DING Zhao FEBRUARY 2014 VOL 5, NO 10. Master degree candidate, College of Economic & Management, Sichuan Agricultural University.

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1 Analysis of relationship between rural energy consumption of biomass resources and income alteration of farmers DING Zhao Master degree candidate, College of Economic & Management, Sichuan Agricultural University. Postal Address: Sichuan Agricultural University. Wenjiang Chengdu Sichuan , China Abstract Biomass energy is an important kind of renewable energy which has drawn more and more attention in the world, and becoming a new industry with huge potential, while the development of biomass energy will affect the layout of agricultural production and farmers' life. Therefore, this paper analyzed the correlation of consumption of biomass energy resources and farmers' life by utilizing the grey correlation method, and found that there is a strong correlation between the consumption of biomass resources i.e., biogas, straw, firewood and the per capita net income of farmers, the development of biomass energy should take full consideration of the impact on peasants. Keywords: Relationship, Energy consumption, Income alteration 1. Introduction Energy is the most important material basis, which has a dominant impact on the development of the society and economy. Biomass energy is an important kind of renewable energy which has drawn more and more attention in the world, and becoming a new industry with huge potential in this day and age. This industry can not only ease the tense situation of fossil energy supply, optimize energy structure and protect national energy security, but also improve and protect the ecological environment. Meanwhile, biomass energy can expand agricultural function and increase the income of farmers, which has a great significance for the achievement of sustainable development. At present,rural energy consumption in China is mainly depend on the primary use of biomass energy resources, such as biogas, straws and firewood. Since the development of biomass energy is the trend of social development, so at this stage, what is the correlation between the biomass and farmers and in what extent the biomass COPY RIGHT 2014 Institute of Interdisciplinary Business Research 76

2 crops or resources affect their lives are worthy of analyzing, under the background that the development of biomass is paid increasingly attention. 2. Literature Review Although there are many researches on the relationship between energy consumption and economic growth, few of them refer to rural bio energy consumption. Studying on the relationship between energy consumption and economic growth, Kraft (1978) did the pioneering research. He utilized the annual data during in American, and found that economic growth will drive the energy consumption. Stern (2000) expanded his own results on the relationship between the GDP and energy by using co-integration method of static single equation and dynamic model. He found that energy consumption has significant effect in the interpretation of GDP alteration, and confirmed the existence of a long-term co-integration relationship between DGP, capital, labor and energy. Wang Jianlin and Zhao Jiajia (2008) examined the relationship between energy consumption and economic growth in China by ARDL method. They also revealed the existence of co-integration relationship between them, and the increase of GDP would cause the increase of energy consumption, but increase of energy consumption does not cause the growth of GDP. Wang Huogen and Shen Lisheng (2008) test the energy consumption and economic growth through panel causality and panel co-integration, by establishing a production function with energy consumption in. Granger causality test results showed that the panel energy consumption is the impetus of economic growth, while economic growth is not the driving force of energy consumption. These papers mainly show that the unilateral causal relationship between energy consumption and economic growth. Liu Fengzhao, Liu Yuanyuan and Pan Xiongfeng (2007) used the vector autoregressive (VAR) model, the generalized forecast error variance decomposition and generalized impulse response analysis method, studied the dynamic characteristics of the energy consumption and economic growth with the data from 1988 to 2005 in China, in the framework of single sector neoclassical production function of three elements i.e., capital, labor and energy. The results showed the impact of economic growth on energy consumption is not very significant in the short-term, while economic growth is an important factor in the growth of energy consumption besides the capital increase in the long term. In COPY RIGHT 2014 Institute of Interdisciplinary Business Research 77

3 addition, energy consumption is the most important essential productive factor after capital, the increase of energy consumption has positive effect on economic growth. Tian Xiaofei (2008) studied the relationship between the total energy consumption together with its components and economic growth. He did this by unit root test, co-integration test and granger causality test with 20 years data since Chinese economic reform. His study also found that there is a two-way causal relationship between total quantity of the economic growth and energy consumption. However, Liang Chaojun(2008) found there is a co-integration relationship between China's energy consumption and economic growth, but not a causal relationship. Although there were no direct analysis on the relationship between biomass energy consumption and economic growth, Sadorsky (2009) studied the relationship between renewable energy consumption and real income per capita. He did so by utilizing panel data from 18 emerging market countries. He found that an increase in real income per capita has a significant positive effect on per capita renewable energy consumption. Apergis and Payne (2010) analyzed the relationship between renewable energy consumption and economic growth. They did so in a multivariate panel framework and the data form 20 OECD countries between 1985 and They revealed that there is co-integration relation among actual GDP, renewable energy consumption, capital and labor, and there is a two-way causal relationship between economic growth and renewable energy consumption both in the short and in the long term. Wang Ying (2008) explored the relationship between renewable energy consumption and economic growth. Her study revealed a long-term equilibrium relationship between real GDP and renewable energy consumption by co-integration test. The empirical results showed that the positive affection from renewable energy consumption to economic growth is limited in the short term, while 1 unit of renewable energy consumption can push the growth of real GDP by 1.39 unit in the long term. Li Jinkai (2008) analyzed agricultural energy consumption and GNP in China. He found that there is a long-term co-integration relationship between them, and the effect of national income on energy consumption is more significant in the short term than in the long term. Nevertheless, there was no granger causal relation between agricultural GDP and energy consumption, and there was unidirectional causality between them as well. In the literatures that I have reviewed, most of them are focus on the causality between energy consumption and economic growth, while causality is only one relationship between COPY RIGHT 2014 Institute of Interdisciplinary Business Research 78

4 energy consumption and economic growth. To study the relationship between the two factors, these researches are no enough. The research method also should be innovated. As a whole, the paper will utilize the method of grey correlation, focuses on the relationship between rural biomass resources as energy consumption i.e., non commercial energy consumption situation in rural area and the rural per capita net income. 3. Data 3.1 Selection of indicators According to the availability and authority of the indicators, the paper choice three major non commercial biomass energy i.e., biogas, straw and firewood as independent variable, the dependent variable is the per capita net income of farmers. The data comes from energy statistics yearbook and Chinese statistical yearbook, and has been converted to coal equivalent unit. 3.2 Descriptive analysis of sample data As can be seen from the Fig.1, the consumption of straw is the biggest and remains higher than that of firewood, which is the ahead of biogas during the span of 12 years. The consumption of straw and firewood show a rising trend as a whole, besides some fluctuation, they are more than ca.65 and ca.44 times bigger than that of biogas. The data of biogas increase gradually but keeps the lowest in the non commercial biomass energy consumption in rural area. The per capita net income of farmers rises significantly, constantly and steadily from ca.1200 Yuan in 1996 to more than 3500 Yuan in On the whole, all of them see an upward trend. 4 Empirical Analysis 4.1 Methodology External world is not only a world of material, but also a world of information. The social and economic activities can not working without information. Research on the social and economic issues will often encounter the situation of incomplete information. In 1945, America cybernetics expert N. Wiener called the unknown object in internal information as black box. Since then, people commonly used color indicates the degree of complete information. The system is divided into three categories i.e., the white system, black system and grey system, when the information is completely clear, not clear and part of clear respectively. More specifically, the grey system is a system possess features of fuzzy level and structure, random COPY RIGHT 2014 Institute of Interdisciplinary Business Research 79

5 dynamic change, imperfect and uncertain data. The model called Grey Model, which reveals a process of continue development and change inside the grey system. Grey system mainly contains four kinds of methods: grey correlation analysis method, grey prediction method, grey statistics and evaluation method, grey situation decision method. The basic method of factor analysis is statistical method in the past, such as regression analysis i.e., linear regression, multiple factors regression, single factor regression, stepwise regression and stepwise regression. Although regression analysis is commonly utilized, it is generally believed has many drawbacks i.e., require a large amount of data, the distribution is linear, workload of calculation is heavy and may appear abnormal phenomenon etc. Considering all of these drawbacks, gray system adopts the method of correlation analysis, which in fact is a dynamic process of quantitative analysis of development trend. More specifically, grey correlation analysis is not only a quantitative comparison of development trend, but also a comparison of geometric relationship of relevant statistical data. This method is essentially geometry analysis and comparison between several kinds of curve, which holds that the closer of geometry, the greater of the degree of correlation. According to this view, grey correlation analysis at least will not appear the abnormal phenomenon, which shows the positive correlation as a negative correlation. 4.2 Grey model The basic model of grey correlation analysis is as follows: A. Establish the reference sequence of dependent variable and comparative sequence of independent variable. B. Nondimensionalization of original data: C. Calculate the absolute value of the difference between sub data columns and parent data columns in each time point, and choose the maximum difference and minimum difference. D. Calculate the gray relational coefficient:. COPY RIGHT 2014 Institute of Interdisciplinary Business Research 80

6 λ is the resolution ratio, and between 0 and 1. Generally E. Calculate the degree of grey relation:. The degree of grey relation is closer to 1, the level of correlation is bigger. According to experience, two factors are considered significant relevance when that degree is larger than 0.6 when the λ= The results analysis Taking the sample data i.e., the consumption of biogas, straw, firewood and the alteration of per capital net income of farmers between 1996 and 2007 into the model of grey correlation analysis. We can get the maximum and minimum of the absolute value of the difference between sub data columns and parent data columns is and 0 respectively. The grey relational coefficient is as show in the Table1. And the degrees of grey relation are: Above all, the consumption of straw is the largest and also the strangest of the relevance with per capita net income of farmers. Although the amount of biogas is the smallest, the degree of correlation with per capita net income is just below straw, higher than that of firewood. While all in all, the degree of grey relation with the per capita net income are all bigger than 0.6, regardless of biogas, straw and firewood, which means the income of farmers is significantly relevant with them. 5. Conclusions The paper has found out that non commercial biomass energy i.e., biogas, straw and firewood is not only closely relate to the life of peasants, but also has a high degree of correlation with per capita net income of them. Therefore, the conclusion suggests that the development biomass energy should not be blind, and even it can bring huge benefits. Because form the micro point of view, the biomass crops is closely relate to the lives of farmer, and in what specific ways COPY RIGHT 2014 Institute of Interdisciplinary Business Research 81

7 the development of biomass energy is benefit to farmers need further exploration and research. The development of biomass energy should take into full account of the impact on farmers. COPY RIGHT 2014 Institute of Interdisciplinary Business Research 82

8 References Apergis, N.,Payne,J.E.. Renewable energy consumption and economic growth: Evidence from a panel of OECD countries [J],Energy Policy, 2010(38): John, Asafu-Adjaye. The Relationship Between Energy Consumption, Energy Prices and Economic Growth:Time series Evidence from Asian Developing Countries.Energy Economics. 2000, 22: 615~625 Kraft, J., Kraft, Analysis on the relationship between energy and GNP.Journal of Energy and Development,1978,(3): Li Jinkai. An empirical analysis of Chinese agricultural energy consumption and national income. Economic Review. 2008,09:70-72 Liang Chaohui. The changing trend of energy consumption under the condition of economic growth: theory and demonstration. Shanghai Economic Review. 2008(2):31-37 Liu Fengchao, Liu Yuanyuan, Pan Xiongfeng. Dynamics of Economic Growth and Energy Consumption in China. Resource Science. 2007,05: Sadorsky, P.. Renewable energy consumption and income in emerging economics. Energy Policy,2009(37): Stern, D. I., Economic growth and energy.encyclopedia of Energy, 2004(2):1-17. Tian Xiaofei. Empirical study on the energy consumption and economic growth. Science and Technology Information. 2008(4):75-77 Wang Huogen, Shen Lisheng. Panel statistical analysis of energy consumption and economic growth in China. The Journal of Quantitative & Technical Economics. 2007(12): Wang Jianlin, Zhao Jiajia. Analysis of energy consumption and economic growth: based on the sample of China.Industrial Technology & Economy. 2008,27(1):86-91 Wang Ying. Time series analysis of renewable energy consumption and economic growth in China. Industrial Technology & Economy. 2008(27):96-99 COPY RIGHT 2014 Institute of Interdisciplinary Business Research 83

9 Annexure(Figure and Table) Fig.1 The consumption of biogas, straw, firewood and the alteration of per capital net income of farmers Table 1 Grey relation coefficient between non commercial energy consumption situation and per capita net income Per Capita Net Income Biogas Straw Firewood COPY RIGHT 2014 Institute of Interdisciplinary Business Research 84