- Email: [email protected]
Analysis of Influencing Factors on Energy Efficiency of Yangtze River Delta Urban Agglomeration Based on Spatial Heterogeneity
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Energy Procedia 00 (2018) 000–000 Energy Procedia 158 Energy Procedia 00(2019) (2017)3234–3239 000–000
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China The 15th International Symposium District Heating and Cooling of Analysis of Influencing Factorsonon Energy Efficiency Yangtze River UrbanofAgglomeration on Spatial Assessing theDelta feasibility using the heat Based demand-outdoor temperature function for aHeterogeneity long-term district heat demand forecast
Guan Rongdia, Tian Lixina, Li Wenchaoa,* I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc
a
a* a* School of Finance &Economics,Jiangsu university, Zhenjiang and 212013,China IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Abstract Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
In this paper, the Yangtze River Delta city group for the study, first, using data envelopment analysis estimates the energy efficiency of the city, and found that the Yangtze River Delta city group showing lower energy efficiency overall. Abstract Within the urban agglomeration 26 prefecture-level cities, Zhoushan is the only one achieving relatively effective energy efficiency. The spatial heterogeneity of energy efficiency in the urban agglomeration in the Yangtze River Delta District heating networks addressed in the literature as onetheofexistence the mostofeffective solutions for clusters decreasing the is then measured usingare the commonly Moran's I index (including local), confirming low-energy efficiency greenhouse emissions the building sector. These systems require high investments are returned through centeredgas around Hefeifrom and clusters of high-energy agglomeration centers centered aroundwhich Hangzhou. Then the spatialthe heat sales.error Duemodel to the changed climate conditions and building renovation policies, heat demand in the future could decrease, is used to test the various factors that affect the energy efficiency of the urban agglomeration in the Yangtze prolonging the investment period. River Delta based onreturn the circumvention of spatial heterogeneity. The conclusion shows that there is a positive The main scope between of this paper is to assess the investment, feasibility ofscale, usingforeign the heattrade demand – outdoor temperature function influence for heat demand correlation industrial structure, and energy efficiency. Government forecast. The district of Alvalade, in Lisbon (Portugal), used as aconclusion, case study.this Thepaper district consisted and system is negatively relatedlocated to energy efficiency. Finally,was based on the putsisforward the of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district policy suggestion of promoting the energy efficiency of the urban agglomeration in the Yangtze River Delta. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were © 2019 The Authors. Published by Elsevier Ltd.model, previously developed and validated by the authors. compared with results a dynamic demand Copyright © 2018from Elsevier Ltd. Allheat rights reserved. This is an open access article under the CC BY-NC-ND licensethe (http://creativecommons.org/licenses/by-nc-nd/4.0/) th th The Selection results showed that when only changeofis the considered, margin of error could be acceptable for some applications Conference on andunder peer-review underweather responsibility scientific committee of the 10 Peer-review responsibility of than the scientific committee ofscenarios ICAE2018 – The 10thInternational International Conference onrenovation (the Applied error in annual demand was lower 20% for all weather considered). However, after introducing Energy (ICAE2018). Applied Energy. scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The Keywords: value of slope increased onMetropolitan average within the range 3.8% up to Moran's 8% perI Index. decade, that corresponds to the energycoefficient efficiency; Yangtze Delta Aggregate; Spatialof Heterogeneous; decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and 1. Introduction improve the accuracy of heat demand estimations.
The main factors affecting energy efficiency are GDP [1], investment [2], industrial structure [3],
© 2017 The Authors. Published by Elsevier Ltd. structure of energy [4] and so on. However, most of the data used in these studies are national or interPeer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and provincial data, which analyzes the spatial heterogeneity of energy efficiency. This study explores the Cooling. Keywords: Heat demand; Forecast; Climate change ** Corresponding author: Li Wenchao. E-mail address: [email protected]
1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102 © 2017 The Authors.under Published by Elsevier Ltd.scientific committee of the 10th International Conference on Applied Energy Selection and peer-review responsibility of the Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. (ICAE2018). 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.998
Guan Rongdi et al. / Energy Procedia 158 (2019) 3234–3239 Author name / Energy Procedia 00 (2018) 000–000
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spatial heterogeneity of energy efficiency using prefecture-level cities in the Yangtze River Delta region (Shanghai as a whole city) as a research unit, the spatial lag / space error model is used to study the influencing factors of energy efficiency, and policy recommendations on promoting low carbon development in the urban agglomeration of the Yangtze River Delta are proposed. 2. Energy Efficiency Calculation 2.1. Data Sources The Yangtze River Delta region includes 25 prefecture-level cities and Shanghai. All data in this paper are from the year of 2016 in Jiangsu Statistical Yearbook, Zhejiang Statistical Yearbook, Shanghai Statistical Yearbook and Anhui Statistical Yearbook. Therefore, the data used for the same period of 2015. 2.2. Energy Efficiency Calculation Results Before calculating efficiency of DEA, input-output indicators need to be set. This paper measures the energy efficiency, so its input indicators for the energy, as some of the statistical yearbook only shows the total amount of urban electricity consumption, so this paper uses the total amount of electricity consumption to characterize urban energy consumption, the per capita GDP to characterize the city's economy output to characterize the unwanted output with carbon emissions. There are many ways of dealing with unwanted output, such as a monotonically decreasing conversion if the unwanted output increases in proportion to the expected output as well as the input of unwanted output [5]. The smaller the value is, the higher the efficiency is. The law does not change the structure of the variables. In theory, the visual environment pollution is the input of energy consumption.
ek =
44 NVCk ´ dk ´ ORk (k = 1, 2,……,8) 12
(1)
In the formula (1), Ek represents the k kinds of energy emission coefficient; NCVk refers to the k kind of energy net calorific value (low); Dk denotes the k kinds of energy default emission factors; ORk means of k kind of energy burn oxidation rate. Taking carbon emissions and electricity consumption as input indicators and GDP per capita as output indicators, as Table 1 : Table 1. Forecasting result of Jiangsu province electricity consumption from2013 to 2020 Serial number
area
Energy efficiency value
Serial number
area
1 2 3 4 5 6 7 8 9 10 11 12 13
Shanghai Nanjing Wuxi Changzhou Suzhou Nantong Yancheng Yangzhou Zhenjiang Taizhou Hangzhou Ningbo Shaoxing
0.17 0.10 0.23 0.41 0.09 0.28 0.32 0.50 0.35 0.40 0.60 0.13 0.77
14 15 16 17 18 19 20 21 22 23 24 25 26
Huzhou Jiaxing Jinhua Zhoushan Taizhou Hefei Wuhu Anshan Tongling Anqing Xuancheng Chizhou Chuzhou
Energy efficiency value 0.88 0.49 0.68 1.00 0.42 0.13 0.18 0.34 0.73 0.14 0.30 0.79 0.19
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Guan Rongdi et al. / Energy Procedia 158 (2019) 3234–3239 Author name / Energy Procedia 00 (2018) 000–000
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As can be seen in Table 1, the energy efficiency in most areas is low. The nature of this is mainly due to the large scale of industries in all cities and regions in various regions and the high energy-consumption and high-emission industries in industries, which in turn leads to the low energy efficiency of cities in the Yangtze River Delta. 3. Energy efficiency of space measurement analysis 3.1. Exploratory Spatial Data Analysis Results The Triangle map is based on the energy efficiency distribution of the Yangtze River Delta urban agglomeration (Figure 1). As can be seen from Figure 1, there is spatial agglomeration effect of energy efficiency and the specific numerical Moran's I index can be calculated. Using the GEODA software, the Moran's I index of the Yangtze River Delta urban agglomeration can be calculated as shown in Figure 2. As can be seen from Figure 2, the energy efficiency of urban agglomerations in the Yangtze River Delta is characterized by spatial heterogeneity, which is mainly characterized by spatial agglomeration. According to GEODA, it can be used to calculate the local Moran's I index for energy efficiency in the Yangtze River Delta urban agglomeration.
Fig.1 Energy efficiency in the urban agglomeration
Fig.2 Moran's I index calculation results
The results in FIG. 1&2 shows that an urban agglomeration of Yangtze energy efficiency does exist some space agglomeration effects. It is manifested as the concentration of low energy efficiency space in Hefei city center and high energy efficiency space centered in Hangzhou City. The main reasons for the accumulation of the low energy efficiency space’s center is Hefei is that Hefei and Chuzhou, Anqing and other cities connected, due to the level of economic development in Anhui Province, energy-saving technologies and environmental awareness have a certain gap comparing to Jiangsu, Shanghai and Zhejiang provinces, eventually led to lower energy efficiency in these cities. At the same time, Hefei, as the capital city of Anhui Province, led to the accumulation of a large amount of resources in Hefei, eventually resulting in making Hefei a low energy efficiency space. Compared with Hefei, the situation in Hangzhou is much better. Hangzhou is connected with Xuancheng, Huzhou, Jiaxing, Shaoxing and Jinhua. Due to geographical reasons, these cities can’t develop large-scale industries and can only be forced to develop their service industries. In recent years, the rapid development of e-commerce and other industries, through the accumulation of time, lead to higher economic output, lead to its high energy efficiency. At the same time, from the geographical point of view, Hangzhou is at the
Guan Rongdi et al. / Energy Procedia 158 (2019) 3234–3239 Author name / Energy Procedia 00 (2018) 000–000
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center of its economy and politics, bringing energy efficiency to Hangzhou and ending up as a point of high energy efficiency in Hangzhou. 3.2. Space measurement results analysis The main factors affecting energy efficiency include: economic development level, energy factors, environmental factors, industrial structure, investment level, government influence, institutional factors, the degree of opening up, the scale factor, the situation of foreign trade. Since the per capita GDP, electricity consumption and carbon emissions per capita have been assumed in the calculation of energy efficiency, there is no spatial impact. Therefore, this paper discusses the impact of other indicators from a spatial point of view (Table 2), and combined with the 2015 provincial statistical data, using GEODA software for leastsquares estimation, space lag estimation and spatial error estimation, the fitting index showing in the Table 3. Table 2. Spatial efficiency of energy efficiency indicators selected factor energy efficiency Industrial Structure Investment level Government influence Institutional factors
variable
Indicator selection
unit
Y
Calculated by DEA
-
Industrial Structure = Value Added of Tertiary % Industry / Gross Regional Product Investment Level= Fixed Asset Investment/ Gross X2 % Regional Product Government Influence= Government X3 % Expenditure/ Gross Regional Product Institutional Factors = State-owned Industrial X4 % Output / Gross Industrial Output Opening up level = foreign investment and Hong Kong, Foreign X5 Macao, total industrial output value of % factors investment / gross industrial output value Scale factor = large and medium-sized industrial output % Scale factor X6 value / gross industrial output value Foreign trade Foreign Trade = Total Import and Export / Gross X7 % level Regional Product Note: The total units of imports and exports in the yearbook are in units of billions of U.S. dollars and need to be converted into 100 million yuan using the average annual exchange rate X1
Table 3. Fitting indicators for OLS, space lag, and spatial error regression Fitting degree index
OLS
Space lags
Space error
AIC
6.24216
6.78779
6.07679
SC
16.6089
18.4503
16.4435
Log-Likelihood
4.87892
5.60611
4.961607
AIC stands for Chi-Chi Information Criterion, SC stands for Schwartz Information Criterion, and LogLikelihood denotes Log-Likelihood Estimate. It is found that although the log-Likelihood value of the space lag model is the largest, the AIC and SC values of the space error model are the smallest, which shows that the fitting degree of the space error estimation is the best, that is, the energy efficiency of the urban agglomeration in the Yangtze River Delta not only exists in space Heterogeneity, and this spatial
Guan Rongdi et al. / Energy Procedia 158 (2019) 3234–3239 Author name / Energy Procedia 00 (2018) 000–000
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heterogeneity mainly for the spatial error. Based on this, we use regression of spatial error model to the above factors to get the final regression results as shown in Table 4: Table 4. Spatial error regression results variable Intercept term X1 X2 X3
coefficient -0.1237643 * (-1.89248) 0.5914971 ** (7.148499) 0.1452483 * (3.9771) -0.5193667 * (-4.963963)
variable X4 X5 X6 X7
coefficient -0.8705298 * (-1.763736) -1.447454 *** (-3.698635) 1.247517 * (1.877632) 0.0976689 * (4.979815)
Note: The values in parentheses are t; *, **, *** indicate that the parameter estimates are significant at significant levels of 10%, 5%, and 1%, respectively.
4. Conclusions and recommendations The correlation coefficient between industrial structure and energy efficiency is 0.59. This shows that the optimization of industrial structure can enhance the urban energy efficiency significantly in the Yangtze River Delta. Changing industrial structure can improve energy efficiency, but the industry type and mode of investment level adjustment is different. Under the situation of supply-side reform, the urban agglomerations of the Yangtze River Delta need to adjust and guide investment categories while further enhancing their investment promotion efforts. Investment tax relief and other measures should be taken to promote investment inflow into industries that promote energy efficiency. (1) The correlation coefficient between industrial structure and energy efficiency is 0.59. This shows that the optimization of industrial structure is of great significance to enhancing the urban energy efficiency in the Yangtze River Delta. The correlation coefficient between investment level and energy efficiency is only 0.15, which indicates that although the investment level can improve energy efficiency, the industry type and mode of investment level adjustment is different. Under the situation of supply-side reform, the urban agglomerations of the Yangtze River Delta need to adjust and guide investment categories while further enhancing their investment promotion efforts. Investment tax relief and other measures should be taken to promote investment inflow into industries that promote energy efficiency. (2) The correlation coefficient between government influence and energy efficiency is -0.52, which shows that government behavior will make energy efficiency lower. The government should further reduce energy subsidies related to energy efficiency and make the market allocate more resources. This will not only reduce the government's financial burden but also increase the efficiency of resource allocation. At the same time, we should step up censorship and ensure that every financial subsidy reflects social fairness. (3) The correlation coefficient of institutional factors and energy efficiency is -0.87. Indicators show that the existence of state-controlled enterprises reduces energy efficiency. It can be seen that the reform of state-owned enterprises is imperative because state-owned enterprises not only reduce energy efficiency but
Guan Rongdi et al. / Energy Procedia 158 (2019) 3234–3239 Author name / Energy Procedia 00 (2018) 000–000
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also drag the market efficiency of society as a whole. However, the reform of state-owned enterprises also needs to assess the situation and needs to be done step by step and can’t be done in one move. (4) The correlation coefficient between the degree of opening up and energy efficiency is -1.45. This shows that in the process of foreign investment, most are not investing in green low-carbon enterprises, but investing in enterprises with high energy consumption and high pollution. the government should pay attention to the quality of foreign enterprises. (5) There is a positive correlation between the scale factor and energy efficiency, with a correlation coefficient of 1.25. This shows that the industrial enterprises in the urban agglomeration of the Yangtze River Delta have a large-scale effect on energy efficiency and are quite obvious. Therefore, the future development of the cities in the Yangtze River Delta should focus on "shutting down and turning around" small-scale enterprises in the region so as to enhance the economies of scale of industrial enterprises in the region. In addition, small-scale enterprises in the region may also be transferred and transferred to underdeveloped areas to promote local economic development and optimize the overall development of the national economy. Acknowledge National Natural Science Foundation of China (No. 71704067,71690242), Humanity and Social Science Foundation of Ministry of Education ( No. 17YJC790080), College Students’ Innovation Foundation of Jiangsu University (No. 201710299004Z). References [1] Qin Q, Li X, Li L, et al. Air emissions perspective on energy efficiency: An empirical analysis of China's coastal areas [J]. Applied Energy,2017,185(p1): 604-614. [2] Zhang Bingbing . Research on China's total factor energy efficiency and its influencing factors under carbon emissions [J] . Ecological Indicators, 2016, 70:480-497. [3] Liu Y, Zhao G, Zhao Y. An analysis of Chinese provincial carbon dioxide emission profiles based on energy consumption structure [J]. Energy Policy, 2016, 96: 524-533. [4] Zang Chuanqin , Liu Yan . Analysis of total factor energy efficiency and its influencing factors in Shandong Province [J]. China Population , Resources and Environment , 2012,20 (8): 107-113. [5] Wu Qi , Wu Chun . Based on DEA study of energy efficiency evaluation model [J]. Management Science , 2009,22 (01): 103112.
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ScienceDirect ScienceDirect
Energy Procedia 00 (2018) 000–000 Energy Procedia 158 Energy Procedia 00(2019) (2017)3234–3239 000–000
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China The 15th International Symposium District Heating and Cooling of Analysis of Influencing Factorsonon Energy Efficiency Yangtze River UrbanofAgglomeration on Spatial Assessing theDelta feasibility using the heat Based demand-outdoor temperature function for aHeterogeneity long-term district heat demand forecast
Guan Rongdia, Tian Lixina, Li Wenchaoa,* I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc
a
a* a* School of Finance &Economics,Jiangsu university, Zhenjiang and 212013,China IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Abstract Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
In this paper, the Yangtze River Delta city group for the study, first, using data envelopment analysis estimates the energy efficiency of the city, and found that the Yangtze River Delta city group showing lower energy efficiency overall. Abstract Within the urban agglomeration 26 prefecture-level cities, Zhoushan is the only one achieving relatively effective energy efficiency. The spatial heterogeneity of energy efficiency in the urban agglomeration in the Yangtze River Delta District heating networks addressed in the literature as onetheofexistence the mostofeffective solutions for clusters decreasing the is then measured usingare the commonly Moran's I index (including local), confirming low-energy efficiency greenhouse emissions the building sector. These systems require high investments are returned through centeredgas around Hefeifrom and clusters of high-energy agglomeration centers centered aroundwhich Hangzhou. Then the spatialthe heat sales.error Duemodel to the changed climate conditions and building renovation policies, heat demand in the future could decrease, is used to test the various factors that affect the energy efficiency of the urban agglomeration in the Yangtze prolonging the investment period. River Delta based onreturn the circumvention of spatial heterogeneity. The conclusion shows that there is a positive The main scope between of this paper is to assess the investment, feasibility ofscale, usingforeign the heattrade demand – outdoor temperature function influence for heat demand correlation industrial structure, and energy efficiency. Government forecast. The district of Alvalade, in Lisbon (Portugal), used as aconclusion, case study.this Thepaper district consisted and system is negatively relatedlocated to energy efficiency. Finally,was based on the putsisforward the of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district policy suggestion of promoting the energy efficiency of the urban agglomeration in the Yangtze River Delta. renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were © 2019 The Authors. Published by Elsevier Ltd.model, previously developed and validated by the authors. compared with results a dynamic demand Copyright © 2018from Elsevier Ltd. Allheat rights reserved. This is an open access article under the CC BY-NC-ND licensethe (http://creativecommons.org/licenses/by-nc-nd/4.0/) th th The Selection results showed that when only changeofis the considered, margin of error could be acceptable for some applications Conference on andunder peer-review underweather responsibility scientific committee of the 10 Peer-review responsibility of than the scientific committee ofscenarios ICAE2018 – The 10thInternational International Conference onrenovation (the Applied error in annual demand was lower 20% for all weather considered). However, after introducing Energy (ICAE2018). Applied Energy. scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The Keywords: value of slope increased onMetropolitan average within the range 3.8% up to Moran's 8% perI Index. decade, that corresponds to the energycoefficient efficiency; Yangtze Delta Aggregate; Spatialof Heterogeneous; decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and 1. Introduction improve the accuracy of heat demand estimations.
The main factors affecting energy efficiency are GDP [1], investment [2], industrial structure [3],
© 2017 The Authors. Published by Elsevier Ltd. structure of energy [4] and so on. However, most of the data used in these studies are national or interPeer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and provincial data, which analyzes the spatial heterogeneity of energy efficiency. This study explores the Cooling. Keywords: Heat demand; Forecast; Climate change ** Corresponding author: Li Wenchao. E-mail address: [email protected]
1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102 © 2017 The Authors.under Published by Elsevier Ltd.scientific committee of the 10th International Conference on Applied Energy Selection and peer-review responsibility of the Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. (ICAE2018). 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.998
Guan Rongdi et al. / Energy Procedia 158 (2019) 3234–3239 Author name / Energy Procedia 00 (2018) 000–000
2
3235
spatial heterogeneity of energy efficiency using prefecture-level cities in the Yangtze River Delta region (Shanghai as a whole city) as a research unit, the spatial lag / space error model is used to study the influencing factors of energy efficiency, and policy recommendations on promoting low carbon development in the urban agglomeration of the Yangtze River Delta are proposed. 2. Energy Efficiency Calculation 2.1. Data Sources The Yangtze River Delta region includes 25 prefecture-level cities and Shanghai. All data in this paper are from the year of 2016 in Jiangsu Statistical Yearbook, Zhejiang Statistical Yearbook, Shanghai Statistical Yearbook and Anhui Statistical Yearbook. Therefore, the data used for the same period of 2015. 2.2. Energy Efficiency Calculation Results Before calculating efficiency of DEA, input-output indicators need to be set. This paper measures the energy efficiency, so its input indicators for the energy, as some of the statistical yearbook only shows the total amount of urban electricity consumption, so this paper uses the total amount of electricity consumption to characterize urban energy consumption, the per capita GDP to characterize the city's economy output to characterize the unwanted output with carbon emissions. There are many ways of dealing with unwanted output, such as a monotonically decreasing conversion if the unwanted output increases in proportion to the expected output as well as the input of unwanted output [5]. The smaller the value is, the higher the efficiency is. The law does not change the structure of the variables. In theory, the visual environment pollution is the input of energy consumption.
ek =
44 NVCk ´ dk ´ ORk (k = 1, 2,……,8) 12
(1)
In the formula (1), Ek represents the k kinds of energy emission coefficient; NCVk refers to the k kind of energy net calorific value (low); Dk denotes the k kinds of energy default emission factors; ORk means of k kind of energy burn oxidation rate. Taking carbon emissions and electricity consumption as input indicators and GDP per capita as output indicators, as Table 1 : Table 1. Forecasting result of Jiangsu province electricity consumption from2013 to 2020 Serial number
area
Energy efficiency value
Serial number
area
1 2 3 4 5 6 7 8 9 10 11 12 13
Shanghai Nanjing Wuxi Changzhou Suzhou Nantong Yancheng Yangzhou Zhenjiang Taizhou Hangzhou Ningbo Shaoxing
0.17 0.10 0.23 0.41 0.09 0.28 0.32 0.50 0.35 0.40 0.60 0.13 0.77
14 15 16 17 18 19 20 21 22 23 24 25 26
Huzhou Jiaxing Jinhua Zhoushan Taizhou Hefei Wuhu Anshan Tongling Anqing Xuancheng Chizhou Chuzhou
Energy efficiency value 0.88 0.49 0.68 1.00 0.42 0.13 0.18 0.34 0.73 0.14 0.30 0.79 0.19
3236
Guan Rongdi et al. / Energy Procedia 158 (2019) 3234–3239 Author name / Energy Procedia 00 (2018) 000–000
3
As can be seen in Table 1, the energy efficiency in most areas is low. The nature of this is mainly due to the large scale of industries in all cities and regions in various regions and the high energy-consumption and high-emission industries in industries, which in turn leads to the low energy efficiency of cities in the Yangtze River Delta. 3. Energy efficiency of space measurement analysis 3.1. Exploratory Spatial Data Analysis Results The Triangle map is based on the energy efficiency distribution of the Yangtze River Delta urban agglomeration (Figure 1). As can be seen from Figure 1, there is spatial agglomeration effect of energy efficiency and the specific numerical Moran's I index can be calculated. Using the GEODA software, the Moran's I index of the Yangtze River Delta urban agglomeration can be calculated as shown in Figure 2. As can be seen from Figure 2, the energy efficiency of urban agglomerations in the Yangtze River Delta is characterized by spatial heterogeneity, which is mainly characterized by spatial agglomeration. According to GEODA, it can be used to calculate the local Moran's I index for energy efficiency in the Yangtze River Delta urban agglomeration.
Fig.1 Energy efficiency in the urban agglomeration
Fig.2 Moran's I index calculation results
The results in FIG. 1&2 shows that an urban agglomeration of Yangtze energy efficiency does exist some space agglomeration effects. It is manifested as the concentration of low energy efficiency space in Hefei city center and high energy efficiency space centered in Hangzhou City. The main reasons for the accumulation of the low energy efficiency space’s center is Hefei is that Hefei and Chuzhou, Anqing and other cities connected, due to the level of economic development in Anhui Province, energy-saving technologies and environmental awareness have a certain gap comparing to Jiangsu, Shanghai and Zhejiang provinces, eventually led to lower energy efficiency in these cities. At the same time, Hefei, as the capital city of Anhui Province, led to the accumulation of a large amount of resources in Hefei, eventually resulting in making Hefei a low energy efficiency space. Compared with Hefei, the situation in Hangzhou is much better. Hangzhou is connected with Xuancheng, Huzhou, Jiaxing, Shaoxing and Jinhua. Due to geographical reasons, these cities can’t develop large-scale industries and can only be forced to develop their service industries. In recent years, the rapid development of e-commerce and other industries, through the accumulation of time, lead to higher economic output, lead to its high energy efficiency. At the same time, from the geographical point of view, Hangzhou is at the
Guan Rongdi et al. / Energy Procedia 158 (2019) 3234–3239 Author name / Energy Procedia 00 (2018) 000–000
4
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center of its economy and politics, bringing energy efficiency to Hangzhou and ending up as a point of high energy efficiency in Hangzhou. 3.2. Space measurement results analysis The main factors affecting energy efficiency include: economic development level, energy factors, environmental factors, industrial structure, investment level, government influence, institutional factors, the degree of opening up, the scale factor, the situation of foreign trade. Since the per capita GDP, electricity consumption and carbon emissions per capita have been assumed in the calculation of energy efficiency, there is no spatial impact. Therefore, this paper discusses the impact of other indicators from a spatial point of view (Table 2), and combined with the 2015 provincial statistical data, using GEODA software for leastsquares estimation, space lag estimation and spatial error estimation, the fitting index showing in the Table 3. Table 2. Spatial efficiency of energy efficiency indicators selected factor energy efficiency Industrial Structure Investment level Government influence Institutional factors
variable
Indicator selection
unit
Y
Calculated by DEA
-
Industrial Structure = Value Added of Tertiary % Industry / Gross Regional Product Investment Level= Fixed Asset Investment/ Gross X2 % Regional Product Government Influence= Government X3 % Expenditure/ Gross Regional Product Institutional Factors = State-owned Industrial X4 % Output / Gross Industrial Output Opening up level = foreign investment and Hong Kong, Foreign X5 Macao, total industrial output value of % factors investment / gross industrial output value Scale factor = large and medium-sized industrial output % Scale factor X6 value / gross industrial output value Foreign trade Foreign Trade = Total Import and Export / Gross X7 % level Regional Product Note: The total units of imports and exports in the yearbook are in units of billions of U.S. dollars and need to be converted into 100 million yuan using the average annual exchange rate X1
Table 3. Fitting indicators for OLS, space lag, and spatial error regression Fitting degree index
OLS
Space lags
Space error
AIC
6.24216
6.78779
6.07679
SC
16.6089
18.4503
16.4435
Log-Likelihood
4.87892
5.60611
4.961607
AIC stands for Chi-Chi Information Criterion, SC stands for Schwartz Information Criterion, and LogLikelihood denotes Log-Likelihood Estimate. It is found that although the log-Likelihood value of the space lag model is the largest, the AIC and SC values of the space error model are the smallest, which shows that the fitting degree of the space error estimation is the best, that is, the energy efficiency of the urban agglomeration in the Yangtze River Delta not only exists in space Heterogeneity, and this spatial
Guan Rongdi et al. / Energy Procedia 158 (2019) 3234–3239 Author name / Energy Procedia 00 (2018) 000–000
3238
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heterogeneity mainly for the spatial error. Based on this, we use regression of spatial error model to the above factors to get the final regression results as shown in Table 4: Table 4. Spatial error regression results variable Intercept term X1 X2 X3
coefficient -0.1237643 * (-1.89248) 0.5914971 ** (7.148499) 0.1452483 * (3.9771) -0.5193667 * (-4.963963)
variable X4 X5 X6 X7
coefficient -0.8705298 * (-1.763736) -1.447454 *** (-3.698635) 1.247517 * (1.877632) 0.0976689 * (4.979815)
Note: The values in parentheses are t; *, **, *** indicate that the parameter estimates are significant at significant levels of 10%, 5%, and 1%, respectively.
4. Conclusions and recommendations The correlation coefficient between industrial structure and energy efficiency is 0.59. This shows that the optimization of industrial structure can enhance the urban energy efficiency significantly in the Yangtze River Delta. Changing industrial structure can improve energy efficiency, but the industry type and mode of investment level adjustment is different. Under the situation of supply-side reform, the urban agglomerations of the Yangtze River Delta need to adjust and guide investment categories while further enhancing their investment promotion efforts. Investment tax relief and other measures should be taken to promote investment inflow into industries that promote energy efficiency. (1) The correlation coefficient between industrial structure and energy efficiency is 0.59. This shows that the optimization of industrial structure is of great significance to enhancing the urban energy efficiency in the Yangtze River Delta. The correlation coefficient between investment level and energy efficiency is only 0.15, which indicates that although the investment level can improve energy efficiency, the industry type and mode of investment level adjustment is different. Under the situation of supply-side reform, the urban agglomerations of the Yangtze River Delta need to adjust and guide investment categories while further enhancing their investment promotion efforts. Investment tax relief and other measures should be taken to promote investment inflow into industries that promote energy efficiency. (2) The correlation coefficient between government influence and energy efficiency is -0.52, which shows that government behavior will make energy efficiency lower. The government should further reduce energy subsidies related to energy efficiency and make the market allocate more resources. This will not only reduce the government's financial burden but also increase the efficiency of resource allocation. At the same time, we should step up censorship and ensure that every financial subsidy reflects social fairness. (3) The correlation coefficient of institutional factors and energy efficiency is -0.87. Indicators show that the existence of state-controlled enterprises reduces energy efficiency. It can be seen that the reform of state-owned enterprises is imperative because state-owned enterprises not only reduce energy efficiency but
Guan Rongdi et al. / Energy Procedia 158 (2019) 3234–3239 Author name / Energy Procedia 00 (2018) 000–000
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also drag the market efficiency of society as a whole. However, the reform of state-owned enterprises also needs to assess the situation and needs to be done step by step and can’t be done in one move. (4) The correlation coefficient between the degree of opening up and energy efficiency is -1.45. This shows that in the process of foreign investment, most are not investing in green low-carbon enterprises, but investing in enterprises with high energy consumption and high pollution. the government should pay attention to the quality of foreign enterprises. (5) There is a positive correlation between the scale factor and energy efficiency, with a correlation coefficient of 1.25. This shows that the industrial enterprises in the urban agglomeration of the Yangtze River Delta have a large-scale effect on energy efficiency and are quite obvious. Therefore, the future development of the cities in the Yangtze River Delta should focus on "shutting down and turning around" small-scale enterprises in the region so as to enhance the economies of scale of industrial enterprises in the region. In addition, small-scale enterprises in the region may also be transferred and transferred to underdeveloped areas to promote local economic development and optimize the overall development of the national economy. Acknowledge National Natural Science Foundation of China (No. 71704067,71690242), Humanity and Social Science Foundation of Ministry of Education ( No. 17YJC790080), College Students’ Innovation Foundation of Jiangsu University (No. 201710299004Z). References [1] Qin Q, Li X, Li L, et al. Air emissions perspective on energy efficiency: An empirical analysis of China's coastal areas [J]. Applied Energy,2017,185(p1): 604-614. [2] Zhang Bingbing . Research on China's total factor energy efficiency and its influencing factors under carbon emissions [J] . Ecological Indicators, 2016, 70:480-497. [3] Liu Y, Zhao G, Zhao Y. An analysis of Chinese provincial carbon dioxide emission profiles based on energy consumption structure [J]. Energy Policy, 2016, 96: 524-533. [4] Zang Chuanqin , Liu Yan . Analysis of total factor energy efficiency and its influencing factors in Shandong Province [J]. China Population , Resources and Environment , 2012,20 (8): 107-113. [5] Wu Qi , Wu Chun . Based on DEA study of energy efficiency evaluation model [J]. Management Science , 2009,22 (01): 103112.
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