Enhanced input-output modelling for improved assessment of supply chain-wide environmental pressures in space and time: the case of China
The PhD defence of Quanliang Ye will take place (partly) online and can be followed by a live stream.
Quanliang Ye is a PhD student in the research group Multidisciplinary Water Management (MWM). Supervisors are dr. M.S. Krol and prof.dr.ir. M. Berger from the Faculty of Engineering Technology (ET).
In the context of the Paris Agreement, Sustainable Development Goals, and circular economy agendas, whoever is responsible for the resource extractions and pollution releases of final goods and services has been debated. The virtual displacement of environmental pressures (EPs) from final consumers to production sites is the prominent issue in these debates. To solve the virtual displacement (or outsourcing) issue of environmental pressures, the consumption-based accounting that relies on the environmental-extended multi-regional input-output (MRIO) model has been widely used to quantify supply chain-wide EPs of consumed goods and services. However, key limitations lie in the conventional MRIO model: 1) the aggregation of products with different environmental properties into homogeneous sectors in the discipline of macroeconomics, and 2) the neglect of temporal dynamic feature of manufactured capital as primary production factors in economic activities.
The goal of this thesis is to develop improved modelling techniques to better capture spatiotemporal virtual displacement of EPs along the entire supply and use chain of products. This thesis proposes two improved models based on the conventional environmentally extended MRIO model to address aforementioned limitations: the hybrid MRIO model and the capital-endogenized MRIO model. The two improved models are applied to answer four research questions, of which the former two are related to the spatial virtual displacement of EPs embodied in trade and the latter two are related to the temporal virtual displacement embodied in capital.
A hybrid multi-regional input-output model of China: integrating the physical agricultural biomass and food system into the monetary supply chain. This chapter develops a symmetric MRIO model that hybridizes the physical food and agricultural biomass system with the monetary supply chain of China. First, the inter-provincial supply, use, and input-output tables in physical units of 84 agriculture, food and forestry products are constructed. These physical supply/use/MRIO tables systematically capture agri-food product flows, at an unprecedented level of product detail, along domestic supply chains within China. Then the physical MRIO table of agri-food products are integrated into the monetary all-sector MRIO table to construct a symmetric hybrid MRIO table of China. The application of our hybrid MRIO model to the case of provincial blue water footprint assessments reveals that the hybrid model enhances both the monetary MRIO table-based approach and the process-based approach from different aspects. For instance, the hybrid MRIO model can reduce the uncertainty of monetary MRIO modelling due to the aggregation of products with different environmental properties into homogeneous sectors. Lastly, uncertainty analysis is implemented to quantify the main sources of uncertainties, and understand the reliability of our new hybrid MRIO model for future sustainable development design.
Effects of production fragmentation and inter-provincial trade on spatial blue water consumption and scarcity patterns in China. This chapter formulates a comprehensive trade disaggregation approach to elaborate the virtual water networks of three trade patterns (i.e., direct final goods trade, intermediate goods trade for the last step of production, and value chain-related trade) within China, and further analyzes the impacts of trade on provincial blue water scarcity by comparing the actual water scarcity with that under a “no-trade” scenario (NTS). In 2012, there was 128 km3 blue water virtually transferred across provinces because of inter-provincial trade. Direct final goods trade contributed the most to the virtual water trade (accounting for 47% of the total), whereas value chain-related trade induced the least (17%). Compared with the results under the NTS, current trade alleviated the water scarcity in provinces under extreme water scarcity but worsened the water scarcity of this nation from a broader scope. It suggests policy makers fully considering specific trade patterns and their impacts on provincial or national water consumption to cope with water scarcity in China.
Linking the environmental pressures of China’s capital development to global final consumption of the past decades and into the future. This chapter developed a new global model for assessing capital formation and use along the global supply chain. It is used to quantify the linkages between capital use and human need production and consumption over the past two decades between six EPs caused by China’s capital formation and domestic as well as foreign consumption. Result show that only 35% of the assets acquired by China from 1995 to 2015, representing 32%-39% of the associated EPs (e.g., water consumption, GHG emissions, and metal ore extractions), have been depreciated, whilst the majority rest will serve future production and consumption. The outsourcing of capital services and the associated EPs are considerable, ranging from 14-25% of depending on the EP indicators. Without accounting for the capital-final consumption linkages across time and space, one would miscalculate China’s environmental footprints related to the six EPs by big margins, from -61% to +114%.
Re-allocating CO2 emissions of capital investment along capital’s full lifespan. This chapter quantifies the temporal displacement of capital and associated carbon emissions within China for the period from 1995−2017. The results show that considering the temporal CO2-emission displacement relieves the emission responsibilities of capital assets for the year of formation, with 25‒46% declinations from conventional accounting methods. To understand this temporal displacement from the past to the future, three capital investment scenarios until 2030, based on different purposes of capital investments (e.g., for further economic growth or for low-carbon development), have been designed. Overall, the existing capital in 2017 will still contribute approximately 10% of China’s carbon emissions in 2030, and account for more than 40% for capital-intensive service sectors like real estate or transportation services. The virtual temporal displacement of carbon emissions associated with capital feeds into a discussion on the equity across generations due to historical and future ‘commitments’ of emissions.
Conclusion. The hybrid MRIO model and the capital-endogenized MRIO model developed and presented in this thesis solved key limitations in conventional IO modelling for environmental pressure assessments. In detail, the hybrid MRIO model combines advantages of both process- and IO table-based approaches, thus enabling to quantify the supply chain-wide environmental pressures of a specific agri-food product. The capital-endogenized MRIO model endogenizes capital investment and consumption into economic production over time, thus enabling to allocate environmental responsibilities of capital activities among different capital activities along capital’s full lifespan. This thesis also has contributions related to datasets, such as a national dataset of inter-provincial trade-linked supply, use and input-output tables, and a capital investment dataset at the provincial level during the period of 1995-2017. Both of models can be used to better assign the environmental responsibilities of our production and consumption in space and time, and provide key information for policy makers, producers, and consumers to rethink their roles in global sustainable development and make their own contributions to deliver a sustainable future.
About the Author
Quanliang Ye (叶全梁) was born on 6th of April 1993 in Shaoxing, Zhejiang Province, China. He loves his four-people family, with a warm-heart mom, an always-optimistic dad, and a beautiful sister. He also loves his hometown which has more than 2,500-year history since ancient China's Spring and Autumn period. In Shaoxing, Quanliang was taken good care of by his family, and completed his pre-university education at Luxun High School in 2011. Quanliang’s background mainly focuses on environmental science, but not in the first place of his education. He obtained a BSc degree of Applied Mathematics at Hohai Univeristy in 2015. After that, he changed his major into environmental science also at Hohai Univeristy, and finally obtained his MSc degree of Environmental Science and Engineering in 2018. Thereafter, in September 2018, Quanliang started his oversea studies at University of Twente, the Netherlands, and now is approaching the end of his PhD. Quanliang’s research aims to better understand and reveal the human effects on natural environments in the context of climate change, globalization and sustainable development. This is built upon cross-disciplinary academic training in mathematics and environmental science, and research experiences with systems modeling like multi-objective optimization, multi-regional input-output analysis, and statistics analysis. At this moment, Quanliang is pretty interested in environmental footprint assessments and sustainable development pathway design. After his graduation, he will continue his European life in Denmark. He likes cooking a lot, and really good at it. He is also a sport guy. He plays several sports every week, including but not limited to badminton, tennis, basketball, and swim (mostly in the summertime).