With China’s rapid economic development, the economic transformation and emission reduction based on energy conservation are the two key problems of Chinese economic development. In this article, we made quantitative analysis to the energy conservation and emission reduction ofJiangsuindustrial restructuring and industrial technology progress by using the model of input-output methods with the data ofJiangsuinput-output table in 2007. The result showed that if energy consumption caused by per unit output in the industry higher than average level of all the industries, we can decrease its proportion of all the industries. However, if the industry is the pillar industry to the national economic development, we cannot largely decrease its proportion. During this background, improving the technical level of theIndustry to reduce the energy consumption is a better choice. This article analyzed the contribution to energy conservation and emission reduction by industrial restructuring and industrial technology progress from the quantitative aspect.
Each county shows high attention to the resources and environment problems based on the situation of resource limitation and environmental serious damage. China has made a commitment of energy saving and emission reduction on the Copenhagen Climate Conference during 17-18 December 2009. As a major economic province in China, Jiangsu is also a province with relatively poor energy. With the rapid economic development in recent years, energy supply and demand keep increasing year after year. In future, economic development has great relationship to the energy in Jiangsu province. And it is necessary to analyze the ways of energy conservation and emission reduction based on the high attention of the “twelfth five-year plan”.
Many scholars have done a lot of research work on energy conservation and emission reduction. S. J. Liu found we should control the commission to the lowest level and the most economical level by analyzing the environmental Kuznets curve [
Firstly, this article builds a mathematical model of inputoutput tables with Jiangsu. Coal, oil and gas are considered as energy according to the input-output tables. We get the total consuming coefficient of oil and coal in each department from input-output tables in 2007, Jiangsu— the consumption of coal, oil and gas corresponding to the addition of one unit output in each department. Then, the sum of this result divided by 42, we can get the consuming amount of coal, oil and gas by increasing one unit of GDP in the existing production structure, and using this result to divided by the consumption amount of coal, oil and gas which caused by one unit additional outputting of each sector, we can get the coefficient of each department. The larger coefficient it is, the consuming amount of coal, oil and natural gas caused by one unit of output in this department is more. When the coefficient of one department is less than 1, it shows the consumption of coal, oil and gas caused by each additional unit output of this sector is less than the amount caused by the increasing of one unit of GDP under the existing production structure. Instead, when the coefficient is higher than 1, it shows the consumption of coal, oil and gas caused by each additional unit output of this sector is more than the amount caused by the increasing of one unit GDP under the existing production structure. Calculation steps and the results are as follows.
In front of 42 departments in the input-output tables, the total consumption coefficient of the coal mining and washing were , which is arranged in order by industry input-output tables. Look-up table can be calculated: . X represents the sum of the coal consumption amount by one unit end-use product and all indirect consumption of it in each production sector.
then is the coal consumption of one unit total production output, it means the amount of coal consumed by each additional unit of GDP in the current economic structure. The total consumption coefficient of the oil and gas in front of 42 departments in the input-output tables were. Look-up table can be calculated:
Y represents the sum of the oil and gas consumption amount by one unit end-use product and all indirect consumption of it in each production sector in the current economic structure. Given
then means the oil and gas consumed by one unit total production output, it means the amount of oil and gas consumed by each additional unit of GDP in the current economic structure. Then , it represents that the consumption amount of coal, oil and gas is 0.1392776 unit when increase one unit of GDP in the current economic structure. In front of 42 departments in the input-output tables, the total consumption amount of the coal, oil and gas were . So it can get 42 departments coefficient:
so is refers to the gap between the total consumption amount of coal, oil and gas which results from one unit output in this department and it caused by one unit GDP addition under the existing production structure.
From the flow statement of input-output tables, we can know output value of 42 industries is respectively , we can get the energy consumption of one GDP unit which is 0.853 tons of standard coal/ million. For example, according to the analysis of the above, if the output of 42 departments respectively increased by 20%, we can calculate the savings in energy consumption resulted from developing some industry instead of the same additional amount of GDP in the existing production structure, calculation process is following:
Then we discuss waste gas reducing effect of 42 industries. In 2007, the total output value of Jiangsu area is 2601.848 billion Yuan, the exhaust gas is 2354.7 hundred million standard cubic meters, so that the exhaust gas of one unit GDP is 0.90501 hundred million standard cubic meters/hundred million Yuan. Change the energy consumption of one unit GDP in the above formula (1) to the exhaust gas of one unit GDP, we can calculate the decrement of the exhaust gas which is represented by, then obtains the formula:
The primary data, the middle data as well as the final data involving in above computational process are shown in
As we can see from above that the advanced manufacturing industry plays a promoting and supporting role in the economy and its energy consumption and emission quantity of exhaust gas are both lower than the average level of other industries, these sectors should be vigorously developed. We can also see from
According to the Twelfth Five-Year Plan of Jiangsu Province, the energy consumption of per unit GDP needs to be reduced by 16 percent, so we should reduce the complete consumption coefficient of the twelve Industries to coal mining and washing industry, oil and gas mining industry. Set refers to the energy (coal, gas and oil) consumption caused by one additional unit of GDP of these twelve industries. If the complete consumption coefficient of the twelve industries to coal mining and washing industry, oil and gas mining
industry fell by 16 percent, the Z value in the previous section will change according to the above calculation process.
The result of the calculation is:
Respectively get the coefficients of the twelve departments:
Set refers to the total output of the twelve departments, under the premise that the total output of the twelve departments do not changed, we must reduce the complete consumption coefficient of the twelve departments to coal, oil and gas to reduce the emissions.
It means reduce by 16%:
From above we got:
Then
Then we replaced energy consumption of per unit GDP by exhaust emission of per unit GDP to calculate the reduction of the exhaust emission caused by per unit of GDP:
All the related raw data, intermediate data and final data are shown in the following
It can be seen from
metallurgical industry, 1561.83 ten thousand tons stan- dard coal and 1657.04 hundred million standard cubic meters. Although the total demand coefficient of metallurgical industry to Coal industry, Oil and gas mining (0.21) is not very large, the total output of metallurgical industry is the second. So we can conclude that the amount of energy saving and exhaust emission depend on the coefficient and the total output of the industry. As we know, the total output of oil processing industry, chemical industry, non-metal manufacturing industry, metallurgical industry, metal manufacturing industry, electricity manufacturing industry and transportation industry was as much as over one hundred billion Yuan, so the reduction of energy consumption and exhaust emissions would be relatively large by reducing the complete consumption coefficient. Besides, as the complete consumption coefficient of the oil processing industry, chemical industry, nonmetal manufacturing industry, electricity industry, gas industry are relatively large, it would also bring significant energy saving and exhaust emission reduction by reducing the complete consumption coefficient of these departments. As a conclusion, we should upgrade the technology, improve the management and eliminate the backward equipment and production capacity of these twelve departments we mentioned above.
The major categories of industrial transformation can be divided into two types: industrial structure adjustment and industrial technology development. In order to realize the target that driven by high consumption of material resources changes to driven by innovation, extensive growth changes to intensive development, we should vigorously develop the service industry and high-tech industry and focus on the industries with low energy consumption, high development potential and large influence on the national economy to improve the proportion of industrial output value in GDP. However, we cannot completely abandon those traditional industries with high energy consumption and high pollution for they play a significant role in the national economy. We should transform these industries by technology upgrading, merger and reorganization, elimination of backward production capacity to reduce the energy consumption and pollution of these industries.
The authors would like to acknowledge to staff form Research Institute of Jiangsu Province Government for comments and suggestions. This study was funded by the National Natural Science Foundation of China (Grant No. 41001377) and Social Sciences Fund of Jiangsu Province (Grant No. 10GLC013).