Thailand

Main findings

2050 projections of power and industrial high temperature heating sectors:

Projected no-action emissions based on current energy mix: 278.1 MtCO₂e /year⁺
Using only local measures, reaching net zero emissions in the modelled sectors requires a mitigation cost of USD 14.80/tCO₂e

Most important needle mover is onshore wind, coupled with openfield PV and storage technologies (batteries and ammonia)
In international collaboration, green electricity trade with other countries helps reduce mitigation costs to USD 10.70 - USD 14.70/tCO₂e. Onshore wind and ammonia storage in Thailand remain essential to reach zero emissions in collaboration

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

Country context

Country parameters

Socioeconomic indicators	Geographical opportunity/limitation	Fossil fuel dependency
GDP: Current USD 450 billion 2022¹ GDP/capita: Current USD 6278 in 2022²	Maximum technical resource potential/*: PV rooftop: 70 GWp PV openfield: 790 GWp Wind onshore: 410 GWp Wind offshore: 530 GWp	Energy imports: 61.6% of the primary energy supply (2020)³
Population: 7.5 million⁴ Population density: 32 /sq km⁵		Fossil fuel rent: Gas: 0.9 % of GDP⁶ Coal: 0 % of GDP⁷ Oil: 0 % of GDP⁸
Emissions: 364.73 MtCO₂e⁹		Carbon intensity of energy: 0.2 kgCO₂/kWh¹⁰

* 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.¹¹ . 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)¹².

Country Context

Thailand's climate mitigation potential is shaped by its prominent progress in growth and poverty reduction. The country has a high GDP and moderately high GDP per capita compared to its ASEAN counterparts. With its flourishing economy this past decade, Thailand struggles with harmonising climate mitigation policies with its economic development, inequality reduction, and environmental sustainability objectives.¹³

Thailand's emissions per capita are high compared to neighboring countries. Heavy reliance on imported fossil energy, particularly natural gas for its power sector,¹⁴ underlines the need for an energy transition. This is particularly crucial considering the country's vulnerability to fluctuating LNG prices, as reflected in its LNG-fired plant projects.¹⁵Thailand also imports part of its electricity from Lao PDR, with a high reliance on coal-fired power plants¹⁶.

The country’s terrain offers considerable potential for harnessing solar and wind resources. This provides a promising avenue for extending its clean energy infrastructure. The country recently laid out initiatives like Thailand 4.0 and Bio-Circular-Green (BCG) economy model¹⁷ to demonstrate a commitment for a more sustainable future.

Country target and policy

Thailand mitigates its greenhouse gas emissions through multi-level and sectoral strategies and policies. Its NDC sets out to be 20% GHG emission reduction from the projected BAU level by 2030. With adequate and enhanced access to technology development and transfer, financial resources, and capacity building support, the target could increase up to 25%.¹⁸

Thailand hasve also submitted its Long Term Low Greenhouse Gas Development Strategy (LT-LEDS) to the UNFCCC. The LT-LEDS aims to achieve carbon neutrality by 2050 and net-zero greenhouse gas emissions by 2065.¹⁹

The overall strategy extends from its overarching National Strategy, to its climate-focused Long Term Low Greenhouse Gas Development Strategy (LT-LEDS) and Climate Change Master Plan, energy-focused Integrated Energy Blueprint, and specific plans from the Ministry of Transport, Industry, and Agriculture and Cooperatives.^20,21

In pursuit of these plans, Thailand has introduced incentives to promote electric vehicle adoption and manufacturing, encourage renewable energy use, and enhance energy efficiency. More recently, the government has provided targeted fuel and electricity subsidies for low-income households in response to escalating energy costs.²²

While Thailand's plans and policies drive progress toward increased climate mitigation, the challenge is balancing its rising energy demand, fueled by robust economic growth, with its commitment to sustainability.

Learn more about Thailand’s specific policy interventions here.

Outlook mitigation potential 2050 - STEVFNs

Up to 269.8 MtCO₂e/y Technical Potential at no additional cost^{^^}

Up to 269.8 MtCO₂e/y Technical Potential at an additional average mitigation cost^{^} of USD10/MtCO₂e

Up to 278.1 MtCO₂e/y Technical Potential at an additional average mitigation cost^{^} of USD20$/MtCO₂e

Up to 278.1 MtCO₂e/y Technical Potential at an additional average mitigation cost^{^} of USD50/MtCO₂e

Up to 278.1 MtCO₂e/y Geographic potential^#

278.1 MtCO₂e/y Technical potential – domestic

Key elements: Onshore wind, requiring ammonia and battery storage to reach zero emissions

278.1 MtCO₂e/y Technical potential – International collaboration

Key elements: Onshore wind, ammonia and battery storage in Thailand. Green electricity trade

^{^^}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 International Collaboration*

*The following still of the D-PACC shows the bar chart for the annualized cost of the main technologies in the highest mitigation scenario for Thailand in autarky and international collaboration. For higher detail, please see the interactive mitigation potential diagrams when exploring the map.

Modelling results summary:

As Thailand sets increasingly stringent emissions reduction targets for the year 2050, the choice of which technology to use becomes critical.
The least-cost emissions are immediately reduced to less than 25% of those in a no-action scenario, which is achieved through a combination of open field PV and onshore wind, dominated by the latter.
There is a steeper cost increase for the abatement of the final 5% of emissions with respect to emissions in a no-action scenario

Role of industrial heating technologies:

Electric industrial heaters are gradually increased as emissions are reduced, and ammonia high temperature heating is installed to support the abatement of the last 3% of BAU emissions

These completely replace fossil heaters which remain in use until these final emissions are reduced.

Role of Storage technologies:

To be able to achieve zero emissions in the power and industrial heating sectors, it is critical that Thailand invests in storage technologies, mainly ammonia storage. With deployment of some conversion of electricity into ammonia using excess renewable generation when demand has already been met, this longer-term storage provides opportunity for flexibility, further reducing the need for fossil generation.
Some battery storage is also required to abate the last 10% towards zero emissions.

International Collaboration Necessity:

In the absence of international collaboration (autarky), Thailand is able to achieve zero emissions in its power and industry heating sectors.
In international collaboration arrangements, the mitigation cost is reduced through green electricity trade with other countries, ranging from USD 10.70 - USD 14.70/tCO₂e in the collaborations modelled here.
Onshore wind in Thailand remains critical for collaboration emissions reductions, and replacing fossil industrial high temperature heating with electric and ammonia heaters.

Facilitative Policies: Illustrative cases

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

Country-specific notes on Methodology

National and International 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, minimizing the net present value of a 30-year project.

First, a baseline case study for no action was set up for Thailand with its current energy mix, determining the no-action emissions on its own (autarky). From this baseline, a 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 mitigation potential in autarky for Thailand, two additional case studies were created:

A combination of countries in autarky, with maximum total emissions defined as the sum of their individual autarky emissions
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 and for the set of countries without 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 green 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 capital and operational costs were translated from detailed OSeMOSYS modelling where available, and estimated based on literature figures for technologies, including 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 generalized STEVFNs system-of-systems model-generator.

This is done for the following benefits:

Reduce barrier to entry for countries to engage with GMPA.
Leverage on national modeling efforts around the world instead of repeating modelling efforts.
Have a scalable, distributed method of managing and updating national modelling data.
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 Thailand by building a “0th order” OSeMOSYS “starter data kit” using the methodology developed by Climate for Compatible Growth (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

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