Prediction of China's coal production-environmental pollution based on a hybrid genetic algorithm-system dynamics model

Abstract

This paper proposes a hybrid model based on genetic algorithm (GA) and system dynamics (SD) for coal production–environmental pollution load in China. GA has been utilized in the optimization of the parameters of the SD model to reduce implementation subjectivity. The chain of “Economic development–coal demand–coal production–environmental pollution load” of China in 2030 was predicted, and scenarios were analyzed. Results show that: (1) GA performs well in optimizing the parameters of the SD model objectively and in simulating the historical data; (2) The demand for coal energy continuously increases, although the coal intensity has actually decreased because of China's persistent economic development. Furthermore, instead of reaching a turning point by 2030, the environmental pollution load continuously increases each year even under the scenario where coal intensity decreased by 20% and investment in pollution abatement increased by 20%; (3) For abating the amount of “three types of wastes”, reducing the coal intensity is more effective than reducing the polluted production per tonne of coal and increasing investment in pollution control.

Introduction

Restricted by the resource endowment and energy structure of less oil, lack of gas, and coal-rich (NDRC, 2008), the coal industry has been and still will be the backbone of China's energy industry, contributing significantly to economic development. Coal consumption has accounted for over 70% during the past sixty years, making China the world's largest producer and consumer of coal since the 1990s (Andrews-Speed, 2004). In 2009, China's gross energy consumption is 3.1 billion tonnes tce (Ton of Standard Coal Equivalent), out of which 2.16 billion tonnes tce is attributed to coal consumption, which is 69.6% of gross primary energy consumption (CNBS, 2010). The Chinese Academy of Engineering consulting report has shown that China's energy structure will face a major adjustment. Along with the rapid development of new energy resources, the proportion of coal consumption will drop gradually, but will still account for 50% of the basic energy in the next 30 to 50 years. However, coal industry currently follows the linear production model of “resource development–production–waste emission”, which is based on a manner of massive production, massive consumption, and massive waste. The model has rapidly promoted the economic development of coal mining areas, but has also resulted in an over-exploitation of coal resources and several waste streams, the structural and functional destruction of the ecological systems, such as land and vegetation destruction, water pollution, air pollution, heavy metal pollution, water loss, soil erosion, and land desertification (Foster, 2001, Ghose and Majee, 2007). According to statistics from 2009, the industrial waste gas emission amounted to 0.436 million tonnes, waste water and solid waste up to 804 million tonnes, and the mining and washing of coal sector amounted to 239 million tonnes. Thus, total pollution amounted to 1041 million tonnes (CNBS, 2010). Furthermore, the destruction and occupation of various lands because of coal mining reached 0.790 million hectares (CNBS, 2010). The ecological environment issue resulting from coal mining has become one of the most important factors that negatively affects human health, deepens the contradiction between the land and humans, and constrains economic and social development (Chadwick et al., 1996, Bian et al., 2010).

For these reasons, determining the output of “three types of wastes (waste gas, waste water, and solid waste)” in various levels of economic development, such as coal energy intensity and environmental treatment investment, and exploring what measures can effectively reduce the amount of “three types of wastes” are important in regard to satisfying the demand for coal energy consumption.

Environmental pollution of coal mining areas occurs in numerous phases, such as mining, storage, and transportation, all of which affect the output of raw coal, mining investment, mining technology, waste management and utilization methods, and so on (Singh and Singh, 2006;Wang, 2006). The effect has a certain level of hysteretic, hidden uncertainty, with complexity and nonlinearity (Michalik et al., 2007). The behavior of the system is difficult to simulate through experiments and must then be achieved through a simulation model. The nature of law between “Economic development level–Coal consumption demand–Coal production–Environmental pollution loads” can be determined by analyzing the simulation results.

System Dynamics (SD) is considered an appropriate approach for predicting the dynamic results of the interactions and analyzing the implications of different policies given such complexities, as proposed by Forrester, 1961, Forrester, 1987. The construction of “causal loop diagrams” and “stock and flow diagrams” is necessary to form an SD context for applications. Within this context, stocks represent the account of a system component, either spatially or temporally (i.e., population). Flows are the rate at which the component flows in or out of the stock. Converters modify rates of change and unit conversions. Principles to develop SD models can be found in a series of literature (Forrester, 1987, Qudrat-Ullah and Seong, 2010, Saleh et al., 2010). The method can effectively incorporate individual system components within a general framework and then comprehensively analyze their interactions. Obtaining knowledge of the environmental concerns, as well as the relevant policy responses, is very meaningful for the sustainability of the development of coal mining areas.

The SD methodology has been used to study environment and energy. For example, Guo et al. (2001) developed an environmental SD model for supporting the Lake Erhai planning task. O'Regan and Moles (2006) proposed an SD model to simulate the interaction between environmental and economic factors in the mining industry. Arquitt and Ron Johnstone (2008) described an SD model developed for the design of a proposed environmental restoration banking system. Assili et al. (2008) proposed an improved mechanism based on SD for capacity payment in a competitive electricity environment.

Recently, an increasing number of publications focused on the applications of SD models in the ecological environment of China's coal production and related issues. Related research can be categorized as follows: (1) SD model of coal production and supply capacity. Fan et al. (2007) developed an SD model considering the investment in coal industry, available reserves, mine construction, and coal supply capability and optimizing the optimal investment size; and (2) an SD model of the sustainable development of the coal industry. Li et al. (2006) established an SD model for the sustainable development of the coal industry, arguing that resource carrying and environmental carrying capacities are the most important factors constraining the sustainable development of the coal industry. Yao and Lu (2008) constructed an urban SD model of a coal city and conducted a case study for the five development model simulations and policy controls in Jixi city, Shanxi province, China. Hou et al., (2009) established a resources–economy–environment SD model in Hebei province, China to predict development trends in coal reserves, coal demand, supply, pollution under various conditions of economic development, industrial structure, and investment in exploration and production. (3) Environmental SD model of coal mining areas. Wen et al., (2008) divided the coal mining environment system into three parts: solid waste, water, and atmosphere. The primary environmental SD model has been proposed.

The above mentioned studies have fully explored the dynamic features of the ecological environment attributable to coal production, as well as the relationships between system elements. However, some issues should be further studied. First, a number of parameters in the SD model are determined primarily by the lookup function or the regression of historical data. The lookup function can well simulate historical period data, but data for future periods are estimated primarily by man-made functions, which may be strongly subjective. The regression equation is typically a large deviation because SD often models a complex system with multiple feedbacks. Therefore, the reliability of the model decreases. To estimate the parameters, “simulation–analysis–revision,” which is similar to hand-style “trial and error” method with a certain degree of blindness, is sometimes used. Ensuring that the estimates key parameters are conducted efficiently and with high quality is difficult. Second, the SD models of the ecological environment of coal primarily focus on historical data simulation and on the mechanism of dynamics features in the existing literature. The models do not conduct scenario–response analysis and corresponding policy proposals according to different scenario economic development levels, investment size, and environment treatment ratio.

On the other hand, Genetic Algorithms (GA) are general-purpose population-based stochastic search techniques that mimic the principles of natural selection and genetics laid down by Charles Darwin. The concept of GA was introduced by Holland (1975) and has proven to be effective in various applications, especially in modeling (Salim and Cai, 1997, Sweeney et al., 2007, Wang et al., 2009). To successfully simulate historical data and reflect the system of “economic development–coal energy demand–coal production–environmental pollution,” the present study utilized GA to optimize the parameters in the SD model.

Therefore, the current research focuses on two issues. (1) A genetic algorithm optimization method is introduced into an SD model to automatically global optimize model parameters and reduce human subjectivity. (2) The environmental pollution load resulting from coal production is predicted in terms of different scenarios, and a number of policy recommendations have been proposed to mitigate environmental pollution in coal mining areas.