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ABSTRACT:Industrialization and urbanization in the developing world have boosted steel demand during the recent two decades. Reliable estimates on how much steel is required for high economic development are necessary to better understand the future challenges for employment, resource management, capacity planning, and climate change mitigation within the steel sector. During their use phase, steel-containing products provide service to people, and the size of the in-use stock of steel can serve as indicator of the total service level. We apply dynamic material flow analysis to estimate in-use stocks of steel in about 200 countries and 3 identify patterns of how stocks evolve over time. Three different models of the steel cycle are applied and a full uncertainty analysis is conducted to obtain reliable stock estimates for the period 1700-2008.Per capita in-use stocks in countries with a long industrial history, e.g., the U.S, the UK, or Germany, are between 11 and 16 tonnes, and stock accumulation is slowing down or has come to a halt. Stocks in countries that industrialized rather recently, such as South Korea or Portugal, are between 6-10 tonnes per capita and grow fast. In several countries, per capita inuse stocks of steel have saturated or are close to saturation. We identify the range of saturation to be 13±2 tonnes for the total per capita stock, which includes 10±2 tonnes for construction, 1.3±0.5 tonnes for machinery, 1.5±0.7 tonnes for transportation, and 0.6±0.2 tonnes for appliances and containers. The time series for the stocks and the saturation levels can be used to estimate future steel production and scrap supply.

The uncertainties of China's gross primary productivity (GPP) estimates by global data-oriented products and ecosystem models justify a development of high-resolution data-oriented GPP dataset over China. We applied a machine learning algorithm