Clean Energy Finance and Investment Roadmap of Thailand

Annex C. Methodology for modelling Thailand’s clean energy finance and investment needs

In the process of developing the Clean Energy Finance and Investment Roadmap, The Creagy ran models to estimate the financing and investment required to meet Thailand’s renewable energy and energy efficiency goals. In particular, the models estimated the following elements:

1. the investment and financing needs required for implementing the Alternative Energy Development Plan (AEDP) 2018 and the draft Energy Efficiency Plan (EEP) 2022, the level of financial assistance needed by public and private financing sources, as well as the distribution of investment costs across the supply chain for the selected renewable energy and energy efficiency technologies

2. the economic impact, including employment and value added, that would be generated from the implementation of the AEDP 2018 and the draft EEP 2022.

The following sections outline the estimation methodology and assumptions.

Model on the investment and financing needs for the implementation of the Alternative Energy Development Plan (AEDP) 2018 and the draft Energy Efficiency Plan (EEP) 2022

This model has two main objectives: (i) estimating the investment needed for the implementation of the AEDP 2018 and the draft EEP 2022; and (ii) estimating the level of financial assistance provided by the government (in the form of subsidies, incentives and grants) and by the private sector, to promote clean energy initiatives.

As shown in Figure A C.1, the assessment involved the following steps:

• Step 1: Identifying the annual plan for new renewable energy installations by fuel type, as per AEDP 2018, and the annual plan for promoting energy efficiency improvements by economic sector, according to the draft EEP 2022

• Step 2: Collecting data on capital expenditures (CAPEX) and operating expenditures (OPEX) for each renewable energy technology used for generating electricity, along with their trends over time, as well as the cost of energy efficiency investments by economic sector

• Step 3: Using data from steps 1 and 2, assess the annual magnitude of the investment needed to reach the targets of the AEDP 2018 and the draft EEP 2022

• Step 4: Determining the extent of government support, private equity and private debt required to achieve the AEDP 2018 and the draft EEP 2022 by using the debt-to-equity ratio and the co‑financing ratio at the different technology readiness levels summarised in Figure A C.1.

Figure A C.1. Process in assessing investment and financing needs for implementation of the AEDP 2018 and the draft EEP 2022

Source: Authors

Access to various financial sources is significantly influenced by the level of technological readiness. Lower levels of readiness are associated with higher risks and uncertainties. As technology progresses through readiness levels, private entities exhibit greater confidence, leading to increased reliance on private debt and reduced dependence on government support. Figure A C.2 illustrates the debt-to-equity ratio for projects with different technology readiness levels, based on previous private investments in renewable energy and EE, as well as the co-financing ratio for each financial instrument provided by the government. This co-financing ratio represents the proportion of the total project investment and the total amount of support funded by the government, relative to the shared financed by the private sector. In this modelling, five financial instruments implemented by the government were analysed, namely grants, investment subsidies, performance subsidies, debt and equity financing, and carbon markets.

Figure A C.2. Past financial support provided by the government at the different technology readiness levels

Source: Authors (Creagy)

Data on the co-financing ratios for the five government financial instruments were obtained from various sources, as described in Table A C.1.

Table A C.1. Co-financing ratio for five government financial instruments

Financial Instrument	Government's scheme	Project Activities	Investment Cost (million THB)	Amount of support (million THB)	Co-financing Ratio	Debt-equity ratio
Grant	Grants for R&D	Waste to Energy (MBT, RDF, Pyrolysis, Power Plant, Refinery)	570	570	1.0	N/A
Investment Subsidy	Direct Subsidy (70:30 / 80:20) for energy efficiency investment	20% and 30% subsidy for energy efficiency investments	881	378	3.3	N/A
Performance Subsidy	DSM Bidding	Subsidy per energy savings for 152 projects	2,687	309	8.7	70%
Preferential Debt Financing	EERF	Revolving fund for renewable energy & EE	14,643	6,057	2.4	70%
Preferential Equity Financing	ESCO Revolving Fund	3 x 6.123 MW Solar PV Farm	2,412	111	21.7	70%
Carbon Market	CDM	38-MW Solar PV Farm	4,220	46	91.1	>70%

Sources: (DEDE, 2023[1]; Energy For Environment Foundation, 2011[2]; Asian Power, 2011[3])

Key assumptions on the investment and financing needs for the implementation of the AEDP 2018

Data on the cumulative installed capacity of renewable energy power plants during 2021-2037 was collected from different sources. The data for 2021 was sourced from the DEDE database while the data for 2037 is the target as per the AEDP 2018. The data between 2022 -2036 was obtained from the PDP 2018 (rev.1). Based on Table A C.2, the new installation of renewable energy power plants can be calculated, as illustrated in Table A C.3.

Table A C.2. New installation of renewable energy power plants during 2022-2037 (MW)

RE Type	2022	2023	2024	2025	2026	2027	2028	2029	2030	2031	2032	2033	2034	2035	2036	2037	Total
Large hydro	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
Small hydro	23*	18	-	6	4	4	6	2	4	2	3	3	28	5	1	55	141
Onshore wind	49	90	90	-	-	-	-	-	-	-	130	-	-	300	657	128	1 444
Solar PV	264	130	130				850	1 650	900	2,250	450	1 600		400	390	110	9 124
Floating Solar PV	-	-	-	-	298	50	-	280	300	250	300	438	140	325	100	175	2 680
Biomass	896*	24	18	146*	58*	56*	196*	179*	297	237	17	926	177	492	277	1 074	3 675
Biogas	330	160	100	-	-	-	-	-	-	50	100	150	-	-	50	50	930
Waste	395	100	-	-	-	-	-	-	-	-	-	-	6	15	14	156	586
Total new installations (MW)*	1 037	522	388	6	302	51	856	1 932	1 501	2 789	1 000	3 117	351	1 537	1 489	1 748	18 579

Note: The total capacity includes only the newly installed units, excluding the retired capacity (noted with a *) of 1 554 MW.

Data regarding CAPEX, OPEX and capacity factor for renewable energy technologies was sourced from multiple references, including IRENA’s Renewable Power Generation Costs in 2022 (August, 2023), Ambition to Action’s Domestic Expenditure and Employment Impacts of Power Sector Development in Thailand (A2A, November, 2019), and Clean, Affordable and Secure Energy for Southeast Asia (CASE)’s study on Towards a collective vision of Thai energy transition: National long-term scenarios and socioeconomic implications (November, 2022). Details are described in Table A C.3.

Table A C.3. CAPEX and OPEX for renewable energy power plants

RE Types	CAPEX (THB/kW)				Fixed OPEX (THB/kW)				Capacity Factor (%)
	2022 - 2024	2025 - 2029	2030 - 2036	2037	2022 - 2024	2025 - 2029	2030 - 2036	2037	CASE	A2A	IRENA	AVG
Small hydro	61 469	61 469	61 469	61 469	1 229	1 229	1 229	1 229		37%	56%	47%
Onshore wind	45 152	39 716	33 287	28 557	1 402	1 302	1 202	1 202	24%	23%	25%	24%
Solar PV	38 096	47 187	36 474	37 460	508	451	378	360	20%	14%	21%	18%
Floating Solar PV	38 875	33 232	27 589	27 589	445	366	287	287	20%	14%	21%	18%
Biomass	59 687	57 299	54 912	54 912	1 652	1 585	1 519	1 519	60%	70%	75%	68%
Biogas	59 687	57 299	54 912	54 912	1 652	1 585	1 519	1 519	60%	70%	75%	68%
Waste	59 687	57 299	54 912	54 912	1 652	1 585	1 519	1 519	60%	70%	75%	72%

Notes:

1. CAPEX and OPEX for Small Hydro were sourced from A2A (2019)

2. CAPEX and fixed OPEX for onshore wind, solar PV, and biomass were sourced from CASE (2022).

During 2022-2024, the CAPEX and OPEX of solar PV were estimated from the following sources: 50% solar rooftop, 50% utility solar, and 0% solar with battery. This ratio was adjusted to 37.5% solar rooftop, 37.5% utility solar, and 25% solar with battery from 2025 to 2029, and 25% solar rooftop, 25% utility solar, and 50% solar with battery from 2030 onwards.

3. The CAPEX of floating solar PV is estimated to be 25% higher than utility solar, while its OPEX is estimated to be 14% lower.

4. CAPEX and fixed OPEX for biomass are used for both biogas and waste.

Table A C.4 illustrates the estimated annual investment needed for the implementation of the AEDP 2018 by multiplying CAPEX data from Table A C.3 and new installations from Table A C.2.

Table A C.4. Investments needed for the implementation of the AEDP 2018 (unit: million THB)

RE Type	2022	2023	2024	2025	2026	2027	2028	2029	2030	2031	2032	2033	2034	2035	2036	2037	Total
Small Hydro	-	1.11	-	0.37	0.25	0.25	0.37	0.12	0.25	0.12	0.18	0.18	1.72	0.31	0.06	3.38	8.67
Onshore Wind	2 198	4 064	4 064	-	-	-	-	-	-	-	4 327	-	-	9 986	21 870	3 655	50 163
Solar PV	10 046	4 952	4 952	-	-	-	40 109	77 859	32 826	82 066	16 413	58 358	-	14 590	14 225	4 121	360 517
Floating Solar PV	-	933	-	-	9 903	1 662	-	9 305	8 277	6 897	8 277	12 084	3 862	8 966	2,759	4 828	77 752
Biomass	-	9 550	1 074	-	-	-	-	-	16 309	13 014	933	50 848	9 719	27 017	15 211	58 975	202 650
Biogas	19 673	5 969	5 969	-	-	-	-	-	-	2 746	5 491	8 237	-	-	2 746	2 746	53 575
Waste	23 605	-	-	-	-	-	-	-	-	-	-	-	329	824	769	8 566	34 093
Total investment (million THB)	55 522	25 469	16 059	0.37	9 903	1 662	40 109	87 164	57 412	104 723	35 442	129 527	13 913	61 382	57 578	82 894	778 761

As mentioned previously, the government employs various financial instruments to promote the adoption of renewable energy, depending on the technological readiness level. The investment needed for each readiness level of AEDP 2018 was estimated using the assumptions described in Table A C.5, which outlines the readiness levels of renewable energy technologies for generating electricity. Table A C.6 displays the estimated investment required for each readiness level.

Table A C.5. Extent of renewable energy investments at each readiness level of renewable energy technologies

RE Type	2022-2025				2026-2030				2031-2037
	Demonstration/ pilot	Deployment	Diffusion	Commercial maturity	Demonstration/ pilot	Deployment	Diffusion	Commercial maturity	Demonstration/ pilot	Deployment	Diffusion	Commercial maturity
Small Hydro		50%	50%				50%	50%				100%
Onshore Wind		50%	50%					0%			50%	50%
Solar PV		50%	50%			20%	50%	30%			30%	70%
Floating Solar PV	50%	50%				20%	50%	30%			30%	70%
Biomass			30%			20%	50%	30%			30%	70%
Biogas			30%	70%		0%	0%	0%			30%	70%
Waste			30%	70%		0%	0%	0%			30%	70%

Table A C.6. Amount of renewable energy investments at each readiness level of renewable energy technologies (Unit: million THB)

RE Type	2022-2025				2026-2030				2031-2037
Demonstration/ pilot	Deployment	Diffusion	Commercial maturity	Demonstration/ pilot	Deployment	Diffusion	Commercial maturity	Demonstration/ pilot	Deployment	Diffusion	Commercial maturity
Small Hydro	-	1	1	-	-		1	1	-	-	-	6
Onshore Wind	-	5,163	5,163	-	-				-	-	19,919	19,919
Solar PV	-	9,976	9,976	-	-	30,159	75,397	45,238	-	-	56,932	132,841
Floating Solar PV	466	466	-	-	-	5,829	14,573	8,744	-	-	14,302	33,371
Biomass	-	-	3,187	7,437	-	3,262	8,154	4,893	-	-	52,715	123,002
Biogas	-	-	9,483	22,127	-				-	-	6,589	15,375
Waste	-	-	7,082	16,524	-				-	-	3,146	7,342
Total investments	466	15,605	34,891	46,088	-	39,250	98,125		-	-	153,604	331,856

The amount of government support for renewable energy investments was estimated by mapping the co-financing ratio of each financing instrument (Table A C.1) to the amount of investments allocated based on the technology's readiness level. The remaining investments were then split into private debt and private equity by multiplying it with the debt-equity ratio (Table A C.1) at the specific readiness level of technology. Table A.C.7 shows the proportion of financing from each source for renewable energy investment, based on the data and assumptions above.

Table A C.7. Renewable energy investments during 2022 – 2037 by source of finance (Unit: million THB)

Source of Finance	Investment (million THB)				Share (%)
	2022-2025	2026-2030	2031-2037	Total	2022-2025	2026-2030	2031-2037	Total
Government support	5,331	13,290	16,369	34,990	5.5%	6.8%	3.4%	4.5%
Private debt	68,533	133,895	361,185	563,613	70.6%	68.2%	74.4%	72.4%
Private equity	23,186	49,065	107,906	180,157	23.9%	25%	22.2%	23.1%
Total investment (million THB)	97,051	196,250	485,460	778,761	100%	100%	100%	100%

Key assumptions on the investment and financing needs for the implementation of the draft EEP 2022

Table A C.8 shows the estimated investment cost per ktoe of energy savings for each sector based on data from the different government programmes.

Table A C.8. Estimated investment cost per ktoe of energy savings for each sector

Sector	Government Program	Total Investment (million THB)	Total energy savings (ktoe)	Investment Cost (Million THB/ktoe)
Industrial	DSM Bidding (2019)	2,687.0	79.6	33.76
Commercial	30% Subsidy & 20% Subsidy	1,960.0	26.9	72.86
Residential	Matching Fund	1,008.0	8.5	119.15
Agricultural	Assume the same cost as residential sector			119.15

Source: Source: (Creagy, 2018[4])

Table A C.9. Energy efficiency investments during 2022 – 2037 by sector (Unit: million THB)

Sector	Saving Targets at 2037 (ktoe)	Investment Cost (Million THB/ktoe)	Total Investment Cost during 2022 -2037 (Million THB)
Industrial	12,432	33.76	419,658
Commercial	3,542	72.86	258,079
Residential	1,774	119.15	211,370
Agricultural	710	119.15	84,596
Total energy efficiency investments	18,458		973,703

As mentioned previously, the government employs various financial instruments to promote the adoption of EE, depending on the technological readiness level. The investment needed for each readiness level of the draft EEP 2022 was estimated using the assumptions described in Table A C.10, which outlines the readiness levels of energy efficiency technologies for different sectors. Table A C.11 displays the estimated investment required for each readiness level.

Table A C.10. Extent of energy efficiency investments at each readiness level of energy efficiency technologies

Sector	Demonstration/pilot	Deployment	Diffusion	Commercial maturity	Total
Industrial	15%	15%	20%	50%	100%
Commercial	15%	15%	20%	50%	100%
Residential	15%	15%	20%	50%	100%
Agricultural	15%	15%	20%	50%	100%

Table A C.11. Amount of energy efficiency investments at each readiness level of energy efficiency technologies (Unit: million THB)

Sector	Demonstration/pilot	Deployment	Diffusion	Commercial maturity	Total
Industrial	62,949	62,949	83,932	209,829	419,658
Commercial	38,712	38,712	51,616	129,039	258,079
Residential	31,706	31,706	42,274	105,685	211,370
Agricultural	12,689	12,689	16,919	42,298	84,596
Total	146,055	146,055	194,741	486,851	973,703

The amount of government support for energy efficiency investments was estimated by mapping the co-financing ratio of each financing instrument (Table A C.1) to the amount of investments allocated based on the technology's readiness level. The remaining investments were then split into private debt and private equity by multiplying it with the debt-equity ratio (Table A C.1) at the specific readiness level of technology. Table A.C.12 shows the proportion of financing from each source for energy efficiency investment, based on the data and assumptions above.

Table A C.12. Amount of energy efficiency investments at each readiness level of energy efficiency technologies (Unit: million THB)

Source of Finance	Amount (million THB)	Share (%)
Government support	82,091	8%
Private debt	600,712	62%
Private equity	290,900	30%
Total investment (million THB)	973,703	100%

Model on the economic impacts from the implementation of the AEDP 2018 and the draft EEP 2022

The purpose of this analysis is to evaluate the economic impact, including employment and value added, resulting from the implementation of the AEDP 2018 and the draft EEP 2022.

As shown in Figure A C.2, the assessment method can be illustrated as follows:

• The total jobs generated from the implementation of the AEDP 2018 can be estimated from the sum of manufacturing jobs, construction and installation jobs, and operation and maintenance jobs. The calculation method is shown in Figure A C.3 and the employment factors are listed in Figure A C.5. For the power plant using biomass, waste, the fuel jobs will be added into the total job.

• The total number of jobs created by implementing the draft EEP 2022 was estimated by multiplying the total investment with the employment factor, measured in jobs per one million United States Dollars (USD) of investment. The employment factor of each economic sector is shown in Figure A C.6.

• The value added generated from the implementation of the AEDP 2018 was estimated using the Economic Impact Model for Electricity Supply developed under the Ambition to Action project, funded by the International Climate Initiative.

• The value added generated from the implementation of the draft EEP 2022 was estimated by using input-output matrix and the Leontief inverse to calculate the value added of each sector.

Figure A C.3. Process of assessing the economic impact from the implementation of the AEDP 2018 and the draft EEP 2022

Source: Authors

Key assumptions on the economic impacts from the implementation of the AEDP 2018

The method for estimation of job generated from the implementation of the AEDP 2018 is shown in Figure A C.4. Key assumptions on employment factors and decline factors are described in Table A C.134. The total jobs generated from the implementation of the AEDP 2018 can be estimated from the sum of manufacturing jobs, construction and installation jobs, and operation and maintenance jobs.

Figure A C.4. Method for estimation of job generated from the implementation of the AEDP 2018

Source: (ASEAN Centre for Energy, 2024[5])

Table A C.13. Employment factors

	Employment factors				Decline factors
	Construction times	Construction	Manufacturing	O&M	2020	2021	2022	2023	2024	2025
Technology	Years	Job Years / MW		Jobs / MW
Hydro	2	7.4	3.5	0.2	98%	98%	97%	97%	96%	96%
Geothermal	2	6.8	3.9	0.4	84%	81%	78%	74%	71%	68%
Solar	1	13	6.7	0.7	86%	84%	81%	78%	75%	73%
Wind	1	3.2	4.7	0.3	99%	99%	99%	98%	98%	98%
Regionality factors					2.30	2.26	2.22	2.18	2.14	2.10

Source: (ASEAN Centre for Energy, 2024[5])

The value added generated from the implementation of the AEDP 2018 was estimated using the Economic Impact Model for Electricity Supply developed under the Ambition to Action project, funded by the International Climate Initiative.

Key assumptions on the economic impacts from the implementation of the draft EEP 2022

The job generated from the implementation of the draft EEP 2022 is estimated by using the employment factor, measured in jobs per one million USD of investment as shown in Figure A C.5.

Figure A C.5. Employment factors, measured in jobs per USD one million USD (USD 2015)

Source: Authors based on data from https://ars.els-cdn.com/content/image/1-s2.0-S2215016120301758-fx1_lrg.jpg.

The value added generated from the implementation of the draft EEP 2022 was estimated by using input-output matrix and the Leontief inverse to calculate the value added of each sector. The energy efficiency investments were allocated to each sector using the assumptions as shown in Table A C.13.

Table A C.14. Allocation of energy efficiency investments across economic sectors based on input/output table

Economic Sector in I/O table	Industrial	Commercial	Residential	Agriculture
D16: Wood and products of wood and cork	1.5%	0%	2%	2%
D20T21: Chemicals and pharmaceutical products	2.5%	2.8%	6%	6%
D22: Rubber and plastic products	0.0%	0.5%	0%	0%
D23: Other non-metallic mineral products	1%	2.5%	4%	4%
D25: Fabricated metal products	1%	1.3%	0.0%	0.0%
D26: Computer, electronic and optical products	20%	18%	15%	15%
D27: Electrical equipment	12%	5%	5%	5%
D28: Machinery and equipment, etc	41%	38%	36%	36%
D31T33: Other manufacturing	0%	0%	6%	6%
D41T43: Construction	9%	20%	14%	14%
D64T66: Financial and insurance activities	2%	2%	2%	2%
D69T82: Other business sector services	10%	10%	10%	10%
Total	100%	100%	100%	100%

Source: (Brown, Soni and Li, 2020[6])

Model on the value chain of solar PV and highly efficient cooling systems

The purpose of this analysis is to assess the investment gaps for selected clean technologies including solar PV rooftops and high-efficiency cooling systems.

As shown in Figure A C.6, the assessment process involves the following steps.

• Gather information on investment allocation and the distribution of local content across the supply chain. The distribution of investment cost for solar PV systems and high-efficiency cooling systems across the supply chain as well as its local content are shown in Table A C.14 and Table A C.15 respectively.

• Draw capacity of new installation and investment costs of solar PV systems from Table A C.2 and Table A C.4 respectively and energy efficiency targets and investments for commercial sectors from Table A C.8.

• Estimate the domestic value of investment in the supply chain by multiplying the ratio of local content and investment costs and estimate the investment gaps of each technology by subtracting the domestic value of investment from the total investment.

Figure A C.6. Process in Assessing Value Chain of Solar PV and High-efficiency Cooling Systems

Source: Authors

Table A C.15. Allocation of energy efficiency investments across economic sectors based on input/output table

	Share	Local content
PV module	34%	10%
Inverter	6%	10%
Racking	9%	47%
BOS and grid connection	17%	32%
Installation	12%	63%
Developer	12%	76%
Fees and contingencies	9%	89%
Total	100%	-

Source: (Ambition to Action, 2019[7])

Table A C.16. Allocation of energy efficiency investments across economic sectors based on input/output table

	Raw materials	Manufacturing	Assembling	Engineering	Total
Share	42.25%	22.75%	15%	20%	100%
Local content	0%	43%	100%	100%	-

Source: (KMUTT, n.d.[8])

References

[7] Ambition to Action (2019), Domestic expenditure and employment impacts of power sector development in Thailand, https://ambitiontoaction.net/wp-content/uploads/2019/12/Domestic-expenditure-and-employment-in-the-Thai-power-sector-November-2019.pdf.

[5] ASEAN Centre for Energy (2024), Job Creation Towards Achieving the Regional Renewable Energy Target, https://aseanenergy.org/job-creation-towards-achieving-the-regional-renewable-energy-target/.

[3] Asian Power (2011), ADB lends $140M for Thai solar projects, https://asian-power.com/project/in-focus/adb-lends-140m-thai-solar-projects.

[6] Brown, M., A. Soni and Y. Li (2020), “Estimating employment from energy-efficiency investments”, MethodsX, Vol. 7, p. 100955, https://doi.org/10.1016/j.mex.2020.100955.

[4] Creagy (2018), Partnership for Market Readiness, https://ghgreduction.tgo.or.th/th/pmr-news-and-activities/download/1556/1036/17.html.

[1] DEDE (2023), Thailand’s renewable energy policy and plan, https://www.thailand-energy-academy.org/assets/upload/coursedocument/file/Renewable%20Energy%20Policy.pdf.

[2] Energy For Environment Foundation (2011), ESCO Revolving Fund, http://www.efe.or.th/escofund.php?task=&sessid=&lang=en.

[8] KMUTT (n.d.), Percentages, https://www.thailog.org/wp-content/uploads/2019/01/54-68.pdf.