of people in the workforce relative to the number of dependents (Lee and Mason 2006). Addressing the challenges of having high fertility rates, particularly in poor households, and the high unemployment and underemployment rates among young workers, the country will fully

6 5 benefit from the demographic transition. Strong reforms are needed in the areas of population management, investment in human capital, and expansion of opportunities in the labor market. Reducing fertility rate is the critical element for the demographic transition. It is a necessary condition for the creation of this rare window of demographic opportunity for continuous economic growth. (See Box 1 for notes on the population projections.) Strong political will is needed in order to increase the contraceptive prevalence rate from the current number of 55% to 70%. Continuous investment in human capital is also important, and the additional two years of schooling (particularly for women, as Increased education of women is also a strong determinant in lowering the total fertility rate or TFR), due to successful implementation of the K to 12 Program, is a good start. By increasing the years of schooling, the wage income will also increase, particularly for young workers. Table 1: Strong Growth Scenario Projections for Real GDP, Population, GDP per Capita, and Electricity Consumption, Year Real GDP at 7% Growth Rate (P billion) Population ( 000) Growth Rate (%) GDP Per Capita (P) Growth Rate (%) Electricity Consumption (GWh) , ,803 74,453 81, , , % 79, % 85, , , % 83, % 89, , , % 88, % 93, , , % 93, % 97, , , % 98, % 102, , , % 104, % 106, , , % 110, % 111, , , % 116, % 116, , , % 123, % 121, , , % 130, % 126, , , % 138, % 131, , , % 146, % 137, , , % 154, % 143, , , % 163, % 149, , , % 173, % 156, , , % 184, % 162, , , % 195, % 169, , , % 207, % 176, , , % 219, % 184, , , % 233, % 192, , , % 247, % 200, , , % 263, % 209, , , % 279, % 217, , , % 297, % 227, , , % 316, % 236,865

7 Note: Real GDP and GDP per capita are at 2000 constant prices. 6

8 7 Box 1: Notes on the Population Projections To project the population until 2100, the United Nations Population Division uses assumptions regarding future trends in fertility, mortality, and international migration. Because future trends cannot be known with certainty, a number of projection variants are produced. The World Population Prospects 2012 Revision uses the same stochastic model for fertility projection as used in the 2010 revision with modifications. The AR1 model used for low fertility countries is estimated using a Bayesian hierarchical model and future long-term fertility levels are more data-driven and countryspecific. Two new stochastic models were also used to project life expectancy at birth for all countries not significantly affected by the HIV/AIDS epidemic. The first model, used for females, is a Bayesian hierarchical approach that models the rate of mortality improvement by level of life expectancy at birth; the second model is used for males, to project the gender gap conditionally on female mortality level. The following projection variants were used to simulate the Philippine population up to Projection Variant Assumptions Fertility Mortality Migration Medium Medium Normal Normal Low Low Normal Normal The low projection variant is used for the strong growth scenario. The medium variant is used for the weak growth scenario. Medium Fertility. A country is assumed to have medium total fertility rate (TFR) if its rate has been declining but the estimated level is above the replacement level of 2.1 children per woman in (The Philippines will reach a TFR of 2.12 in ) Low Fertility. A country is assumed to have low TFR if its rate is half a child lower than that of the medium variant. That is, countries with a TFR of 3 children per woman in the medium variant have a TFR of 2.5 children per woman in the low variant. (The Philippines will reach a TFR of 2.09 in ) Normal Mortality. For most countries where mortality was assumed to follow a declining trend starting in 2010, life expectancy was generally assumed to rise over the projection period. In contrast with fertility assumptions, only one variant of future mortality trends (median path) was used for standard projection. Normal Migration. Based on past international estimates and considered the policy stance on future international migration flows, the projected levels of net migration are generally kept constant over the next decades. After 2050, it is assumed that net migration will gradually decline and reach zero by The following are the data sources in projecting the Philippine population: Total Population: National censuses from 1960 through 2010, and with estimates of the subsequent trends in fertility, mortality, and international migration. Total Fertility: Maternity-history data from the 1993 National Demographic Survey (NDS), and the National Demographic and Health Surveys (NDHS) of 1998, 2003, 2008, and Infant and Child Mortality: Estimates from the 1998, 2003, 2008, and 2013 NDHS; 2006 Family Planning Survey; and estimates from UNICEF as published in Life Expectancy at Birth: Infant and child mortality estimates from the 1998, 2003, 2008, and 2013 NDHS; 2006 Family Planning Survey; official estimates from a life table of 2006; and the West model of the Coale-Demeny Model Life Tables and the Lee-Carter method. International Migration: Estimates of net international migration derived as the difference between overall population growth and natural increase through 2010, and on information on Filipino emigrants admitted by the main countries of immigration.

9 8 The changing age structure due to reduction in the country s TFR is a necessary but insufficient condition for harvesting the demographic dividend. It should be given the right kind of policy, particularly in the labor market, to absorb the first batch of young individuals (aged 20 to 24) who will enter the workforce. Reforms must be made in the labor market to provide the young workers with higher employment opportunities. The strong reform scenario simulates the case where employment rate is increased, coupled with the lowering of fertility rate and increase in the years of schooling (additional two years) that will benefit the young workers. Under the strong reform scenario, the support ratio will be greater than 0.50 starting 2025 and will be highest at 0.55 from 2055 to This scenario creates a relatively wider demographic window of opportunity. This means that in 2025, 50 effective workers are supporting themselves and 50 effective consumers. By 2040, 54 effective workers are supporting themselves and 46 other effective consumers, thus providing the economy with additional savings. The other scenario, the weak-growth scenario, assumes an average GDP growth of 4% annually from 2016 to 2040, similar to the average growth rate of GDP from 1990 to With this economic growth, the expected per capita income growth of the country is 2.58% and per capita income is P140,791 ($3,061) by 2040 (see Table 2). For the energy sector, the power subsector in particular, long-term visioning is of prime importance as investments in most new facilities for generation and transmission are lumpy in nature. It takes several years to put up a base load power plant, especially as environmental and social impact studies are factored in. In this regard, it is critical to plan ahead and coordinate the power requirement and the corresponding generation and transmission that will support the vision of strong growth. Forecasts of electricity consumption for 2040 under the two scenarios were obtained using a single-equation error correction model, a dynamic model that integrates short-run dynamics with a long-run relationship. In the present case, the short-run dynamics is modeled by relating annual growth rates of electricity consumption to growth rates of the predictor variables: GDP, electricity price, and temperature. The long-run relationship between electricity consumption and real GDP appears as an extra term in the model. Because there may be disequilibrium (referred to as disequilibrium error ) in the short-run, this extra term is regarded as the error correction mechanism (ECM), which corrects for the disequilibrium. ECM was estimated using annual data from 1992 to The forecast values were computed by assuming that electricity price and temperature follow the historical trend. Figure 4 shows the trend in actual electricity consumption for together with the forecasts for , both including transmission loses and own-consumption of generation plants. The forecasted trend is the electricity consumption that supports the assumed GDP per capita growth under each scenario. Electricity consumption is expected to grow at an annual average rate of 4.3% under the strong-growth scenario but only 2.4% under weak-growth. We consider the forecasts as lower bounds, if we allow for the possibility of lower electricity prices and higher temperatures. However, electricity consumption will also be influenced by demand-side management through the use of more efficient appliances, lighting fixtures, and smart metering (see e.g. Strbac 2008; Moura and de Almeida 2010; EIA 2014). Thus, the net effect on electricity consumption is unclear. We also point out that electricity consumption in the model is primarily driven by aggregate real GDP. Hence, it cannot reflect the effects of changes in GDP s components. Future modeling efforts should account for the structural changes within the economy since electricity consumption largely depends on this, i.e., the gross value added share of agriculture, industry, and services.

10 9 Table 2: Weak Growth Scenario Projections for Real GDP, Population, GDP per Capita, and Electricity Consumption, Year Real GDP at 4% Growth Rate (P billion) Population ( 000) Growth Rate (%) GDP Per Capita (P) Growth Rate (%) Electricity Consumption (GWh) , ,803 74,453 81, , , % 76, % 83, , , % 77, % 84, , , % 79, % 86, , , % 81, % 88, , , % 83, % 90, , , % 85, % 92, , , % 87, % 95, , , % 89, % 97, , , % 91, % 99, , , % 94, % 102, , , % 96, % 104, , , % 98, % 107, , , % 101, % 109, , , % 104, % 112, , , % 106, % 115, , , % 109, % 117, , , % 112, % 120, , , % 115, % 123, , , % 118, % 126, , , % 122, % 129, , , % 125, % 132, , , % 129, % 136, , , % 132, % 139, , , % 136, % 142, , , % 140, % 146,383 Note: Real GDP and GDP per capita are at 2000 constant prices.

11 10 Figure 4: Strong and Weak Growth Scenarios of Electricity Consumption, (GWh) 240,000 ACTUAL FORECAST 200, ,000 STRONG GROWTH 120,000 80,000 WEAK GROWTH 40, Given the 2040 vision for the economy and the forecast for electricity consumption, what is the needed generating capacity that corresponds to the visions of strong-growth and of weakgrowth? If the generating capacity is sufficient to meet the consumption, what does this mean for the well-being of Filipinos in terms of electricity prices? The direction of policies that the government takes is critical in influencing the outcome. We take numerical exercise to compute the required generating capacity at each projected electricity consumption level for the two scenarios. Formal modeling requires that supply and demand be determined simultaneously at each point in time. For illustration purposes, electricity consumption is modeled separately when computing the generation requirement. To estimate the net generating capacity, estimates for installed from various fuel sources by grid in Luzon, Visayas, and Mindanao are needed. This requires solving for the optimal mix of fuel sources over time based on the least-cost rule while taking into account environmental and health concerns. Inasmuch as a fully theoretical and operational model of investment planning and coordination is yet to be developed for the Philippines, we focus here on the conceptual issues and illustrate how policies with regards to fuel mix might affect the growth trajectory of the country and the well-being of Filipinos. For the two scenarios, strong- and weak-growth projections, we consider the current policy stance of the government as stated in the Department of Energy s (DOE) Department Circular , Guidelines for the Policy of Maintaining the Share of Renewable Energy (RE) in the Country. The policy statement in Section 2 of the circular is to maintain the share of (RE) in power generation by adopting at least 30 percent share of RE in the country s total power generation capacity. For the numerical exercise, the fuel mix is pegged at 30% share of RE, 30% natural gas, 30% coal, and 10% others, hereafter referred to as Policy 1, fuel mix.

12 11 Under the strong-growth scenario, we also present an alternative policy on the fuel mix, which favors increased temporary utilization of the lesser-cost resources but accounts for environmental costs. This alternative policy is based on ADB (2013), whereby coal, the cheapest fuel, will still be the main fuel source in Asia and the Pacific (Figure 5). Considering RA 9513 or the Renewable Energy Act of 2008, a fuel mix that favors increased use of renewables, both conventional and variable, is also presented. Thus, the exercise under the strong-growth scenario considers four policy regimes that target the following fuel mix by 2040: 1) ; 2) the alternative policy of temporary utilization of lesser-cost resource; 3) increased use of conventional renewables; and 4) increased use of variable renewables. Inasmuch as we do not model for the optimal fuel mix, the policy regimes above are just four of the many possible configurations of fuel mix. A caveat is in order for Policy 3: the fuel share of conventional renewables in our assumption is for illustration purposes as the share may hit a hard constraint depending on the availability of natural reserves, e.g., water for hydropower. Figure 5: Fuel Mix Based on Installed Capacity, 2015 and 2040 (%) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% (a) 2015 Fuel Mix 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% (b) 2040 Fuel Mix Source of basic data: ADB (Asian Development Bank) Energy Outlook for Asia and the Pacific. Mandaluyong City, Philippines: ADB. Table 3 presents our assumptions about the fuel mix under four policy regimes for the Philippines. We arrive at these considering the fuel mix by grid in Luzon, Visayas, and Mindanao provided in Box 2 as percentage of installed capacity and as percentage of energy generation (consumption). The share allocation of installed capacity by load baseload and mid-merit to peaking plus ancillary, in turn, is determined by the peak demand per grid, taking into account our electricity consumption forecasts (see Box 3). The share allocation of energy generation (consumption) by load is determined by the load factor and the per unit energy for each load. The per unit load energy is derived from the load duration curve by grid.

13 12 We consider the type of load and geographical location (grid) in our assumption. Not all fuel sources or technologies are suitable for all types of load. Geographical location also matters. For Luzon, technologies for baseload include coal, natural gas, geothermal, base hydro and the must-dispatch variable renewables. The variable renewables include solar, wind, biomass, and run-off river hydro. Mid-merit and peak include peaking hydro, peaking natural gas, and oil. For Visayas and Mindanao, technologies for baseload include coal, geothermal, base hydro and the must-dispatch variable renewables. Mid-merit and peak include peaking hydro and oil. Table 3: Assumptions on Fuel Mix Share for Policies 1, 2, 3, and 4 (%) Installed Capacity Mix Energy Consumption Mix Coal Natural Conventional Variable Natural Conventional Variable Others Gas RE RE Coal Gas RE RE Others Policy 1: Policy 2: Temporary utilization of the lesser-cost resource Policy 3: Increase utilization of conventional renewables (hydro and geothermal) Policy 4: Increase utilization of Variable Renewables (solar, wind, biomass, run-off river hydro) Note: 2016 data is a carry over of the fuel mix as of June Conventional renewables include hydro (19%) and geothermal (10%). Variable renewables include wind (2%), solar (1%), and biomass (1%). *Sum not equal to 100 due to rounding up

14 13 Policy 1: Coal Natural Gas Installed Capacity Mix Conventional RE Box 2: Fuel Mix by Grid (%) Variable RE Others Coal Natural Gas Generation Mix Conventional Variable RE RE Luzon Visayas Mindanao *Sum not equal to 100 due to rounding up Others

15 14 Box 2: Fuel Mix by Grid (%) Policy 2: Temporary utilization of the lesser-cost resource Installed Capacity Mix Coal Natural Gas Conventional RE Variable RE Others Coal Natural Gas Generation Mix Conventional Variable RE RE Luzon Visayas Mindanao *Sum not equal to 100 due to rounding up Others

16 15 Box 2: Fuel Mix by Grid (%) Policy 3: Increase utilization of conventional renewables (hydro and geothermal) Installed Capacity Mix Coal Natural Gas Conventional RE Variable RE Others Coal Natural Gas Generation Mix Conventional RE Variable RE Luzon Visayas Mindanao *Sum not equal to 100 due to rounding up Others

17 16 Box 2: Fuel Mix by Grid (%) Policy 4: Increase utilization of Variable Renewables (solar, wind, biomass, run-off river hydro) Installed Capacity Mix Generation Mix Coal Natural Gas Conventional RE Variable RE Others Coal Natural Gas Conventional RE Variable RE Luzon Visayas Mindanao *Sum not equal to 100 due to rounding up Others

18 17 Box 3: Fuel Mix by Load and by Grid (%) Strong Growth Scenario Weak Growth Scenario Base Mid-Peak-Ancillary Base Mid-Peak-Ancillary Luzon % 35% 65% 35% % 40% 59% 41% % 39% 60% 40% % 38% 61% 39% % 37% 61% 39% Visayas % 25% 75% 25% % 30% 70% 31% % 33% 67% 34% % 35% 65% 36% % 37% 62% 38% Mindanao % 41% 59% 41% % 39% 60% 40% % 38% 60% 40% % 38% 61% 39% % 37% 62% 38% *Sum not equal to 100 due to rounding up

19 18 The evolution of generation charges for each type of fuel source largely depends on how technology develops over time (see Viswanathan et. al. 2006; ADB 2013; van Kooten 2013; and Knittel et. al. 2015). In the absence of data to enable forecasting future price trends, we use the 2015 average generation charges to illustrate welfare impacts under the two policy regimes for strong growth and the current policy regime for the weak growth. Table 4 in column (a) is the average generation charges in 2015 for each type of fuel source by grid in Luzon, Visayas, and Mindanao. Fuel Type Table 4: Generation Price by Source for Policy 1 and 2 (P/kWh) Policy 1 (a) Average Environmental Tax (b) Policy 2 and 3 (a+b) Luzon Visayas Mindanao Luzon Visayas Mindanao Coal Geothermal Hydro Must-Dispatch RE Natural Gas Oil Note: Carbon tax is computed based on UNFCCC (United Nations Framework Convention on Climate Change) Tracking Greenhouse Gases: An Inventory Manual (see Box 2). Sources of basic data: Meralco Average Generation Charge by Fuel Type for Luzon; kuryente.org Power Supply Agreements ; Visayan Electric Company (VECO) Generation Rates for Visayas and Mindanao; FIT rates as published by ERC for Must-Dispatch RE. The generation charges under policy regimes (2) and (3) are adjusted to incorporate the negative externality from carbon emissions and other particulates, i.e., the price should reflect its true social cost. Taking this into account, the generation charges are adjusted by imposing the appropriate environmental taxes per kwh of corresponding CO 2 emissions. Taking the social cost of carbon equals $25 per ton of carbon (Tol 2013) or equivalently $91 per ton of CO 2 (1 tc = 3.67 tco 2), and downscaling by the Philippines share of world GDP (0.44%) as suggested by Gayer and Viscusi (2014), we get a $1 carbon tax per tco 2. Adjusting further for the cost of particulates and sulfur dioxide, albeit small, we arrive at a conservative estimate of environmental tax equal to $3 per tco 2. Conversion of environmental tax for the various fuel sources is computed following the UNFCCC (2011) Inventory Manual (see Box 4). Column (b) of Table 4 shows the values for the environmental tax, while the last column provides the adjusted generation charges that are used in the numerical exercise.

20 19 Box 4: Carbon Tax Computation AAAAAAAAAAAAAA EEEEEEEEEEEEEEEEEEEEEEEEEE TTTTTT PP kkkkkk = [TTTTTTTTTT CCCC 2 EEEEEEEEEEEEEEEEEE (tttttt 2 ) CCCCCCCCCCCC TTTTTT (PP/tttttt 2 )] NNNNNN GGGGGGGGGGGGGGGGGGGG (kkkkh) CCCC 22 EEEEEEEEEEEEEEEEEE (ttcccc 22 ) = FFFFFFFF CCCCCCCCCCCCCCCCCCCCCC FF (TTTT) CCCCCCCCCCCC EEEEEEEEEEEEEEEE FFFFFFFFFFFF FF tttt TTTT CCCCCCCCCCCC SSSSSSSSSSSS FF (tttt) FFrrrrrrrrrrrrrr oooo CCCCCCCCCCCC OOOOOOOOOOOOOOOO FF where, P = Philippine peso tttttt 22 = tttttttttttt (1000 kkkk) CCCC 2 FF = FFFFFFFF SSSSSSSSSSSS TTTT = TTTTTTTTTTTTTTTTTTTT (1 TTTT iiii 1,000,000,000,000 oooo jjjjjjjjjjjj) = rrrrrrrrrr oooo tthee mmmmmmmmmmmmmmmmmm wwwwwwwwhtt oooo CCCC 2 44 gggggggggg tttt tthee mmmmmmmmmmmmmmmmmm wwwwwwwwhtt oooo mmmmmmmm CC 12 gggggggggg mmmmmmmm aaaaaa iiii uuuuuuuu tttt cccccccccccccc ffffffff CC tttt CCCC 2 FFFFFFFF CCCCCCCCCCCCCCCCCCCCCC FF (TTTT) = NNNNNN GGGGGGGGGGGGGGGGGGGG FF (kkkkh) kkkkkkkk TTTT kkkkh kkkkkkkk Fuel Source Carbon Emission Factor (tc/tj) Fraction of Carbon Oxidized Coal Natural Gas Others (i.e., Oil) Source: UNFCCC (United Nations Framework Convention on Climate Change) Tracking Greenhouse Gases: An Inventory Manual. Generation capacity is the amount of electricity a generator produces over a specific period of time. For example, a generator that operates with a 1 megawatt (MW) capacity consistently for 1 hour will produce 1 Megawatt hour (MWh) of electricity. If it operates at only half that capacity for 1 hour, it will produce 0.5 MWh of electricity. Many generators do not operate at their full capacity all the time; they may vary their output according to conditions at the power plant, fuel costs, and/or as instructed by the electric power grid operator (EIA, 2015). Given the generation price and the fuel mix assumptions under the three illustrative policy regimes, the required installed capacities for strong- and weak-growth scenarios are calculated using our numerical exercise.

21 20 Table 5 provides the parameters, formulas, and assumptions used in our numerical exercise. To estimate the projected installed capacity and gross generation for the county, we compute them by grid. Using our forecast of electricity consumption (EEEE) for the Philippines in Figure 2, we allocate the consumption by grid (EEEE GG ) using the historical average (aa) (see Table 5, Lines 1-3). The peak demand by grid (PPPP GG ) is computed by dividing EEEE GG by the grid load factor (LLLL GG ). Then the flow in MWh units is converted to stock (MW) by dividing by 8760 hours (Table 5, Line 5). The load factor (LLLL GG ) is based on DOE s Philippine Energy Plan. We then need to compute the requirement for ancillary services, the regulating reserve (RRRR GG, 4% of peak demand), contingency reserve (CCCC GG, largest unit capacity online for the grid), and dispatchable reserve (DDDD GG, second largest unit capacity online) (IIEE, 2014), Table 5, Lines 7-9. Once the peak demand (PPPP GG ) is known, the installed capacity by load (NNNNNN LL ) net of maintenance and station service can be obtained by applying the capacity shares (CCCC LL ) by load, i.e., baseload, mid-merit, and peaking (Table 5, Lines 10-11). We used capacity factors based on the DOE's Power Development Plan by grid for base, mid-merit, and peaking loads respectively (see Table 5). The installed capacity for ancillary services NNNNNN AA (Table 5, Line 12) net of maintenance and station service is simply the summation of all reserves (RRRR GG + CCCC GG + DDDD GG ). The gross installed capacities for each load (GGGGGG LL ) and ancillary services (GGGGGG AA ) are simply obtained by including the maintenance and station service (Table 5, Lines 13-16). From here, we get the gross installed capacity by grid (GGGGGG GG ), (Table 5, Line 17) by summing up GGGGGG LL and GGGGGG AA. The next step is to compute the share allocation of installed capacity (θθ LLLL ) by load per grid. For simplicity, we lumped together mid-merit, peaking, and ancillary as others. Thus, we only have two loads (LL), base and others. The share of installed capacity by load per grid (θθ LLLL ) is just GGGGGG LL divided by GGGGGG GG (Table 5, Line 19). Our assumption on fuel (technology) share ββ FFFFFF (Table 5, Line 21), representing the four policy regimes (see Box 2) takes into account θθ LLLL. The share allocation of energy consumption (δδ LLLL ) by load per grid (Table 5, Line 23) is computed dividing per-unit energy by load by grid (Table 5, Line 22) by the load factor, i.e., ρρ LLLL LLLL GG. Our assumption on fuel (technology) share μμ FFFFFF in energy generation (consumption) (Table 5, Line 25) for the four policy regimes (see Box 2) takes into account δδ LLLL. The next step is to compute the power generation by type of fuel and grid (GG FFFF ) (Table 5, Line 26). This is simply obtained by multiplying the electricity consumption by grid (EEEE GG ) by the fuel share as percent of electricity consumption (μμ FFFFFF ). Generation price by fuel source by grid (GGGG FFFF ) is given in Table 3. Generation cost by fuel source for each grid (GGGG FFFF ) is then obtained by multiplying the generation by the price (GG FFFF GGGG FFFF ), (Table 5, Line 28). While we assumed that generation price is constant until 2040, the blended generation charge (BBBBBB GG ) varies depending on the policies, which differ according to the fuel mix. Summing up the cost of generation over all fuel types and dividing by the total power generation FF GGGG FFFF, we obtain the FFFF GG FFFF per-unit blended generation charge for each grid (BBBBBB GG ), (Table 5, Line 29).

22 21 Table 5: Parameters and Formulas Used for the Numerical Exercise Parameter Variable Unit Formula Description 1 Electricity Consumption EEEE GWh Projected using error correction model (Figure 2). Equal to Gross Generation. 2 Share of Electricity aa % Historical average from 1991 to 2014: 74% for Consumption by Grid 3 Electricity Consumption by Grid Luzon, 13% for Visayas, 13% for Mindanao. EEEE GG MWh EEEE aa 1000 GG refers to Grid: Luzon, Visayas, and Mindanao 1 GW = 1000 MW 4 Load Factor LLLL GG % Based on the DOE's Philippine Energy Plan assumptions per grid; 73% for Luzon, 69% for Visayas, 72% for Mindanao. Assumed constant for all years. 5 Peak Demand by Grid (Non-Coincident) PPPP GG MW EEEE GG LLLL GG 8,760hrrrr Based on the DOE's Power Development Plan computation of peak demand 6 Peak Demand (Non- PPPP PPPP Peak demand for the Philippines GG Coincident) GG 7 Regulating Reserve RRRR GG MW PPPP GG.04 Assists in frequency control by providing automatic primary and/or secondary frequency response, equivalent to 4% of peak demand. 8 Contingency Reserve CCCC GG MW Intended to take care of the loss of the largest synchronized generating unit or the power import from a single grid interconnection, whichever is larger. The Sual Power Plant Unit 1 with GW capacity is assumed to serve as CCCC GG ; Kepco-Salcon Unit 1 with GW capacity for Visayas; a coal fired power plant with GW capacity for Mindanao. 9 Dispatchable Reserve DDDD GG MW 2nd largest unit capacity online. The Sual Power Plant Unit 2 with GW capacity is assumed to serve as DDDD GG ; Kepco-Salcon Unit 2 with GW capacity for Visayas; a coal fired power plant with Capacity Share by Load (Load LL: Base, Mid-merit, Peaking) 11 Net Installed Capacity by Load 12 Net Installed Capacity for Ancillary 13 Maintenance Capacity Factor 14 Station Service Capacity Factor 15 Gross Installed Capacity by Load 16 Gross Installed Capacity for Ancillary 17 Gross Installed Capacity by Grid GW capacity for Mindanao. CCCC LL % Based on the DOE's Power Development Plan capacity share assumptions by grid for base, mid-merit, and peaking loads respectively for all grids: 67%, 23%, 10% NNNNNN LL MW PPPP GG CCCC LL Required installed capacity by base, mid-merit, and peaking load net of maintenance and station service. NNNNNN AA MW RRRR GG + CCCC GG + DDDD GG Required installed capacity to satisfy ancillary services net of maintenance and station service. bb % Assumed to be 90% of gross installed capacity. cc % Assumed to be 90% for base and 95% for midmerit, peaking, and ancillary services of gross installed capacity + maintenance service. GGGGGG LL MW NNNNNN LL bb cc Required installed capacity by base, mid-merit, and peaking load plus maintenance and station service. GGGGGG AA MW NNNNNN AA bb cc Required installed capacity to satisfy ancillary services plus maintenance and station service GGGGGG GG MW GGGGGG LL + GGGGGG AA Required installed capacity by Luzon, Visayas, and Mindanao

23 22 18 Gross Installed Capacity GGGGGG 19 Share of Installed Capacity by Load by Grid 20 Fuel Source (Technology) 21 Fuel Share by Load by Grid as Percent of Installed Capacity GGGGGG GG GG Required installed capacity for the Philippines θθ LLLL % GGGGGG LL GGGGCC GG Note: LL θθ LLLL = 100% FF Fuel sources include coal, natural gas, conventional renewables, variable renewables, and oil. Conventional renewables include geothermal and hydro. Variable renewables include the must-dispatch solar, wind, biomass, and run-off river hydro. ββ FFFFFF % Share in the fuel mix by grid is given in Box 3 as percent of installed capacity. Note that not all fuel sources or technologies are suitable for all types of load. Geographical location also matters. The following list the technologies by load and by grid: 1. Luzon - Baseload: coal, natural gas, geothermal, base hydro and the must-dispatch variable renewables. Note: θθ LLLL = FF ββ FFFFFF - Mid-merit and peak: peaking hydro, peaking natural gas, and oil 2. Visayas and Mindanao - Baseload: coal, geothermal, base hydro and the must-dispatch variable renewables - Mid-merit and peak: peaking hydro, and oil 22 Per-unit Energy by Load by Grid 23 Share of Energy Consumption by Load by Grid 24 Sum Total of Load Share in Peak Demand 25 Fuel Share by Load by Grid as Percent of Electricity Consumption ρρ LLLL % Per-unit base energy is assumed 67% for all grids. Per-unit mid-to-peak is assumed 5% for δδ LLLL Luzon and Mindanao; and 2% for Visayas. % ρρ LLLL LLLL GG dd % The parameter (dd) is based on the load duration curve in 2014 actual utilization of the different fuel sources. Based on DOE s data, share in peak demand of base, mid-merit, and peaking are 67%, 23%, and 10% respectively. Share of ancillary is 15% computed as NNNNNN AA. Thus, sum total of load PPPP GG share in peak demand, dd = 115% μμ FFFFFF % ββ FFFFFF dd δδ LLLL as percent of generation mix. Fuel share (installed capacity) by load multiplied by the sum total of load share in peak demand (dd) taken as a share of energy consumption by load divided by the share of energy consumption by load. 26 Generation by Fuel Source by Grid GG FFFF kwh EEEE GG μμ FFFFFF Generation Price by Fuel Source by Grid GGGG FFFF P/kWh Given in Table Generation Cost by Fuel GGGG FFFF P GG FFFF GGGG FFFF

24 23 Source by Grid 29 Blended Generation Charge by Grid 30 Blended Generation Charge BBBBBB GG P/kWh FF GGGG FFFF FF GG FFFF BBBBBB P/kWh FFFF GGGG FFFF FFFF GG FFFF The result of the numerical exercise for the Philippines is presented in Table 6. Gross installed capacities and gross generation are summed across grid to obtain the values for the country corresponding to the three policy regimes under the weak- and strong-growth scenarios. The result for each grid is given in Box 5. Under the strong-growth scenario regardless of policy regime, the result shows that the country needs to increase its installed capacity from 18.9 GW in 2016 to about 49 GW in 2040, a 159% increase. This required installed capacity in 2040 is close to the 2012 installed capacity of Thailand and Indonesia, which are at about 53 GW and 48 GW, respectively. Gross generation would increase from 85,434 GWh in 2016 to 236,865 GWh in 2040, a 177% increase. Under the weak-growth scenario, the result shows that the country needs to increase its installed capacity from 18.5 GW in 2016 to about 31 GW in 2040; a 64% increase. Gross generation would increase from 83,204 GWh in 2016 to 146,383 GWh in 2040, a 76% increase. Table 6: Generation Capacity in Strong and Weak Growth Scenarios, Indicator Strong Growth Scenario Population Growth Rate (%) GDP per Capita (P) 79, , , , ,173 GDP per Capita Growth Rate (%) Electricity Consumption = Gross Generation (MWh) 85,434, ,382, ,576, ,496, ,865,100 Installed Capacity (MW) 18,983 24,143 30,545 38,682 49,096 Blended Generation Charge (P/kWh) Strong Growth Scenario with Policy Strong Growth Scenario with Policy Strong Growth Scenario with Policy Strong Growth Scenario with Policy Weak Growth Scenario with Policy 1 Population Growth Rate (%) GDP per Capita (P) 76,155 87, , , ,791 GDP per Capita Growth Rate (%) Electricity Consumption = Gross Generation (MWh) 83,204,540 95,137, ,676, ,681, ,383,200 Installed Capacity (MW) 18,539 20,912 23,803 27,185 31,103 Blended Generation Charge (P/kWh) Notes: 1. Strong growth scenario has an annual 7% GDP growth rate and low variant population growth rate. 2. Weak growth scenario has an annual 4% GDP growth rate and medium variant population growth rate. 3. Policy 1: mix, Policy 2: temporary increased utilization of the lesser-cost resource, and Policy 3: increased use of conventional renewables, and Policy 4: increased use of variable renewables. Policy 2, 3 and 4 take account of environmental tax.

25 Box 5: Generation Capacity in Strong- and Weak- Growth Scenarios by Grid, Indicator Luzon Strong Growth Scenario Electricity Consumption = Gross Generation (MWh) 63,221,648 82,423, ,246, ,527, ,280,174 Peak Demand (MW) 9,886 12,889 16,614 21,350 27,410 Installed Capacity (MW) 13,969 17,753 22,446 28,412 36,047 Blended Generation Charge (P/kWh) Strong Growth Scenario with Policy Strong Growth Scenario with Policy Strong Growth Scenario with Policy Strong Growth Scenario with Policy Weak Growth Scenario with Policy 1 Electricity Consumption = Gross Generation (MWh) 61,571,360 70,401,809 81,160,684 93,744, ,323,568 Peak Demand (MW) 9,628 11,009 12,692 14,659 16,939 Installed Capacity (MW) 13,644 15,384 17,504 19,983 22,855 Blended Generation Charge (P/kWh) Visayas Strong Growth Scenario Electricity Consumption = Gross Generation (MWh) 11,106,506 14,479,738 18,664,880 23,984,519 30,792,463 Peak Demand (MW) 1,837 2,396 3,088 3,968 5,094 Installed Capacity (MW) 2,549 3,252 4,124 5,233 6,652 Blended Generation Charge (P/kWh) Strong Growth Scenario with Policy Strong Growth Scenario with Policy Strong Growth Scenario with Policy Strong Growth Scenario with Policy Weak Growth Scenario with Policy 1 Electricity Consumption = Gross Generation (MWh) 10,816,590 12,367,885 14,257,958 16,468,621 19,029,816 Peak Demand (MW) 1,790 2,046 2,359 2,725 3,148 Installed Capacity (MW) 2,489 2,812 3,206 3,667 4,201 Blended Generation Charge (P/kWh) Mindanao Strong Growth Scenario Electricity Consumption = Gross Generation (MWh) 11,106,506 14,479,738 18,664,880 23,984,519 30,792,463 Peak Demand (MW) 1,761 2,296 2,959 3,803 4,882 Installed Capacity (MW) 2,464 3,138 3,974 5,037 6,397 Blended Generation Charge (P/kWh) Strong Growth Scenario with Policy Strong Growth Scenario with Policy Strong Growth Scenario with Policy Strong Growth Scenario with Policy Weak Growth Scenario with Policy 1 Electricity Consumption = Gross Generation (MWh) 10,816,590 12,367,885 14,257,958 16,468,621 19,029,816 Peak Demand (MW) 1,715 1,961 2,261 2,611 3,017 Installed Capacity (MW) 2,406 2,716 3,094 3,535 4,047 Blended Generation Charge (P/kWh)