Economic Input-Output Life Cycle Assessment of the 180 Indusrial and Service Sectors in Thailand

Aweewan Mangmeecahi

Executive Summary

A process-based approach is commonly used for performing life-cycle assessments (LCAs) of the environmental impacts of products or services. The current research proposes an economic input-output life cycle assessment (EIO-LCA) approach for the Thai economy comprised of 180 industrial sectors to assess the production and service values, energy consumptions, and GHG emissions. The work presented here proposes an EIO-LCA approach for the Thai economy of 180 industrial sectors to evaluate production and service values, energy consumption, and GHG emissions.

Chapter 1 consists of an overview of LCA studies and their implications. LCA is the study of the environmental aspects and the potential impacts throughout a product’s life from the point of raw materials to the grave. Economic input-output life cycle assessment or EIO-LCA is a model approach of LCA. EIO-LCA takes a more aggregate view of all industrial and service sectors in an economy and makes use of two major simplifications. In Thailand, a few studies have attempted to develop an EIO-LCA model.
Chapter 2 describes research related to EIO-LCA and its connotations. Research works on EIO-LCA, implication of EIO-LCA, and Hybrid EIO-LCA were summarized and discussed. A few studies of EIO-LCA have been conducted in Thailand. However, the results report very greatly because researchers rely on different data sources and assumptions. Researchers have applied EIO-LCA model for sharing the GHG responsibility between the developed countries and developing countries.

The data sources and methods used in this study are detailed in Chapter 3. Thailand’s Input-Output Table (I-O Table) has been reported by Office of the National Economic and Social Development Board (Office of the National Economic and Social Development Board, 2005). The energy data are from Department of Alternative Energy Development and Efficiency (2005). Due to the aggregate of data, some sectors are assumed to consume energy/baht equally. The energy consumption is converted to CO2 eq emissions using emission factors derived from secondary data.

Chapters 4 and 5 are the overall implications of the model that focuses on the agricultural sector. The model limitations are also discussed. Chapter 4 focuses on the effects of the agricultural sector using EIO-LCA model to estimate the life cycle energy consumption and greenhouse gas emissions (GHGs). The results here show that by assuming 1 million baht of final demand (purchaser price) of the agricultural sector (sectors 1-24), themodel demonstrates the entire supply chain (180 sectors) related to the agricultural sector. For example, the first sector is the paddy sector. It covers the growing rice activities and also the production of both glutinous and non-glutinous paddy and its by-products e.g. straw. In terms of economic value, the top five economic activity values is the paddy sector itself, followed by the fertilizer and pesticide sectors, the basic industrial chemical sector, petroleum refinery, and petroleum and natural gas. With respect to CO2 emissions, the sector that produces the largest GHG emissions is the electricity sector (1,246 kg CO2), followed by the paddy sector (760 kg CO2), the fertilizer and pesticide sector (513 kg CO2), the basic industrial chemical sector (325 kg CO2), and the cutlery and hand tools sector (263 kg CO2). By looking at the energy and GHG emissions of the whole supply chain, the agricultural sector may find strategies to reduce the use of fertilizer and pesticide as well as rely on saver energy modes of transportation e.g. rail.

Chapter 5 shows the implication on food transportation impacted by using the EIO-LCA model. Transportation is the second largest CO2 emission sector in Thailand (Department of Alternative Energy Development and Efficiency 2013, Energy Policy and Planning Office 2013a). Among the modes of transportation, road transportation is the dominant form of cargo transport, accounting for 80 percent of all modes (tons of product) (Thailand Transport Portal 2015). The results show that, among all agricultural sectors, the vegetable sector shares the highest CO2 emissions with transportation. In other words, transportation emissions of all modes of cargo transport account for 6 percent of total supply chain emissions. This suggests that transportation in the agricultural sectors yield a relatively small portion of transportation emissions. By changing the transportation mode from freight to rail, emissions would be expected to be reduced. If 50 percent of road transportation is shifted to rail transportation, CO2 emissions will be diminished by 30 percent.

EIO-LCA model suffers from limitations through high levels of aggregation. It must be noted that the model is developed from assumptions. In this study, the energy data is reported for only 18 major sectors while the IO table is reported in a more disaggregate view (180 sectors). Future research should focus on product-level EIO-LCA to disaggregate economic sectors in order to create a hybrid of the process model and EIO-LCA approaches