Our research aims to combine economic input-output accounts with environmental and energy data in order to understand drivers of environmental impacts, energy and emissions growth at the regional, national as well as global level. Ultimately, our research is suitable to form policies based on production as well as consumption. This often requires the use of environmental-economic life cycle assessment (LCA) in order to estimate carbon footprints, or structural path analysis (SPA) techniques to quantify the environmental impact of global supply chains from leading industries.
Input-Output Analysis (IOA) is a well-established analytical model within economics and systems of national accounts. Wassily Leontief developed IOA in the late 1930’s as an analytical quantitative framework to investigate the complex interdependencies within the US economy. Initially, the method was used to evaluate inter-industry requirements in the US economy, but it was quickly adopted by other fields such as energy, environmental pollution and material flow analysis. If you want to learn more about IOA, read the "Crash Course to Input-Output Analysis" by Erik Dietzenbacher.
In our team we use IO techniques exclusively for environmental-economic-energy (E3) analysis. We stress both, new methodological advances as well as applicability of our work to policy makers.
Development of Hybrid Life Cycle Assessment Tools
On the side of methodological development we have focussed on improving input-output tables to reflect better technological diversity of environmentally sensitive sectors. Main focus has been on representing the electricity production sector in China's input-output table in a high resolution. Such work consists in combining bottom-up life cycle inventory data of products with the top down input-output data in order to mitigate weaknesses of both individual techniques. On a policy level the work contributes to a more accurate analysis of CO2 emissions embodied in international trade of products. The peer-reviewed papers can be found here and here.
More specifically, in regards to China we also used IOA to analyse the embodied energy use in the supply chain of energy-intense industries:
This figure shows a comparsion of main emitting sectors in China across provinces for the year 2009. Here we found a high disparity of technology use of the same industry in different regions and provinces of China and recommend that Chinese policy makers ought to focus on improving industrial efficiency in low developed provinces of China.
For the Chinese electricity production sector we showed the high disparity of CO2 emissions associated with provincial production and consumption:
Here we found that the differences between provincial CO2 emissions embodied in electricity consumption (red bar) versus production (blue bar) can be as high as the total domestic emissions of the Dutch electricity sector (in the case of Beijing), or as high as the emissions from France's electricity sector (in the case of Guangdong Province). The full paper can be viewed here.
Development of supply chain emissions analysis
Input-output analysis techniques have also been developed that enhance our understanding of how consumption activities, at the scale of regions, industries and enterprises, drive production activities elsewhere in the global economy. Applications of these techniques have been used to infer the potential influence different actors in the economy may have over their indirect greenhouse gas emissions.
An analytical and diagrammatic approach for mapping flows of embodied emissions through the global economy has been developed. The resulting maps (such as the one illustrated in the figure below) help to visualise and explain how the differences between production-based and consumption-based accounts of emissions arise and provide insight into the varying role played by different industries at different stages of the global production system. For a comprehensive description of the mapping approach and application to the global economy please see Skelton et al., 2011.
Methodological developments have allowed us to measure the maximum potential influence different actors have over emissions reductions from indirect supply chain sources. The figure below illustrates the breakdown of influence different EU industries have over their direct and upstream indirect emissions sources.
Collectively, in 2007, EU industry had the potential to influence over one gigatonne of carbon dioxide emissions from non-EU sources via the international supply chain linkages of its constituent companies. Focusing on the EU manufacturing industry, initial engagement with suppliers from Chinese metals and manufacturing industries were found to offer the greatest potential for driving emissions reductions from non-EU sources, as illustrated below. A detailed analysis into the influence of EU industry is reported in Skelton, 2013.
A computational model has also been developed that allows us to estimate the collective carbon footprint of a group of enterprises. The model reflects the scale and structure of the world's leading enterprises, removes double-counting caused by the presence of enterprises within each other’s supply chains and deals with uncertainty introduced by incomplete information. Through the use of Monte Carlo simulation and linear programming techniques, results are reported in the form of probability density distributions such as those illustrated below for a system of three enterprises.
Currently we are continuing to develop the Chinese environmentally-extended input output tables to include non CO2 pollutants, such as VOC, PM2.5, NOX and SO2. Given the recent all time high pollutant levels in Beijing-Tianjin and the whole North East of China, we aim to better understand the linkage between internationally traded products and local pollutants. This project includes an integrated assessment of atmospheric science, health models and economic development.
Lindner, S., Legault, J., Guan, D., "Disaggregating the Electricity Sector of China’s Input-Output Table for Improved Environmental Life-Cycle Assessment", 2013. Available at: Economics Systems Research (ESR) DOI: 10.1080/09535314.2012.746646
Lindner, S., Legault, J., Guan, D., "Disaggregating Input-Output Models with Incomplete Information". Available at: Economics Systems Research (ESR), Volume 24, Issue 4, 2012. DOI:10.1080/09535314.2012.689954
Lindner, S. Liu, Z., Guan, D., Geng, Y., Li, X., "CO2 emissions from China’s power sector at the provincial level: Consumption versus production perspectives", Renewable and Sustainable Energy Reviews, Volume 19, 164 -172, 2013. DOI:org/10.1016/j.rser.2012.10.050
Lindner, S., "A hybrid-unit energy input-output model to evaluate embodied energy and life cycle emissions for China’s economy", Accepted at: Journal of Industrial Ecology, 2013.
Skelton, A., 2013 (in Press). EU corporate action as a driver for global emissions abatement: A structural analysis of EU international supply chain carbon dioxide emissions. Global Environmental Change. http://dx.doi.org/10.1016/j.gloenvcha.2013.07.024
Skelton, A., Guan, D., Peters, G.P., Crawford-Brown, D., 2011. Mapping flows of embodied emissions in the global production system. Environmental Science & Technology. 45, 10516–10523. http://dx.doi.org/10.1021/es202313e
Liu Z, Geng Y, Lindner S, Zhao H, Fujita T, Guan D. "Embodied Energy Consumption in Chinese Industries", Energy Policy, 2012. (http://dx.doi.org/10.1016/j.enpol.2012.07.016).
Guan, D; Liu, Z; Yong, G; Lindner, S; Hubacek, K., "The Gigatonne Cap in China’s CO2 Inventories". Nature Climate Change, Volume 2, Issue 8, 2012. doi:10.1038/nclimate1560