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Philippines

Main findings

2050 projections of power and industrial high temperature heating sectors:  

  •  Projected no-action emissions based on current energy mix: 185.9 MtCO2e /year+ 

  • Using only local measures, reaching net zero requires an average abatement cost of USD 22.20/tCO2e 

  • Average mitigation cost in the collaborations where the Philippines participates modelled here range from USD 7.80 - USD 22.00/tCO2e 

  • Most important needle mover is battery storage coupled with onshore wind and open field PV. 

+Emissions are the annualized value for a 30-year project starting in 2050 (i.e., divided by 30 from the total project emissions) 

 

Country context

Country parameters

Socioeconomic indicators 

 

Geographical opportunity/limitation 

 

Fossil fuel dependency 

GDP: Current USD 408 billion 20221 

GDP/capita: Current USD 3528 20222 

Maximum resource potential*:  

 

PV rooftop: 90 GWp 

PV open field: 230 GWp 

Wind onshore: 170 GWp 

Wind offshore: 690 GWp 

Energy imports: 56% of the primary energy supply (2020)3  

Population: 115.6 million4 

 

 

Fossil fuel rent: 

Gas: 0.2 % of GDP5 

Coal: 0.1% of GDP6 

Oil: 0 % of GDP7 

 

Emissions: 249 MtCO2e8 

 

Carbon intensity of energy: 0.26 kgCO2/kWh9 

* The technical potential is a first order estimate calculated based on a generalised set of land / ocean area exclusion constraints and technical parameters for each technology. Differences to other literature can occur due to different modelling assumptions.

** The technical potential for renewable energy sources used in STEVFNs including wind onshore, wind offshore, open field and residential rooftop solar PV is estimated by a Python-based simulation pipeline.10 The pipeline applies temporally and spatially-resolved simulation models of the open-source python packages GLAES (Geospatial Land Eligibility for Energy Systems) and RESKit (Renewable Energy Simulation Toolkit)11.

Country Context

The Philippines, given its unique archipelagic nature and susceptibility to the adverse effects of climate change, places significant emphasis on emissions mitigation efforts. The country's per capita emissions are lower than other ASEAN nations, mainly due to its second-largest population – a home to a growing middle class, young growing populace, and large domestic market, which rapidly drive its current economic progress.12 

Currently, the Philippines relies on its large coal-fired power sector and is facing challenges related to the depletion of its natural gas reserves, leading to high electricity prices under the fluctuating global energy market13. The country's geographic attributes endow it with considerable potential for harnessing solar, wind, and geothermal energy resources.14, 15

Overall, to transition its substantial population from a middle-income to a high-income country, the Philippines may push forward climate mitigation actions to ensure the development of not only a strong developed economy, but also low-carbon. 

Country target and policy

The Philippines’ NDC sets out a target GHG emissions reduction and avoidance of 75% for the period 2020-2030 against the projected BAU cumulative emission, of which 2.71% is unconditional and 72.29% is conditional.16 The country has not yet submitted its Long-Term Low-Emissions Development Strategy. 

The Philippines has laid down robust targets and future development strategies, building on its Ambisyon Natin 2040 vision and the overarching Philippines Development Plan. This emphasis is particularly evident in the power sector, as outlined in the Philippine Energy Plan, Power Development Plan, and National Renewable Energy Program. Additionally, the country's mitigation efforts are reinforced by various key policies, including those governing renewable energy auctions, electric vehicle industry incentives, and energy efficiency regulations.16,17  

Phasing out fossil-fuel infrastructure becomes is paramount in propelling the country toward sustainable development in the future.18

Learn more about The Philippines’ specific policy interventions here.  

Outlook mitigation potential 2050 - STEVFNs

Summary: 

Up to 172.7 MtCO2e/y Technical Potential at no additional cost^^ 

Up to 179.3 MtCO2e/y Technical Potential at additional average mitigation cost^ of USD 10/tCO2e 

Up to 179.3 MtCO2e/y Technical Potential at additional average mitigation cost^ o USD 20/tCO2e 

Up to 185.9 MtCO2e/y Technical Potential at additional average mitigation cost^ o USD 50/tCO2e 

Up to 185.9 MtCO2e/y Geographic potential# 

 

185.9 MtCO2e/y Technical potential – domestic 

Key elements: Battery storage with onshore wind with support of open field PV 

185.9 MtCO2e/y Technical potential – International collaboration  

Key elements: Green electricity trade in collaboration with Singapore, Malaysia and Indonesia. High reliance of Indonesia onshore wind to reach zero emissions. Onshore wind sited in Philippines is the most important needle pusher for this four-country collaboration 

^^This refers to a change in the technology mix that would result in the same system cost as the current policy scenario. It does not take into account costs associated with transiting to a different technology mix.  

^This refers to the additional average system cost with reference to the current policy scenario, costs expressed in USD 

#Geographical potential is estimated only for the sectors considered in GMPA. GMPA tries to consider the cheapest and biggest mitigation options/sectors, however other mitigation options/sectors also exist so actual geographical potential is larger. As GMPA adds more sectors, this number will get closer to matching the actual theoretical limit. 

 

Dynamic Pareto Abatement Cost Curves (D-PACC) in Autarky and Collaborations* 

*The following stills of the D-PACC show bar charts for the annualised cost of the main technologies in the highest mitigation scenario for each case study. For higher detail, please see the interactive mitigation potential diagrams when exploring the map. 

 

Modelling results summary: 

  • As Philippines sets increasingly stringent emissions reduction targets for the year 2050, the choice of which technology to use becomes critical. 

  • The replacement of fossil power plants with onshore wind is essential from early emissions reductions, and this technology continues to drive the reductions towards zero along with openfield PV and battery storage.

Role of Electric high temperature heating: 

  • High temperature heating shifts from fossil heaters to electric heaters, which are necessary to reach zero emissions.

Evolving role of Offshore wind: 

  • Offshore wind is an important technology in moderate emissions reductions efforts, but becomes unnecessary in least-cost optimal technology mixes past 30 MtCO2e in emissions, where onshore wind is more beneficial.

  • It would be more beneficial for the Philippines to invest in onshore wind towards a zero emissions goal, rather than invest in a more expensive technology which may be stranded as the emissions budget is further reduced. 

Benefits of International collaboration:

  • In the absence of international collaboration (autarky), Philippines is able to reach zero emissions in its power sector and industrial heating, requiring a mitigation cost of USD 22.20/tCO2e. 

  • In its collaborations in configurations modelled here, the Philippines can reach zero emissions in the modelled sectors at an average mitigation cost ranging from USD 7.80 - USD 22.00/tCO2e

  • The above suggests that there are some collaborations which will bring a marginal (smaller with respect to other configurations) benefit to countries participating, whereas other configurations will have higher impact in mitigation costs.

 

We have included some illustrative case studies of effective policy interventions in particular countries and cities. 

  1. Power
  2. Energy
  3. Industry
  4. Transport
  5. Agriculture

National and Collaboration Modelling (STEVFNs) 

National and international modelling was performed using STEVFNs energy system model generator. Modelling was performed by setting annual emissions limits and finding the cost-optimal technology mix that meets all hourly demands with the emissions constraints. 

This modelling assumes a greenfield model built in 2050 to meet fixed electricity and high-temperature heating demand, miniming the net present value of a 30-year project.  

First, a baseline for no action was set up for Philippines with its current energy mix, determining the no action emissions on its own (autarky). From this baseline, the linear reduction towards zero emissions was determined that would constrain the scenarios. A total of eleven scenarios with emissions constraints ranging from those obtained for no action to zero were run to build the cost-optimal technology mixes shown in the Dynamic Pareto Abatement Cost Curve (D-PACC). 

After determining the total emissions in autarky for Philippines, two additional case studies were created:  

  1. A combination of countries in autarky, with maximum total emissions defined as the sum of their individual autarky emissions 
  2. A combination of countries collaborating (adding electricity and ammonia transport between them), with maximum total emissions defined as the sum of their individual autarky emissions  

Then, the methodology implementing emissions constraints in eleven scenarios  with respect to the sum of the set of countries no action emissions were set up. Results from these then build the D-PACC for collaboration. In some cases, the set will not be able to reach zero emissions in the modelled sectors when modelled independently, as their individual reductions make the problem infeasible at some level of constraint for total emissions. These, however, may reach zero (or at least higher emissions reduction) when energy trade is enabled through electricity and ammonia transport. 

Data 

Electricity and high-temperature heating demand are projected to 2050, following the estimates from high-resolution modelling performed in OSeMOSYS. Technology costs were translated from detailed OSeMOSYS modelling where available, and estimated based on literature figures for technologies such as high-voltage direct current (HVDC) submarine cables. See detailed methodology page for specifics on this data.  

Additional Detailed National Modelling  

In the pilot, additional “detailed national modelling” was performed using OSeMOSYS energy system model-generator. These are supposed to emulate potentially different energy system models currently used by different countries. In future phases of GMPA, countries will be encouraged to share their national models and data. These will be translated to the generalised STEVFNs system-of-systems model-generator.    

This is done for the following benefits:  

  1. Reduce barrier to entry for countries to engage with GMPA. 
  2. Leverage on national modelling efforts around the world instead of repeating modelling efforts.  
  3. Have a scalable, distributed method of managing and updating national modelling data.  
  4. Ensure the input assumptions on GMPA are “owned” by countries so that they are comfortable enough with the results for the results to be useful as a basis to bring parties onto the negotiation table.  

In the pilot, a detailed national modelling was performed for Philippines by building a “0th order” OSeMOSYS “starter data kit” using the methodology developed by CCG that is applied to more than 60 countries around the world. The model determines the least cost optimal technology mix pathway from 2015-2070 to meet all end-use energy demands given some emissions constraints. The sectors included are power, transport, industry, household, and commercial sectors. 

If you would like to see D-PAC curves for detailed national modelling, please contact GMPA. 

Contact

For additional information, please contact the GMPA consortium at info@mitigationatlas.org

References

1.         World Bank. GDP (current US$). World Bank Open Datahttps://data.worldbank.org/indicator/NY.GDP.MKTP.CD (2022).

2.         World Bank. GDP per capita (current US$). World Bank Open Datahttps://data.worldbank.org/indicator/NY.GDP.PCAP.CD (2022).

3.         IRENA. IRENA Energy Profile: Philippines. https://www.irena.org/-/media/Files/IRENA/Agency/Statistics/Statistical_Profiles/Asia/Philippines_Asia_RE_SP.pdf (2022).

4.         World Bank. Population, total. World Bank Open Datahttps://data.worldbank.org/indicator/SP.POP.TOTL (2022).

5.         World Bank. Natural gas rents (% of GDP) | Data. https://data.worldbank.org/indicator/NY.GDP.NGAS.RT.ZS (2022).

6.         World Bank. Coal rents (% of GDP). World Bank Open Datahttps://data.worldbank.org/indicator/NY.GDP.COAL.RT.ZS (2022).

7.         World Bank. Oil rents (% of GDP). World Bank Open Datahttps://data.worldbank.org/indicator/NY.GDP.PETR.RT.ZS (2022).

8.         Gütschow, J. & Pflüger, M. The PRIMAP-hist national historical emissions time series (1750-2022) v2.5. https://doi.org/10.5281/zenodo.10006301 (2023).

9.         Our World in Data. Carbon intensity vs. GDP per capita. Our World in Datahttps://ourworldindata.org/grapher/carbon-intensity-vs-gdp?tab=table (2021).

10.       NewClimate Institute & Climate Analytics. Wind and solar benchmark for 1.5C world: Technical Annex. https://ca1-clm.edcdn.com/assets/Wind-and-solar-benchmarks_Technical-Annex_nov2023.pdf?v=1700559344 (2023).

11.       Ryberg, D. S., Tulemat, Z., Stolten, D. & Robinius, M. Uniformly constrained land eligibility for onshore European wind power. Renewable Energy 146, 921–931 (2020).

12.       WorldBank. Philippines Overview. World Bankhttps://www.worldbank.org/en/country/philippines/overview (2023).

13.       IEEFA. Philippines. IEEFA https://ieefa.org/region/philippines.

14.       Delos Santos, A. S. A. Renewable Energy in the Philippines. (2016).

15.       ASEAN. ASEAN Climate Change Report 2020.pdf. (2020).

16.       IEA. Policy Database – Data & Statistics. IEA https://www.iea.org/policies.

17.       Climate Action Tracker. Philippines. Climate Action Trackerhttps://climateactiontracker.org/countries/philippines/ (2022).