In this paper, the input–output model is extended with assets. It allows us to examine the various assets that are held and used in production. The requirements of assets that must be held by each sector can thus be specified. Extending the input–output model with assets provides a better alternative to the capital stock matrix in the standard Systems of National Accounts. The input–output model is extended by taking the depreciation of fixed assets into full account. This extension allows for the calculation of total holding coefficients that express the amount of assets that are required to be held in each sector in order to satisfy a unit of final demand. In addition, a dynamic version of the extended model is presented. The extended input–output model has been widely applied in China for various purposes.

The input-output analysis developed by American economist Wassily Leontief has found wide applications all over the world. In current inputoutput tables, the term input means the consumption of various essential factors in economic activities. In addition to consuming some means of production, a production process also controls stocks of many means of production, natural resources and so on, such as capital assets, land, circulating capital and labour force. For example, in farming a certain amount of land must be occupied and in mining some mineral resources should be possessed. Any producer who wishes to exist and develop in competition will control and employ a certain amount of skilled labour force, including experienced managing personnel, engineers and workers. To acquire an output there should be not just input flows but also 'occupancy' in the sense of control over or possession of stocks from which many of these flows originate.

本文构建了一种能够反映中国加工贸易特点的非竞争(进口)型投入占用产出模型,提出了一个国家全部出口与分部门、分大类商品的单位出口对国内增加值和就业的拉动效应的计算方法,从数学上证明了出口总值等于出口商品所包含的完全国内增加值与完全进口额之和,并据此编制了2002年中美两国的非竞争(进口)型投入占用产出表,测算和分析了中美两国出口对各自国内增加值和就业的影响。

Develop a methodological framework for the estimation of the increases in domestic value-added and employment in a country in response to increases in its exports, in the aggregate as well as disaggregated by commodity and by destination. Apply this methodology empirically to the estimation of the increases in Chinese domestic value-added, or equivalently, Chinese GDP, and employment, as a consequence of increases of US$1,000 in aggregate Chinese exports to the United States and, say, US$1,000 in the exports of Chinese textiles to the United States, and to the World. The methodology is applicable to the analysis of the effects of exports to any specific country, or groups of countries, or to the World as a whole.

We develop an input–output methodology to estimate how Chinese exports affected the country's total domestic value added (DVA) and employment in the years 2002 and 2007. For every US$1000 dollar of Chinese exports in 2007 (2002), DVA and employment are estimated to be US$591 (US$466) and 0.096 (0.242) person-year, respectively. To implement these estimations, we use hitherto unpublished Chinese government data to construct several completely new datasets, including an input–output table with separate input–output and employment-output coefficients for processing exports, non-processing exports, and output for domestic use. We hypothesize that, in comparison with the export sector, China's domestic sector would be relatively autarkic due to China's history of central planning. We expect that exports would generate less DVA and employment than output for domestic use. Processing exports, which are highly dependent on imported inputs, would similarly generate less DVA and employment than non-processing exports. Our findings support these expectations. For both 2002 and 2007, the DVA and employment effects of domestic final demand were higher than those of non-processing exports, which were in turn higher than those of processing exports. However, with the progress of economic reforms, we found that the total DVAs of exports and domestic final demand have converged from 2002 to 2007.

Feeding 1.2 billion Chinese is a critical issue both for China and for the world. This paper presents a systematic integrated method (SIM), with the key elements of input-occupancy-output analysis, nonlinear variable coefficient forecasting equations, and minumum sum of absolute value technique to predict China's grain output. Since 1980 this approach has been successfully implemented in China, and is appreciated by China's top leaders and responsible governmental agencies.

主要介绍了我国学者陈锡康研究员所创立的投入占用产出技术，从静态模型和动态模型两方面完整地阐述了投入占用产出思想、模型。

This article describes the three main stages of the development of input–output techniques in China, examines the sectors in which input–output has been employed, and considers which input–output applications have been found useful.

This paper develops a multi-year lag Input-Holding-Output (I-H-O) Model on education with exclusion of the idle capital to address the reasonable education structure in support of a sustainable development strategy in China.

Water pricing plays a crucial role in water resources management. Water shadow price is an important reference in setting water price. It was said that in practice it is almost impossible to obtain water shadow price by solving a linear programming model. In this paper we use water conservancy economy input-occupancy-output tables of the nine Chinese major river basins, and combining input–output analysis method with linear programming method we develop a linear programming model with restrictions on the final demand, total output, trade balance and water availability. We estimate the water shadow prices for industrial water and productive water for the nine Chinese major river basins in 1999 and compared these results with the real industrial water price in China in 1999. Then, for operational purposes and to estimate the shadow prices of industrial water and productive water more easily, and using a Gauss–Newton nonlinear simulation method, we present two nonlinear models that relate the ratio of the volume of water used to the total water resources volume with the shadow prices to predict shadow prices of industrial water and productive water in 2020 and 2030 in China and its nine major river basins.