ars.els-cdn.com · web viewchauvel, a. and lefebvre, g., 1989. petrochemical processes, part 2:...

Forecasting global developments in the basic chemical industry for

environmental policy analysis

M.L.M. Broeren, D. Saygin, M.K. Patel

ANNEXES

A. Trade analysis background information

Table 3: List of countries included in the trade analysis (Section 2.5) by world

region

World region Included countriesAsia Pacific Japan, KoreaChina and India China, IndiaEurope France, Germany, Italy, Netherlands, Poland, United

KingdomMiddle East and Africa Saudi Arabia, Iran, Kuwait, EgyptNorth America United States, CanadaOther Developing Asia Indonesia, Thailand, Malaysia, Singapore, Taiwana

South America Brazil, Venezuela, Argentina, Colombia, Trinidad, ChileTransition Economies Russia, Ukraine

a. Taiwan is not included as a country in the UN’s Comtrade database due to

political reasons. However, the database includes a territory ‘Other Asia, not

elsewhere specified’. In theory, all trade with Asia that is reported to

Comtrade without specifying a partner country is included here. However,

since Taiwan is neither a separate country nor considered part of China, all its

trade is reported in this category, along with other unspecified trade with Asia.

For this report, we assume that ‘Other Asia, not elsewhere specified’ is

equivalent to Taiwan, estimating that the share of other unspecified Asian

trade is minor in comparison. For further information regarding this issue, visit

http://unstats.un.org/unsd/tradekb/Knowledgebase/Taiwan-Province-of-China-

Trade-data.

Table 4: List of UN Comtrade products included in the trade analysisa (Section

2.5) and their product codes (classification: Harmonized System as reported).

Ammonia (2814) and methanol (290511) not shown.

HVCs and derivatives Polymers

Ethylene 290121Polyethylene - specific gravity <0.94 in primary form

390110

Propene (propylene) 290122Polyethylene - specific gravity >0.94 in primary form

390120

Buta-1, 3-diene and isoprene 290124

Polymers of ethylene nes, in primary forms

390190

Benzene 290220 Polypropylene in primary forms39021

0

Styrene 290250Propylene copolymers in primary forms

390230

Ethylbenzene 290260Polystyrene, expansible in primary forms

390311

1,2-dichloroethane(ethylene dichloride) 290315

Polystyrene, except expansible in primary forms

390319

Vinyl chloride (chloroethylene) 290321

Styrene-acrylonitrile (SAN) copolymers, primary forms

390320

Ethylene glycol (ethanediol) 290531Acrylonitrile-butadiene-styrene (ABS) copolymers

390330

Oxirane (ethylene oxide) 291010Polymers of styrene except SAN or ABS in primary form

390390

Ethanal (acetaldehyde) 291212Polyvinyl chloride in primary forms

390410

Acetic acid 291521Acrylic polymers nes, in primary forms

390690

Cumene 290270Polyethylene terephthalate, in primary forms

390760

Butanols nes 290514Polyesters nes, unsaturated, in primary forms

390791

Octanol(octyl alcohol), isomers 290516 Polyesters nes, in primary forms

390799

Propylene glycol (propane-1,2-diol) 290532 Polyurethanes in primary forms

390950

Phenol (hydroxybenzene), salts 290711

Bisphenol A, diphenylolpropane, salts 290723Methyloxirane (propylene oxide) 291020Acetone 291411Acrylic acid, salts 291611Dioctyl orthophthalates 291732Hexamethylenediamine, its salts 292122Acrylonitrile 292610

a. Each country reports its own import and export flows for each commodity to

the Comtrade database, specifying a partner country. However, the reports of

the same physical trade flow reported by one country (e.g. as export) and its

partner country (as import) are not always equal. We therefore only consider

the reported export flows.

B. Forecasting model data input

Table 5: Population per world region, millions. 2006 population taken from CB

(2010), extrapolation based on IEA (2010).

2006 2010 2015 2020 2025 2030 CAGR, %/yrOECD 1,205 1,234 1,271 1,310 1,332 1,355 0.5

PAC 200 201 201 201 198 195 -0.1 EUR 541 549 563 579 587 596 0.4 NA 464 484 507 531 547 563 0.8

Non-OECD 4,997 5,248 5,552 5,879 6,091 6,317 1.0

C&I 2,430 2,527 2,640 2,759 2,814 2,870 0.7 MEA 1,155 1,257 1,394 1,545 1,671 1,808 1.9 ODA 701 736 772 810 833 857 0.8 SA 374 392 408 426 436 446 0.7 EIT 338 336 338 339 338 337 0.0

Global 6,203 6,481 6,823 7,189 7,423 7,672 0.9

Table 6: GDP per world region, billion USD2009/yr. 2006 GDP taken from CB

(2010), extrapolation based on IEA (2010).

2006 2010 2015 2020 2025 2030 CAGR, %/yr

OECD 40,687 41,42245,43

5 49,870 54,865 60,396 1.7 PAC 6,813 6,871 7,475 8,132 8,632 9,162 1.2

EUR 16,756 17,01718,468 20,061 22,094 24,349 1.6

NA 17,117 17,53519,49

3 21,677 24,140 26,884 1.9Non-OECD 27,176 34,498

45,919 61,745 74,598 90,300 5.1

C&I 11,667 16,37223,68

9 34,322 42,332 52,289 6.4 MEA 4,559 5,467 6,720 8,262 9,750 11,514 3.9 ODA 3,700 4,369 5,810 7,804 9,387 11,319 4.8 SA 3,560 4,149 4,916 5,826 6,722 7,758 3.3 EIT 3,689 4,140 4,783 5,533 6,406 7,420 3.0

Global 67,862 75,92091,35

4 111,615 129,463150,69

5 3.4

Table 7: Overview of regional data used for production cost calculations. Data

given for 2006 unless stated otherwise. Oil price of 50 USD/bbl.

OECD Non-OECD SourcesPAC EUR NA C&I MEA ODA SA EIT

GeneralInterest rate, %/yr 8.6 8.7 8.2 12.1 14.0 11.1 15.0 12.6 Damodaran, 2012;

Economist, 2006Location factor (NA=100) 130 110 100 90 125 125 125 125 Gielen, 2003Labour cost factor (EUR=100) 82 100 97 14 56 97 24 15 ILO, 2011EthyleneSEC (excl. feedstock), GJ/t HVCs

Average technology 13.2 15.6 18.3 16.7 18.3 19.7a 17.1 21.3b IPTS/EC, 2003 ; Saygin et al.,

2011aBPT c 12.6 12.6 13.0 12.6 13.3 12.6 12.6 12.6

SEC (excl. feedstock), GJ/t ethylene

Average technology 22.3 27.3 24.1 30.2 21.9 23.9 22.1 38.7 IPTS/EC, 2003 ; Saygin et al.,

2011aBPT c 24.2 24.2 19.7 24.2 14.9 24.2 24.2 24.2

Average age of capacity, year 25 25 25 10 10 10 20 25 IEA, 2009aFeedstock mix, %

Ethane 0 7 47 5 60 62 52 0 OGJ, 2006Propane 1 5 16 3 14 5 7 0Butane 2 6 4 0 3 5 2 7

OECD Non-OECD SourcesPAC EUR NA C&I MEA ODA SA EIT

Naphtha 97 74 28 67 23 27 39 91Gas oil 0 8 4 24 0 0 0 2

Energy prices, USD/GJEthane 8.0 8.0 6.6 6.2 1.2 5.8 8.0 2.1 See below tablePropane 11.9 12.0 10.1 11.3 9.4 10.3 10.3 n.a.Butane 11.9 11.5 11.8 n.a. 9.4 10.7 10.6 9.2Naphtha 13.7 13.6 15.0 14.0 9.7 12.8 12.7 11.0Gas oil 12.0 14.2 10.5 12.0 n.a. n.a. n.a. 8.8Weighted average 13.7 13.0 9.9 13.0 4.5 8.2 10.0 10.9

By-product prices d, USD/tPropylene 926 926 926 926 926 926 926 926 Chemical Week,

2006bButadiene 859 859 859 859 859 859 859 859Benzene 929 929 929 929 929 929 929 929Toluene 874 874 874 874 874 874 874 874Xylene 910 910 910 910 910 910 910 910

AmmoniaSEC (incl. feedstock), GJ/t

Average technology 35.7 36.3 38.0 47.4 40.9 45.4 40.1 46.1 Prince, 2007 ; Saygin et al.,

2011aBPT e 28.0 28.0 28.0 28.0 28.0 28.0 28.0 28.0

Average age of capacity, year 25 30 30 20 15 20 20 25 IFA, 2002; IFDC, 2008

Feedstock mix, %Natural gas 89 82 98 33 96 80 100 100 Saygin et al.,

2011aCoal 0 0 0 60 4 0 0 0Oil 11 18 2 7 0 20 0 0

Energy prices, USD/GJNatural gas 7.8 7.6 6.2 5.0 1.1 1.9 2.6 1.8 See below tableOil 8.9 8.9 7.7 12.5 n.a. 11.3 6.4 n.a.Coal n.a. n.a. n.a. 1.4 2.8 n.a. n.a. n.a.Weighted average 7.9 7.9 6.2 3.5 1.2 3.8 2.6 1.8

MethanolSEC (incl. feedstock), GJ/t

Average technology 37.0 33.7 35.8 43.7 34.3 37.0 33.6 38.8 UNIDO, 2010BPT e 32.4 32.4 32.4 32.4 32.4 32.4 32.4 32.4

Average age of capacity, year 15 25 20 5 10 10 10 30 MI, 2010bFeedstock mix, %

Natural gas 100 63 84 20 99 100 100 100 MI, 2010bCoal 0 0 16 75 1 0 0 0Oil 0 37 0 5 0 0 0 0

Energy prices, USD/GJNatural gas 7.8 7.3 6.1 5.4 1.0 1.9 1.8 1.4 See below tableOil 8.9 8.9 7.7 12.5 n.a. 11.3 6.4 n.a.Coal n.a. n.a. 1.2 1.6 2.8 n.a. n.a. n.a.Weighted average 7.8 7.9 5.3 3.0 1.0 1.9 1.8 1.4

ChlorineSEC (incl. electricity), GJ/t

Average technology 11.9 13.1 15.3 15.4 13.7 13.7 15.2 n.a. Saygin et al., 2011aBPT f 11.9 11.9 11.9 11.9 11.9 11.9 11.9 n.a.

Average age of capacity g, year 25 25 25 10 10 10 20 n.a.Technology mix, %

Mercury 0 38 12 36 20 20 14 n.a. Saygin et al., 2011aDiaphragm 0 20 70 34 33 33 64 n.a.

Membrane 100 42 18 30 47 47 22 n.a.Energy prices, USD/GJ

Natural gas 7.8 7.5 6.3 4.8 0.7 1.9 4.1 n.a. See below tableElectricity 26.3 21.9 17.2 20.5 7.4 20.1 21.1 n.a.Weighted average 10.8 9.6 9.7 6.8 2.1 5.7 9.1 n.a.

By-product pricesd, USD/tCaustic soda 264 264 264 264 264 264 264 n.a. Chemical Week,

2006ba) No SEC data given by Saygin et al. (2011a), therefore value taken from DEDE (n.d.).

b) No SEC data given by Saygin et al. (2011a), therefore calculated by assuming world average SEC is 16.9

GJ/t HVCs (as in Saygin et al., 2011a).

c) The most efficient plants in Europe according to IPTS/EC (2003) for ethane (13.3 GJ/t HVCs) and

naphtha (12.6 GJ/t HVCs) are used as the reference BPT. Data is converted from GJ/t ethylene using

feedstock yields (Neelis et al., 2005) and the Solomon definition of HVCs. Values assumed appropriate

for 2006. All regions are assumed to use 100% naphtha for BPT, except MEA (100% ethane) and NA

(50% naphtha, 50% ethane).

d) Prices for US are assumed valid for all regions.

e) For BPT, all regions are assumed to use 100% natural gas based production.

f) As BPT, all regions are assumed to use the membrane chlor-alkali process.

g) Assumed equal to steam crackers (HVCs).

References for energy prices

Ethane – PAC: price assumed equal to Europe (minor importance); EUR and

MEA: McKinsey (2008); NA: DeWitt (2009); C&I: source no longer available;

ODA: CS (2011); SA: UNIPAR (2006); EIT: Roland Berger (2008).

Propane – PAC, EUR and NA: DeWitt (2009); C&I, ODA and SA: assumed

equal to 81% of naphtha price (average for other world regions; minor

importance); MEA: McKinsey (2008).

Butane – PAC: assumed equal to propane price (minor importance); EUR and

NA: DeWitt (2009); MEA: McKinsey (2008); ODA, SA and EIT: assumed equal

to 84% of naphtha price (average for other world regions; minor importance).

Naphtha – PAC, EUR and NA: DeWitt (2009); C&I: weighted average of BMI

(2010) and Wenshan (n.d.); MEA: McKinsey (2008); ODA: average of IEA

(2006a) and Roland Berger (2008); SA: source no longer available; EIT: Roland

Berger (2008) and natural gas prices.

Gas oil – PAC, EUR, NA and C&I: source no longer available (minor

importance); EIT: assumed equal to 80% of naphtha price (minor importance).

Natural gas – All regions: Agrium (2007). These sources show country-level

natural gas prices. For world regions, the country-level prices are weighed by the

production capacity of each chemical. Therefore, because some countries are large

producers of methanol but not ammonia, or vice versa, natural gas prices can

differ per chemical.

Oil – All regions: EIA (2010a), assuming industrial prices are 5% higher.

Coal – NA: EIA (2010b); C&I: Sagawa and Koizumi (2007); MEA: source no

longer available.

Electricity – All regions: EIA (2010c); IEA 2006b; Rosen and Houser (2007)

Table 8: Overview of fixed capital costs used for production cost calculations

(expressed per unit of capacity) and global utilization factors.

Fixed capital costsa, USD2006/t/yr

Global utilization factor

Based on

HVCs Ethane: Propane: Butane:

Naphtha: Gas oil:

13701680170020502400

80% Worrell et al., 2000; Chemical Week, 2010

Ammonia Natural gas:Fuel oil:

Coal:

5708001370

85% Lako, 2009; EFMA, 2000; Chemical Week, 2006c

Methanol Natural gas:Fuel oil:

Coal:

265370635

80%b HCP, 2001

Chlorine Diaphragm:Mercury:

Membrane:

104011101000

80%b IPTS/EC, 2001; Chauvel and Lefebvre, 1989

a) All costs updated to base year 2006 using Chemical Engineering’s Plant Cost

Index. Fixed capital costs are assumed valid for the U.S. and updated for

different regions using the location factors shown in Table 7.

b) Assumed equal to utilization factor for HVCs.

Table 9: Transportation costs of ethylene, ammonia and methanol between

world regions, in 2006 all in USD2006 per tonne of product. Transportation costs

between two regions are assumed to be symmetrical, and trade within a region is

assumed free. Based on shipping costs from (CMAI, 2007; Fertilizer Week, 2008;

ICIS, 2011). Existence of pipelines was taken into account when estimating costs

for land-based transportation.

PAC C&I EUR MEA NA ODA SA EIT

Eth

ylen

e

PAC 0 146 354 265 220 105 350 300C&I 146 0 256 168 302 110 321 300EUR 354 256 0 180 215 320 220 30MEA 265 168 180 0 261 230 248 100NA 220 302 215 261 0 300 130 250ODA 105 110 320 230 300 0 338 350SA 350 321 220 248 130 338 0 300EIT 300 300 30 100 250 350 300 0

Am

mon

ia


Met

hano

l


C. Production cost forecast up to 2030

In order to calculate the production costs for each five year period up to 2030, all cost

parameters are updated from their 2006 values shown in Table 7. We applied a

number of methods for this purpose. These are summarized as follows:

Energy prices are adapted based on the price changes estimated by IEA (2010)

for several fuels, following the Current Policies scenario. Our 2006 energy

prices shown in Table 7 are assumed to follow the same trend as these fuels.

The increase in energy prices affects both process energy and feedstock

energy.

o The 2006 prices of ethane, naphtha, gas oil and electricity are linked to

the global oil price. The prices at which HVC by-products are sold are

also assumed to increase along with the oil price.

o The prices of propane, butane and natural gas are linked to the

estimated trend in natural gas prices. For natural gas prices, IEA

distinguishes between prices in Asia Pacific, Europe and North

America, so we follow these regional developments. The rest of the

world is assumed to follow the average price increase of these three

regions.

o The coal prices are linked to the development of the OECD steam coal

imports.

The SECs of new capacity based on average technology or BPT improve at 1

%/yr (Neelis et al., 2007). These improvement rates lead to a slight

convergence of the SECs of average capacity. In addition, we assume that the

capacity that already exists in 2006 will also improve its energy efficiency (for

instance through retrofits), at a rate of 0.5 %/yr (Phylipsen et al., 2002).

Interest rates and labour costs are assumed to be dependent on a world

region’s GDP per capita. For labour costs, the data from ILO (2011) shows

that a higher GDP/capita correlates with higher wages in the chemical sector.

We derive a linear relationship to estimate the increase in labour costs between

2006 and 2030 based on GDP/capita estimates (shown in Table 5 and Table

6). Similarly, higher GDP/capita correlates with lower interest rates and we

derive a linear relationship to estimate the developments.

For chlorine, we derive a linear trend line for the average price development of

salt for 2001-2010 from US production statistics (USGS, 1995-2010). We

extrapolate this trend to 2030. The prices of rock salt (production input) and

caustic soda (chlorine by-product) are assumed to follow the same trend

All feedstock mixes, location factors and operating rates are kept constant

between 2006 and 2030.

D. Calculation methods for energy, CO2 emissions (direct and indirect),

investment costs, energy costs

Final energy use

The annual final process energy use (FEUr,t,i) for the production of a chemical i in

region r and year t is estimated using Equation 3.

FEU r ,t ,i=UF i∗(CAPOld ,r ,t ,i∗SECOld , r ,t ,i+ ∑n=2010

t

{NPC BPT ,r ,n ,i∗SEC BPT , r ,n , i+ NPC Avg, r ,n , i∗SEC Avg ,r , n ,i }) (Equation 3)

Where:SECj,r,t,i= SECj,r,2010,i * (1 – IRj)t - 2010

FEUr,t,i = Final process energy use for the production of chemical i in

world region r in year t, PJ/yr

UFi = Capacity utilization factor for chemical i, %

CAPOld,r,t,i = Old production capacity (existed in 2010) for chemical i still

in operation in year t in region r, kt/yr

NPCBPT,r,n,i = New BPT production capacity for chemical i built in region r

in year n, kt/yr

NPCAvg,r,n,i = New average technology production capacity for chemical i

built in region r in year n, kt/yr

SECj,r,t,i = SEC of capacity of type j (old, new average, new BPT) for

chemical i in region r in year t, GJ/t

IRj = SEC improvement rate of capacity of type j (old, new

average, new BPT), %/yr

We include a global utilization factor (UFi), which is estimated based on Chemical

Week, and is kept constant for 2010-2030.

CO2 emissions

The total CO2 emissions of the chemical sector depend on the regional fuel mix, as

well as the fuel mix of the power sector (IEA, 2011c). We distinguish between direct

emissions, which result from on-site activities (e.g. fuel combustion), and indirect

emissions, which stem from the (typically off-site) generation of electricity used for

the production of chemical products.

The direct emissions are determined by splitting the final process energy use for each

group of chemicals between various fuels based on the regional feedstock mix. We

distinguish between 1) coal, 2) ethane, butane and propane, 3) naphtha and heavy oil,

and 4) natural gas. We divide the process energy use (excluding electricity) from the

production of the four chemicals in each world region among these fuels in proportion

to the feedstock mix (see Annex B).

The direct CO2 emissions for each world region in each time period are determined by

multiplying the final process energy use by fuel with its emission factor (IPCC, 2006)

Equation 4.

CO2direct , r ,t=∑F

FEU r ,t , F∗EFF (Equation 4)

Where: CO2direct,r,t = Direct CO2 emissions of the basic chemical sector in

world region r in year t, Mt/yr

FEUr,t,F = Final process energy use of fuel F in region r in year t, PJ/yr

EFF = Emission factor of fuel F, t CO2/GJ

Please note that during ammonia production, unlike the steam cracking and methanol

production processes, all carbon present in the fuels entering the process is emitted as

carbon dioxide, since no carbon is stored in the product. To account for this, the

feedstock energy of 20.7 GJ/t ammonia is added to the FEU in Equation 4.

Furthermore, the concentrated carbon dioxide produced along with ammonia is often

used for urea production, absorbing 0.77 t CO2/t urea produced. We therefore assume

that all urea is co-produced in ammonia plants and subtract the sequestered carbon

dioxide from the calculated emissions. We assume that global urea production

volumes follow the same trend as ammonia.

Furthermore, a region’s indirect emissions depend on its electricity use and power

mix, as shown in Equation 5.

CO2indirect , r , t=FEU r , t , el∗EF el ,r (Equation 5)

Where: CO2indirect,r,t = Indirect CO2 emissions of the basic chemical sector in

world region r in year t, Mt/yr

FEUr,t,el= Final electricity use in region r in year t, PJ/yr

EFel,r = Emission factor of power generation in region r, t

CO2/GJ

We use the emission factors for power generation as provided by IEA (2011c).

Annualized fixed capital costs & energy costs

The annualized fixed capital costs are the annual expenses related to the investments

in new production capacity and their financing. They are the product of fixed capital

costs, annuity factors and location factors. Since we do not know the fixed capital

costs of capacity existing in 2010 (old capacity), we cannot include their depreciation

and financing costs. As indicated in Section 3.2.1 in the main text, we assume that

BPT equipment with higher energy efficiency is more expensive. Furthermore, no

learning effects are included. We calculate the annualized fixed capital costs using

Equation 6.

A FC Cr ,t=∑i

∑n=2006

t

ar , n∗LF r∗∑j

( FC CRef , i , j∗NPCr ,n , i , j) /( SECr , n ,i

SECRef ,n ,i)

(Equation 6)

Where: AFCCr,t = Total annualized fixed capital costs for capacity built after

2010 for region r in year t, million USD

FCCRef,i,j = Fixed capital costs for a production facility making chemical

i from feedstock j in reference region, valid for 2006, USD/t/yr

NPCr,n,i,j = New production capacity for chemical i using feedstock j

placed in region r in year n, Mt/yr

SECRef,n,i = SEC for production of chemical i in reference region

(corresponding with ICRef,i,j) in year n, GJ/t

SECr,n,i = SEC for production of chemical i in region r in year n, GJ/t

ar,n = Annuity factor for region r in year n, calculated from regional

interest rates given in Table 7 assuming an economic lifetime of 25 years

LFr = Location factor of region r (see Table 7)

Finally, when implementing BPT, the higher annualized fixed capital costs will be

partly offset by lower energy and feedstock costs. Therefore, we calculate the regional

energy costs, by multiplying the energy use with the average fuel price (i.e.

accounting for feedstock mix), see Equation 7.

ECr ,t=∑i

FEU r ,t ,i∗P r ,t ,i (Equation 7)

Where: ECr,t = Energy costs for the chemical sector in region r in

year t, million USD/yr

FEUr,t,I = Final energy use (process and feedstock) in region r in

year t for chemical i, PJ/yr

Pr,t,i = Average energy price in region r in year t for

producing chemical i, weighted by feedstock mix, USD/GJ

E. References (for electronic annex only)

Agrium, 2007. Natural Gas and Nitrogen Fertilizer, Presentation. WSU ALL Extension Conference,

Spokane, Washington.

BMI, 2010. India Oil and Gas Report Q2 2010. Business Monitor International.

Chauvel, A. and Lefebvre, G., 1989. Petrochemical Processes, Part 2: Major Oxygenated, Chlorinated

and Nitrated Derivatives. Editions Technip, Paris, France.

Chemical Week, 2006c. Product focus – Ammonia. Vol. 168, issue 29, August 30, pp. 43.

Chemical Week, 2010. Product focus – Ethylene. Vol. 172, issue 7, March 22, pp. 33.

CS, 2011. Petronas Chemical Group - Equity Research.

https://doc.research-and-analytics.csfb.com/docView?

language=ENG&source=ulg&format=PDF&document_id=869073601&serialid=V9i

%2FSVZ8LtigF0G81byUL5lNHbR8%2FN1qWyz2AirtfmI%3D edn, Credit Suisse.

Damodaran, A., 2012. Country Default Spreads and Risk Premiums .

http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/ctryprem.html.

DEDE, n.d. A Study on Energy Efficiency Index in Petrochemical Industry - Department of Alternative

Energy Development and Efficiency. Ministry of Energy, Thailand.

DeWitt, 2009. Historical Pricing - Sample data 2006.

http://www.dewittworld.com/portal/HPP/ComparePrices.aspx, DeWitt & Company -

Petrochemical Portal.

Economist, 2006. Economic and Financial Indicators. The Economist, vol. 381, no. 8509, pp. 152-153.

EIA, 2010a. Heavy Fuel Oil Prices for Electricity Generation for Selected Countries. U.S. Energy

Information Administration. http://www.eia.gov/emeu/international/hfoforelec.html.

EIA, 2010b. Steam Coal Prices for Industry for Selected Countries. U.S. Energy Information

Administration. http://www.eia.gov/countries/prices/coalprice_industry.cfm.

EIA, 2010c. Electricity Prices for Industry for Selected Countries. U.S. Energy Information

Administration. http://www.eia.gov/countries/prices/electricity_industry.cfm.

HCP, 2001. Hydrocarbon Processing, Petrochemical Processes 2001. Gulf Publishing Company,

Houston, Texas, U.S.A.

IEA, 2006a. Monthly Oil Market Report – June 13th 2006. International Energy Agency, , Paris,

France. http://omrpublic.iea.org/omrarchive/13jun06pri.pdf.

IEA, 2006b. Key World Energy Statistics 2006. International Energy Agency, Paris, France.

IEA, 2011c. CO2 Emissions from Fuel Combustion – 2011 Highlights. International Energy Agency,

Paris, France. http://www.iea.org/co2highlights/.IFA, 2002. Database: Fertilizer consumption by

country/region, International Fertilizer Industry Association, Paris, France.

IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Prepared by the National

Greenhouse Gas Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T. and

Tanabe K. (eds). Published: IGES, Japan.

ILO, 2011. LABORSTA Labour Statistics Database - Wages in manufacturing, International Labour

Organization. Geneva, Switzerland. http://laborsta.ilo.org/.

IPTS/EC, 2003. Reference document on Best Available Techniques in the Large Volume Organic

Chemical Industry. Integrated Pollution Prevention and Control (IPPPC), European

Commission, Brussels.

Lako, P., 2009. Energy conservation potential of the nitrogen fertilizer industry. Energy Research

Centre of the Netherlands, ECN-E-09-011.

McKinsey, 2008. Chemicals in the Middle East 2020 - Local opportunities and global implications.

McKinsey & Company / Gulf Petrochemicals and Chemicals Association.

Prince, A., 2007. Initiating New Projects in the Ammonia Sector, International Fertilizer Industry

Association. Workshop on Energy Efficiency and CO2 Reduction Prospects in Ammonia

Production, Viet Nam.

Rosen, D. H., Houser, T., 2007. China Energy – A Guide for the Perplexed. Center for Strategic and

International Studies and Peterson Institute for International Economics.

http://www.iie.com/publications/papers/rosen0507.pdf.

Sagawa, A., Koizumi, K., 2007. Present State and Outlook of China's Coal Industry. Institute of Energy

Economics, Japan, Tokyo, Japan.

UNIDO, 2010. Global Industrial Energy Efficiency Benchmarking – An Energy Policy Tool. Working

paper, November 2010. United Nations Industrial Development Organisation, Vienna, Austria.

UNIPAR, 2006. UNIPAR Releases Results for the Second Quarter and First Half of 2006.

http://www.mzweb.com.br/unipar/web/arquivos/UNIPAR_ER_2Q06_eng.pdf , União de

Indústrias Petroquímicas S.A., August 8, Rio de Janeiro, Brazil.

USGS, 1995-2010. Salt – Statistics and Information, Mineral Yearbooks 1995 – 2010. United States

Geological Survey. http://minerals.usgs.gov/minerals/pubs/commodity/salt/.

Wenshan, W., n.d. The current situation of China's synthetic ammonia and urea production based on

natural gas and oil. China Nitrogen Fertilizer Industry Association, China,

http://www.fertilizer.org/ifacontent/download/5575/88184/version/1/file/103.pdf.

ars.els-cdn.com · web viewchauvel, a. and lefebvre, g., 1989. petrochemical processes, part 2:...

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