, 董洁芳1,2,31. 中国科学院新疆生态与地理研究所, 乌鲁木齐 830011;
2. 中国科学院大学, 北京 100049;
3. 新疆大学资源与环境科学学院, 乌鲁木齐 830046
摘要: 深入了解能源消费与经济增长的关系对挖掘新疆节能空间具有重要意义。基于2005-2014年新疆能源消费与经济增长相关数据，运用灰色关联分析，分别对新疆各产业能源消费和能源消费种类与经济增长的相关性进行实证分析。结果表明：1）不同产业能源消费与经济增长关联程度差异显著，其中工业与建筑业二者的能源消费与经济增长的关联度相同且最大。2）在工业能源消费中，制造业能源消费与经济增长的关联程度最大，且其占工业能源消费的比重最大。3）不同能源消费种类与经济增长的关联度也显著不同，其中煤炭能源消费量与经济增长的关联度最高，石油能源消费量与经济增长的关联度最低。这说明新疆的经济增长属于要素投入型的经济增长方式，对能源的依赖性较强；同时，由于要考虑国家战略需求，新疆的能源消费种类结构存在一定程度的不合理性。本工作基于对新疆产业能源消费、能源消费种类与经济增长的研究结论，给出相应的政策建议。
关键词: 能源消费经济增长灰色关联分析新疆
Xinjiang is not only one of the major export energy provinces, but also one of the strategic energy reserve bases in China. Energy plays an important role in the entire economy system and has extensive and close relationships with various economic and social sectors in Xinjiang. Driven by the rapid growth of economy in Xinjiang, there is inevitably a continuous increase in energy consumption, and consequent environmental pollution and ecological problems arise. The energy consumption in Xinjiang has a series of problems such as irrational structure, low utilization efficiency, etc[1]. For instance, in 2014, coal accounted for a proportion as high as 65.1 % of the total energy consumption of Xinjiang and the total coal consumption reached 2 570 000 t. Energy conversion efficiency in Xinjiang was 68.50 % in 2014, which was 93.20 % of the national average level of the same period, and the worse is that low energy efficiency in Xinjiang is only equivalent to 32.4 % of the national energy efficiency (GDP according to the fixed price in 1978). It is clear that environmental and ecological problems caused by energy consumption will become important factors preventing the economy of Xinjiang from realizing sustainable development in the future[2, 3]. More important is that Xinjiang accounts for one-sixth of Chinese territory. If green development is achieved in Xinjiang, it will have important practical significance for China's economic transition.
1 Literature reviewAcademically, many scholars have developed different theoretical and modeling methods to explore the relationship between the energy consumption and economic growth extensively [4-15]. Most of these studies suggest that there is a correlation between economic growth and energy consumption, but conclusions drawn in these studies were different. Among the methods developed, the GRA is a common analysis method which was used to analyze the correlation[5, 9, 16-17]. Lin et al.[17] adopted the GRA to analyze the relationships among economy, energy utilization, and CO2 emissions in Taiwan. Li[9] used the GRA to explore the correlation between the economic growth of China and a variety of China's energy consumption structure. Yuan et al.[18] employed the GRA to divide China's economic development and energy consumption into four stages, and drew some conclusions regarding policy values by analyzing the relational degree between the two in each stage. Wang et al.[19] applied the GRA to study Liaoning province, and the results suggested that the industrial sector had the highest energy consumption but a relatively low correlation with economic growth and that the energy consumption of coal had an insignificant correlation with economic growth. Chen et al.[20] applied the GRA to do research on industrial pollutant emission and its potential influence factors, and investigated the causes of the Kuznets curve of industrial pollutant emission in Inner Mongolia. In addition, numerous scholars have also introduced other study methods to explore the relationship between energy consumption and economic growth[21-28]. These studies include the study conducted by Odhiambo on Tanzania using the ARDL bound test method and the study conducted by Lin et al. on South Africa using the co-integration method and Granger causality test method. Both the studies suggest that there is a certain causal relationship between energy consumption and economic growth[29-30]. Shyamal Paul et al., Erdal et al., and Tsani studied the causal relationships between energy consumption and economic growth in India, Turkey, and Brazil, respectively[23, 27, 31]. At the same time, other scholars have carried out more extensive theoretical analyses and empirical studies of energy efficiency, energy prices, and issues of energy and economic growth using analytical frameworks of energy, environment, and economy[6, 8, 24, 32-46].
To sum up, in-depth understanding of the relationship between energy consumption and economic growth is of great practical significance for realizing the sustainable development in different countries and regions and for exploring energy-saving potential. The present work attempts to use the perspective of structure, based on the data related to the energy consumption and economic growth of Xinjiang from 2005 to 2014, with the purpose of proposing some structural optimization suggestions regarding energy consumption and energy consumption types in various industries in Xinjiang, exploring its energy-saving potential, and realizing its green and sustainable development.
2 Study method and data source2.1 Study methodThe grey relational analysis (GRA) is a method of measuring the relational degree between factors based on the similarity or difference degree of their developmental trends[16, 47-49]. This method makes up for deficiencies of the variance analysis, principal component analysis, regression analysis, and other mathematical statistics methods used in system analysis, and imposes no higher requirements on the sample size or typical regularity of samples. The results are easy to calculate, and quantification results generally match the results of qualitative analysis. So the method is widely applied in many disciplines.
The mathematical modeling process of the GRA is described below.
1) Establish the reference sequence (X0) and comparison sequence (Xi) of the original sequence
| ${\mathit{\boldsymbol{X}}_0} = \left\{ {{x_0}\left( 1 \right), {x_0}\left( 2 \right), \cdots, {x_0}\left( k \right)} \right\}\left( {k = 1, 2, \cdots, m} \right), $ | (1) |
| ${\mathit{\boldsymbol{X}}_i} = \left\{ {{x_i}\left( 1 \right), {x_i}\left( 2 \right), \cdots, {x_i}\left( k \right)} \right\}\left( {k = 1, 2, \cdots, n} \right), $ | (2) |
2) Estallish standardization. For the purposes of ensuring uniformity of the original sequence and making it convenient to contrast reference and comparison sequences, adopt the initialization operator for dimensionless processing. The calculation formulas are given as follows.
| ${x'_0}\left( k \right) = {x_0}\left( k \right)/{x_0}, $ | (3) |
| ${x'_i}\left( k \right) = {x_i}\left( k \right)/{x_i}, $ | (4) |
| ${\mathit{\boldsymbol{X'}}_{\rm{0}}} = \left\{ {{{x'}_0}\left( 1 \right), {{x'}_0}\left( 2 \right), \cdots, {{x'}_0}\left( k \right)} \right\}\left( {k = 1, 2, \cdots, m} \right), $ | (5) |
| ${\mathit{\boldsymbol{X'}}_i} = \left\{ {{{x'}_i}\left( 1 \right), {{x'}_i}\left( 2 \right), \cdots, {{x'}_i}\left( k \right)} \right\}\left( {k = 1, 2, \cdots, n} \right), $ | (6) |
3) Calculate the absolute difference [Δi(k)] between the reference and comparison sequences at each moment using the formula
| ${\Delta _i}\left( k \right) = \left| {{{x'}_i}\left( k \right)-{{x'}_0}\left( k \right)} \right|\left( {i = 1, 2, \cdots, n} \right).$ | (7) |
| ${\Delta _i}\left\{ {{\Delta _i}\left( 1 \right), {\Delta _i}\left( 2 \right), \cdots, {\Delta _i}\left( k \right)} \right\}.$ | (8) |
| ${\varepsilon _i} = \left( {{\Delta _{{\rm{min}}}} + \rho {\Delta _{\max }}} \right)/\left( {{\Delta _i}\left( k \right) + \rho {\Delta _{\max }}} \right), $ | (9) |
| ${\gamma _i} = \frac{1}{k}\sum\limits_{k = 1}^m {{\varepsilon _i}, } $ | (10) |
5) Rank the relational degrees from high to low. The foot that γ1 < γ2 means that the comparison sequence X2 is similar to the reference sequence X0, and in this case the relational degree between the two is high.
2.2 Data source and processingThe data used in this work was compiled based on Xinjiang Statistical Yearbook (1999-2015). In view of technological advances, energy utilization efficiency, consistency of energy consumption, and other influencing factors, we use data related to the GDP of Xinjiang in 2005-2014 and the terminal energy consumption and energy consumption types of various industries to establish three grey relational models, and we mainly study the relational degrees between economic growth and energy consumption of various industries and the correlation relationships between economic growth and consumption of energy such as coal, petroleum, natural gas, hydropower, wind power, solar energy, etc.
The reference sequences used in the three grey relational models are all GDP (unit: 100 million yuan). Specifically, comparison sequence of the first grey relational model consists of six major industries (unit: 10 000 t standard coal), namely, the energy consumption of the agriculture, forest, farm, fish, and water conservation industries (X1), the energy consumption of the industrial sector (X2), the energy consumption of the construction industry (X3), the energy consumption of the transportation, storage, and postal industries (X4), the energy consumption of the wholesale, retail, hospitality and catering industries (X5), and the domestic energy consumption (X6).
The industrial sector occupies the highest proportion in the energy consumption. It is necessary tofurther investigate the gray correlations between the the internal industry categories. Therefore, the comparison sequence of the second grey relational model consists of three types of industrial energy consumption in the industrial sector (unit: 10 000 t standard coal), namely, the energy consumption of mining industry(Y1), the energy consumption of manufacturing(Y2), and the energy consumption of production and supply of electricity, gas, and water(Y3).
The comparison sequence of the third grey relational model consists of four types of total energy consumption (unit: 10 000 t standard coal), namely, the total consumption of coal (Z1), the total consumption of petroleum (Z2), the total consumption of natural gas (Z3), and the total consumption of hydropower, wind power, solar energy, and other energy sources (Z4).
The GRA is used to calculate grey relational coefficients and relational degrees between the energy consumptions of the six major industries, the consumptions of three type industrial energy in industrial sector, and the four types of total energy consumption on the one hand and the GDP on the other hand.
3 Empirical results and discussion3.1 GRA of economic growth and industrial energy consumption in XinjiangDuring the period of 1997-2014, there was a high correlation between energy consumption and economic growth in Xinjiang. The GDP of Xinjiang and its total energy consumption presented almost the same variation trends (see Fig. 1), which shows relatively strong dependence of the economic growth of Xinjiang on energy. As indicated by the GRA, among the six major industries of Xinjiang related to energy consumption, the energy consumption of the industrial sector (X2) and construction industry (X3) presented the highest relational degrees of 0.87, followed by the transportation, storage and postal industries (γ4=0.75), agriculture, forest, farm, fish and water conservation industries (γ1=0.69), wholesale, retail, hospitality and catering industries (γ5=0.67), and the domestic energy consumption (γ6=0.55). More details are in Table 1.
Fig. 1
 | Download: JPG larger image |
Fig. 1 Total energy consumption, total energy production, and GDP of Xinjiang(1997-2014) | |
Table 1
 Table 1 Relational coefficients between energy consumption and GDP in various industries in Xinjiang (2005-2014)
| Table 1 Relational coefficients between energy consumption and GDP in various industries in Xinjiang (2005-2014) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The energy consumption of the industrial sector not only had the highest relational degree with economic growth, but also accounted for the highest proportion in the total energy consumption of Xinjiang, with the mean proportion of 71.3 % in the past decade. Compared to the energy consumption of the industrial sector, the energy consumption of the construction industry not only had a significant correlation with economic growth, but also was at a relatively low level with only 0.9 % of the total energy consumption of Xinjiang on average, suggesting that the construction industry had both a relatively strong driving effect for the economic growth of Xinjiang and a very high potential for energy saving. At the same time, the energy consumption of the transportation, storage and postal industries (X4) and the energy consumption of the wholesale, retail, hospitality, and catering industries (X5) were both relatively low, accounting for 7.4 % and 2.2 %, respectively, on average in the total energy consumption of Xinjiang (as shown in Fig. 2), showing low energy consumption and high economic efficiency of the transportation industry and the service sector-based tertiary industry. The energy consumption of the agriculture, forest, farm, fish, and water conservation industries (X1) of Xinjiang had the relational degree of 0.69 with economic growth, revealing its relatively significant correlation with economic growth. This fact can be explained by the large-scale development and relatively high mechanization degree of agriculture in Xinjiang as a major agricultural province, and it has a certain driving effect for its economy.
Fig. 2
 | Download: JPG larger image |
Fig. 2 Developmental trends of energy consumptions in various industries in Xinjiang (1997-2014) | |
3.2 GRA of economic growth and energy consumption of the industrial sector in XinjiangThe previous section showed that the energy consumption of the industrial sector accounted for the highest proportion of total energy consumption with the average proportion of 71.3 % in the sample period. Therefore, it is necessary to further investigate the gray correlation between the energy consumption of the internal industry categories and economic growth in Xinjiang. The results are presented in Table 2. As indicated by the GRA, the energy consumption of manufacturing (Y2) presented the highest relational degree with economic growth (γ2=0.88) compared with the other two types of industry in industrial sector of Xinjiang. This result can be interpreted as follows. The energy consumption of manufacturing almost always kept the highest position in the past 10 years with the average proportion of 68.8 %, followed by the energy consumption of production and supply of electricity, gas, and water (Y3) with a relational degree of 0.81 with economic growth of Xinjiang. However, its proportion in total industrial energy consumption was the lowest, and the average proportion was 11.6 % in the sample period. Finally, the relational degree between the energy consumption of mining industry (Y1) and economic growth was the lowest (γ1=0.60), but its proportion in total industrial energy consumption was high with the average proportion of 19.6 % in the sample period. This shows that the growth of industry, especially the growth of manufacturing in Xinjiang, strongly depends on energy. Therefore, Xinjiang needs to increase investment in science and technology and improve energy efficiency gradually for gatting rid of the association ultimately between economic growth and energy consumption in Xinjiang.
Table 2
Table 2 Relational coefficients between the energy consumption of industrial sectors and GDP in Xinjiang (2005-2014)
| Table 2 Relational coefficients between the energy consumption of industrial sectors and GDP in Xinjiang (2005-2014) | |||||||||||||||||||||||||||||||||||||||||||||||||||
3.3 GRA of economic growth and energy consumption types in XinjiangXinjiang has a rich reserve of various types of energy, but different types of energy differ significantly in terms of their proportions in the total energy consumption. As a result, the energy utilization structure of Xinjiang is irrational and specifically, there is a prominent problem of excessive dependence of energy consumption on coal resources, as shown in Fig. 3. The proportion of coal energy consumption in the total energy consumption has always maintained a stable level of above 60 %, presenting the highest relational degree with economic growth (γ1=0.85), as shown in Table 3. This suggests that coal energy occupies an important position in the economic development of Xinjiang. Contrarily, the consumption of petroleum energy presents the lowest relational degree with economic growth(γ2=0.60), and its proportion in the total energy consumption of Xinjiang shows a declining trend. This is mainly because petroleum dominates the energy output of Xinjiang, while coal, due to its excessively high transportation costs, dominates the energy consumption of Xinjiang. At the same time, the proportion of the consumption of clean energies such as water, wind, solar energy, etc., presents a continuously rising trend. The proportion of natural gas energy consumption is relatively stable, reaching 7.3 % and 15.2 % of the total energy consumption in 2014, respectively, presenting relatively high relational degrees with economic growth, reaching 0.83 and 0.72 in 2014, respectively.
Fig. 3
 | Download: JPG larger image |
Fig. 3 Proportions of various energy consumption types in the total energy consumption in Xinjiang (1997-2014) | |
Table 3
Table 3 Relational coefficients between energy consumption types and GDP in Xinjiang (2005-2014)
| Table 3 Relational coefficients between energy consumption types and GDP in Xinjiang (2005-2014) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
4 Conclusions and policy implications4.1 Conclusions and discussionAs indicated by the results of empirical analysis, the relational degrees between energy consumption and economic growth differ significantly among industries in Xinjiang. The industrial sector and construction industry present the same highest relational degree between energy consumption and economic growth, but the energy consumption of the industrial sector is the highest while that of the construction industry is low. The energy consumption of the transportation, storage and postal industries, and wholesale, retail, hospitality, and catering industries are relatively low, and their grey relational degrees with economic growth are also relatively high. The energy consumption of manufacturing had the maximum correlation degree with economic growth during the period of 2005-2014. Moreover, the proportion of energy consumption of manufacturing in the industrial sector is the highest, followed by production and supply of electricity, gas and water industry, and the mining industry.
The proportion of the industrial energy consumption in the total energy consumption of Xinjiang is above 62 %, and presents an increasing trend over years. In 2014, the proportion of the industrial energy consumption in the total energy consumption in Xinjiang reached 77 % and its grey relational degree with economic growth was 0.87, which indirectly supports the extensive expansion stage of Xinjiang’s secondary industry and relatively low efficiency of energy utilization. Measured by the GDP created by unit energy consumption, the energy utilization efficiency of Xinjiang is only about 40 % of the national average. In addition, according to data released by the China Statistical Yearbook and Xinjiang Statistical Yearbook, the energy consumption elasticity coefficient of Xinjiang is 0.95, while that of China is only 0.29 in 2014. Meanwhile, the average energy consumption elasticity coefficient of Xinjiang is as high as 1.164, while that of China is only 0.614. The result of comparative analysis suggests that economic growth in Xinjiang depends strongly on energy, and there is great potential for improvement in energy efficiency. In Xinjiang the energy utilization efficiency of the secondary industry is 15 % of that of the primary industry and 21 % of that of the tertiary industry. The energy consumption of the construction industry and that of the industrial sector have the same rational degree with economic growth, but the energy utilization efficiency of the former is 16.43 times that of the latter, which suggests that the construction industry has a close relationship with economic growth and that its development significantly promotes the growth of GDP. At the same time, the demand and driving effect of the construction industry on other industries facilitate the development of other industries and promote economic growth. The transportation, storage and postal industries, the wholesale, retail, hospitality and catering industries and other service and manufacturing industries have relatively strong relational effects, and promote economic development to a very large extent. The energy consumption of the agriculture, forest, farm, fish, and water conservation industries is relatively high, and has a relatively significant correlation with economic growth. This can be explained by the large-scale development and relatively high mechanization degree in the primary industry in Xinjiang and by its relatively strong dependence on the industrial sector and agricultural service industry, which suggests that the development of the primary industry has a certain driving effect on the economic growth of Xinjiang. With the development of economy and the continuous rise of living standards, the domestic energy consumption in Xinjiang presented an obvious rising trend, as shown in Fig. 2. This is mainly due to significant transformations of energy consumption patterns in the modern society, increase in use of air conditioners, water heaters, refrigerators, and other high-power household appliances as well as automobiles, and the consumption of energy in large amounts by heating, lighting, cooking, and so forth. However, the grey relational degree between the domestic energy consumption and economic growth is only 0.55, which is far lower than that of the other industries.
The relational degree between energy consumption and economic growth also differs significantly among the specific energy consumption types concerned. For instance, coal energy consumption has the highest relational degree with economic growth, which is far larger than the sum of consumption of the other energy types, while petroleum energy consumption, contrarily, has the lowest relational degree with economic growth. This is mainly because, in spite of Xinjiang being a major energy producer in China with increasing total energy consumption annually, its total energy consumption is still lower than its total energy yield. In addition to meeting its regional demands, the total energy yield still leaves a considerable portion that can be transported to other regions. This also suggests that Xinjiang, as a strategic energy reserve base in China, needs to adapt its energy utilization to the demands of China’s national strategies and to balance the consumption of different types of energy in its economic development. Furthermore, due to the long distances from Xinjiang to China’s inland energy consumption markets and low cost of long-distance transportation of coal, it is impossible to eliminate strong dependence of its economic growth on coal resource consumption within a short term. Also, the consumption of natural gas energy and clean energies such as water, wind, solar energy, etc., has significant correlation with economic growth, and also accounts for a considerable proportion in the energy consumption structure of Xinjiang. This is mainly because Xinjiang, with the purpose of effective elimination of the current excessive dependence on coal resources and full exploration of rich local wind and solar energy resources, has promoted development of clean energies and encouraged use of the green, environment-friendly, safe, and reliable natural gas, wind power, and solar energy resources.
4.2 Policy implicationsThe fragile ecology, backward technical conditions, and inefficient energy utilization in Xinjiang have exerted high requirements and posed great challenges to the coordinated developments of its economy, energy, and environment. In addition, as an underdeveloped region, Xinjiang will still prioritize economic growth in the years to come, and will not be able to transform itscoal-based energy consumption structure, imposing a lot of pressure on the coordinated and sustainable development of its economy, energy, and environment[1, 50]. In this work, we use the structural perspective to analyze the energy consumption in Xinjiang, and find out energy conservation space without affecting economic growth in Xinjiang. On that account, this work proposes some structure optimization suggestions regarding energy consumption and energy consumption types of various industries in Xinjiang, based on the obtained conclusions, with the purpose of exploring its energy-saving potential and realizing its sustainable development.
First, Xinjiang should adjust the industrial structure, especially the industrial sector, and focus on the developments of the construction and service industries. Currently, Xinjiang’s secondary industry still accounts for an excessively high proportion, and, although it has the highest relational degree with economic growth, its energy consumption is the highest, which suggests that the economic growth of Xinjiang depends on energy consumption and that its economy is still energyinput-dependent. For this reason, close attention should be paid to the adjustment of the industrial structure in Xinjiang, with the purpose of restraining or eliminating high-energy consumption and low-efficiency industries. The energy consumption of the transportation, storage and postal industries, and the energy consumption of the wholesale, retail, hospitality, and catering industries are very low, accounting for only about 8 % of the total energy consumption of Xinjiang, but their grey relational degrees with economic growth are relatively high. Therefore, Xinjiang should encourage development of service industries, improve its development transportation conditions, optimize its internal and external transportation connections, reshape its presence in the economic geographic landscape of the country, and increase the proportion of industrial service industries in its economy.
Secondly, Xinjiang should advocate utilization and development of clean and renewable energies and promote optimization of the energy structure. The energy consumption structure of Xinjiang is based on coal, followed by natural gas and petroleum. The renewable energies such as water, wind, solar energy, etc., only account for a maximum of 7.8 % of its total energy consumption. Furthermore, the consumption of renewable energy has a significant correlation with economic growth, is characterized by high cleanness and low carbon and other pollutions, and can effectively promote coordinated and sustainable development of its economy and environment. Therefore, Xinjiang should fully explore its rich wind energy and solar energy resources, increase its scientific, technological, and capital investments, develop safe, reliable, green, and environment-friendly wind power, solar energy, and other clean and renewable energy sources, and realize unification of economic and ecological benefits. At the same time, natural gas has a relatively high relational degree with economic growth and is characterized by low pollutions. Therefore, in the future, efforts should be continuously made to increase the proportion of natural gas consumption in the total energy consumption of Xinjiang and to realize the goal of optimizing its energy consumption structure. The ecology is very fragile in Xinjiang. So it is necessary to realize the low carbon development firstly in China.
Thirdly, Xinjiang should transform economic growth patterns, increase energy utilization efficiency, and achieve transition from quantity-oriented growth to quality-oriented growth. The consumption of coal, petroleum, and natural gas in Xinjiang accounts for above 90 % of its total energy consumption. Especially, in 2014 the energy consumption elasticity coefficient of Xinjiang was still as high as 0.95, which was far higher than the national average of only 0.29 in the same period. The results show clearly that the energy utilization efficiency of Xinjiang is on the low side, and its energy consumption structure depends strongly on fossil resources such as coal, petroleum, and natural gas. Therefore, in Xinjiang, the top priority is to transform the high-input and high-energy consumption and high-pollution and low-efficiency development pattern, strengthen the introduction and exploration of energy-saving and consumption-reducing technologies, increase investments in energy-related technical innovations, and choose a new-type industrial development path characterized by low energy consumption, low pollutions, and high efficiency.
References
| [1] | Dong J f, Wang Q, Deng C, et al. How to Move China toward a green-energy economy:from a sector perspective[J].Sustainability, 2016, 8(4):337.DOI:10.3390/su8040337 |
| [2] | Wang Q, Chen Y. Barriers and opportunities of using the clean development mechanism to advance renewable energy development in China[J].Renewable and Sustainable Energy Reviews, 2010, 14(7):1989–1998.DOI:10.1016/j.rser.2010.03.023 |
| [3] | Wang Q, Chen X. Energy policies for managing China's carbon emission[J].Renewable and Sustainable Energy Reviews, 2015, 50:470–479.DOI:10.1016/j.rser.2015.05.033 |
| [4] | Liu L H, Yin C B. Gray relational analysis of energy consumption and energy structure in rural areas of China during 1999-2010[J].Acta Agriculturae Zhejiangensis, 2014, 26(2):503–509. |
| [5] | Kose E, Burmaoglu S, Kabak M. Grey relational analysis between energy consumption and economic growth[J].Grey Systems:Theory and Application, 2013, 3(3):291–304.DOI:10.1108/GS-06-2013-0010 |
| [6] | Berk I, Yetkiner H. Energy prices and economic growth in the long run:theory and evidence[J].Renewable & Sustainable Energy Reviews, 2014, 36:228–235. |
| [7] | Zhang C L, Tan Z L. Correlation Analysis on the Energy Consumption Structure and Economic Growth:Taking Shandong Province as an Example[J].Sustainable Development of Industry and Economy, 2014, 869/870:914–918. |
| [8] | Shahbaz M, Hye Q M A, Tiwari A K, et al. Economic growth, energy consumption, financial development, international trade and CO2, emissions in Indonesia[J].Renewable & Sustainable Energy Reviews, 2013, 25(5):109–121. |
| [9] | Li X Y. Analysis of Relationship between Energy Consumption and Economic Growth Based on Gray Relevance[J].Journal of Chongqing University(Social Science Edition), 2010, 16(5):31–35. |
| [10] | Al-Mulali U, Che N B C S. The impact of energy consumption and CO2, emission on the economic growth and financial development in the Sub Saharan African countries[J].Energy, 2012, 39(1):180–186.DOI:10.1016/j.energy.2012.01.032 |
| [11] | Li F, Dong S C, Li X, et al. Energy consumption-economic growth relationship and carbon dioxide emissions in China[J].Energy Policy, 2011, 39(2):568–574.DOI:10.1016/j.enpol.2010.10.025 |
| [12] | Hossain M S. Panel estimation for CO2 emissions, energy consumption, economic growth, trade openness and urbanization of newly industrialized countries[J].Energy Policy, 2011, 39(11):6991–6999.DOI:10.1016/j.enpol.2011.07.042 |
| [13] | Mudakkar S R, Zaman K, Khan M M, et al. Energy for economic growth, industrialization, environment and natural resources:Living with just enough[J].Renewable & Sustainable Energy Reviews, 2013, 25(3):580–595. |
| [14] | Lee C C, Chang C P. The impact of energy consumption on economic growth:Evidence from linear and nonlinear models in Taiwan[J].Energy, 2007, 32(12):2282–2294.DOI:10.1016/j.energy.2006.01.017 |
| [15] | Asafu-Adjaye J. The relationship between energy consumption, energy prices and economic growth:time series evidence from Asian developing countries[J].Energy Economics, 2000, 22(6):615–625.DOI:10.1016/S0140-9883(00)00050-5 |
| [16] | Luo X, Wang Y, Zhao J, et al. Grey relational analysis of an integrated cascade utilization system of geothermal water[J].International Journal of Green Energy, 2015, 13(1):14–27. |
| [17] | Lin S J, Lu I J, Lewis C. Grey relation performance correlations among economics, energy use and carbon dioxide emission in Taiwan[J].Energy Policy, 2007, 35(3):1948–1955.DOI:10.1016/j.enpol.2006.06.012 |
| [18] | Yuan C, Liu S, Fang Z, et al. The relation between Chinese economic development and energy consumption in the different periods[J].Energy Policy, 2010, 38(9):5189–5198.DOI:10.1016/j.enpol.2010.05.004 |
| [19] | Wang L, Feng-Ming X I, Wang J Y. Analysis of grey correlation between energy consumption and economic growth in Liaoning Province, China[J].Chinese Journal of Applied Ecology, 2016, 27(3):920–926. |
| [20] | Chen G Y, Li H T, Liang T, et al. Analysis of the Environmental Kuznets Curve Relationship Between Environmental Pollution and Economic Growth of Inner Mongolia[J].Chinese Agricultural Science Bulletin, 2012, 28(17):284–291. |
| [21] | Chontanawat J, Hunt L C, Pierse R. Does energy consumption cause economic growth:Evidence from a systematic study of over 100 countries[J].Journal of Policy Modeling, 2008, 30(2):209–220.DOI:10.1016/j.jpolmod.2006.10.003 |
| [22] | Tiwari A. Primary Energy Consumption, CO2 Emissions and Economic Growth:Evidence from India[J].Indian Economic Review, 2012, 47(2):165–189. |
| [23] | Erdal G, Erdal H, Esengün K. The causality between energy consumption and economic growth in Turkey[J].Energy Policy, 2008, 36(10):3838–3842.DOI:10.1016/j.enpol.2008.07.012 |
| [24] | Mahadevan R, Asafu-Adjaye J. Energy consumption, economic growth and prices:A reassessment using panel VECM for developed and developing countries[J].Energy Policy, 2007, 35(4):2481–2490.DOI:10.1016/j.enpol.2006.08.019 |
| [25] | Jobert T, Karanfil F. Sectoral energy consumption by source and economic growth in Turkey[J].Energy Policy, 2007, 35(11):5447–5456.DOI:10.1016/j.enpol.2007.05.008 |
| [26] | Paul S, Bhattacharya R N. Causality between energy consumption and economic growth in India:a note on conflicting results[J].Energy Economics, 2004, 26(6):977–983.DOI:10.1016/j.eneco.2004.07.002 |
| [27] | Phung B T. Energy consumption and economic growth in Vietnam:threshold cointegration and causality analysis[J].International Journal of Energy Economics & Policy, 2011, 1(1):1–17. |
| [28] | Alam M J, Begum I A, Buysse J, et al. Energy consumption, carbon emissions and economic growth nexus in Bangladesh:Cointegration and dynamic causality analysis[J].Energy Policy, 2012, 45(2):217–225. |
| [29] | Lin B, Wesseh P K. Energy consumption and economic growth in South Africa reexamined:a nonparametric testing apporach[J].Renewable & Sustainable Energy Reviews, 2014, 40:840–850. |
| [30] | Odhiambo N M. Energy consumption and economic growth nexus in Tanzania:an ARDL bounds testing approach[J].Energy Policy, 2009, 37(2):617–622.DOI:10.1016/j.enpol.2008.09.077 |
| [31] | Tsani S Z. Energy consumption and economic growth:a causality analysis for Greece[J].Energy Economics, 2010, 32(3):582–590.DOI:10.1016/j.eneco.2009.09.007 |
| [32] | Fan F, Lei Y. A review on energy, economy and environment(3E) system[J].Ecological Economy, 2013(12):42–48. |
| [33] | Saboori B, Sulaiman J. Environmental degradation, economic growth and energy consumption:evidence of the environmental Kuznets curve in Malaysia[J].Energy Policy, 2013, 60(5):892–905. |
| [34] | Saboori B, Sulaiman J. CO2 emissions, energy consumption and economic growth in association of Southeast Asian Nations (ASEAN) countries:Acointegration approach[J].Energy, 2013, 55(1):813–822. |
| [35] | Gillingham K, Rapson D, Wagner G. The rebound effect and energy efficiency policy[J].Review of Environmental Economics & Policy, 2015, 10(1):8–88. |
| [36] | Azevedo I M L. Consumer end-use energy efficiency and rebound effects[J].Annual Review of Environment & Resources, 2014, 39(1):393–418. |
| [37] | Wang M Y, Cao T, Shen X L. Analysis of Beijing energy economy and environment (3E) system architecture and index system structure[J].Advanced Materials Research, 2014, 1010-1012:1875–1882.DOI:10.4028/www.scientific.net/AMR.1010-1012 |
| [38] | Arouri M E H, Youssef A B, M'Henni H, et al. Energy consumption, economic growth and CO2, emissions in Middle East and North African countries[J].Energy Policy, 2012, 45(6):342–349. |
| [39] | Han S, Zhu J. Research on the dynamic relationship of the energy-economy-environment (3E) system-based on an empirical analysis of China[J].Energy Procedia, 2011, 5(1):2397–2404. |
| [40] | Lin C S, Liou F M, Huang C P. Grey forecasting model for CO2, emissions:a Taiwan study[J].Advanced Materials Research, 2012, 518-523(11):1664–1668. |
| [41] | Acaravci A, Ozturk I. On the relationship between energy consumption, CO2, emissions and economic growth in Europe[J].Energy, 2010, 35(12):5412–5420.DOI:10.1016/j.energy.2010.07.009 |
| [42] | Chang C C. A multivariate causality test of carbon dioxide emissions, energy consumption and economic growth in China[J].Applied Energy, 2010, 87(11):3533–3537.DOI:10.1016/j.apenergy.2010.05.004 |
| [43] | Menyah K, Wolde-Rufael Y. Energy consumption, pollutant emissions and economic growth in South Africa[J].Energy Economics, 2010, 32(6):1374–1382.DOI:10.1016/j.eneco.2010.08.002 |
| [44] | Dong F, Tan Q M, Zhou D Q, et al. The effect of technological progress, industrial structure and opening-up degree on Chinese energy consumption:an empirical study based on the two-step method, the grey relational analysis and co-integration test[J].China Population Resources & Environment, 2010, 20(6):22–27. |
| [45] | Gasparatos A, Gadda T. Environmental support, energy security and economic growth in Japan[J].Energy Policy, 2009, 37(37):4038–4048. |
| [46] | Soytas U, Sari R. Energy consumption, economic growth, and carbon emissions:challenges faced by an EU candidate member[J].Ecological Economics, 2009, 68(6):1667–1675.DOI:10.1016/j.ecolecon.2007.06.014 |
| [47] | Deng J L. Introduction to grey system theory[J].Journal of Grey System, 1989, 1(1):1–24. |
| [48] | Deng J L. To analyze the connotation and extension (C & E) of grey theory[J].Journal of Grey System, 2012, 24(4):293–298. |
| [49] | Camelia D, Emil S, Liviu-Adrian C. Grey relational analysis of the financial sector in Europe[J].Journal of Grey System, 2013, 25(4):19–30. |
| [50] | Wang Q, Chen Y. Energy saving and emission reduction revolutionizing China's environmental protection[J].Renewable and Sustainable Energy Reviews, 2010, 14(1):535–539.DOI:10.1016/j.rser.2009.08.006 |
