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Li, L.; Pan, C.; Ling, S.; Li, M. Ecological Efficiency of Urban Industrial Land in China. Encyclopedia. Available online: (accessed on 10 December 2023).
Li L, Pan C, Ling S, Li M. Ecological Efficiency of Urban Industrial Land in China. Encyclopedia. Available at: Accessed December 10, 2023.
Li, Lei, Chenzi Pan, Shuai Ling, Mingqi Li. "Ecological Efficiency of Urban Industrial Land in China" Encyclopedia, (accessed December 10, 2023).
Li, L., Pan, C., Ling, S., & Li, M.(2023, July 12). Ecological Efficiency of Urban Industrial Land in China. In Encyclopedia.
Li, Lei, et al. "Ecological Efficiency of Urban Industrial Land in China." Encyclopedia. Web. 12 July, 2023.
Ecological Efficiency of Urban Industrial Land in China

Industrial land is an indispensable strategic resource in urban development that plays an indispensable role in ensuring the industrial space of urban construction and development. Measuring and analyzing the eco-efficiency of industrial land utilization (ECILU) can provide insights into how to maximize the input–output ratio of industrial land and ensure the sustainable development of land resources and economies.

urban industrial land model

 1. Background

The eco-efficiency of industrial land utilization (ECILU) will be used to evaluate the utilization rate of industrial land from both economic and ecological aspects. Its improvement is the key to China’s economic transformation and ecological civilization construction, and it is an urgent task to be faced at this stage. At present, the Chinese urbanization process is rapidly developing. As the carrier of the industrial economy, industrial land has also shown a steady growth trend, but there remain widespread problems, such as disorderly use, idle waste, low floor area rate, and low output [1]. The land-use model with high extensiveness, high pollution, and low efficiency restricts the sustainable development of Chinese industries. Considering the vast Chinese territory and the differences between the realities of various economic zones, general administrative guidance at the national level is difficult to truly implement. Therefore, it is an urgent task at this stage to take effective governance measures for different places, improve the allocation mode of industrial land resources, and increase the ECILU to better alleviate the local problems of industrial land in different cities and metropolitan areas and realize intensive, economical, and efficient use of land and sustainable development throughout the country. In addition, China’s original intention and goal of building a “resource-saving” and “environment-friendly” society also require us to embrace the industrialization of sustainable development that is oriented toward integrating ecological factors into the evaluation of industrial land efficiency while maintaining the economic output of steady national growth, thereby striving to reduce the negative external effects of resource waste and environmental pollution to improve economic output and social well-being to a greater extent while maintaining public interests [2][3].

2. Comparison of the Eco-Efficiency of Industrial Land Utilization

After the integration and analysis of the data, this research used DEA-SOLVER Pro 5.0 to operate and uses the undesirable output SBM model to measure the ECILUs in 78 cities from 2007 to 2018. According to the calculation results, the efficiency values could be divided using the equal width method according to the regional difference of a city’s ECILU [4]: low efficiency (≤0.25), medium-low efficiency (0.25~0.50), medium-high efficiency (0.50~0.75), and high efficiency (≥0.75), where the number of cities in different levels is presented below (Table 1).
Table 1. Statistics of ECILUs from 2007 to 2018.
Year Mean Maximum Minimum ECILU Levels
Low Medium–Low Medium–High High
2007 0.2699 1.0000 0.1024 49 23 2 4
2008 0.2709 1.0000 0.0942 50 20 5 3
2009 0.2432 0.7068 0.0891 56 16 6 0
2010 0.2574 1.0000 0.1056 52 21 3 2
2011 0.2475 1.0000 0.0998 49 26 2 1
2012 0.2876 1.0000 0.0863 44 27 5 2
2013 0.2707 0.6887 0.0810 45 26 7 0
2014 0.2804 0.7620 0.0953 34 38 5 1
2015 0.3026 0.8825 0.1111 35 34 8 1
2016 0.3169 1.0000 0.1773 33 37 6 2
2017 0.3574 1.0000 0.1613 24 44 5 5
2018 0.4103 1.0000 0.1239 17 42 11 8
By counting the efficiency values and grade proportions of prefecture-level cities, this research analyzed the classification and temporal variation characteristics of the ecological efficiency value of industrial land in each city (Figure 1). First, throughout the period, the average ECILU improved from 0.2699 to 0.4103. Second, the average ECILU showed characteristics of increasing during this period. It decreased from 2007 to 2008, and from 2009 to 2018; although there was no significant increase, the overall trend showed the evolutionary characteristics of fluctuating growth. Finally, the number of cities with low ECILUs decreased yearly, and gradually evolved to the level of medium and high efficiency. There were 49 inefficient cities in 2007 and 17 in 2018, and the proportion of cities with medium-high efficiency also increased from 7.69% in 2007 to 24.36% in 2018. It can be seen that although the number of medium-high-efficiency cities increased yearly, the ECILUs in most cities in China were still at a low level.
Figure 1. Evolution trend of the ECILUs in 2007–2018.
To understand the distribution of the ECILUs in different regions more intuitively, this research found the average change trend of the ECILU in all cities and cities in the four major economic zones over the years based on the results above. Figure 2 shows that the ECILUs in China and the four major economic zones showed steady yearly increases in the time dimension, and the ECILUs in the four major economic zones from high to low were the Western, Eastern, Central, and Northeastern Economic Zones. Moreover, it can be seen from Figure 2 that the line of “All samples” was below the “Western Economic Zone” and “Eastern Economic Zone” and above the “Central Economic Zone” and “Northeastern Economic Zone” throughout the period from 2007 to 2018. This means that the ECILUs in the Western Economic Zone and the Eastern Economic Zone were higher than the average efficiency of all cities over the 12 years, and the ECILUs in the Central and Northeastern Economic Zones were lower than the average efficiency of all cities. In addition, at the regional dimension level, the ECILUs in the Western Economic Zone reached its peak in 2008 and 2012, and the Eastern Economic Zone reached its peak in 2010 and 2012; then, both of them decreased for one year and increased steadily. The ECILU in the Central Economic Zone steadily increased from 2007 to 2017, but the growth rate in 2018 was significantly accelerated, while the ECILU in the Northeastern Economic Zone decreased in 2016, but increased again in the next year.
Figure 2. Evolution trend of the ECILUs in 2007–2018.

3. Spatial and Temporal Differences in Eco-Efficiency of Industrial Land Utilizations in Metropolitan Areas

To further analyze the changes in the ECILUs in different metropolitan areas over the assessed 12 years, this research analyzed the spatial and temporal differences of 78 cities’ ECILUs in 13 metropolitan areas by using averaging and variance operations. First, this research took three years as a cycle and selected the grouping data of the years 2007, 2009, 2011, 2013, 2015, and 2017 for comparative analysis (Figure 3).
Figure 3. Comparison of the ECILUs in metropolitan areas: (a) 2007, (b) 2009, (c) 2011, (d) 2013, (e) 2015, and (f) 2017.
The results showed that the ECILUs in XA and CJ decreased significantly, and the ECILUs of the other 11 metropolitan areas showed a fluctuating upward trend from 2007 to 2018. The ECILUs in CJ, XZQ, QD, ZZ, GZ, and XA reached more than 0.5, showing a high level. The ECILUs of SY and WH were generally concentrated in 0.3~0.5, and the efficiency was low. It can be seen that the size distribution of the ECILU was not divided by the economic development level of the economic zone it belonged to. For example, although the economic development levels of BJ and NJ in the Eastern Economic Zone were relatively high, there were only a few years when the ECILUs were more than 0.5. Although SY and CJ are both in the Northeastern Economic Zone, the ECILU was obviously in the opposite state of one high and one low, and the reason for this phenomenon is worth further discussion.
Based on comparing the ECILUs in 13 metropolitan areas, this research investigated the close degree of the relationship between the cities in different metropolitan areas using the variance of the ECILUs between cities covered by the metropolitan area to judge the overall unity and synergy of each metropolitan area in the development of industries. That is, the smaller the variance index value, the higher the degree of synergy between cities in the metropolitan area, and the greater the index value, the greater the differences between cities and the lower the synergy. The 100-fold variance index for ECILU for each metropolitan area is shown below (Table 2, Figure 4).
Figure 4. Variance indexes of ECILUs in the metropolitan areas 2007–2018.
Table 2. Variance indexes of the ECILUs in the metropolitan areas from 2007 to 2018.
2007 0.83 0.56 0.01 5.65 6.03 0.30 0.09 0.65 1.18 0.16 8.94 9.70 0.14
2008 1.06 0.49 0.01 2.40 5.98 0.39 0.06 0.41 1.70 0.27 4.51 11.8 0.37
2009 0.98 0.40 0.01 1.42 1.76 0.38 0.08 0.26 1.36 0.18 2.71 4.81 0.21
2010 5.31 0.42 0.01 0.90 1.66 0.48 0.07 0.31 0.39 0.09 4.89 3.33 0.13
2011 0.39 0.33 0.07 1.18 0.35 0.29 0.01 0.32 0.12 0.07 8.17 1.81 0.06
2012 1.23 0.60 0.09 1.95 0.33 15.2 0.08 0.88 0.23 1.05 7.97 2.48 0.03
2013 1.39 0.74 0.04 1.40 0.27 5.70 0.20 0.89 0.21 1.52 2.77 1.99 0.00
2014 1.10 0.96 0.02 0.71 0.42 7.15 0.00 1.17 0.47 1.74 2.90 1.63 0.00
2015 1.56 1.27 0.01 1.10 0.50 9.50 0.01 1.68 0.18 2.52 3.47 1.49 0.01
2016 1.91 0.30 0.00 1.21 0.64 12.8 0.00 1.47 0.23 1.70 4.24 1.77 0.03
2017 5.79 0.27 0.00 3.88 0.96 0.90 0.01 2.23 0.56 0.62 4.66 6.57 0.00
2018 6.89 0.77 0.01 4.68 0.82 1.43 5.24 6.34 8.89 0.77 6.99 5.73 0.00
According to Table 2 and Figure 4 above, during the 12 years from 2007 to 2018, the variance indexes of XZQ, GZ, and NJ were significantly larger than those of other metropolitan areas, and the degree of urban synergy was also relatively low. The variance indexes of SH and CD showed a trend of first decreasing and then increasing. The variance index of the cities in CD reached its peak in 2008. Then, like SH, CD’s variance index decreased steadily in 2010 and increased yearly from 2016. BJ reached its first peak in 2010, then decreased in the following year, and then had stable fluctuations for nearly six years, but increased again after 2017. The variance index of XZQ had two peaks, namely, in 2012 and 2016, indicating that there was a large gap in the ECILUs between cities within the metropolitan area and that urban synergy was reduced. The index value of QD was low in the first 11 years but rose to the middle level of the 13 metropolitan area samples in 2018.
In general, the ECILUs can allow observers to see the degree of industrial development in a region from a quantitative perspective. However, concurrently, the degree of synergy between cities is also an important factor when measuring the sustainable development of a metropolitan area. Based on the comprehensive consideration of ECILUs in the metropolitan areas and cities above, and taking the average value and variance index as the reference, this research divided the 13 metropolitan areas into four categories: First, the high efficiency–high synergy metropolitan areas were represented by CS, QD, ZZ, and CS. Second, the high efficiency–low synergy metropolitan areas were represented by SH, XZQ, GZ, and CD. Third, the low efficiency–high synergy metropolitan areas were represented by SY, NJ, and WH. Fourth, the low efficiency–low synergy metropolitan area was represented by BJ.

4. Analysis of the Influencing Factors of the Eco-Efficiency of Industrial Land Utilization

This research used the SBM model to effectively measure the ECILU in each city and metropolitan area and undertook a comparative analysis and summary. Furthermore, this research investigated all kinds of factors and mechanisms that affect efficiency. Based on the Tobit regression model of the maximum likelihood estimation method and panel data of 78 prefecture-level cities from 2007 to 2018, this research conducted an empirical analysis according to the classification of the four economic zones, namely, the Eastern, Northeastern, Central, and Western regions. The specific effects of each variable are shown in Table 3.
Table 3. Tobit regression analysis results of the ECILUs.
Explanatory Variable Explained Variable: the ECILU
All Samples Eastern Zone Northeastern Zone Central Zone Western Zone
gdp 0.1213337 ***
0.1320097 ***
0.0770858 ***
0.0926126 ***
ic −0.0038424
−0.0825258 **
0.2991415 **
rpq −0.0528232 ***
−0.0590623 ***
−0.0501857 ***
−0.0332701 **
−0.0572474 ***
drm −0.0325802 **
−0.0378645 *
icl −0.0240317 **
−0.0254135 **
−0.0972003 ***
rg −0.0053223
−0.010946 **
0.0767977 ***
−0.0562089 **
wp −0.0019175
−0.0157632 **
−0.0131907 *
−0.0221559 **
sp −0.0245648 ***
−0.0253258 ***
−0.0245787 **
−0.0136374 *
−0.0451033 **
Note: *, **, *** respectively indicate that the variables were significant at the levels of 10%, 5%, and 1%, and the values in brackets are p-values.
The regression results of the factors affecting the ECILUs in various cities from 2007 to 2018 showed that, in the empirical study, from the perspective of all the samples, the variables of urban scale, regional population quality, degree of regional marketization, infrastructure construction level, and sulfur dioxide pollution had significant effects on the ECILUs in various prefecture-level cities selected in the sample, and the influencing coefficients were 0.1213337, −0.0528232, −0.0325802, −0.0240317, and −0.0245648, respectively. From the perspective of the economic zones:
  • The urban scales of the Eastern, Central, and Northeastern Economic Zones had an appreciable impact on their ECILUs, and the regression coefficients were all positive. The industrial economy affected the ecological efficiency of industrial land by providing support for ecological environment protection, but there were significant differences in the effects of different regions [5], which may have been the reason why the urban scale did not have a significant impact on the Western Economic Zone. Therefore, each economic zone should pay attention to the positive role of urban scale, and strengthen the introduction of regional factors and industrial upgrading by expanding the economic scale of cities.
  • Industrial structure had a negative correlation with the Eastern Economic Zone and a positive correlation with the Western Economic Zone. Within a certain range, the improvement in the industrial structure level will affect the allocation mode of various resources and transfer them in the direction conducive to industrial development. However, when the industrial structure level reaches a certain standard under the combined effect of the siphon effect and the negative externality of environmental pollution, it may hinder the improvement of ECILU. Existing research showed that large cities mainly improve eco-efficiency by influencing the tertiary industries, and other small- and medium-sized cities mainly improve eco-efficiency via the secondary industries [6]. Therefore, it is suggested that the Western Economic Zone should strengthen industrial and ecological construction in the construction of metropolitan areas, and also innovate the internal inter-city cooperation mechanism [7].
  • Regional population quality had a significant impact on the ECILUs of all economic zones, and the impact coefficients were all negative. The allocation of labor among industries and regions is a direct manifestation of the efficiency of economic operation [8]. The negative correlation shown in the regression results may have been due to the existence of a large surplus of industrial labor, which had a negative impact on the ECILUs [9]. It is recommended that the economic zones appropriately reduce the proportion of industrial labor to slow down the phenomenon of labor surplus.
  • The degree of regional marketization had an appreciable impact on the ECILU only in the Central Economic Zone, and the regression coefficient was −0.0378645. The vitality of the market is crucial to industrial efficiency [10]. Industrial agglomerations and transfers have become the main ways for China to improve industrial efficiency [11]. However, when the negative crowding effect caused by industrial agglomeration brought by the marketization level is greater than the positive scale effect, it will have a negative impact on the ECILU.
  • There was a significant negative correlation between the level of infrastructure construction and ECILU in the Eastern and Central Economic Zones. Cross-regional transportation and other infrastructure construction provide logistic guarantees for local industrial agglomerations, and industrial agglomerations can also promote the use of urban land resources and reduce the total cost of industrial activities through external economies of scale [12][13]. However, in fact, the eastern and central parts of China are flat and the spatial distribution of public transportation services is uneven [14]. This research suggests that scientific planning of road transportation and other infrastructure should be strengthened, road utilization should be improved, and an efficient connection between public transportation and land use should be established [15].
  • The area of regional green coverage had a negatively correlated and significant impact on the Northeastern and Western Economic Zones, and a positively correlated impact on the Central Economic Zone. Compared with the Central and Eastern Economic Zones, it can be explained that the more green areas in the Northeastern and Western Economic Zones may compress industrial land and limit the growth of the industrial economy, while the green areas in the Central Economic Zone may greatly improve the local ecology. Improving ECILU entails the common development of “economy” and “ecology.” Therefore, each economic zone should jointly promote industrial economic growth and environmental protection and realize their harmony.
  • The regression coefficients of industrial wastewater pollution and sulfur dioxide pollution for ECILU were both negative. This is consistent with existing research. Industrialization has brought serious pollution problems to the ecological environment, resulting in the decline of water and air quality, soil structure destruction, etc., and has severely weakened the ecosystem service functions of concentrated industrial areas [16]. Therefore, strengthening the city’s management of industrial production factors and reducing pollutant emissions is one of the important ways to improve ECILU on the basis of building a sustainable city [17].

5.  Summary

The ECILU was not the same as the level of local industrial economic development; therefore, ECILU evaluation based on economic benefits as the output index will no longer apply to the needs of high-quality urban development. In the future, when evaluating ECILU, the local government should establish a better evaluation system that includes economic, social, ecological, and other factors, rather than blindly pursuing economic development and sacrificing social and environmental benefits. Meanwhile, in order to increase the growth of the industrial economy and the ECILU while ensuring environmentally friendly development, it is necessary to reinforce the flow of resources and factors between cities and promote coordination between cities in the metropolitan area to truly implement the policy guidance of local governments in improving the utilization of industrial land, optimizing the spatial layout of urban land, and promoting the coordinated and high-quality development of cities.


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