Decomposed Sources of Green Productivity Growth for Three Major Urban Agglomerations in China 1876 6102 © 2016 The Authors Published by Elsevier Ltd This is an open access article under the CC BY NC N[.]
Available online at www.sciencedirect.com ScienceDirect Energy Procedia 104 (2016) 481 – 486 CUE2016-Applied Energy Symposium and Forum 2016: Low carbon cities & urban energy systems Decomposed Sources of Green Productivity Growth for Three Major Urban Agglomerations in China Feng Taoa, Huiqin Zhanga, Xiaohua Xiab,c * a Institute of Industrial Economics, Jinan University, 601 Huangpu Avenue West, Guangzhou, 510632, China.P.C b School of Economics, Renmin University of China, 59 Zhongguancun Street, Beijing 100872, China c Institute of China’s Economic Reform and Development,Renmin University of China, 59 Zhongguancun Street, Beijing,100872,China Abstract This study introduces the global Malmquist-Luenberger productivity index to measure and decompose green productivity growth for the three major urban agglomerations across China over the period 2003-2013 As the first study focusing on green productivity of emerging cities in developing countries, its results show that the major source of green productivity growth is technical progress rather than efficiency improvement Efficiency deterioration damages green productivity growth in Yangtze River Delta and Pearl River Delta Finally, this study finds determinants of green productivity growth are very different across these three urban agglomerations © 2016 The Authors Published by Elsevier Ltd This is an open access article under the CC BY-NC-ND license © 2016 The Authors Published by Elsevier Ltd (http://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and/or peer-reviewofunder responsibility of of CUE Peer-review under responsibility the scientific committee the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems Keywords: green productivity, global Malmquist-Luenberger index, urban agglomerations Introduction The emergence of urban agglomerations is an important phenomenon in the development of regional economy in China The three major urban agglomerations Yangtze River Delta, Pearl River Delta and Beijing-Tianjin-Hebei in the eastern coast of China have become main driver of industrialization and urbanization of the whole country, and they are also key regions to support China as “world factory” The three major urban agglomerations which made up 2.8% of the territory gathered 18% of the people, created 36% of the gross domestic products (GDP) in 2013 [1] However, China paid high costs in terms of huge energy consumption and pollution emissions for economic prosperity in the last decades It will be impossible for cities to continue the traditional industrialization and urbanization mode, characterized * Corresponding author Tel.: +86-10-8250-9079; fax: +86-10-8250-9079 E-mail address: xiaxh.email@gmail.com 1876-6102 © 2016 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 the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems doi:10.1016/j.egypro.2016.12.081 482 Feng Tao et al / Energy Procedia 104 (2016) 481 – 486 by high inputs, high energy consumption, high pollution emission but low efficiency In 2013, Municipal districts of the three urban agglomerations consumed electricity accounted for 45.39% of the whole country, and emitted waste water, SO and Soot (dust) accounted for 35.95%, 16.42%, 19.16%, respectively, of the whole country [2] Therefore, the three urban agglomerations will play important role in finishing the task of energy saving and emission reduction in China in the future Since energy and environment are hard constraints for economic growth, we cannot precisely evaluate economic quality until the negative effect of environmentally harmful by-products has been incorporated into the conventional measures of productivity Chung et al [3] creatively introduced the MalmquistLuenberger productivity index (ML index) to calculate environmentally sensitive productivity growth, namely the green productivity growth However, ML index is noncircular and faces linear programming infeasibility when measuring cross-period directional distance functions (DDFs) To overcome the weakness of ML index, Oh [4] integrated the concept of the global production possibility set (PPS) and the DDF and proposed the Global Malmquist-Luenberger (GML) productivity index as an alternative version of the ML index Oh [4] also provided methods to decompose the GML index to judge sources of green productivity growth In recent years, GML has been widely used to measure green productivity growth in regions and countries especially in China Wang and Feng [5] used GML index to measure and decomposed the environmental productivity of China during 2003-2011 Fan et al [6] applied GML index to estimate and decompose the total factor CO emission performance of 32 industrial sub-sectors in Shanghai (China) However, the study of green productivity of cities during the industrialization process in developing countries is largely neglected in the green productivity benchmarking literature Cities play a major role in energy consumption and environment protection during the industrialization in developing countries and this study therefore focuses on calculating and decomposing the green productivity growth for the three major urban agglomerations in China The determinants driving green productivity growth are also discussed in this article Methodology 2.1 The GML Productivity Index Under a panel of k =1,…, K cities and t =1,…, T time periods, for city k at time period t, the inputs and outputs set can be assumed as outputs, , where the production technology can produce M desirable , and J undesirable outputs, , by using N inputs, The production possibility set, therefore, can be represented by: (1) For the purpose of incorporating undesirable outputs, Chung et al [3] introduced the DDF which can be defined as follows: (2) is a direction vector, and denotes value of the DDF Taking the direction vector, g, as where weight, the DDF seeks the maximal increase of desirable outputs while decreasing undesirable outputs [7] Compared with the traditional ML index, GML index improve the definition of PPS A global G P1 * P * P * * PT It envelopes all contemporaneous benchmark technology is defined as P benchmark technologies by establishing a single reference PPS from a panel data on inputs and outputs of relevant DMUs Referencing the GML index proposed by Oh [4], this study defines the global DDF and Feng Tao et al / Energy Procedia 104 (2016) 481 – 486 the global Luenberger index with a slack-based measure (SBM) [8] The GML index can be expressed as follows: D G ( x t , y t , bt ) D G ( x t 1 , y t 1 , bt 1 ) GPEC t ,t 1 u GPTC t ,t 1 u GSTC t ,t 1 u GSEC t ,t 1 (3) GMLt ,t 1 ( x t , y t , bt , x t 1 , y t 1 , bt 1 ) where GML has been decomposed four components when assuming constant returns to scale (CRS), according to the methods of Färe et al [9], Ray and Desli [10] and Grosskopf [11], such as the pure efficiency change, GPEC, the pure technical change, GPTC, the scale effect on technical change, GSTC, and the scale effect on efficiency change, GSEC 2.2 Data The sample covers 51 cities at prefecture level across the three urban agglomerations in China in the period 2003-2013 Input and output variables are needed to measure the green productivity using the GML index Inputs include capital stock, labor force and electricity consumption Desirable output is real GDP, and undesirable outputs cover industrial wastewater, SO and soot (dust) emissions All data are collected or calculated from Chinese City Statistical Yearbook [2], China Statistical Yearbook [12] Results and discussions 3.1 Temporal trends of green productivity growth The temporal trends of cumulative green productivity growth across the three major urban agglomerations in China are depicted in Fig In general, the index of green productivity of three urban agglomerations grew slowly over the studied period The green productivity of the Beijing-Tianjin-Hebei was higher than the two other agglomerations in most of years and achieved a stable growth trend in every year However, for both Yangtze River Delta and Pearl River Delta, the cumulative index fell down in 2011 The most likely reason is that the global financial crisis and domestic economic downturn caused a big shock to these two export-oriented agglomerations Fig Green productivity growth for three major urban agglomerations in China, 2003-2013 3.2 Decomposed sources of green productivity growth The decomposed components of green productivity growth calculated as cumulative value and geometric mean value, respectively, are reported in Table The growth rate of green productivity in the Yangtze River Delta is highest among the three urban agglomerations during the studied period It 483 484 Feng Tao et al / Energy Procedia 104 (2016) 481 – 486 indicates that sustainability of economic growth in the Yangtze River Delta is better than the two others when considering environmentally harmful by-products Judging from the components of GML, the main contributor of green productivity growth is pure technical changes, of which the cumulative index (GPTC) is 32.5% in the Yangtze River Delta, 28.4% in the Pearl River Delta and 17.2% in the Beijing-TianjinHebei, respectively Both pure efficiency changes (GPEC) and scale efficiency changes (GSEC) are higher than in the Beijing-Tianjin-Hebei, but lower than in the two other urban agglomerations This indicates that efficiency improvements can promote green productivity growth in the Beijing-TianjinHebei, but prevent green productivity growth in the Yangtze River Delta and the Pearl River Delta Table Green productivity growth and its components of cities in the three major urban agglomerations of China: 2003-2013 urban agglomerations cumulative value geometric mean value GML GPEC GPTC GSEC GSTC GML GPEC GPTC GSEC GSTC Beijing-Tianjin-Hebei 1.251 1.046 1.215 1.022 0.981 1.021 1.004 1.018 1.002 0.998 Yangtze River Delta 1.301 0.998 1.338 0.987 1.000 1.025 0.999 1.028 0.998 1.000 Pearl River Delta 1.188 0.970 1.348 0.956 0.959 1.017 0.997 1.029 0.995 0.995 3.3 Determinants of green productivity growth In order to investigate the determinants of green productivity growth, this study established an econometric model incorporating the following determinants such as urban agglomeration intensity (AG), environmental regulations (ER), industrial structure (IS), endowment structure (K/L), foreign direct investment (FDI) and infrastructure conditions (INFRA) All data are collected or calculated from Chinese City Statistical Yearbook [2], China Statistical Yearbook [12] Hausman tests support the fixed effects model and the results for three subsamples are reported in Table For the Yangtze River Delta and the Pearl River Delta, the coefficients for agglomeration intensity, AG, are positive and significant, while its squared terms, AG2, carry a negative and significant sign It implies that the relationship between agglomeration intensity and green productivity growth is an inverted-U curve That is to say, below a critical value, the increase of urban agglomeration intensity can promote green productivity growth; however, it may damage green productivity growth when above the critical value However, for the Beijing-Tianjin-Hebei, the coefficients of both AG and AG2 are not significant For the three subsamples, the coefficients for environmental regulations are significantly positive Therefore, we provide empirical evidence for the “Porter hypothesis” [13, 14] That is to say, for the three major urban agglomerations in China, strict environmental regulations can lead to “win-win” situations, where both economic prosperity and environmental quality can be improved The coefficients of industrial structure are negative and significant for both Yangtze River Delta and Beijing-Tianjin-Hebei It shows that the rise of the proportion of industry obstacles green productivity growth because industry is the main source of pollutant emissions in China’s cities For both the Yangtze River Delta and Pearl River Delta, the coefficients of capital-labour ratio are significantly negative, implying that the rise of capital-labour ratio hinders green productivity growth When capital-labour ratio increases, the labour-intensive industries are substituted by capital-intensive industries, most of which in China are heavy chemical industries dirtier than light ones The coefficients for FDI are significantly positive only in the Yangtze River Delta, revealing that FDI can promote green productivity growth only in the Yangtze River Delta This result is similar to Wen [15], who suggested that the impacts of FDI on productivity were different across regions in China That is to say, the “pollution havens hypothesis” doesn’t exist in our sample 485 Feng Tao et al / Energy Procedia 104 (2016) 481 – 486 Table The regression results for determinants of green productivity growth Variables Beijing-Tianjin-Hebei Yangtze River Delta Pearl River Delta Fixed effects model Random effects model Fixed effects model Random effects model Fixed model AG -0.020 -0.033 0.205*** 0.172*** 0.244*** (0.036) (0.034) (0.047) (0.040) (0.056) (0.046) AG2 0.001 0.003 -0.0225** -0.0179** -0.0213*** -0.0196*** (0.005) (0.005) (0.010) (0.009) (0.007) (0.006) 0.040*** 0.042*** 0.012*** 0.014*** 0.001 0.006* (0.006) (0.005) (0.003) (0.003) (0.003) (0.003) -0.521*** -0.401*** -0.957*** -0.813*** 0.072 -0.003 (0.164) (0.138) (0.187) (0.155) (0.314) (0.174) 0.007 -0.002 -0.015** -0.008** 0.007 -0.006** (0.013) (0.007) (0.006) (0.004) (0.010) (0.002) 0.001 -0.001 0.006** 0.005* 0.006 -0.004 ER IS K/L FDI effects Random effects model 0.158*** (0.007) (0.006) (0.003) (0.003) (0.007) (0.006) INFRA 0.003 0.003 0.001 0.001 -0.000 -0.004*** (0.004) (0.003) (0.002) (0.002) (0.003) (0.001) Constant 1.118*** 1.128*** 1.465*** 1.346*** 0.693*** 1.098*** (0.120) (0.108) (0.111) (0.0920) (0.219) (0.124) Hausman test 5.44 R-squared 0.570 Observations 130 11.42 0.563 130 290 14.07 0.344 290 90 90 Note: (1) Standard errors are given in parentheses (2) *** Significant at 1%, ** Significant at 5%, * Significant at 10% Conclusions This is the first study of green productivity growth and its decomposed sources in cities of the three major urban agglomerations across China The results calculated by GML index show that the cumulative growth rate of green productivity in the Beijing-Tianjin-Hebei was higher than the two other agglomerations in most of years during the studied period Green productivity growth most benefits from technical changes rather than efficiency changes for the three agglomerations Efficiency deterioration significantly prevents green productivity growth in the Yangtze River Delta and the Pearl River Delta The determinants driving green productivity growth are different across the three urban agglomerations The relationship between urban agglomeration and green productivity growth shows an inverted “U” for cities in the Yangtze River Delta and the Pearl River Delta FDI inflows can improve green productivity growth only in the Yangtze River Delta, while environmental regulations can promote green productivity growth in all three agglomerations The rise of proportion of industry may obstacle green productivity growth in the Yangtze River Delta and the Beijing-Tianjin-Hebei For the Yangtze River Delta and the Pearl River Delta, the rise of capital-labor ratio may hinder green productivity growth 486 Feng Tao et al / Energy Procedia 104 (2016) 481 – 486 Acknowledgements The authors acknowledge funding from the Project 71333007 supported by National Natural Science Foundation of China, and the Project 14JZD021 supported by Ministry of Education of China References [1] State Council of China National New-Type Urbanization Plan (2014-2020) 2014 (In 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and the Pearl River Delta, China Papers in Regional Science 2014; 93: 341-365 Biography Xia Xiaohua is an associate professor at Renmin University of China He obtained B E degree from CUST(Changsha), MBA degree from Jinan University, and Ph.D degree in economics from Sun Yat-sen University He had been worked as a postdoctoral fellow at Peking University He has published over 20 peer-reviewed journal papers ... export-oriented agglomerations Fig Green productivity growth for three major urban agglomerations in China, 2003-2013 3.2 Decomposed sources of green productivity growth The decomposed components of green productivity. .. trends of green productivity growth The temporal trends of cumulative green productivity growth across the three major urban agglomerations in China are depicted in Fig In general, the index of green. .. study therefore focuses on calculating and decomposing the green productivity growth for the three major urban agglomerations in China The determinants driving green productivity growth are also