A nonlinear programming approach to strategic planning of coal chemical industry with carbon constraints in China

Abstract

Coal chemical industry plays a critical role in China’s economic growth and energy security. However, its carbon-intensity characteristics cause a large number of CO2 emissions during coal chemicals production. Facing intense pressure to reduce CO2 emissions, it is urgent to seek synergistic development between CO2 emissions reduction and coal chemical engineering. A nonlinear programming (NLP) approach is proposed to optimize the deployment of China’s coal chemical industry under carbon constraints. The NLP model is pursuing the minimum CO2 emission per unit value of gross output of coal to chemicals sector (CPUVGC) with simultaneously satisfying economic growth. Twelve main categories coal chemical products and six measures or technologies of CO2 emission reduction are taken into consideration in the NLP model, based on which a short-term (2020), mid-term (2030) and long-term (2050) deployment of coal chemical industry under restriction of CO2 emissions are investigated, and sensitivity or uncertainty analysis of effects of crude oil price (COP), which have a significant impact on coal chemicals price, on CO2 emission reduction target also is performed. Three scenarios involved 100% (positive), 50% (moderate) and 25% (conservative) of the predicted target of CO2 emissions reduction from different technologies or measures of CO2 emissions reduction are analyzed in different periods. At the end, the development roadmap (2020-2030-2050) of coal chemical industry under carbon constraints is plotted and some specific suggestions and safeguard measures are also provided to guarantee implement of the planning.