Endogenising capital in MRIO models

Transcription

1 Endogenising capital in MRIO models Carl-Johan Södersten*, Richard Wood*, Edgar Hertwich** *Norwegian Institute of Science and Technology (NTNU) **Yale School of Forestry and Environmental Studies

2 Endogenising capital in MRIO models And how it would constitute an tremendously formidable add-on to the IELab Carl-Johan Södersten*, Richard Wood*, Edgar Hertwich** *Norwegian Institute of Science and Technology (NTNU) **Yale School of Forestry and Environmental Studies

3 Why? Capital still exogenous in MRIO models (final demand) Footprint calculations do not account for impacts associated with the capital goods used in the production processes Minx et al (2011): 21% to 31% of Chinese GFCF emissions ( ) can be assigned to the production of exports How are capital goods consumed across industries? How much of the impacts associated with the construction and maintenance of capital goods are embodied in the goods and services that we consume? How does the endogenisation affect service sectors? Minx, Jan C., et al. "A carbonizing dragon : China s fast growing CO2 emissions revisited." Environmental science & technology (2011):

4 Importance of capital 80% Share of total final demand constituted of GFCF (2011) % 60% Percentage of total 50% 40% 30% 20% 10% Euros 0% 0

5 Different measures of capital Gross fixed capital formation (GFCF) Consumption of fixed capital / depreciation (CFC) (Capital services)

6 MRIO Sectors / regions Sectors / regions => (I-A) -1 = L + Interindustry capital requirement matrix K => (I-(A+K)) -1 = L new Gross fixed capital formation Consumption of fixed capital

7 Data EXIOBASE v countries / regions 200 products, 163 industries Time series (nowcasted to 2015) GFCF as vector of final demand, CFC as row vector of primary inputs External detailed capital databases (EUKLEMS, WORLDKLEMS, national statistical offices) KLEMS: 13 countries (10 European plus JPN, AUS & USA) (update sept 2017: 27 countries) 8 products, 32 industries different capital metrics available (GFCF, CFC, Capital services, etc)

8 Procedure external capital data available Available capital use data => disaggregate to EXIOBASE EXIOBASE KLEMS K matrix DEU GFCF DEU,EXIO CFC DEU,EXIO

9 Procedure no external capital data available No available capital use data => generic capital matrix EXIOBASE KLEMS K matrix generic MEX CFC MEX,KLEMS GFCF MEX,KLEMS GFCF MEX,EXIO CFC MEX,EXIO

10 Results household emissions Dotted curves: consumption-based CO2 emissions without capital endogenised Solid curves: consumption-based CO2 emissions with capital endogenised

11 Total emissions from final demand Dotted green curves: consumption-based CO2 emissions without capital endogenised Solid green curves: consumption-based CO2 emissions with capital endogenised Yellow curves: production-based CO2 emissions

12 Dotted curves: net traded consumption-based CO2 emissions without capital endogenised Solid curves: net traded consumption-based CO2 emissions with capital endogenised (Only 5 largest trading partners are shown (in terms of traded emissions) Net-traded emissions

13 Net-traded materials Dotted curves: net material footprint without capital endogenised Solid curves: net material footprint with capital endogenised

14 CO2 Multipliers (kgco2/meur) : Services : Non-Services Carl-Johan Södersten IELab conference 2017 NTNU Industrial Ecology

15 Change in CO2 Multipliers : Services : Non-Services

16 Change in CF Multipliers : Services : Non-Services

17 Input requirements for IELab implementation Country capital distribution matrices (GFCF / CFC) Concordance matrices (+ distribution proxies?) How to handle countries with missing data Optional parameters / constraints: Years with missing data Historical stressor intensities (older assets used in current production) Balancing constraints Capital data for regionalisation (e.g. mining in Australia)

18 Challenges CFC data in constant prices -> Use GFCF price indices for CFC Using GFCF as a proxy temporal aspect When disaggregating CFC To EXIOBASE 200-product resolution (assumes same capital investment structure) Across countries (assumes same historical trade patterns) Generic capital distribution matrix RAS proxy -> Age of capital assets create scenarios Stressor intensity matrix changes over time Stressor intensities in constant prices => create scenarios (e.g. avg intensities over the last n years)

19 Conclusions Difference between consumption-based and production-based impacts increased further for most countries Increase in emissions embodied in trade Minor reallocations of emissions between countries, major reallocations of emissions between products (multiplier change) Could perhaps be an useful feature of the IELab?

20 Thank you for your attention! Comments, questions? Carl-Johan Södersten IIOA conference 2017 NTNU Industrial Ecology

21 GFCF per product category Carl-Johan Södersten IIOA conference 2017 NTNU Industrial Ecology

22 Goverment expenditures per product category 'Public administration and defence services; compulsory social security services (75)' 'Health and social work services (85)' 'Education services (80)' 'Research and development services (73)' 'Recreational, cultural and sporting services (92)' 'Plastics, basic' 'Supporting and auxiliary transport services; travel agency services (63)' 'Other waste for treatment: waste water treatment' Carl-Johan Södersten IIOA conference 2017 NTNU Industrial Ecology

23 Global household expenditures per product category Carl-Johan Södersten IIOA conference 2017 NTNU Industrial Ecology

24 Comparability of results Final demand categories: hh, GFCF, gov, NGO, etc: y tot New final demand, no GFCF: y nocap d = SL y old old tot d = SL y new new nocap d = SL ( y + y ) ( y GFCF K ) new new nocap res = -å resi i j ij