# Compilation of a City-Level & Four-Digit Industry Code MRIO Table Based on Firm-Level Data
## 2 Background of MRIO-Compilation in China
Due to the time-consuming and labor-intensive characteristics of input-output tables at the national level, the compilation of the IO tables mentioned above has participated in by the NBS in China. Scholars have also applied different methods to compile MIRO, mainly at the country or subnational levels, such as provinces, states or counties [18]. For example, [PERSON] et al. [23] demonstrated the construction of a 1997 to 2011 time series of nested interregional-international MRIO tables with the case of the Chinese economy, where each of the 30 provinces is distinguished with 135 industry sectors inside it and linked with 185 world countries. Although it features a large-scale, subnational and international IO relationship, it fails to provide high-resolution inter-regional connections inside China. [PERSON] and [PERSON] [12] used China and Japan's inter-regional IO tables, the OECD inter-country IO tables, and China and Japan's regional customs statistics to compile the regionally extended inter-country IO table. However, the table segments the Chinese domestic region into four parts, namely China Coast, center, west and northeast, which is rough of China's great regional heterogeneity and large economy. In addition, based on the modified gravity model, [PERSON] et al. [24] compiled the Chinese MRIO table for 2012 among 30 economic sectors in 30 regions (30 provinces excluding Hong Kong, Macao, Taiwan and Tibet due to data unavailability). The compilation of the 2012 MRIO table is based on the IO table for 30 Chinese provinces published by the NBS. It is the first MRIO table to reflect China's economic development pattern after the 2008 global financial crisis [24]. However, it aggregates the 42 economic sectors into 30 sectors because there are 30 sectors in the Chinese MRIO tables for both 2007 and 2010. Therefore, the aggregation of sectors might lead to bias in the IO analysis. The input-output analysis method based on the IO table is used not only in the research of industrial structure problems but is also widely applied in economic planning, policy simulation, industrial value development, price measurement, etc. In particular, in the field of environmental research, specific input-output tables have also been developed, such as the U.S. environmentally extended input output (EEIO) models [25]. Most studies on the environmental and social impacts associated with consumption require extended MRIO tables for better empirical results [26]. For example, based on an MRIO table for German Federal States, the research from [PERSON] et al. [27] presents a net economic impact assessment of the German Renewable Energy Act on the economy of the state \"North-Rhine Westphalia\". [PERSON] et al. [28] used a new U.S. MRIO model to determine the economic and energy impacts resulting from the installation of wind energy farms in 10 U.S. states. From a practical point of view, input-output data and methods play an important role in China's current economic structural transformation, as well as in the analysis of regional, resource and environmental economic linkages. Combining network analysis tools, [PERSON] et al. [29] analyzed the regional and sectoral structure of the Chinese economy with the MRIO tables of China in 2007 and 2012 [16; 22]. Some researchers have used Chinese MRIO to perform environmental studies, such as carbon emissions, air pollution, water consumption, and other energy use. For example, [PERSON] and [PERSON] [30] compared emission flows in China for 2002 and 2007 by two methods: the bilateral trade (EEBT) and MRIO model. They find that the results from the two approaches differ significantly and the analysis based on the MRIO method is more applicable for consumption-based studies. [PERSON] et al. [31] applied an environmentally extended multiregional input-output approach to measure carbon inequality for households in China's 30 regions and concluded that economic growth contributes to an overall reduction in carbon inequality in China. [PERSON] et al.