European emission trades from the vehicle industry 

Mathieu Delpierre

Introduction:

Industrial Ecology is a new field that has been developed from 1980s and adopts an interdisciplinary approach (with engineering, social and environmental sciences) to tackle sustainable problems. Many techniques can be linked to it, such as LCA (Life Cycle Analysis), MFA (Mass Flow Analysis) and EIOA (Environmental Input-Output Analysis). Each of them considers specific aspects of sustainable problems, with its advantages and limits. Concerning EIOA, courses offer the possibility to students to get a deeper understanding of this tool, firstly developed by Nobel Laureate Wassily Leontief in 1970s. This technique, first developed for economic purposes, seeks – to put it simple – to understand the interactions between final demand and production. With time, this tool has also been used in environmental purposes or analysis and can nowadays answer important and interesting questions such as: “what are the emissions linked to a specific product and in which sector do they occur?” or “how the production pattern would change if a country went fully vegetarian?”. Some questions may sound a bit unrealistic but can provide relevant insights.

From my experience, one concept that I found interesting with the concept of EIOA is the study of the carbon trades. To make it more concrete: imagine that a French person buys a car. He may have bought the product in France, but the car may have been manufactured in Germany, the electronic pieces may have been assembled in Japan and the raw materials may have been extracted in China. So, in global, the carbon footprint of the car would not occur only in France, during its use. This example has no sources, it is just to show the idea. There can be what are called ‘carbon trades’ between countries and in the context of fighting the climate change, highlighted by the Paris Agreement in 2015, I personally find this concept really interesting. A sector in which I wanted to apply the carbon trades principles is the car industry, responsible for amongst the highest CO₂ emissions in the world. Another aspect that is also interesting from EIOA is the ability to consider temporal evolution. Based on the data collected through time by National Statistical Offices, a trend through the years can be drawn.

My assignment:

From this basis, I decided to conduct a small research to answer the following question:

“How did the emission trades between the EU and the RoW (Rest of the World) in the vehicle industry evolve between 2005 and 2011? What are the lessons that can be taught from it?”

A result that would be debatable to me is to see that European countries decrease their carbon footprint, but at the expense of other countries. To answer the research question, I used Exiobase 3.0, a “Multi-regional Environmentally Extended Supply and Use/Input Output database” (EXIOBASE Consortium, 2018). To express it simply, it provides a lot of data in Excel files, indicating which industry from which country sends how many products to which industry in which country, in the form of matrices. With the help of a characterization matrix, I could turn the input-output values into kg CO₂-eq. By using some mathematical formula (r=bLy, with L the Leontief inverse, y the final demand, b the emission coefficients matrix and r the total emission vectors in kg CO₂-eq), I could calculate the carbon trades. The main steps that I achieved are: (if you want the Python code, please ask it in the comments)

• Isolate only two categories of product/service: “Motor vehicles, trailers and semi-trailers” and “Sale, maintenance, repair of motor vehicles, motor vehicles parts, motorcycles, motor cycles parts and accessories” from Exiobase
• Combine the two categories above to consider only one category called “vehicle industry”, for results’ simplicity.
• Create a group “EU” which includes all its members, so I can make comparisons with the other countries categories
• Consider the final demand for “Vehicle industry” only from the EU category but the production must consider all categories.
• Create tables though time to see an evolution.

My results:

Below are presented some graphs of the results I obtained from my work:

The graphs above show the carbon trades occurring in the world due only to European demand in the “vehicle industry” category, through different years. The last graph shows the evolution between 2005 and 2011. Firstly, what can be noticed is that most of the carbon trades due to European demand in “vehicle industry” actually occurs within the EU. Secondly, on a global scale, a decrease in emissions is observed, especially after 2008 so there may be links made with the economic crisis. Nevertheless, a reduction of circa 20% occurred between 2005 and 2011. Thirdly, the unexpected importance (to me) of Mexico and Indonesia in the role of supplying the European demand in “vehicle industry”.

To understand better the roles of these countries and this quite unexpected result, I have achieved some further research with other resources and found out that:

• Mexico is ranked 7th for vehicle motor production in the world and 80% of the Mexican vehicle’s production is exported. The EU is the third foreign market, reinforced with Mexico-EU FTA (Free Trade Agreement) from 2000 (Naftabeyond, 2017; OICA, 2018; Player, Partner, & Investment, 2001)
• Indonesia is a growing market in Asia. Consequently, many countries want to invest there even if the Japanese brand Toyota still possesses the largest share. Vehicles are the fifth export sector in Indonesia (Workman, 2018). On different segments, Europe accounts for 15% of the exports (2^nd client) (OEC, 2018).

To support these further researches, the Observatory of Economic Complexity has been really helpful and provided interesting visual presentations on the economic trades between countries (OEC, 2018).

Obviously, some potential errors can still be present in the Python code but the results are coherent. Furthermore, I wished there were more recent data from Exiobase (2011 was the latest year recorded when I conducted this assignment) in order to have a “closer” result in time. Finally, the role of Indonesia and its importance remain a bit unclear and requires probably further research for a deeper comprehension.

Conclusion:

To conclude, my study showed no obvious trend through time to “outsource” the carbon emissions due to the European demand in “vehicle industry”. However, Mexico and Indonesia still possess a significant share in it and this situation may be improved. Therefore, the trade between Mexico and the EU could be reconsidered by implementing stricter environmental regulations for example. The role of Indonesia should be further studied in order to improve its position. Finally, the trend for emissions’ decrease should be kept for the future. 

Bibliography

EXIOBASE Consortium. (2018). Exiobase. Retrieved August 21, 2018, from https://www.exiobase.eu/

Naftabeyond, Y. S. (2017). Mexico’s auto industry, 23 years since NAFTA and beyond. Retrieved from https://www.cargroup.org/wp-content/uploads/2017/08/Sandoval.pdf

OEC. (2018). OEC: The Observatory of Economic Complexity. Retrieved June 19, 2018, from https://atlas.media.mit.edu/en/

OICA. (2018). World motor vehicle production by country and type. OICA. Retrieved from http://www.oica.net/wp-content/uploads/By-country-2017.pdf

Player, A. G., Partner, S., & Investment, F. O. R. (2001). The automobile sector in Mexico. Brussels. Retrieved from http://www.economia-snci.gob.mx/sic_php/pages/bruselas/pdfs/FS02AUT.pdf

Workman, D. (2018). Indonesia’s Top 10 Exports. Retrieved June 17, 2018, from http://www.worldstopexports.com/indonesias-top-10-exports/