Promises and pitfalls in environmentally extended input–output analysis for China: A survey of the literature

Abstract

As the largest developing economy, China plays a key role in global climate change. Environmentally extended input-output analysis (EE-IOA) is an important and insightful tool seeing widespread use in studying large-scale environmental impacts in China: calculating and analyzing greenhouse gas emissions, carbon and water footprints, pollution, and embedded energy. Chinese EE-IOA are hindered, however, by unreliable data and limited resolution. This paper reviews the body of literature regarding EE-IOA for China in peer-reviewed journals and provides an overview of the articles, examining their methodologies, environmental issues addressed, and data utilized. This paper further identifies the shortcomings in using inputoutput analyses to gauge environmental impacts in China. Potentially fruitful areas of expansion in Chinese EE-IOA research are denoted, including under-researched environmental issues, underutilized methodologies, and techniques to disaggregate data to move beyond the limitations inherent in official Chinese input-output data.

Unequal household carbon footprints in China

“…Furthermore the GTAP team used detailed trade price indices and trade data to link up the individual country tables and establish sectoral interlinkages, while for this study only shares of total outputs and importshares 6,10 were used to estimate domestic and imported demand. For further systematic and detailed treatment of uncertainties in multi-regional input-output models we refer the interested reader to the literature 8,9,26,29,31,[34][35][36][37][38] .…”

“…The input-output tables were used in full detail of 135 sectors, going beyond existing studies which use more aggregated IO tables (8-40 sectors) due to for example a focus on time series analysis and the constraint of backwards compatibility of input-output tables 2,14,15,25,26 . CO2 from fossil fuels energy use data has been compiled from the latest revised data 27 , which is up to 10% lower than the official Chinese Energy Statistical Yearbook 28 .…”

Review of the Water Footprint Project within Geographically Delineated Area

“…Due to the data intensive and bottom-up nature of traditional life cycle assessment, LCA can be excellent for measuring direct inputs, but presents difficulties when accounting for indirect inputs [15]. Hybrid LCA and IO-LCA provide top-down approaches to LCA that accounts for both direct and indirect inputs.…”

China's changing diet and its impacts on greenhouse gas emissions: an index decomposition analysis

“…Most of these studies examined food production and agriculture in Western nations, such as the European Union, United States and Australia, with studies outside these countries, such as in India, being relatively fewer (Herrero et al 2011;O'Mara 2011;Weiss and Leip 2012;Cederberg et al 2013). As the world's largest producer of GHGs and with increasing influence in the Pacific Rim, there is great interest in GHG production in China, in general (Su and Ang 2013;Hawkins et al 2015), but only a few studies have looked specifically at GHG production in China associated with the agricultural sector and food production industries (Wang 2010;Lin et al 2014). In parallel, while the food and beverage industry has been commonly identified in decomposition analyses of energy use and GHG emissions (Ang and Zhang 2000;Ma and Stern 2008;Zhao et al 2010), its use in determining the drivers for foodrelated GHG emissions has been limited (Hawkins et al 2015).…”

“…As the world's largest producer of GHGs and with increasing influence in the Pacific Rim, there is great interest in GHG production in China, in general (Su and Ang 2013;Hawkins et al 2015), but only a few studies have looked specifically at GHG production in China associated with the agricultural sector and food production industries (Wang 2010;Lin et al 2014). In parallel, while the food and beverage industry has been commonly identified in decomposition analyses of energy use and GHG emissions (Ang and Zhang 2000;Ma and Stern 2008;Zhao et al 2010), its use in determining the drivers for foodrelated GHG emissions has been limited (Hawkins et al 2015). This study fills this gap in three ways.…”