6. Application of AIM/Local Model to India using
Area and Large Point Sources
Manmohan Kapshe1, Amit Garg2, and Priyadarshi R. Shukla1
Summary. India's emissions inventory estimates indicate that Large Point Sources(LPS) contribute above 60% of CO2 and SO2 emissions. Uneven distribution ofenergy resources, unbalanced regional development and the present higheconomic growth has led to emission patterns with dispersed hotspots. The policy
making to address the environmental concerns thus rests on the assessment of
future emissions and the options to mitigate them. The paper shows, using
AIM/Local model with GIS interface, that Indian CO2 emissions shall continue torise steadily till 2030, whereas the SO2 emissions shall decline after 2020, creatinga natural decoupling of Greenhouse Gas (GHG) and local emissions. The carbon
mitigation analysis, under three global policy regimes, indicates substitution ofcoal by gas, besides pushing energy efficient and low carbon technologies. Under
all the scenarios, LPS contribute a major share of emissions, with industrial
centers and large cities growing into major hotspots of emissions. Paper suggeststhat these spots would be the major focus of future emissions mitigation policy
analysis for applications of formal tools like the AIM/Local model.

6.1 Introduction
Industrial development has contributed significantly to economic growth in India
over last few decades; however, industrialization has not been uniform. Large andmodern urban centers coexist with traditional rural and agrarian economy. The
varying sectoral growth rates, consumption patterns and resource endowments
have led to widely different regional and sectoral emission distributions. Some of
the regions have experienced fast industrialization, and increasing air pollution insuch areas is becoming an important environmental issue. Coal based thermal
power plants, steel and cement plants have been major contributors to CO2 andSO2 emissions emanating from fossil fuel consumption. Transport sector is amajor contributor to urban air pollution. Emissions from large industries are
growing at a rate faster than the national average. High concentration of pollution
in India is not due to a lack of sound environmental policy regime, but due to alack of implementation at the local level. The LPS emissions are growing much
faster than the national average due to growing population, increasing
urbanization and higher consumption levels. Therefore, there is a need to estimate

1
2

Indian Institute of Management, Ahmedabad 380015, India
Winrock International India, New Delhi 110057, India

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Kapshe, M. et al.

future emissions both from LPS and from area sources to prepare an
implementation plan for emission mitigation.
This paper presents the analysis of Indian emissions using AIM/Local model.
Indian emissions inventory for carbon dioxide (CO2), methane (CH4), nitrous
oxide (N2O), sulfur dioxide (SO2) and nitrogen oxides (NOX) at sectoral levels for1995 and 2000 shows that LPS contributed to Indian CO2 and SO2 emissions to alarge extent (above 60%) while for the other gases LPS contribution was below
10% (Garg et al. 2002). Therefore, we have projected CO2 and SO2 emissions
temporally and regionally using AIM/Local model at area and LPS levels. 382
LPS and 466 area sources (Indian districts) are covered for the base year 2000.
New LPS are added to reach 457 (2010), 523 (2020) and 587 (2030) based onpresent investment proposals for new plant installations, retrofitting and expansion
options for the existing plants, and policy dynamics. The LPS share in Indian CO2emissions is projected to increase marginally from 64% to 65% in 2030 while thatof SO2 increases from 62% to 70% up to 2020 and decreases marginally to 66% in
2030. The total CO2 emissions in India are projected to increase almost 3.15 times
to 2945 Mt-CO2 in 2030. However SO2 emissions grow only about 1.15 times
during the same period to 5.8 Mt-SO2 in 2030 indicating a disjoint between GHG
and local pollutant emissions in future. This is mainly due to adoption of flue gas
desulfurisation and clean coal technologies in the power sector, and considerable
reduction in sulfur contents in petroleum products. Some of these policy dynamics
are already visible in India. The paper also demonstrates this Indian emission
dynamics and would be useful for policy makers and researchers.

6.2 Methodology
The emission sources may be broadly classified as LPS, area sources and line
sources (mainly for transportation sector). In the present analysis, we havecombined the area and line sources since activity data for transport sector at line
source level is not available. The combined representation is called area sources.
The aggregate national emissions are the sum of LPS and area source emissions.
For future emission projections at LPS and area source levels, AIM/Local modelwith Geographical Information System (GIS) support has been used.
The AIM/Local model follows the approach of linear programming to find an
optimal solution by selecting a combination of technologies with the least cost
while satisfying the given constraints of fulfilling the demand and meeting theenvironmental targets and/or energy supply constraints in the specific region (AIM
Project Team 2002). This model estimates the emissions from the LPS and areasource, which can be used to calculate the total emissions from a region. Emission
calculation methodology used in AIM/Local model is in line with therecommended methodology of the Intergovernmental Panel on Climate Change(IPCC 1996) and follows a similar approach as used by (Li et al. 1999) and (Garget al. 2001a, 2001b). A detailed description of the emission estimation
methodology used in AIM/Local model is given in the Appendix.

6. Application of AIM/Local Model to India

95

Indian AIM/Local model is developed for five major sectors namely power
generation, industrial, transport, residential and agriculture sectors. The choice of
these sectors is based on their importance in the national energy consumption. The
industrial sector covers fifteen major industry types. Each sector is modeled withconsiderable technological details about consumption of different energy forms,
emission of various gases, cost components, and technological shares. Powergeneration and industrial sectors provide all the LPS for the analysis whereas theother sectors have been modeled as area sources.
The AIM/Local model, suitable for estimating future emissions from LPS andarea sources, is demand driven. The enduse sectoral demands in turn depend uponnational macro-economic growth projections. Based on the last 30-year time series
GDP data, government projections and expert opinion, the Indian GDP is assumed
to grow in real terms by 6% per annum on an average during 2000-2015, by 5%during 2015-2025, by 4% during 2025-2035 and by 3% in the later half of the 21st
century under the reference scenario. The Indian GDP grows 4.8 times at anannual rate of 5.2% and population rises from the present 1 billion to 1.35 billion
between the years 2000 to 2030 (UN 1998) indicating a four-fold increase in percapita income levels.
The industrial enduse sector demands saturate in the long run following alogistic model. This is divided into LPS and area demand. Excess demand over
and above the capacity of the LPS has been taken as area source demand in case ofindustries where the total estimated national demand exceeded the total capacityof all the LPS considered. The autonomous energy efficiency improvements(AEEI) in enduse technologies supplying these demands capture improvements
due to better management practices, learning curve, improved infrastructure,
retrofitting for the existing demand technologies and incremental technologicalinterventions.
The AIM/Local model also captures the present policy dynamics to reduce
anthropogenic air pollution by various measures including fuel quality
improvements, adopting cleaner technologies and stricter enforcement of emission
regulations. Fuel quality improvement includes coal beneficiation, increased useof imported coal (lower ash and higher calorific value than the average Indiancoal), and reduction in sulfur contents of petroleum products.
Production quantity for an LPS has been estimated on the basis of the sectoral
demand elasticity and the past production trends of the plant, wherever available.
Information regarding the new LPS till year 2010 has been taken from various
data sources like CMIE (2002b) and policy documents of the government. New
LPS locations beyond the year 2010 have been estimated based on pastdevelopment trends, retrofitting expansion options for the existing plants, studies
related to suitability of industrial locations in India, and present policy dynamics.
These are however indicative and not conclusive since the actual LPS locations
may vary as future unfolds.

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6.3 Data Sources and Coverage
Many diverse data sources were utilized since there is no comprehensive database
covering all the types of emitters for India. These included published documentsof the Government of India, state governments, government organizations andinstitutions, industry federations and autonomous organizations covering various
sectors and fuels (Garg et al. 2001a, 2001b, 2002). Future LPS data was mainly
taken from published reports and databases like CMIE (2002a, 2002b). These
provide status information of the various planned investment projects in India(power, refineries, cement, steel and fertilizer plants, etc.) till almost 2010.
Coupled with the retrofitting and capacity augmentation options for the existing
plants, present policy directives of the government and expert opinion, LPS
information for the next 30 years was assimilated. We have tried to cross verify
each existing LPS data using more than one data source providing a profoundrichness and robustness to the base data. We have projected CO2 and SO2
emissions in the present paper since LPS have a dominant share only for these twoemission types. Table 1 provides the LPS coverage over the years.

6.4 Results
6.4.1 Reference scenario
The reference scenario assumes the dynamics-as-usual, i.e. continuation of macroeconomic (including structural changes in the economy), demographic and energy
sector trends (such as autonomous energy efficiency improvements and penetraTable 1. Large point source coverage over the years for India
SectorSub-sectors

LPS covered
2010
2020

2030

82
12
10
85
31
33
28
19
63
3
8
5
3

111
17
16
98
41
38
28
21
64
4
9
6
4

131
20
22
110
52
43
29
23
66
5
10
7
5

150
23
28
123
62
48
30
26
68
5
11
8
5

382

457

523

587

2000
EnergyIndustrial
processes

Total

Power (coal & oil)Power (natural gas)
Steel
CementFertilizer
Paper
Sugar
Caustic soda
H2SO4 manufacturing
Aluminium
Copper ore smelting
Lead ore smelting
Zinc ore smelting6. Application of AIM/Local Model to India

97

tion of clean and renewable fuels and technologies), as well as government policy
trends. There are no direct climate change policy interventions in the reference
scenario.
The reference scenario analysis shows that during 2000-2030, energy use willgrow three times and carbon emissions from energy will grow 2.7 times (Fig. 1).
Under the dynamics-as-usual, the energy system shall continue to depend on fossil
fuels, primarily the domestic coal (Fig. 2). Between years 2000 and 2030, carbon
intensity of GDP declines at 1.9% per year (Fig. 3). The improvement in carbon
intensity is contributed mainly by the decline in energy intensity at 1.5% annual
rate and the rest is contributed by the substitution of the share of coal in energy by
gas and marginally by renewable energy. Future emissions of CO2 and SO2 for thereference scenario are shown in Table 2. Although CO2 emissions grow annuallyat 3.4%, the SO2 emissions rise at much lower rates. This is due to policies that are
already under implementation as well as increasing public pressure on national
policy makers for local pollutants, which is not the case for the GHG emissions.
The CO2 and SO2 emission trajectories move in closer bands till 2010 due tocontinuation of existing vintages. Thereafter, while CO2 emissions continue torise, the SO2 emissions begin to decline following the Kuznets curve phenomenon(Kuznets 1958). The GHG and local pollutant emissions are thus decoupled infuture. SO2 emission reduction happens from mandatory use of Flue Gas
Desulfurisation (FGD) in large coal power plants, introduction of low sulfur
diesel, washing of coal and stricter enforcement of local air quality regulations.
These policies however do not affect the GHG emissions.
20
18
EnergyCarbonElectricity

GDP

16
14
Index

12
10
8
Past trends

6
4
Future projections

2
0
1975

1980

1985

1990

1995

2000

2005

2010

2015

2020

Year

Fig. 1. Energy, carbon, electricity and GDP (reference scenario projections)

2025

2030

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Kapshe, M. et al.

60
50

Coal
Natural gas
Renewables
Biomass

Oil
Hydro
Nuclear

2010

2015

Exa Joule

40
30
20
10
0
2000

2005

2020

2025

2030

Year
Fig. 2. Energy demand from 2000-2030 (reference scenario projections)

1.8
1.6
1.4

Electricity

Index

1.2
CarbonEnergy1
0.8
0.6
Past trends

Future projections

0.4
0.2
0
1975

1980

1985

1990

1995

2000

2005

2010

2015

2020

Year

Fig. 3. GDP intensities of energy, electricity and carbon (reference scenario)

2025

2030

6. Application of AIM/Local Model to India

99

There is an interesting emission dynamics evolving in India and transport sector
is at the center of this development. The sulfur content in the diesel oil supplied tothe metropolitan cities (Delhi, Mumbai, Chennai and Kolkata) has been decreased
during the year 2000 from 1% sulfur by weight to 0.25% by the Indian refineries
as per the Indian government directives. The sulfur content has been further
reduced to 0.05% by weight in Delhi by late 2001. This has resulted in an
appreciable decrease in SO2 emissions from the transport sector in these cities.
These four cities account for almost 8% of all India diesel consumption in 2000.
Diesel, in turn is almost 40% of the national petroleum product consumption andtransport sector accounts for almost three fourths of this consumption. Although
the effect of this emission dynamics may not be felt in the overall national SO2
emissions, which have continued to rise, the long-term implications are appreciable.
Besides these there has been recent Supreme Court judgment that has orderedEuro II standards to be followed for all new cars in India, which will be upgradedto Euro IV by 2005 (Mashelkar et al. 2002). While this is not very significant forSO2 control, it shows the mind-set of the judiciary and policymakers to control
local pollution. Moreover it is necessary to have low sulfur diesel for meeting theemission norms beyond Euro II. The reduction in SO2 emissions would be further
strengthened by another recent policy decision on mandatory washing of coal thatis used 700 Km away from the mine mouth. This measure is aimed at reducing fly
ash and also simultaneously reduces some sulfur. Since, over a third of coal isused beyond 700 Km, this measure is expected to start reducing SO2 from coal use
in near future. This policy dynamics manifests in a reduction in SO2 emissions infuture even though the absolute energy consumption, and therefore CO2
emissions, continue to rise.
Coal remains the mainstay of the Indian energy system but its use becomescleaner due to higher penetration of clean coal technologies (WB 1997). Coal
consumption increases about 2.5 times during 2000-2030 from 310 Mt in 2000.
About 90% of the Indian coal product consumption is by LPS for power
generation and industry. Residential coal consumption (area sources) for cooking
purpose is mainly limited to lower middle class households in semi-urban areas.
The urban households normally use LPG and kerosene, while fuel wood, dung
cakes and electricity are also consumed in small proportions. Commercial
establishments like hotels and restaurants consume some coal for cooking but theirconsumption is miniscule in comparison to the LPS coal consumption. Biomass
supplies the rural energy demand to a large extent with kerosene supplementing it
partially.
The sectoral fuel consumption indicates continued dominance of power sector
in coal use and transport in petroleum products with each having 70 percent sharein 2030. Transport sector coal consumption is negligible for future years due tophasing out of coal based steam traction from Indian Railways. LPS dominate
power sector consumption while transport sector has area source dominance.
Power sector share in natural gas consumption increases to more than half fromthe present one third, caused by increasing competitiveness of Combined Cycle
Gas Turbine technologies (CCGT) for electricity generation (Shukla et al. 1999).

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Gas consumption is also LPS dominated and rises rapidly in industries likefertilizers and petro-chemicals. While the share of gas in primary energy still
remains low, the trends suggest a rising penetration of gas, most of which would
have to be imported.
The distribution range of LPS emission in the total national emissions isindicated in Table 2. The largest 50 LPS contribute almost 50% of all India
emissions in 2000, which decreases to 41% in 2030 as the emissions from smaller
LPS increase (Table 3). The CO2 and SO2 emissions from LPS and area sources
over the years for reference case are illustrated in Fig. 4 and Fig. 5 respectively.
Table 2. Share of LPS emission in all India emissions
Emission details
CO2

SO2

Number of LPS
LPS emissions (Mt-CO2)
All India emissions (Mt-CO2)
LPS/total (%)
Number of LPS
LPS emissions (Mt-SO2)
All India emissions (Mt-SO2)
LPS/total (%)

2000

2010

2020

2030

303
630
983
64
368
3.12
5.02
62

374
989
1556
64
437
3.96
5.87
67

435
1418
2189
65
499
4.35
6.25
70

495
1912
2945
65
559
3.83
5.77
66

Table 3. Distribution range of LPS emissions (% of all India emissions)
CO2 emissions

SO2 emissions

Largest LPS

2000

2010

2020

2030

2000

2010

2020

2030

1 to 25
26 to 50
51 to 75
76 to 100
101 to 200
All others

35.2
11.5
5.7
3.7
6.7
1.3

32.5
11.0
5.4
3.9
7.8
2.7

31.0
10.9
5.7
4.1
8.6
3.0

31.5
11.0
5.6
4.2
8.7
3.9

37.1
12.0
5.2
3.1
4.2
0.6

38.0
12.8
5.6
3.5
5.8
1.8

37.3
13.0
6.0
4.0
7.2
2.2

32.5
12.4
5.9
3.9
8.2
3.4

Table 4. LPS contribution to CO2 energy sector emission (% of all India emissions)
SectorPower
Steel
CementFertilizer
Paper
Aluminum

2000

2010

2020

2030

41.3
12.4
9.0
0.7
0.4
0.1

41.5
11.3
9.6
0.3
0.5
0.2

43.0
10.2
8.8
0.2
0.5
0.2

48.0
8.0
7.5
0.2
0.5
0.3

6. Application of AIM/Local Model to India

101

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Kapshe, M. et al.

2000

2030

Million2 Ton Mt-CO
Million
Mt-CO
2 Ton

15

< 3
3-6
6-9
9 - 12
12 -1515 - 1820

18 - 21

40

> 21

0
5
10

Fig. 4. Regional distribution of CO2 emissions in India for 2000 and 2030 in reference
scenario (see color plates)
Note: Circles show emissions from large point sources.

6. Application of AIM/Local Model to India

103

2000

2030

Million 2Ton Mt-SO
Million
Mt-SO
2 Ton

0.11

< 0.010.01- 0.017
0.017- 0.026
0.026- 0.035
0.035- 0.044
0.044- 0.053

0 .15

0.053- 0.060

0.20> 0.060

0
0.03
0.07

Fig. 5. Regional distribution of SO2 emissions in India for 2000 and 2030 in reference
scenario (see color plates)
Note: Circles show emissions from large point sources.

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Kapshe, M. et al.

Table 5. LPS contribution to SO2 energy sector emission (% of all India emissions)
SectorPower
Steel
CementFertilizer
Paper
Aluminum

2000

2010

2020

2030

44.5
11.6
4.7
0.5
0.5
0.3

47.1
13.1
6.1
0.2
0.7
0.3

46.5
14.7
7.1
0.1
0.9
0.4

40.0
16.0
8.4
0.1
1.2
0.5

The sectoral shares (Tables 4 and 5) indicate that LPS emissions would
continue to be major contributors to national CO2 and SO2 emissions in future.
The share of LPS in SO2 emissions decreases marginally in the later years ascompared to CO2 emissions. This is because the SO2 emissions from LPS as well
as some area sources such as transport sector reduce considerably but theemissions from the small-scale industries like brick making, which depend on
coal, continue to grow. These small establishments though essentially point
sources are spread all over and have been classified as area source in the model.
This reduction of LPS share in SO2 emissions further indicates that the disjoint
between the GHG and local pollutant emissions, discussed earlier, is unfolding
gradually in India.
6.4.2 Climate change mitigation scenarios
India does not have any GHG emission mitigation commitments presently.
However rising emission trends necessitate an understanding and analysis ofcarbon mitigation policy options for India. These policies prompt technology andfuel substitution by changing the relative costs of competing fuels in favor of
those with lower carbon contents. Carbon mitigation would therefore require
technological transformation of Indian energy system. Efficiency improvement
measures, cleaner technology and cleaner fuels will penetrate faster andinfrastructure to facilitate these has to be built. This will need investments ininfrastructure to support the new technologies and reforms to remove the barriers
for their penetration. Societies with strong reduction commitments would
therefore have to start thinking differently.
The reference case provides a platform for analyzing implications of alternate
carbon mitigation scenarios for India. The present analysis considers a reference
scenario that assumes a world that is akin to the B2 scenario of Special Report on
Emissions Scenarios (SRES) (IPCC 2000). Climate intervention scenarios are thenconsidered that presume alternate Kyoto-plus regimes aimed at stabilizing longrun concentrations at 550, 650 and 750 parts per million volume (ppmv) of CO2
concentrations. The global emission budgets are as per SRES and have been
allocated between world regions, with around 7.5% share of global carbon

6. Application of AIM/Local Model to India

105

emissions as the cumulative emission budgets for India during the 21st century.
The present analysis is for the period 2000-2030.
Some important results are indicated in Table 6 and Fig. 6. Inertia of existing
technological stock in the energy system prevents significant fuel substitution till theyear 2010 for meeting carbon mitigation targets and coal continues to dominate theenergy system. However coal consumption declines drastically in the long-term for
550 ppmv emission targets. Decline in gas consumption in medium and long term inthis scenario is due to increasing penetration of carbon free technologies likerenewable and nuclear for power generation. However coal is mainly substituted bynatural gas in power sector. While natural gas exhibits early penetration, renewablepenetration is late due to their high initial investment costs. Due to the rising gas
prices with higher use of gas in later years, the renewable energy penetration
increases with rising mitigation targets.
Table 6. Results of climate change policy scenarios
Parameter
Coal #Gas #CO2 (Mt-CO2)
SO2 (Mt-SO2)

750 ppmv
2010 2020 2030
-2
-5
-18
+1
+3
+13
1408 1907 2431
5.82 6.13
5.43

650 ppmv
2010 2020 2030
-4
-17
-32
+1
+10
+13
1327 1775 2229
5.67 5.39
4.87

550 ppmv
2010 2020 2030
-9
-29
-51
+1
+18
-9
1254 1621 1998
5.42
4.79
3.81

# Percentage changes over the reference scenario consumption.

3000
Reference case
750ppmv
650ppmv
550ppmv

CO 2 emissions (Mt-CO 2)

2500
2000
1500
1000
500
0
2000

2005

2010

2015

2020

Year

Fig. 6. Carbon dioxide stabilization scenarios (2000-2030)

2025

2030

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Kapshe, M. et al.

Coal
Power
Plant
Coal
power
plant

Gas
GasPower
powerPlant
plant

Reference
ReferenceScenario
scenario

550
ppmv
550
ppmvScenario
scenario

Million
Mt-COTon
2
< 3
3–6
6–9
9 – 12
12 – 15
15 – 18
18 – 21> 21

Fig. 7. Comparison of CO2 emissions from large power plants in reference and 550 ppmv
stabilization scenarios in 2030 (see color plates)6. Application of AIM/Local Model to India

107

Table 7. Supply and demand side contributions to emission reductions (%)
Mitigation scenario
750 ppmv
650 ppmv
550 ppmv

Supply side
Demand side
Supply side
Demand side
Supply side
Demand side

2010

2020

2030

32
68
36
64
40
60

53
47
56
44
63
37

71
29
72
28
75
25

Analysis of carbon mitigation scenarios provides some useful insights regarding
energy supply and demand side contributions to emissions reductions (Table 7). In
the early periods (up to 2010), the demand sectors show more flexibility than the
supply side by contributing more to carbon emission reduction since endusetechnological stock turnover is faster due to their relatively shorter lifetimes. But
in later periods, the supply side contribution increases, finally reaching almost athree-fourth share in the total mitigation in 2030. These, however, include
emission reduction due to lower power generation requirements as demand side
energy efficiencies improve.
The inertia of supply side technology turnover is due to their much longer
lifetimes, high investment requirements and longer gestation period forinfrastructure development. The supply side mitigation options are primarily
associated with fuel switching from coal to natural gas. Since India is not endowedwith much of natural gas resources, most of this gas would be imported andtherefore has national energy security issues attached. Long-term options include
penetration of carbon-free technologies like nuclear and renewable technologies.
Judged from a market perspective, there are large inefficiencies on the demandside due to capital shortages, risk, high transaction costs and weak financial
markets. The supply side is better organized and has lower inefficiencies. This
dynamics is demonstrated by the spread of power sector LPS emissions in future(Fig. 7). The fuel switching from coal to gas, especially in the coastal areas and in
the demand centers of northern India is apparent. There are more gas-based power
plants in the 550 ppmv case as compared to the reference case. Gas based power
plants contribute 15% of power sector CO2 emissions in 2030 in the reference casewhile it almost doubles in 550 ppmv case despite the fact that the emissions froman equivalent gas based power plant are 40% lower than a coal plant (Garg andShukla 2002). SO2 reduction is even stronger in the 550 ppmv case since gas emits
no sulfur.
Carbon mitigation also acts as a push policy for cleaner technologies especiallyin the industrial sectors. Autonomous energy efficiency improvements provide theinitial options for carbon mitigation in India since the average energy efficiencies
in most sectors are well below international averages. Improved oxygen furnacewith gas recovery replaces existing open hearth furnace for steel production,
improved dry process with pre-heaters and pre-calcinators replaces/augments theexisting wet/dry process in cement industry, vertical shaft and high draught kilns

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Kapshe, M. et al.

replace clamp type and Bull’s trench kilns in brick industry, CNG and electric cars
partially replace diesel and petrol cars etc.

6.5 Conclusions
Analysis of LPS emissions highlights sectors and plants where mitigation efforts
should be targeted for cost-effectiveness. The main contributors to Indianemissions presently are about 70 LPS (50 power, 5 steel and 15 cement plants),
thus offering a good opportunity for focusing mitigation efforts. Power sector isthe predominant emission source for CO2 and SO2. Operational improvements(like heat rate reduction, excess air control etc.), better maintenance, reducing
transmission and distribution losses in the power sector would go a long way inemissions mitigation. The other policy options are switching from coal to lower
carbon content fuels like natural gas, sequestering the emitted carbon from LPS,
increasing renewable and nuclear technology penetration etc. Carbon emission
mitigation analysis under 750, 650 and 550 ppmv scenarios demonstrate this fuelshift.
Energy efficiency improvement measures in other sectors like steel, cement,
caustic soda, sugar, brick making and fertilizer would improve productivity while
reducing overall emissions. Demand side sectors contribute more to carbon
emission mitigation in the short run while supply side (mainly power generation)
contributes more in the long run. The longer life, higher investment requirements
and longer gestation periods of supply side technologies as compared to thedemand side explain this.
Apart from LPS, there are many small, moving and concentrated point sources
like vehicles in urban transport. These are covered under area sources in thepresent analysis. Mitigation options have to be carefully planned for these sources
since some of the above mentioned policy options, though useful for small but
distributed sources as well, would not be cost-effective for them since
implementation efforts would be substantial. For example, although transport
sector sources contribute around one tenth to India’s CO2 equivalent GHG
emissions and almost a third to NOX emissions, their characteristics (large
numbers and low emissions per source) necessitate huge mitigation efforts ascompared to very focused mitigation efforts for LPS. However measures like
improving diesel and gasoline quality and stricter vehicle emission norms will
reduce local pollution levels and to a certain extent GHG emissions as well.
Transport sector emissions from Delhi are an interesting case for such analysis
where introduction of low sulfur diesel in recent years reduced SO2 emissions
significantly. Subsequent shift to mandatory use of Compressed Natural Gas(CNG) in public vehicles further reduced sulfur and particulate emissions, besides
reducing carbon emissions. These policies, introduced by the Indian Supreme
Court orders, are not necessarily cost-effective. A superior mix of policies would
minimize long-term cost for achieving the desired air quality standards at city

6. Application of AIM/Local Model to India

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level. The AIM/Local model is well suited to analyze future emission from all
sectors and to derive such policies.
The results from our study have demonstrated that the LPS would continue tobe responsible for considerable part of the Indian carbon emissions. Powergeneration and industry would contribute almost 75% of CO2 emissions. Since
these sectors have a natural dominance of LPS, mitigating emissions from LPS
would become even more important in future. However, Indian GHG and local
pollutant emission trajectories have a disjoint in future as SO2 emissions decline
due to deeper penetration of FGD in power sector, improved fuel quality resulting
in lower sulfur and ash contents, stricter enforcement of emission regulations andefficiency improvements. Although GHG and local pollutant emission mitigation
targets for a country are often useful as overall policy targets, the marginal
mitigation cost for achieving each target varies across regions and sectors. The
LPS analysis contributes to effectiveness of emissions mitigation by indicating thelocations and sectors where controls can lead to maximum benefits. The present
work is a step in this direction for India.

References
AIM Project Team (2002) AIM-Local: a user's guide. AIM interim paper, IP-02-01.
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Garg A, Ghosh D, Shukla PR (2001c) Integrated energy and environment modelling andanalysis for India. OPSEARCH of India 38
Garg A, Kapshe M, Shukla PR, Ghosh D (2002) Large point source (LPS) emissions from
India: regional and sectoral analysis. Atmospheric Environment 36: 213-224
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Singh DV, Singhal S (2002) Report of the expert committee on auto fuel policy.
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111

Appendix: AIM/Local Modeling Approach
The AIM/Local model, suitable for estimating future emissions from large point
sources and area sources, is demand driven. The model uses linear programming
approach to arrive at the optimal combination of technologies with least cost to
satisfy the service demand while meeting the environmental targets and / orenergy supply constraints in a specific region. It calculates emissions from LPS
and area sources separately. The total emissions from a region are then obtainedby summing up the LPS and area source emissions.
Emissions from large point sources (LPS)The calculation of emissions from energy combustion may be done at threedifferent levels referred to as tiers 1, 2 and 3 in the IPCC Guidelines (IPCC 1996).
Tier-1 methodology, concentrates on estimating the emissions from the carbon
content of energy kind supplied to the country as a whole or to the main energy
combustion activities. This is a simple method and emissions from all sources ofcombustion are estimated on the basis of the quantities of energy kind consumed
and average emission factors. Tier-2 estimation methodology is based on detailedenergy/technology information covering stationary and mobile sources. It is moredetailed than tier-1 methodology but uses the same concept of energy kindconsumption based emission coefficients like CO2 emissions per unit of coal
combustion. Tier-3 is similar to tier-2 except that the emission coefficients are
based on enduse demands like CO2 emissions per unit of power generated.
AIM/Local model uses a combination of tier-2 and 3 methodology of emission
estimation. LPS emissions in the model are estimated by two different approaches.
Model follows an approach similar to tier-2 for the estimates of emissions fromthe LPS for energy consumption. The data required includes information about the
production quantity, production process, energy combustion by various
technologies, emission coefficients for the energy kind and pollution removal
technologies used. The emissions are estimated by multiplying the energy
consumption by each technology with respective emission coefficients for thatenergy kind.
In this model, tier-3 approach is used for estimation of emissions from theindustrial processes. The data requirement for this approach includes production
quantity, production process and emissions factors per unit of production.
Emissions are estimated by multiplying the production quantity by thecorresponding emission coefficients.
Net emissions from an LPS in both the above approaches are calculated byaccounting for the pollution removal factor due to the pollution removal
technology.
Thus emissions from LPS for energy consumption and production processes are
given by
LPS

Ql

LPS

= R lLPS × {∑ ( E lLPS
, k × f k ) + ∑ (V l ,ν × f ν )}
k

ν

(1)

112

Kapshe, M. et al.

Where,LPS
Ql :Net emission from large point source l

RlLPS :LPS
E l, k :Release rate of pollutants after removal technology of large point
source l
Energy consumption of energy kind k for large point source l

fk:

Emission coefficient of energy kind k

V lLPS,ν :Production quantity of production process ν for large point source l

fv:
k:
ν :

Emission coefficient of production process νEnergy kind
Production process

Emissions from area sources (AS)In the present study the start year of modeling has been taken as 1995. India had
25 States and 6 Union territories in 1995. These are further subdivided in 466
small administrative areas called districts. Due to reorganization of states andsubdivision of districts, this number has changed over the years but for themodeling purpose administrative boundaries as of 1995 have been used.
Information collection on the district level for all the required parameters isdifficult due to limited data availability. AIM/Local model provides the flexibilityof regional definition on two levels and facilitates estimation of aggregate
emissions for a larger region and allocation of the same to the smaller areas of theregion based on suitable allocation index for each sector.
In this model, an approach similar to the tier-2 method has been used foremission estimates from area sources. The emissions for a sector are estimated bymultiplying the consumption for each energy kind in a technology with respective
emission coefficient for the energy kind and the production quantity for eachproduction process with respective emission coefficient.
This can be given by the following equation:
AS
AS
AS
AS
AS
Q j = ∑ ( E j , k × f j , k ) + ∑ ( V j ,ν × f j ,ν )

(2)

ν

k

E j ,k = E j ,k −AS

V ASj ,ν = V j ,ν −∑E l ,k

LPS

(3)

∑

V lLPS,ν(4)l∈{point sources belong to sector j}l∈{point sources belong to sector j}

Where,AS

Qj :Emissions from sector j from area sources6. Application of AIM/Local Model to India
AS

113

Emission coefficient of energy kind k from sector j , taking into

f j,k :

account the effect of removal technologies
Energy consumption of energy kind k for sector j

AS
j,k :

E
E j,k :

Total energy consumption of energy kind k for sector jAS

f j ,ν :Emission coefficient of production process ν from sector j , taking

ASV j ,ν :into account the effect of removal technologies
Production quantity of production process ν for sector j

V j, k :Total production quantity of production process ν for sector jjSector:

The sectoral emissions estimated on the national level are then allocated to the
districts based on a suitable allocation index like district population, area, roaddensity etc., for which the information is available on district level. Suitableparameters, which were considered as the major drivers for emissions from aparticular sector, were used to generate this index. The emissions from district
i can thus be given byASAS
q i = ∑ (Q j ×j

I i, j)∑ I i, j(5)iWhere,ASqi :Emission in district i from area sources

I i, j :Emission intensity index for sector j in district i

Total emissions
Equation (1) gives the emissions from the point sources and equation (2) gives theemissions from a sector in a region. The total emissions from sector j in a region
can thus be given byAS
Qj =Qj +LPS∑l∈{point sources belong to sector j}

Ql

(6)

The total emissions at the district level can be calculated by summing up theallocated area source emissions and point source emissions from a district.
AS
Qi = qi +∑l∈{point sources in district i}

LPS

Ql

(7)