Dynamic Convergence of Green Total Factor Productivity in Chinese Cities
Abstract
:1. Introduction and Literature Review
- (1)
- Research on GTFP in different industries. For instance, Feng [17] investigated the sources of GTFP changes and its inefficiency of China’s metal industry during 2000–2015 using a meta-frontier approach. They found GTFP in China’s metal industry increased by 11.52% annually. Technological progress was the most critical driving factor. Along with that, they also found that the reduction of the gap in regional technology uses played a significant role in promoting GTFP growth. Finally, they concluded that the declines in scale efficiency and pure technical efficiency were two inhibitors to economic growth. Using a global DEA, Zhu [18] analyzed the GTFP of China’s mining and quarrying industries for the period of 1991–2014 with regard to technology, scale and management. Their results showed that, during the sample period, the DEA of China’s mining and quarrying industries increased by 71.7% with technological progress being the most important contributor. In addition. the decline in scale efficiency and management efficiency were two inhibitors to economic growth. Technological progress was determined by using the technological efficiency change index decomposed from the Malmquist index. Chen [19] measured the GTFP growth of China’s 36 industrial sectors from 2000 to 2014. The research showed that, considering energy consumption and environmental undesirable outputs, the industrial GTFP goes backwards by 0.02% per year on average.
- (2)
- Research on the relationship between GTFP and its influencing factors. Li [20] examined the heterogeneous impacts of financial development on GTFP for 40 countries over the period of 1991 to 2014. In developing countries, an inverted U-shaped relationship existed between financial development and GTFP. In developed countries, the development of banking and insurance industries tended to adversely affect GTFP. Lei [8] employed the meta-frontier Malmquist–Luenberger index to measure GTFP and then built a panel model to investigate the nonlinear effects of both governmental and civil environmental regulation on GTFP in 30 provinces of China in 2007–2016. Results show that the degrees of the governmental and civil environmental regulation and GTFP display single environmental awareness threshold and regulatory foundation threshold. Lu [21] studied land transfer marketization (LTM) on GTFP and its mechanisms. They found LTM had a significant promoting effect on the improvement of GTFP values for cities in China and the effect was also significant in the eastern, central and western regions, indicating that the application of land transfer policy that regulated regional economic development was an important factor in most regions in China.
- (3)
- Research on the GTFP in different regions. Rusiawan [22] evaluated the effect of CO2 intensity to the TFP in Indonesia, they stated that GTFP would improve productivity growth and emission reduction. Xia [23] studied the provincial GTFP in China. Their results showed little evidence for neither traditional productivity growth nor green productivity growth for most provinces, despite dramatic and continuous GDP growth in the country. In addition, after incorporating environmental factors, productivity performance improved in some provinces, but deteriorated in some others. Focusing on the primary provinces along China’s belt and road (BRI) route, Liu [24] used a global Malmquist–Luenberger (GML) index based on SBM directional distance function to evaluate provincial GTFP and quantitatively analyzed the BRI’s net effect on provincial GTFP. The results indicated a relatively good development for provincial GTFP, with technological progress being its main driving force. Shao [25] used a stochastic frontier analysis method to study Shanghai’s industrial green development transformation. Shao’s results showed an overall upward trend for the industrial GTFP in Shanghai with technical efficiency changes being identified as the main factor.
2. Research Methodology
2.1. DEA–Malmquist Index Method
2.2. Spatial Clustering
2.3. Convergence Analysis
3. Computational Methods
3.1. Green Total Factor Productivity
3.2. Spatial Autocorrelation Analysis
3.2.1. Global Spatial Autocorrelation
3.2.2. Local Spatial Autocorrelation
3.3. Analysis of Convergence
4. Index Selection and Data Source
- (1)
- Local GDP. The unit GDP is 100 million yuan (CNY). According to the GDP index, all GDP figures are converted using the 2008 consumer prices as the base with the unit being100 million CNY;
- (2)
- energy consumption. Due to a lack of primary energy consumption data in many Chinese cities, we use electricity consumption data instead, with the unit being 10,000 kWh. Electricity consumption data are collected by computers and are more accurate;
- (3)
- Fixed-assets Investment. We use urban fixed-assets investment; the unit is 100 million CNY. Due to the lack of conversion data, the current year price is used;
- (4)
- Labor population. The number of urban employees is adopted for this and the unit is ten thousand;
- (5)
- The emission of pollutants. It is measured in volume of industrial sulfur dioxide emission. The unit is ton. Choosing sulfur dioxide emission as the index for the emission of pollutants is because 70% industrial pollutants in China is sulfur dioxide emission [60] and such data are easy to acquire.
5. Results
5.1. Temporal Trend
5.2. GTFP Analysis of Cities in Different Regions
5.3. Spatial Effects of Green Total Factor Productivity Index
5.4. Analysis of Convergence of GTFP
6. Discussion
- (1)
- The values of GTFP show a tendency of increases, indicating that China’s GTFP continues to improve. The changes in the values of GTFP were influenced by changes in the values of technology change index and technology efficiency index. Before 2012–2013, it was mainly the comprehensive effects of technology change index and technology efficiency index, while after 2012–2013, the effect of technology change index was the main factor. In the spatial perspective, the eastern region has the highest values of GTFP over the past nine years, followed by the central region and the west region being the lowest;
- (2)
- Based on the calculated values of Moran’s Index, it seems that the GTFP shows a certain level of global spatial autocorrelation. The univariate local Geary index graph shows that the values of GTFP reveal a weak high-high clustering and a low-low clustering. This indicates that the technology diffusion between adjacent cities is relatively weak;
- (3)
- The calculated values of national and three regional -convergence index values show that the values of GTFP have both convergence and divergence. The overall trend is a slightly divergent one. Moreover, the -convergence trend in China and the three regions is basically the same. The divergence of GTFP measures indicates that there are certain efficiency differences among regions and among cities. Whereas the convergence of GTFP measures indicates that the efficiency of different regions gradually converges. That is, the difference in technology and management levels among regions gradually becomes smaller over time.
7. Conclusions and Suggestions
7.1. Conclusions
7.2. Suggestions
- (1)
- The country should increase its investment in research and development, as well as raise the level of technology and management in rural regions. Simultaneously, China should reinforce international cooperation in energy technologies, learning advanced technologies and management experience among countries and improve the level of China’s GTFP. We also suggest focusing on strengthening the flows of capital, technology and talent across the country and promoting exchanges and cooperation between regions so as to narrow the GTFP between regions;
- (2)
- Industrial structure affects energy consumption and pollutant emissions. Cities should highly value industrial restructuring, develop the tertiary industry and reduce the cities’ reliance on energy-intensive industries to reduce energy consumption and pollutant emission.
Author Contributions
Funding
Conflicts of Interest
References
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Variable | Number | Minimum | Maximum | Average | Standard Deviation |
---|---|---|---|---|---|
GDP | 2367 | 75.79 | 25,041.21 | 1893.46 | 2502.36 |
SO2 | 2367 | 92.00 | 782,000.00 | 56,632.28 | 56,239.62 |
energy | 2367 | 8055.00 | 14,860,200.00 | 905,354.34 | 1481,008.20 |
fixed assets | 2367 | 40.44 | 17,245.76 | 1323.55 | 1476.49 |
Labor | 2367 | 4.21 | 986.87 | 55.87 | 81.75 |
Time | Effch | Techch | Pech | Sech | Tfpch |
---|---|---|---|---|---|
2008–2009 | 0.975 | 1.049 | 0.981 | 0.993 | 1.022 |
2009–2010 | 1.013 | 0.991 | 1.019 | 0.994 | 1.004 |
2010–2011 | 1.026 | 1.019 | 1.008 | 1.018 | 1.046 |
2011–2012 | 1.032 | 1.000 | 1.013 | 1.019 | 1.033 |
2012–2013 | 1.054 | 0.947 | 1.056 | 0.998 | 0.998 |
2013–2014 | 0.971 | 1.035 | 0.989 | 0.982 | 1.005 |
2014–2015 | 0.995 | 1.049 | 0.994 | 1.001 | 1.043 |
2015–2016 | 0.943 | 1.214 | 0.968 | 0.997 | 1.146 |
Mean | 1.001 | 1.036 | 1.003 | 0.997 | 1.036 |
Region | Effch | Techch | Pech | Sech | Tfpch |
---|---|---|---|---|---|
National | 1.001 | 1.036 | 1.003 | 0.997 | 1.036 |
Eastern | 1.001 | 1.039 | 1.003 | 0.998 | 1.040 |
Central | 1.002 | 1.036 | 1.003 | 1.000 | 1.038 |
Western | 1.001 | 1.032 | 1.007 | 0.994 | 1.032 |
2008–2009 | 2009–2010 | 2010–2011 | 2011–2012 | |
---|---|---|---|---|
Moran’s I | 0.053 | 0.148 | 0.087 | 0.021 |
Z-score | 1.3596 | 4.0041 | 2.1924 | 0.6164 |
p-value | 0.0750 | 0.0050 | 0.019 | 0.2480 |
2012–2013 | 2013–2014 | 2014–2015 | 2015–2016 | |
Moran’s I | 0.216 | 0.210 | 0.058 | 0.176 |
Z-score | 5.4571 | 4.9489 | 1.4542 | 4.2730 |
p-value | 0.0010 | 0.0010 | 0.073 | 0.001 |
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Peng, Y.; Chen, Z.; Lee, J. Dynamic Convergence of Green Total Factor Productivity in Chinese Cities. Sustainability 2020, 12, 4883. https://doi.org/10.3390/su12124883
Peng Y, Chen Z, Lee J. Dynamic Convergence of Green Total Factor Productivity in Chinese Cities. Sustainability. 2020; 12(12):4883. https://doi.org/10.3390/su12124883
Chicago/Turabian StylePeng, Yuanxin, Zhuo Chen, and Jay Lee. 2020. "Dynamic Convergence of Green Total Factor Productivity in Chinese Cities" Sustainability 12, no. 12: 4883. https://doi.org/10.3390/su12124883
APA StylePeng, Y., Chen, Z., & Lee, J. (2020). Dynamic Convergence of Green Total Factor Productivity in Chinese Cities. Sustainability, 12(12), 4883. https://doi.org/10.3390/su12124883