Appendices

Supplementary data

Technological parameter assumptions

Energy supply technologies

Parameter assumptions on power generation technologies (US$ kW^-1). Ranges represent the differences among the regions, which are derived from IEA estimates.

Table 1: Parameter assumptions on power generation

	Initial cost				O&M cost
	2020	2030	2040	2050	2020	2030	2040	2050
Coal-PC	600-2100	600-2100	600-2100	600-2100	20-55	20-55	20-55	20-55
Coal-USC	800-2600	800-2600	800-2600	800-2600	30-80	30-80	30-80	30-80
Coal-IGCC	1100-2900	1000-2750	950-2725	900-2700	50-100	45-95	42-95	40-95
Coal-IGFC	1216-3205	1105-3039	1050-3012	995-2984	56-111	50-106	47-106	45-106
Coal-IGCC CCS	3650-6250	3300-5800	2975-5450	2650-5100	165-220	150-205	135-192	120-180
Coal-IGFC CCS	4034-6908	3647-6411	3288-6024	2929-5637	184-245	167-228	150-214	134-200
Gas-GT	504-990	504-990	504-990	504-990	18-27	18-27	18-27	18-27
Gas-CC	560-1100	560-1100	560-1100	560-1100	20-30	20-30	20-30	20-30
Gas-ACC	648-1272	648-1272	648-1272	648-1272	23-35	23-35	23-35	23-35
Gas-MACC	788-1547	788-1547	788-1547	788-1547	30-45	30-45	30-45	30-45
Gas-GTFC	910-1788	910-1788	910-1788	910-1788	35-52	35-52	35-52	35-52
Gas-CC CCS	2200-3200	1850-2750	1675-2575	1500-2400	70-100	60-85	55-78	50-70
Gas-FC CCS	2111-3070	1775-2639	1607-2471	1439-2303	81-116	70-99	64-90	58-81
Oil	930-1826	930-1826	930-1826	930-1826	35-52	35-52	35-52	35-52
Nuclear	2800-6600	2750-5100	2625-4800	2500-4500	120-225	120-225	120-225	120-225
Hydrogen	1000-1000	1000-1000	1000-1000	1000-1000	25-25	25-25	25-25	25-25
Hydropower-Large	1600-2700	1700-2700	1725-2700	1750-2700	40-65	40-65	40-65	40-65
Hydropower-Small	2000-4050	2100-4050	2125-4050	2150-4050	40-80	40-80	42-80	45-80
Geothermal	2150-3600	2100-3450	2025-3325	1950-3200	45-70	40-70	40-68	40-65
Biomass	1600-2500	1550-2450	1325-2400	1100-2350	55-85	55-85	48-85	40-85
Biomass CCS	4150-5850	3850-5450	3350-5025	2850-4650	145-200	135-190	118-175	100-165
Solar PV centralized	600-1830	380-1250	325-1110	270-970	10-28	10-26	10-25	10-24
Solar PV decentralized	650-3430	410-2220	350-1935	290-1650	10-52	10-46	10-45	10-44
CSP	4900-6500	4200-5750	3725-5125	3250-4500	200-260	170-230	150-205	130-180
Wind-Onshore	1040-3190	1020-2980	1010-2915	1000-2850	26-80	26-76	26-75	26-74
Wind-Offshore fixed	2960-4960	1860-3080	1570-2670	1280-2260	65-130	50-95	45-82	40-70
Wind-Offshore floating	4440-7440	2790-4620	2355-4005	1920-3390	98-195	75-142	68-124	60-105

Table 2: Parameter assumption on production of hydrogen energy carriers

		Initial cost (US$ kW-1)	O&M costs (US$ kW-1)	Conversion efficiency (–)
Hydrogen	Electrolysis	1014	15	69%
	Coal	2670	134	60%
	Coal with CCS	2780	139	58%
	Gas	910	33	76%
	Gas with CCS	1360	41	69%
	Biomass	2333	222	60%
	Biomass with CCS	3091	290	55%
	Imported synthetic methane	910	33	76%
	Imported ammonia 1	306	12	84%
Ammonia 2	Electrolysis	855	13	53%
	Coal	2175	109	48%
	Coal with CCS	2810	141	48%
	Gas	905	23	49%
	Gas with CCS	1260	32	49%
	Biomass	6320	316	41%
	Biomass with CCS	8165	408	41%
Synthetic hydrocarbons	Synthetic fuel	760	30	73%
	Synthetic methane	735	30	77%

Solar and wind power potential

Table 3: Potential for solar and wind power in 2050 (EJ yr^-1)

Region	Centralized PV	Decentralized PV	Offshore wind-fixed	Offshore wind-floating	Onshore wind
ARG	50.84	0.37	4.65	45.51	30.58
AUS	124.95	0.71	16.79	114.88	71.93
BRA	92.87	1.76	9.57	64.96	20.55
CAN	47.03	0.36	9.71	154.17	58.83
CHN	115.05	7.16	14.51	22.47	40.79
IDN	6.27	2.21	7.67	22.87	0.59
IND	28.40	8.18	2.66	25.63	14.83
JPN	0.89	0.84	0.64	60.25	1.10
KOR	0.47	0.35	1.29	13.59	0.58
MEX	28.42	0.97	4.41	27.21	6.54
MYS	0.89	0.35	0.00	0.00	0.00
NZL	2.49	0.11	0.61	64.49	1.63
RUS	78.09	0.80	59.36	186.87	88.76
THA	3.60	0.66	0.00	0.00	0.72
TUR	11.97	0.58	0.06	4.76	3.28
USA	96.40	3.89	10.17	72.75	71.89
VNM	2.46	0.83	4.42	10.66	0.56
XAF	326.07	6.19	3.92	134.12	94.19
XCS	56.04	0.61	2.24	2.65	37.40
XE10	3.58	0.62	1.58	6.28	6.56
XE15	21.07	3.87	18.57	128.01	21.78
XE3	2.68	0.30	0.27	3.34	2.00
XEA	20.17	0.35	1.40	14.62	10.56
XEEI	4.59	0.29	1.44	3.45	10.46
XENI	3.86	0.32	0.09	2.26	1.11
XEWI	2.20	0.14	0.70	65.79	2.34
XLM	64.77	1.88	15.25	294.10	12.40
XME	50.69	2.89	0.66	20.59	12.33
XNF	51.71	1.20	1.50	39.38	22.09
XOC	1.16	0.06	3.26	64.22	0.58
XSA	21.92	2.40	1.13	15.21	3.23
XSE	8.04	1.86	0.20	17.26	1.00
ZAF	29.20	0.27	0.25	39.03	12.18

Table 4: Land suitability factor on centralized solar PV and onshore wind

Land category	Solar PV	Wind
Water bodies	0	0
Evergreen Needleleaf Forests	0	0.02
Evergreen Broadleaf Forests	0	0.02
Deciduous Needleleaf Forests	0	0.02
Deciduous Broadleaf Forests	0	0.02
Mixed Forests	0	0.02
Closed Shrublands	0	0.02
Open Shrublands	0.02	0.2
Woody Savannas	0.02	0.2
Savannas	0.02	0.2
Grasslands	0.03	0.2
Permanent Wetlands	0	0
Croplands	0.01	0.3
Urban and Built-up Lands	0	0
Cropland/Natural Vegetation Mo-saics	0	0.2
Permanent Snow and Ice	0	0
Barren	0.01	0.05

Traditional biomass share

Regression results for traditional biomass share. Region codes are dummy variables for each region. Region codes denote the dummy variables for each region. Numbers in parenthesis indicates t-value. Asterisks indicate p-value ranges at :$p\le 0.001(\ast \ast \ast )$, $p\le 0.01(\ast \ast )$, $p\le 0.05(\ast )$, $p\le 0.1()$.

Table 5: Paramters on trational biomass share estimation

Variable	Coefficient	t-value
Intercept	-2.80	-25.16 (***)
PPPP	-0.05	-9.58 (***)
AUS	3.04	11.84 (***)
BRA	2.75	24.86 (***)
CAN	2.51	11.29 (***)
IDN	4.26	38.04 (***)
IND	4.25	40.44 (***)
JPN	-3.67	-23.59 (***)
KOR	-1.41	-8.33 (***)
MEX	3.03	24.1 (***)
NZL	2.31	4.77 (***)
RUS	-0.47	-3.95 (***)
THA	3.90	26.7 (***)
TUR	2.65	18.34 (***)
USA	2.05	10.06 (***)
VNM	4.17	28.92 (***)
XAF	6.33	57.6 (***)
XE10	2.03	14.22 (***)
XE15	2.47	15.91 (***)
XE3	2.59	13.95 (***)
XENI	1.84	10.12 (***)
XNF	1.37	11.55 (***)
XOC	4.94	15.13 (***)
XSA	4.38	38.54 (***)
XSE	4.68	38.41 (***)
ZAF	3.03	18.64 (***)

Adjusted R² = 0.977

Energy demand technologies

Table 6: Technological parameter assumptions on steel sector

	Initial cost			Lifetime (year)	Energy saving (MJ/*)	Unit (*)
	2010	2030	2050
Coke oven, efficient	16	16	16	30	0	t-coke
Coke oven, high efficient	41	41	41	30	1	t-coke
COG latent heat recovery	8	8	8	30	0	t-coke
CDQ	78	78	78	30	1	t-coke
Sintering furnace, efficient	1	1	1	30	0	t-steel
Sintering furnace, high-efficient	40	40	40	30	1	t-steel
Blast furnace	0	0	0	30	0	t-steel
Blast furnace w/CCS	433	433	433	30	-3	t-steel
BFG recovery	15	15	15	30	4	t-steel
TRT	21	21	21	30	0	t-steel
TRT, more efficient	26	26	26	30	0	t-steel
BOG recovery	2	2	2	30	1	t-steel
BOG latent heat recovery	27	27	27	30	0	t-steel
EAF, scrap pre-heating	7	7	7	30	0	t-steel
EAF, efficient	60	60	60	30	0	t-steel
Continuous casting, efficient	24	24	24	30	1	t-steel
Continuous casting, more efficient	59	59	59	30	1	t-steel
Hydrogen DRI	855	855	855	30	11	t-steel

Note: Cost unit is US$ * ^-1. Asterisk refers to the technology specific unit. Initial costs are represented as additional cost relative to the reference technology, excluding Hydrogen-DRI. For Hydrogen-DRI, energy consumption is shown at the energy saving column. For blast furnace with CCS, energy saving is provided with negative number, as it consumes additional energy for CO₂ capture.

Table 7: Technological parameter assumptions on cement sector

	Initial cost			Lifetime (year)	Energy saving (MJ/*)	Unit (*)
	2010	2030	2050
Vertical mill	5	5	5	30	0	t-cement
SP/NSP	2	2	2	30	3	t-cement
Cement kiln w/CCS	78	78	78	30	1	t-cement
Vertical fuel mill	5	5	5	30	0	t-cement

Note: Cost unit is US$ * ^-1. Asterisk refers to the technology specific unit.

Table 8: Technological parameter assumptions on other industry sector

	Initial cost			Lifetime (year)	Energy efficiency
	2010	2030	2050
Boiler, fossil, existing	140	140	140	20	0.36
Boiler, fossil, Lv1	145	145	145	20	0.40
Boiler, fossil, Lv2	155	155	155	20	0.41
Boiler, fossil, Lv3	165	165	165	20	0.51
Boiler, fossil, Lv4	185	185	185	20	0.55
Boiler, fossil, Lv5	215	215	215	20	0.62
Boiler, biomass	173	173	173	20	0.49
Boiler, electric heating	126	126	126	20	0.90
Industrial HP Lv1	370	370	370	20	3.00
Industrial HP Lv2	370	370	370	20	3.60
Boiler, district heating	173	173	173	20	0.80
Boiler, hydrogen	173	173	173	20	0.70
Boiler, ammonia	173	173	173	20	0.70
Furnace, fossil, existing	204	204	204	30	0.70
Furnace, fossil, advanced	213	213	213	30	0.80
Furnace, fossil, with CCS	229	229	229	30	0.73
Furnace, biomass	163	163	163	30	0.80
Furnace, electricity	154	154	154	30	0.90
Furnace, district heat	163	163	163	30	0.80
Furnace, hydrogen	163	163	163	30	0.80
Furnace, ammonia	163	163	163	30	0.80
Power electronics, electricity, existing	9	9	9	20	0.80
Power electronics, electricity, advanced	14	14	14	20	0.84
Power electronics, electricity, w/ inverter	52	52	52	20	0.99
Power electronics, fossil, existing	9	9	9	20	0.50
Power electronics, biomass, existing	9	9	9	20	0.50
Power electronics, heat, existing	9	9	9	20	0.50
Electrochemical, existing	0	0	0	30	1.00
Other, fossil, existing	0	0	0	30	1.00
Other, biomass, existing	0	0	0	30	1.00
Other, electricity, existing	0	0	0	30	1.00
Other, heat, existing	0	0	0	30	1.00

Note: Cost unit is US$ GJ^-1, which is measured as cost per energy service or useful energy.

Table 9: Technological parameter assumptions on passenger transport sector

	Initial cost			Lifetime (year)	Efficiency (MJ/km)
	2010	2030	2050
LDV, conventional	18	18	18	12	3.5
LDV, new	20	20	20	12	3.6
LDV, high-eff Lv1	21	21	21	12	3.0
LDV, high-eff Lv2	22	22	21	12	2.6
LDV, high-eff Lv3	22	22	22	12	2.5
LDV, HEV	23	23	23	12	2.1
LDV, PHEV	27	23	23	12	1.8
LDV, BEV	56	27	26	12	1.1
LDV, FCEV	43	28	25	12	1.4
Bus, conventional	130	130	130	15	10.1
Bus, new	150	150	150	15	8.9
Bus, high-eff	150	150	150	15	8.0
Bus, HEV	197	180	179	15	7.6
Bus, BEV	630	275	265	15	7.0
Bus, FCEV	272	200	186	15	7.2
Rail, electricity, conventional	14000	14000	14000	20	0.3
Rail, electricity, new	14000	14000	14000	20	0.2
Rail, electricity, high-eff	15000	15000	15000	20	0.2
Rail, other, conventional	14000	14000	14000	20	0.3
Rail, other, new	14000	14000	14000	20	0.2
Rail, other, high-eff	15000	15000	15000	20	0.2
Air, conventional	125000	125000	125000	30	2.6
Air, new	150000	150000	150000	30	2.5
Air, high-eff	190000	200000	213333	30	1.7

Note: Cost unit is 1000 US$ vehicle^-1. Energy efficiency unit for non-road modes is MJ pkm^-1.

Table 10: Technological parameter assumptions on freight transport sector

	Initial cost			Lifetime (year)	Efficiency (MJ/km)
	2010	2030	2050
Small truck, conventional	34	34	34	15	9.6
Small truck, new	37	37	37	15	8.5
Small truck, high-eff	37	37	37	15	8.2
Small truck, HEV	56	49	48	15	6.5
Small truck, NGV	58	58	58	15	8.5
Small truck, BEV	175	69	66	15	3.7
Small truck, FCEV	135	76	64	15	5.7
Large truck, conventional	100	100	100	15	14.0
Large truck, new	110	110	110	15	12.3
Large truck, high-eff	127	127	127	15	12.0
Large truck, NGV	187	187	187	15	12.3
Large truck, HEV	128	114	113	15	9.4
Large truck, BEV	1248	360	335	15	9.0
Large truck, FCEV	404	208	174	15	10.0
Rail, electricity, conventional	14000	14000	14000	20	0.4
Rail, electricity, new	15000	15000	15000	20	0.3
Rail, other, conventional	14000	14000	14000	20	0.4
Ship, existing	8050	8050	8050	20	1.0
Ship, high-eff	22344	22344	22344	20	0.5

Note: Cost unit is 1000 US$ vehicle^-1 for road and rail, 1000 US$ GJ^-1 for ship, respectively. Energy efficiency unit for non-road modes is MJ tkm^-1.

Table 11: Technological parameter assumptions on residential sector

	Initial cost			Lifetime (year)	Efficiency
	2010	2030	2050
Space heating, coal stove	260	260	260	15	0.75
Space heating, oil stove	203	203	203	15	0.75
Space heating, gas stove	182	182	182	15	0.90
Space heating, district heating	350	350	350	15	0.90
Space heating, geothermal	308	308	308	15	1.00
Space heating, air coditioner, conventional	308	273	202	15	3.56
Space heating, air coditioner, high-eff	323	286	212	15	4.20
Space heating, air coditioner, BAT	339	300	222	15	5.22
Space heating, biomass stove	350	350	350	15	0.70
Space cooling, air coditioner, conventional	308	273	202	15	3.56
Space cooling, air coditioner, high-eff	323	286	212	15	4.20
Space cooling, air coditioner, BAT	339	300	222	15	5.22
Water heating, coal, conventional	199	199	199	15	0.70
Water heating, coal, high-eff	317	317	317	15	0.80
Water heating, oil, conventional	119	119	119	15	0.70
Water heating, oil, high-eff	190	190	190	15	0.80
Water heating, gas, conventional	119	119	119	15	0.80
Water heating, gas, high-eff	190	190	190	15	0.90
Water heating, electric heating	79	79	79	15	0.90
Water heating, heat pump, conventional	353	301	214	15	4.00
Water heating, heat pump, efficient	392	334	238	15	5.00
Water heating, heat pump, high-eff	431	367	261	15	5.50
Water heating, solar thermal	696	696	696	15	1.00
Water heating, biomass	292	292	292	15	0.80
Water heating, fuel cell	1908	509	339	15	0.77
Cooking, coal, conventional	849	849	849	15	0.30
Cooking, coal, efficient	1431	1431	1431	15	0.37
Cooking, oil, conventional	564	564	564	15	0.40
Cooking, oil, efficient	951	951	951	15	0.50
Cooking, gas, conventional	564	564	564	15	0.40
Cooking, gas, efficient	951	951	951	15	0.50
Cooking, electric heating	611	611	611	15	0.72
Cooking, IH heater	696	696	696	15	1.19
Cooking, biomass, conventional	849	849	849	15	0.20
Cooking, biomass, efficient	1905	1905	1905	15	0.40
Incandescent lamp	5	5	5	2	1.00
Fluorescent lamp	42	42	42	4	4.50
LED lamp, conventional	105	105	105	8	6.07
LED lamp, efficient	210	210	210	8	10.71
LED lamp, more efficient	263	263	263	8	14.29
Kerosene lamp	5	5	5	2	0.50
Other appliances, conventional	478	478	478	10	0.80
Other appliances, efficient	521	521	521	10	1.00
Other appliances, high-eff	573	573	573	10	1.10
Other appliances, advanced	611	611	611	10	1.20

Note: Cost unit is US$ GJ^-1, which is measured as cost per energy service or useful energy. Efficiency for thermal insulation measures relative efficiency improvement where insulation Lv5 equals 1.

Table 12: Technological parameter assumptions on commercial sector

	Initial cost			Lifetime (year)	Efficiency
	2010	2030	2050
Space heating, coal	240	240	240	15	0.75
Space heating, oil	140	140	140	15	0.75
Space heating, gas	140	140	140	15	0.90
Space heating, district heating	374	374	374	15	0.90
Space heating, geothermal	374	374	374	15	1.00
Space heating, air coditioner, conventional	231	204	151	15	3.56
Space heating, air coditioner, efficient	243	215	159	15	4.20
Space heating, air coditioner, high-eff	254	225	167	15	5.22
Space heating, biomass	525	525	525	15	0.70
Space cooling, gas HP, conventional	490	490	490	15	1.25
Space cooling, gas HP, efficient	490	490	490	15	1.35
Space cooling, air coditioner, conventional	308	273	202	15	3.56
Space cooling, air coditioner, efficient	323	286	212	15	4.20
Space cooling, air coditioner, high-eff	339	300	222	15	5.22
Water heaging, coal, conventional	209	209	209	15	0.71
Water heaging, coal, efficient	275	275	275	15	0.78
Water heaging, oil, conventional	146	146	146	15	0.76
Water heaging, oil, efficient	192	192	192	15	0.87
Water heaging, gas, conventional	146	146	146	15	0.76
Water heaging, gas, efficient	192	192	192	15	0.87
Water heaging, electric heating	112	112	112	15	0.90
Water heaging, heat pump, conventional	441	376	267	15	4.00
Water heaging, heat pump, efficienty	490	418	297	15	5.00
Water heaging, heat pump, high-eff	539	459	327	15	5.50
Water heaging, solar thermal water heater	1403	1403	1403	15	1.00
Water heaging, biomass-fired water heater	385	385	385	15	0.70
Water heaging, fuel cell water heater	1931	515	343	15	0.77
Cooking, coal, conventional	88	88	88	15	0.30
Cooking, coal, efficient	149	149	149	15	0.37
Cooking, oil, conventional	59	59	59	15	0.40
Cooking, oil, efficient	99	99	99	15	0.50
Cooking, gas, conventional	59	59	59	15	0.40
Cooking, gas, efficient	99	99	99	15	0.50
Cooking, electric heating	58	58	58	15	0.70
Cooking, IH heater	61	61	61	15	0.80
Cooking, biomass	88	88	88	15	0.20
Incandescent lamp	5	5	5	2	1.00
fluorescent lamp	42	42	42	4	4.50
LED lamp, conventional	105	105	105	8	6.07
LED lamp, efficient	210	210	210	8	10.71
LED lamp, more efficient	263	263	263	8	14.29
Kerosene lamp	5	5	5	2	0.50
Other appliances, old type	454	454	454	10	0.72
Other appliances, conventional type	478	478	478	10	0.80
Other appliances, efficient type	521	521	521	10	1.00
Other appliances, best available technology	573	573	573	10	1.10
Other appliances, advanced technology	611	611	611	10	1.20

Note: Cost unit is US$ GJ^-1, which is measured as cost per energy service or useful energy. Efficiency for thermal insulation measures relative efficiency improvement where insulation Lv5 equals 1.

Supplementary data on energy service estimation

Industry

Table 13: Regression results for cement production.

Variable	Coefficient	t-value
Intercept	-303.54	-13.29 (***)
LGDPP	228.73	27.2 (***)
AUS	-157.42	-9.02 (***)
CAN	-136.86	-8.05 (***)
CHN	641.77	27.79 (***)
IDN	250.91	11.89 (***)
IND	454.40	16.13 (***)
JPN	109.01	6.24 (***)
KOR	801.22	54.88 (***)
MEX	117.50	7.82 (***)
MYS	419.52	26.52 (***)
NZL	-225.83	-14.18 (***)
RUS	129.00	7.89 (***)
THA	471.79	25.52 (***)
TUR	410.60	26.92 (***)
USA	-245.99	-14.07 (***)
VNM	540.48	18.62 (***)
XAF	369.57	13.7 (***)
XCS	234.81	10.04 (***)
XE10	158.85	10.61 (***)
XE3	260.68	15.69 (***)
XEA	1006.35	25.06 (***)
XEEI	298.84	14.32 (***)
XENI	285.96	13.07 (***)
XEWI	-177.73	-8.83 (***)
XLM	131.12	7.98 (***)
XME	268.67	17.6 (***)
XNF	432.41	21.03 (***)
XOC	184.89	8.5 (***)
XSA	439.23	15.15 (***)
XSE	255.51	11.65 (***)
ZAF	84.73	5.28 (***)

Adjusted R² = 0.949 Region codes are dummy variables for each region. Region codes denote the dummy variables for each region. Numbers in parenthesis indicates t-value. Asterisks indicate p-value ranges at $p\le 0.001(\ast \ast \ast )$, $p\le 0.01(\ast \ast )$, $p\le 0.05(\ast )$, $p\le 0.1()$.

Table 14: Regression results for other industry energy service demand.

Variable	Coefficient	t-value
Intercept	1.99	155.73 (***)
LVA_INDP	0.40	40.76 (***)
AUS	0.56	4.23 (***)
CAN	0.75	4.76 (***)
IDN	-0.43	-10.37 (***)
IND	-0.50	-18.38 (***)
MYS	0.56	4.76 (***)
NZL	0.88	2.96 (**)
RUS	0.77	15.39 (***)
USA	0.53	11.91 (***)
XAF	-1.21	-39.42 (***)
XCS	0.72	9.26 (***)
XE10	0.31	4.48 (***)
XEA	1.78	14.83 (***)
XEEI	0.83	10.49 (***)
XME	0.51	11.39 (***)
XOC	0.68	3.47 (***)
XSA	-0.72	-17.75 (***)
XSE	-1.13	-22.88 (***)
ZAF	0.42	4.85 (***)

Adjusted R² = 0.954 Numbers in parenthesis indicates t-value. Asterisks indicate p-value ranges at $p\le 0.001(\ast \ast \ast )$, $p\le 0.01(\ast \ast )$, $p\le 0.05(\ast )$, $p\le 0.1()$.

Transport

Table 15: Regression results for total passenger transport demand.

Variable	Coefficient	t-value
Intercept	-1.07	-43.22 (***)
LGDPP	0.85	70.44 (***)
AUS	0.92	22.29 (***)
BRA	0.23	6.29 (***)
CAN	0.86	20.69 (***)
IDN	0.40	11.36 (***)
JPN	0.43	10.46 (***)
KOR	0.42	10.96 (***)
MYS	0.56	15.11 (***)
NZL	0.97	24.18 (***)
RUS	0.35	9.1 (***)
THA	0.42	11.71 (***)
TUR	-0.27	-7.32 (***)
USA	1.06	24.65 (***)
VNM	-0.24	-6.59 (***)
XE10	0.40	10.31 (***)
XE15	0.49	11.89 (***)
XE3	0.31	8.26 (***)
XEA	0.41	8.98 (***)
XEEI	0.50	13.7 (***)
XENI	0.19	5.51 (***)
XEWI	-0.72	-16.41 (***)
XLM	0.18	4.99 (***)
XNF	-0.19	-5.36 (***)
XOC	0.34	9.1 (***)
ZAF	0.48	13.37 (***)

Adjusted R² = 0.976 Numbers in parenthesis indicates t-value. Asterisks indicate p-value ranges at $p\le 0.001(\ast \ast \ast )$, $p\le 0.01(\ast \ast )$, $p\le 0.05(\ast )$, $p\le 0.1()$.

Table 16: Regression results for the passenger transport modal share of private cars.

Variable	Coefficient	t-value
Intercept	-0.32	-3.67 (***)
LGDPP	0.97	29.38 (***)
BRA	1.69	19.56 (***)
CAN	2.29	24.1 (***)
CHN	-1.95	-21.14 (***)
IDN	-0.43	-4.81 (***)
IND	-1.95	-19.79 (***)
JPN	-2.04	-21.75 (***)
KOR	-1.47	-16.47 (***)
MEX	3.35	38.32 (***)
MYS	2.24	25.66 (***)
NZL	1.70	18.3 (***)
RUS	-1.44	-15.89 (***)
USA	3.06	31.1 (***)
XAF	2.33	22.81 (***)
XE10	-0.62	-6.79 (***)
XE15	-0.91	-9.52 (***)
XE3	-0.68	-7.6 (***)
XEA	0.69	5.17 (***)
XEEI	-1.47	-16.35 (***)
XENI	1.15	12.95 (***)
XEWI	-0.96	-9.49 (***)
XLM	2.15	24.56 (***)
XME	0.86	9.88 (***)
XNF	-0.76	-8.71 (***)
XSA	-0.87	-8.85 (***)
XSE	0.64	7.07 (***)

Adjusted R² = 0.953 Region codes are dummy variables for each region. Numbers in parenthesis indicates t-value. Asterisks indicate p-value ranges at $p\le 0.001(\ast \ast \ast )$, $p\le 0.01(\ast \ast )$, $p\le 0.05(\ast )$, $p\le 0.1()$.

Table 17: Regression results for the passenger transport modal share of buses.

Variable	Coefficient	t-value
Intercept	0.53	4.15 (***)
LGDPP	0.42	8.82 (***)
AUS	-1.42	-12.68 (***)
BRA	1.87	19.5 (***)
CAN	0.69	6.17 (***)
CHN	-0.73	-6.94 (***)
IDN	1.70	17.05 (***)
IND	-0.71	-6.15 (***)
JPN	-3.05	-27.74 (***)
KOR	-0.77	-7.61 (***)
MEX	3.02	30.95 (***)
MYS	2.08	21.29 (***)
NZL	0.49	4.53 (***)
RUS	-2.39	-23.61 (***)
THA	0.92	9.6 (***)
TUR	0.85	8.7 (***)
USA	1.09	9.26 (***)
VNM	1.54	13.67 (***)
XAF	3.27	26.71 (***)
XCS	-0.86	-8.38 (***)
XE10	-1.10	-10.81 (***)
XE15	-1.97	-17.54 (***)
XE3	-0.97	-9.78 (***)
XEA	1.19	6.89 (***)
XEEI	-1.90	-19.25 (***)
XEWI	-1.75	-14.38 (***)
XLM	2.22	22.72 (***)
XME	0.52	5.32 (***)
XNF	-1.18	-12.04 (***)
XSA	0.69	5.98 (***)
XSE	0.41	3.94 (***)
ZAF	0.32	3.37 (***)

Adjusted R² = 0.929 Region codes are dummy variables for each region. Numbers in parenthesis indicates t-value. Asterisks indicate p-value ranges at $p\le 0.001(\ast \ast \ast )$, $p\le 0.01(\ast \ast )$, $p\le 0.05(\ast )$, $p\le 0.1()$.

Table 18: Regression results for the passenger transport modal share of domestic aviation.

Variable	Coefficient	t-value
Intercept	-1.81	-30.76 (***)
LGDPP	0.55	23.16 (***)
AUS	0.80	7.83 (***)
BRA	1.60	16.72 (***)
CAN	2.69	26.2 (***)
CHN	-0.65	-6.79 (***)
IND	-1.90	-19.42 (***)
JPN	-1.60	-15.67 (***)
KOR	-1.51	-15.3 (***)
MEX	3.51	36.19 (***)
MYS	2.15	22.19 (***)
NZL	2.25	22.2 (***)
RUS	-1.12	-11.16 (***)
THA	-0.44	-4.58 (***)
TUR	-0.58	-5.93 (***)
USA	4.09	39.02 (***)
VNM	0.72	7.42 (***)
XAF	0.83	8.33 (***)
XCS	-1.36	-13.77 (***)
XE10	-2.99	-29.54 (***)
XE15	-1.70	-16.56 (***)
XE3	-3.94	-39.42 (***)
XEEI	-3.23	-32.68 (***)
XLM	1.50	15.76 (***)
XME	0.50	5.16 (***)
XNF	-1.97	-20.73 (***)
XSA	-1.50	-15.32 (***)

Adjusted R² = 0.952 Region codes are dummy variables for each region. Numbers in parenthesis indicates t-value. Asterisks indicate p-value ranges at $p\le 0.001(\ast \ast \ast )$, $p\le 0.01(\ast \ast )$, $p\le 0.05(\ast )$, $p\le 0.1()$.

Table 19: Regression results for the passenger transport modal share of international aviation.

Variable	Coefficient	t-value
Intercept	-2.95	-25.92 (***)
LGDPP	1.09	24.52 (***)
AUS	0.56	5.09 (***)
BRA	1.43	15.1 (***)
CAN	2.18	19.74 (***)
CHN	-0.50	-4.91 (***)
IND	-1.20	-10.74 (***)
JPN	-2.06	-18.98 (***)
MEX	2.66	27.44 (***)
MYS	3.35	34.66 (***)
NZL	2.82	26.48 (***)
RUS	-2.19	-21.68 (***)
THA	1.71	18.04 (***)
TUR	0.63	6.54 (***)
USA	2.20	18.96 (***)
VNM	1.37	12.62 (***)
XAF	3.86	32.95 (***)
XE10	-1.19	-11.72 (***)
XE15	-0.66	-5.92 (***)
XE3	-1.43	-14.4 (***)
XEA	2.40	14.69 (***)
XEEI	-2.99	-30.48 (***)
XENI	1.61	16.56 (***)
XEWI	-0.81	-6.75 (***)
XLM	2.70	28.16 (***)
XME	1.87	19.45 (***)
XSA	1.28	11.49 (***)
XSE	3.41	33.86 (***)
ZAF	0.73	7.68 (***)

Adjusted R² = 0.947 Region codes are dummy variables for each region. Numbers in parenthesis indicates t-value. Asterisks indicate p-value ranges at $p\le 0.001(\ast \ast \ast )$, $p\le 0.01(\ast \ast )$, $p\le 0.05(\ast )$, $p\le 0.1()$.

Table 20: Regression results for total freight transport demand for road and rail.

Variable	Coefficient	t-value
Intercept	-0.84	-13.42 (***)
LGDPP	0.65	31.09 (***)
AUS	0.84	18.32 (***)
BRA	0.33	7.51 (***)
CAN	1.48	32.4 (***)
CHN	0.23	4.38 (***)
IDN	-0.41	-8.47 (***)
IND	-0.34	-6 (***)
JPN	-0.73	-16.15 (***)
MYS	0.39	9.02 (***)
NZL	0.35	7.85 (***)
RUS	1.12	25.28 (***)
THA	0.25	5.57 (***)
TUR	-0.30	-6.94 (***)
USA	0.94	19.79 (***)
VNM	-0.35	-6.35 (***)
XAF	-0.88	-14.54 (***)
XCS	0.47	9.45 (***)
XE10	0.26	5.79 (***)
XEA	0.33	3.93 (***)
XEEI	0.95	20.52 (***)
XENI	-0.56	-11.83 (***)
XEWI	-0.99	-20.19 (***)
XME	0.22	5.13 (***)
XNF	-0.45	-9.8 (***)
XOC	-0.34	-6.06 (***)
XSA	-0.98	-17.06 (***)
XSE	-0.94	-18.69 (***)
ZAF	0.75	16.94 (***)

Adjusted R² = 0.965 Region codes are dummy variables for each region. Numbers in parenthesis indicates t-value. Asterisks indicate p-value ranges at $p\le 0.001(\ast \ast \ast )$, $p\le 0.01(\ast \ast )$, $p\le 0.05(\ast )$, $p\le 0.1()$.

Table 21: Regression results for the freight transport modal share of road transport.

Variable	Coefficient	t-value
Intercept	1.01	10.07 (***)
LGDPP	0.48	14.79 (***)
AUS	-2.56	-42.4 (***)
BRA	-1.05	-18.12 (***)
CAN	-2.91	-48.23 (***)
CHN	-1.96	-26.68 (***)
IDN	1.76	26.47 (***)
IND	-1.41	-16.88 (***)
JPN	-0.32	-5.43 (***)
MEX	-0.89	-15.73 (***)
MYS	2.27	40.13 (***)
NZL	-1.07	-18.17 (***)
RUS	-4.23	-72.9 (***)
THA	1.61	27.17 (***)
USA	-3.11	-48.87 (***)
VNM	1.57	19.49 (***)
XAF	1.03	11.59 (***)
XCS	-3.37	-49.38 (***)
XE10	-2.21	-38.06 (***)
XE15	-1.07	-17.63 (***)
XE3	-2.27	-38.79 (***)
XEA	-1.90	-14.77 (***)
XEEI	-3.75	-60.56 (***)
XENI	-1.70	-25.81 (***)
XEWI	-0.80	-12.14 (***)
XLM	1.06	16.79 (***)
XME	0.90	15.83 (***)
XNF	0.33	5.17 (***)
XSA	1.18	14.12 (***)
XSE	0.89	12.53 (***)
ZAF	-1.84	-31.01 (***)

Adjusted R² = 0.979 Region codes are dummy variables for each region. Numbers in parenthesis indicates t-value. Asterisks indicate p-value ranges at $p\le 0.001(\ast \ast \ast )$, $p\le 0.01(\ast \ast )$, $p\le 0.05(\ast )$, $p\le 0.1()$.

Table 22: Regression results for freight domestic navigation transport demand.

Variable	Coefficient	t-value
Intercept	-3.14	-6.49 (***)
LGDP	0.56	14.96 (***)
AUS	1.82	21.19 (***)
BRA	2.08	20.21 (***)
CAN	2.75	29.99 (***)
CHN	3.53	27.69 (***)
IDN	2.65	28.04 (***)
IND	1.19	10.73 (***)
JPN	2.92	24.26 (***)
KOR	3.47	38.4 (***)
MEX	1.75	17.92 (***)
NZL	1.21	12.81 (***)
RUS	1.97	18.03 (***)
THA	0.41	4.82 (***)
TUR	1.20	13.26 (***)
USA	2.28	14.85 (***)
XAF	0.74	7.71 (***)
XCS	-2.55	-29.69 (***)
XE10	-1.37	-14.08 (***)
XE15	2.60	16.67 (***)
XE3	0.70	8.19 (***)
XEEI	0.48	5.55 (***)
XEWI	2.27	26.67 (***)
XLM	2.12	21.74 (***)
XNF	2.14	23.58 (***)
XOC	1.86	14.4 (***)
XSA	0.88	9.93 (***)
XSE	2.32	27.02 (***)

Adjusted R² = 0.961 Region codes are dummy variables for each region. Numbers in parenthesis indicates t-value. Asterisks indicate p-value ranges at $p\le 0.001(\ast \ast \ast )$, $p\le 0.01(\ast \ast )$, $p\le 0.05(\ast )$, $p\le 0.1()$.

Table 23: Regression results for freight international navigation transport demand.

Variable	Coefficient	t-value
Intercept	-1.35	-5.29 (***)
LGDP	0.58	30.71 (***)
BRA	0.57	7.38 (***)
CHN	1.36	16.12 (***)
IDN	-1.50	-20.02 (***)
IND	-1.16	-14.6 (***)
JPN	0.93	11.34 (***)
KOR	2.03	27.38 (***)
MEX	-0.42	-5.5 (***)
MYS	-1.29	-17.38 (***)
NZL	0.18	2.28 (*)
THA	0.39	5.31 (***)
TUR	-0.84	-11.28 (***)
USA	1.85	19.68 (***)
XE15	2.34	24.6 (***)
XE3	-1.18	-15.46 (***)
XENI	1.55	20.6 (***)
XLM	1.35	17.74 (***)
XME	2.39	30.26 (***)
XNF	0.91	12.2 (***)
XSA	-0.92	-12.51 (***)
XSE	3.36	45.66 (***)
ZAF	1.57	21.29 (***)

Adjusted R² = 0.955 Region codes are dummy variables for each region. Numbers in parenthesis indicates t-value. Asterisks indicate p-value ranges at $p\le 0.001(\ast \ast \ast )$, $p\le 0.01(\ast \ast )$, $p\le 0.05(\ast )$, $p\le 0.1()$.

Buildings

Table 24: Regression results for residential energy service demand.

Service	Variable	Coefficient	t-value	Adjusted R2
Space heating	Intercept	1.29	3.32 (***)	0.866
	LGDPP	0.31	3.13 (**)	NA
	LHDD	1.10	18.68 (***)	NA
	CAN	-0.71	-5.03 (***)	NA
	JPN	-1.55	-15.56 (***)	NA
	KOR	-1.27	-9.53 (***)	NA
	NZL	-1.44	-7.62 (***)	NA
	TUR	-1.26	-8.53 (***)	NA
	USA	-0.72	-7.09 (***)	NA
	XE10	-0.78	-5.07 (***)	NA
	XE15	-0.65	-6.15 (***)	NA
	XEEI	-0.89	-3.68 (***)	NA
	XEWI	-0.75	-4.53 (***)	NA
Space cooling	Intercept	-3.62	-4.45 (***)	0.989
	LGDPP	0.89	4.13 (***)	NA
	LCDD	0.59	9.15 (***)	NA
	CAN	0.81	9.27 (***)	NA
	KOR	-0.77	-8.14 (***)	NA
	NZL	-3.04	-26.85 (***)	NA
	USA	1.23	15.57 (***)	NA
Water heating	Intercept	-1.28	-5.4 (***)	0.884
	LGDPP	0.68	10.31 (***)	NA
	HDD	0.20	16.45 (***)	NA
	KOR	-0.23	-5.11 (***)	NA
	TUR	-0.91	-16.2 (***)	NA
	USA	-0.12	-3.89 (***)	NA
	XE10	-0.22	-3.9 (***)	NA
	XE15	-0.56	-24.31 (***)	NA
	XEWI	-0.78	-8.08 (***)	NA
Other Appliances	Intercept	-0.99	-3.85 (***)	0.970
	LGDPP	0.72	10.64 (***)	NA
	BRA	-0.67	-8.1 (***)	NA
	KOR	-0.42	-8.23 (***)	NA
	TUR	-0.83	-12.67 (***)	NA
	USA	0.44	12.33 (***)	NA
	XE10	-0.58	-8.11 (***)	NA
	XE15	-0.44	-13.67 (***)	NA
	XEWI	-0.50	-5.48 (***)	NA
	XNF	-0.54	-3.81 (***)	NA
Cooking	Intercept	-0.43	-7.22 (***)	0.914
	LPOP	1.13	64.03 (***)	NA
	BRA	0.82	6.57 (***)	NA
	CAN	0.45	4.36 (***)	NA
	XE10	0.59	9.49 (***)	NA
	XEEI	0.77	5.69 (***)	NA
	XNF	1.07	8.93 (***)	NA

Region codes are dummy variables for each region. Numbers in parenthesis indicates t-value. Asterisks indicate p-value ranges at $p\le 0.001(\ast \ast \ast )$, $p\le 0.01(\ast \ast )$, $p\le 0.05(\ast )$, $p\le 0.1()$.

Table 25: Regression results for commercial energy service demand.

Service	Variable	Coefficient	t-value	Adjusted R2
Space heating	Intercept	-2.82	-7.76 (***)	0.861
	LGDPP	1.16	10.59 (***)	NA
	LHDD	0.76	16.52 (***)	NA
	JPN	-0.85	-12.41 (***)	NA
	NZL	-0.36	-5.14 (***)	NA
	USA	-0.64	-6.21 (***)	NA
	XE15	-0.55	-11.55 (***)	NA
	XEWI	-0.59	-6.92 (***)	NA
Space cooling	Intercept	-1.34	-2.97 (**)	0.950
	LGDPP	0.52	4.49 (***)	NA
	LCDD	0.13	3.49 (***)	NA
	KOR	0.71	10.23 (***)	NA
	NZL	-0.81	-14.37 (***)	NA
Other services	Intercept	-1.75	-14.09 (***)	0.835
	LGDPP	1.06	28.51 (***)	NA
	USA	0.42	4.66 (***)	NA
	XE15	-0.46	-11.65 (***)	NA
	XEWI	-0.81	-11.78 (***)	NA

Region codes are dummy variables for each region. Numbers in parenthesis indicates t-value. Asterisks indicate p-value ranges at $p\le 0.001(\ast \ast \ast )$, $p\le 0.01(\ast \ast )$, $p\le 0.05(\ast )$, $p\le 0.1()$.

Footnotes

1. Cost unit is presented in US$/t-NH₃.

2. Cost unit is presented in US$/t-NH₃.