Soeren Lindner University of Cambridge

4CMR April 2012

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WIOD application

Country level IO tables

China’s IO table Electricity

sector entry

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Show two ways to disaggregate China’s electricity sector in the 2007 national IO table into the following sub-sector categories, based on an estimation technique:

why How what

Electricity production Pulverized coal plants (sub-c)PC coal plants (super-crit)

transmission and PC coal plants (ultra-super critical)distribution (T&D) Wind power plants

Solar Nuclear powerHydro powerNatural gas power plantsT&D

Sector entry 23 in 42x42 IO table of ChinaElectricity production, transmission and

distribution, heat and water supply

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1) Motivation

2) Chinas electricity sector

3) Method for Disaggregation

4) Results

5) Conclusion

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Methodological improvement Lenzen (2011): Mismatch of economic IO data and environmental satellite

accounts Joshi (1998): “Product Environmental LCA using IO techniques”: he suggests

disaggregation of certain sectors may improve accuracy

Other authors suggesting to introduce more process detail to IOA – LCA : Hendrickson et al. (1998), Suh (2003), Lenzen (2001)

Application to MRIO studies Wiedmann et al. (2007): suggests to disaggregate sectors whenever possible to

improve accuracy Su et al. (2010): quantifies uncertainty of CO2 embodied in trade due to sector

aggregation for Chinese IO tables

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Disaggregation is more important for sectors whose sub-sectors have vastly different environmental coefficients (coal vs. wind power)

China’s electricity sector is arguably one of the most important sectors for climate policy: 80% of China’s emissions stem from electricity production

If emissions inventories are done from a consumption based perspective, then China’s electricity sector represents a source of emissions for many countries

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Several arguments for that in the literature:

Policy perspective: Peters and Hertwich (2004): Production factors and pollution embodied in

trade, theoretical development .

Lenzen et al. (2005):”This work has shown that it is important to explicitly consider the production recipe, energy use structure and CO2 emissions of trading partners, in order to arrive at realistic figures for CO2 embodied in trade, and hence for the national contribution to emissions, based on consumer responsibility”

Relevance for Post-Kyoto emissions inventory: Peters and Hertwich (2007): Post Kyoto GHG inventories: production vs.

consumption

Why particularly China? Export emissions = 20% of national emissions

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Taken from: Davies et al. (2011). Net exporting countries in blue, importing countries in red

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Insufficient data availability often limits disaggregation.

A method by Wolsky (1984) allows to estimate inputs from new sectors into common sectors, common sectors into new sectors and inputs/outputs in the intra matrix if the total output of new sectors are known.

This method has been extended and applied by us to the case of China’s electricity sector

Essentially, we estimate technical coefficients for the new sectors. Constraints need to be met, for that we build weight factors.

A B c*1 c*2 FD X

ABc*1 x*1c*2 x*2

new sectors

common sectors

new sectorscommon sectors

Intra matrix

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In order to make this estimation as accurate as possible, we need to gather as much relevant information as possible and incorporate it into the disaggregation

This is why we compare two cases: one with limited information, another run with more detailed information

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Point 1: non-uniform distribution of electricity generation across the country:

Region

Central China Henan, Hunan Jiangxi Hubei SichuanChongqing

Eastern China Shanghai Jiangsu Anhui Fujian Zhejiang

North East Jilin, Heilongjiang Lianoning

North West Ningxia, Shaanxi Gansu , Xinjiang Qinghai

South China Guizhou Guangdong Yunnan Guangxi

North China GridShanxi Shandong BeijingInner Mongolia Tianjing Hebei

Provinces with more than 90% of power

generation by fossil fuels

Provinces between 50 - 80% of

power generation by fossil fuels

Provinces with less than 50% of power generation by fossil

fuels

CEYB, 2008

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In Lindner et al (under review) (2007 data) Red bars include the emissions from inter-provincial electricity trade respecting province level technology for electricity generation Blue bars include emissions from province electricity generation (including exports, excluding imports)

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Point 2: A body of literature points out that industry sectors are not evenly

distributed across provinces and regions in China (Demurger,2002; Batisse and Poncet, 2006).

Instead, due to several factors (FDI, competition) industry agglomeration occurs and provinces are specialized.

Hypothesis: Chinese Industries are likely to consume a specific electricity mix depending on

local generation and industry presence, which should be incorporated into the disaggregation.

Marriott (2007) found the same for the US.

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Coal Coal Hydro Wind …USC sub-C90% 7% 3% 0

x 30%Aluminium production x 45%

60% 5% 25% 10%x 20%x 50%

0% 10% 60% 30%Steel production x 50%

x 5%

Sum 39 8.1 35.9 17

70.5 6.2 16.9 6.5Steel productionAluminium production

Production levels of industries in three provinces (expressed in %)

Province 1

Province 2

Province 3

Aluminium production

Aluminium production

Steel production

Steel production

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Case 1: Disaggregation is done based on deriving input and output weight factors using

national electricity generation Exception: split of T&D sector from production sector (modification)

Total 549.29Power generation 304.15Transmission& Distribution 245.14

Investment in Chinese power sector (bill. RMB)

% Hydro Coal Sub-c Coal SC Coal USC NG Nuclear Wind Solar PvNorth China 0.03 0.83 0.08 0.01 0.00 0.01 0.04 0.00Central China 0.40 0.50 0.05 0.01 0.00 0.00 0.04 0.00East 0.12 0.63 0.02 0.19 0.00 0.03 0.01 0.00North East 0.09 0.81 0.07 0.00 0.01 0.00 0.02 0.00South 0.47 0.41 0.07 0.00 0.01 0.04 0.00 0.00Northwest 0.29 0.56 0.06 0.03 0.04 0.00 0.02 0.01

National Average 0.22 0.64 0.06 0.03 0.01 0.01 0.02 0.01

Electricity Generation in 6 operating power transmission and distribution networks in China

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A B c*1 c*2 FD XABc*1 x*1c*2 x*2

new sectors

common sectors

new sectorscommon sectors

Intra matrix

Blue: output weight factors Red: input weight factors

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Case 2: Use of more detailed information to estimate output weight factors and input

weight factors Output weight factors: Derive the industry specific electricity consumption mix by using 30 province IO

tables (merged into six power grid systems). Input weight factors: Allocation of industry input to the new power generation sectors should not

solely be based on the electricity output of the new sectors, but also on how the money is spent within a year to generate said output.

Technology Median Low HighCoal (SC) 11.2 9.5 12.9Coal (USC) 12.3 10.3 14.3Coal (sub-c) 8.5 7.5 9.5Natural Gas 20.3 12.6 28.1Nuclear 9.4 7.9 10.9Hydroelectricty 13.4 12.0 14.8Wind power 14.7 13.9 15.5Solar PV 15.3 14.7 16.0

Electricity O&M prices by Generation type (RMB/kWh)

Source: IEA (2010) 17 Lindner et al. Disaggregation

New input weight factors:

Some manual allocation has to be done done:

Technology Median Low HighCoal (SC) 0.07 0.06 0.06Coal (USC) 0.05 0.04 0.04Coal (sub-c) 0.54 0.48 0.44Natural Gas 0.02 0.01 0.02Nuclear 0.01 0.01 0.01Hydroelectricty 0.30 0.28 0.23Wind power 0.01 0.01 0.01Solar PV 0.01 0.01 0.01

Input weight factors (% )

common sector Coal SC Coal USC Coal sub-c NG plant Nuclear Hydroelectricity Wind power Solar pvCoal mining and processing 0.11 0.08 0.81 0 0 0 0 0Petroleum processing and coking 0.02 0.03 0.05 0.9 0 0 0 0Transport and warehousing 0.11 0.08 0.81 0 0 0 0 0Crude petroleum and natural gas products 0.02 0.03 0.05 0.9 0 0 0 0Water production and supply 0.11 0.08 0.81 0 0 0 0 0Gas production and supply 0 0 0 1 0 0 0 0

Allocation across generation types

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Finally, direct and indirect CO2 emissions intensities can be calculated e = CO2 satellite account (el. Price assumption: 0.8 RMB/ kwh)

Technology

Hydroelectricity 18Coal sub-c 1000Coal super-c 900Coal USC 750Natural gas 400Nuclear 45Wind power 10Solar PV 30

CO2 intensity (gCO2/kwh)

Source: Nsakala, 2009

( ) 1−= −ε e I A

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CO2 emissions intensity of 42 industries in China, 2007

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Comparison of both disaggregation runs

Difference in CO2 emissions intensity between 2 disaggregation model runs. 1 = no difference, < 1= L 1 higher, > 2, L2 higher 21 Lindner et al. Disaggregation

In this work we disaggregated the electricity sector of the monetary IO table using an extension of Wolsky’s method

The task included to derive input/output weight factors that are used in the methodology to estimate a disaggregated technical coefficient matrix

Using additional external information seems to not change the results for total embodied emissions significantly, probably because summing indirect emissions along the supply chain will result in approaching the average again.

The uncertainty in the additional information used for deriving new Input/output factors is already quite high

Disaggregation based on national electricity generation mix for the national table is probably sufficient

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There are several approaches to disaggregation of the electricity sector and we chose only one of them. Alternative: hybrid model

To capture the “industry consumption mix” effect in international emissions embodied in trade a multi-region China model can be connected to an international MRIO

Data quality (province-level) is important for such a task.

This also counts for the province-level energy consumption data (in case of a hybrid model)23

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