China’s Economic Gene Mutations

What is mutation of an economy? Is there any gene in the economy? Production functions with electricity have the characteristics of a gene, and it can reflect the mutation of the economy by electricity economics. This book typically tries to provide a new perspective of study on China’s economy by electricity economics. As one of the necessary inputs, electricity has been commonly used in almost all types of productions, and electricity data can also serve as an accurate measure for economic productions in nearly all kinds of economic activities. It is a useful data source for reviewing economic activities that require accurate and reliable electricity data from firm to sector, to industry, and to national economy. By means of electricity economics, the economic development in China will be reviewed in this chapter. It will provide some new scenarios on economic activities for some sectors; the primary, secondary, and tertiary industry; and the national economy.

China’s economy bids farewell to the period of high growth near 10 % in the past three decades. What will it be in the future? How do we avoid risk of stagflation and convert the challenges to opportunities of economic development by the fiscal and monetary policies? It will be tried in this chapter by studying the economic gene mapping of China. Simulations of economic development until 2025 have been executed with our multi-Agent Response Equilibrium (ARE) model which will be introduced in Part Two in this book. Policy selection is like doing experiments to test many policy simulations by ARE and then to find a better one. It has tested many scenarios of combining fiscal and monetary policies each year during 2015–2025. Two different policy simulations have been studied in this chapter: one is based on the fiscal and monetary policies in 2014 without any new policy during 2015–2025 as BAU scenario, and another one is to select some fiscal and monetary policies in 2015–2016 as policy scenario. Two economic gene mappings have been studied based on the two simulations. One finding is that the BAU would increase marginal representative factor productivity of the economy a little higher than policy scenario. Another finding is that a mutation occurred in primary industry, secondary industry, and national economy in 2012, respectively, a mutation in tertiary industry happened in 2014, and there would be no mutations in the three industries and national economy until 2025. The third finding is marginal representative factor productivity in primary industry would have a great increase during 2012–2025 based on the gene map, and perhaps it could be a productivity revolution. The fourth finding is there would be an economic cycle in China’s economy in 2015–2025, the peak growth would be around 2019–2021, and the low growth periods would be in 2015–2017 and 2023–2025. Policy scenario would decline the peak growth and rise lower growth of the economy. As for the inherent rule of the economy in China, there would be an increment of the productivity in the next few years if there were no economic events and strategic mistakes on national economy. Therefore, the mutation of national economy in 2012 would be an era to turn up productivity from lower level to higher one. In this case, the challenges could be an opportunity in China’s economic development.

Economic system is a large and complex system. It consists of a great number of economic men that interlink and interact with each other, which renders the whole system particular functions and characteristics. To carry out research on economic issues, appropriate models are indispensable tools. Plenty of scholars in the world have done a wide variety of researches based on certain economic modeling techniques. In this chapter, we will provide a brief review of popular models in the area of national economy. To start with, the input–output table will be introduced, since it is the most classical means to express the national economy situations effectively and clearly, and it serves as the foundation of not only some existing economic models but also the Agent Response Equilibrium (ARE) model proposed in this book. Then, we focus on the Computable General Equilibrium (CGE) model and the Agent-Based Computational Economics (ACE) model, which are useful tools that appear frequently in both academic papers and practical applications. In fact, our model is proposed and developed based on the analysis of the strengths and weaknesses of these models. Therefore, we would like to present our knowledge of existing economic modeling methodologies before introducing our model in detail in the following chapters.

The system of national economy is extremely large and complex. As a result, it is an undoubtedly difficult task to build models for economic system and simulate its operation. In order to cope with this issue, we need to find some advanced methodologies from the perspective of complex system. The system engineering is an option, but its effect is comparatively limited to deal with the uncertainty problems and distributed intelligence in the system. With the development of innovative ideas and techniques of artificial intelligence, authors have improved the theory and proposed the methodology of intelligent engineering [1]. From our point of view, it is a reasonable and feasible choice to serve as the foundation for modeling complex economic systems. It plays an important role in designing our model and coding our programs. In this chapter, the basic theory of intelligent engineering will be introduced. Particularly, we focus on two aspects to express the typical contents of intelligent engineering, i.e., generalized model and intelligent space. We hope it will be useful and inspiring for researchers to solve problems related to large complex systems with distributed intelligence.

This chapter is regarded to be the core of the study tool, because it covers the introduction of our Agent Response Equilibrium (ARE) model. To deal with the modeling of national economy which is quite a large and complex system, authors propose ARE model on basis of intelligent engineering theory and multi-agent system technique. Principal elements in the national economy and their interactions are all considered in the model, i.e., production sectors, banks, residents, the government, and various kinds of markets. With the help of ARE model, it is possible to simulate the operation of the economic system and conduct some policy experiments. All the simulation results in other chapters in this book come from the ARE model. In this chapter, we will provide readers with the foundation and overview of the model. Firstly, the concept of agent will be described, and the multi-agent system techniques will be outlined. Then, the framework, components, and features of our ARE model will be introduced. Last but not least, we will present some details of the model, such as the assumptions, database, rule base, communication mechanism, etc.

In this chapter, we will go on with the introduction of our original ARE model, which is a tool to simulate the operation of national economic system. The foundation and overview of the model has been introduced in Chap. 6. More details will be focused on in this chapter. First and foremost, we will offer the detailed presentation of various sorts of agents in ARE model one by one, i.e., the sector agent, market agent, international commodity market agent, government agent, central bank agent, commercial bank agent, and resident agent. Then, we will provide some information of our computer program for readers especially who are interested in computer programming and want to know more about our computer program which is the basis of building ARE model. The flow chart and computer programming environment are described, respectively. Furthermore, some typical statements and the input data statements of our computer program will be shown. Lastly, we will exhibit the parameter settings. We hope that readers can come to understand our ARE model after reading Chap. 6 and this chapter, and we even look forward to the improvement and criticism of the model from readers.

There is a cycle of around 9 years in economic growth in China, which is like a sine curve. It can be roughly seen from the curve that each of the four periods of valley, growth, peak, and fall will take about 2.5 years. As we discussed in Chap. 1, the economic growth valley years were 1981, 1989, and 1998; thus, on the basis of the economic cycle, the next growth valley would be in around 2007–2008. Reviewing electricity data, the growth of electricity demand was falling monthly to enter the valley period in 2007, which means the economic cycle is active in China’s economic operation. However, the global economic crisis happened in 2008, and China’s economy was affected sharply by both China’s economic cycle and the global crisis because imports and exports in the Chinese economy are closely related to the global economy. Realizing the economic recession was serious, the Chinese government decided to make a huge investment to stimulate the economy in early 2009, and then the economy grew fast for 2 years, especially the growth in electricity demand, which was in double digits. However, there were some problems such as bubbles in real estate, excess capacity in many sectors of manufacture, etc., and some economists criticized the action of the investment. It will be simulated by using our Agent Response Equilibrium (ARE) model as discussed in Chaps. 6 and 7. In this chapter, the evaluation rules of economic simulation will be introduced to show the test and evaluation of the ARE model, and the economic growth in 2007 will be discussed as the basis for the simulation based on the input–output (I/O) table in 2007. Then the economic simulations for 2008, 2009, and 2010 by the ARE model will be tested by the rules, which show very small errors. Experiments on different policies for economic operation can be simulated on a computer by the ARE model.

Economic system is very complex with a lot of uncertainties, and it is very difficult to be simulated and modeled mathematically. However, it can be simplified to a few sectors of the productions and economic activities. Business decision making in the economic activities can be simulated by an intelligent agent based on some rules such as maximum profits of the productions and economic activities. This is the methodology of ARE model. Based on the economic simulations of China’s economy from 2011 to 2014, the simulation results in each year are shown and discussed, and the errors between simulated results by ARE model and published data by National Bureau of Statistics of China have been compared in this chapter.