The Super-SBM-Undesirable and Malmquist productivity index evaluation models are constructed by taking natural and social resources consumption as input indices, while social and economic development were listed as desirable output indices, leaving pollution, disasters and accidents as undesirable output indices. Model evaluation results are computed using MATLAB.
4.3.1. Result Analysis of Technical Efficiency of Time Cross Section
(1) Analysis of spatio-temporal evolution of comprehensive technical efficiency:
We take the representative years of 2006, 2010, 2015 and the average value of the Yangtze River Economic Belt 2006–2015 comprehensive technical efficiency of sustainable development for spatial and temporal evolution analysis. The comprehensive technical efficiency of provinces and municipalities is sorted according to the calculation results. ArcGIS 10.2 was used to draw the spatio-temporal evolution map of YERB comprehensive technical efficiency in sustainable development from 2006–2015. The deeper red shading in the map represents higher comprehensive technical efficiency of a given province or city (
Figure 1).
(a). Through the geographical distribution map of the mean of sustainable development efficiency in
Figure 1, we can find that the overall level of sustainable development of the YERB is not very high. Among the 11 provinces and municipalities, only Shanghai and Guizhou have an average annual comprehensive technical efficiency of more than 1. That is, only Shanghai and Guizhou have achieved optimal levels of input and output during the study period. While vigorously developing their socioeconomic situations, Shanghai and Guizhou have paid attention to the efficient allocation of resources, the protection of ecological environment, the prevention and control of natural disasters and the maintenance of social order.
(b). Furthermore, from
Figure 1, we can see that the comprehensive technical efficiency gap between the 11 provinces and municipalities in the YERB is growing. In 2006, the technical efficiency variance of 11 provinces and municipalities was 0.024 and the extreme value was 0.477, among which Shanghai (1.215) had the highest efficiency and Jiangxi (0.738) had the lowest efficiency; In 2010, the technical efficiency variance of 11 provinces and municipalities was 0.056 and the extreme value was 0.764, of which Shanghai (1.326) was the most efficient and Yunnan (0.561) was the least efficient; By 2015, the technical efficiency variance of 11 provinces and municipalities was 0.107, the extreme value was 1.074, the highest efficiency was Shanghai (1.584), and the lowest efficiency was Jiangxi (0.509). We can see that comprehensive technical efficiency of the YERB has continuously deteriorated. In 2006, the average technical efficiency of 11 provinces and municipalities was 0.988, of which 7 provinces and municipalities had efficiency greater than 1, with no provinces and municipalities with efficiency less than 0.6. In 2010, the average technical efficiency of 11 provinces and municipalities was 0.903, of which 5 provinces and municipalities were more than 1, and 1 provincial and municipality was less than 0.6. In 2015, the average technical efficiency of 11 provinces and municipalities was 0.785, of which only 3 provinces and municipalities had efficiency greater than 1 and 3 provinces and municipalities had efficiency less than 0.6. It shows that 11 provinces and municipalities in the YERB have facilitated many types of development in the past ten years. The level of sustainable development varies among provinces and municipalities. In exploring the road of sustainable development, there are not only provinces and municipalities with successful transformation of development mode, but also provinces and municipalities with backward development mode. Due to the different development concepts and objectives of the provinces and municipalities, the gap of technical efficiency between the provinces and municipalities in the YERB is widening. On the other hand, because of the influence of international and national environment, most of the 11 provinces and municipalities take GDP as the main index to measure the development level of the region, ignoring the constraints of resources, environment and social order, and ultimately make the overall governance efficiency of the economic belt deteriorating.
(c). In the upper reaches of the YERB, the comprehensive technical efficiency level of Guizhou in the past decade has always been in a relatively effective state, with an average value of 1.176; The efficiency level of Sichuan has changed from slow decline in 2006–2010 to high-speed growth in 2010–2015. By 2015, the efficiency of Sichuan has reached 1.023, becoming one of the three effective provinces and municipalities; The efficiency level of Chongqing and Yunnan dropped sharply, from 1.014 to 0.671 and 1.022 to 0.568 respectively. Hunan, Hubei, Jiangxi and Anhui, located in the middle reaches of the YERB, have the same level of development resources and conditions because of their similar geographical location, and have the same level of development policies, resulting in similar trends in the level of efficiency change, showing a yearly gradual decline. Then, the efficiency level of Jiangsu and Zhejiang located in the lower reaches of the YERB is low and decreases yearly. It shows that the social progress and economic growth of Jiangsu and Zhejiang, as the major economic provinces in China, have been achieved at the expense of resources and environment in the past decade. Jiangsu and Zhejiang provinces should pay enough attention to the problem, optimize and upgrade the development model in the future, and adhere to the road of sustainable development. Finally, Shanghai, located at the forefront of the YERB, has maintained a relatively effective level of comprehensive technical efficiency in the past decade, and its efficiency value has always been ranked first. With its unique geographical position and policy support, Shanghai has rapidly developed into a financial center of China and an international metropolis. Compared with Jiangsu and Zhejiang provinces, Shanghai’s rapid development is not based on sacrificing resources and environment, but on technological innovation, making full use of existing resources, and pursuing the coordinated and balanced development of society, economy, culture and ecosystem.
(2) Analysis of reasons for projection based on production front surface
To explore the causes of ineffective comprehensive technical efficiency of the studied provinces and municipalities, a projection analysis from the perspective of input output is presented and determines the gap between the attribute ideal values. In the Super-SBM-Undesirable evaluation model, when the comprehensive technical efficiency is less than 1, the size of relaxation variables
reflect the reason of efficiency loss.
,
,
respectively represent the input redundancy rate, desirable output insufficiency rate, undesirable output redundancy rate. Taking 2015 as an example, this paper makes a static projection analysis of the ineffective provinces and municipalities from three angles: input redundancy, desirable output insufficiency and undesirable output redundancy rates. According to
Figure 1, there are 8 provinces and municipalities which, based on our assessment are inefficient. In decreasing order of efficiency, they are Jiangxi, Hubei, Yunnan, Anhui, Jiangsu, Zhejiang, Chongqing and Hunan. The slack are shown in
Table 4.
From the input point of view, there are different degrees of redundancy in the input of natural resources in the eight ineffective provinces and municipalities (where input redundancy rates are negative), thus resulting in the low utilization efficiency of natural resources, which challenge their future developments. Besides being influenced by the redundancy of natural resources input, the redundancy of social resource inputs is also an important reason for the inefficiency of sustainable development in Jiangsu, Zhejiang and Chongqing. The higher the redundancy rate, the lower the utilization of funds and labor. In the future development process, the three provinces and municipalities need to improve the capital, labor distribution system, management structure and other aspects to achieve the full use of social resources. From the perspective of output, the eight ineffective provinces and municipalities have different degrees of social development output shortage, of which Jiangxi is the most serious, the desirable output shortage rate is 35.99%, followed by Hubei, 34.14%. Those areas are less affected by the insufficient output of economic development, where only Zhejiang is strongly affected by it, where the insufficiency rate of desirable output is 10.79%. It shows that under the goal of sustainable development, the eight ineffective provinces and municipalities should further develop in terms of social progress and economic growth. Among them, the task of the future development of Jiangxi and Hubei is the most arduous. On the undesirable output redundancy rate, there are very high undesirable output redundancies in those areas. It further shows that in the development of the past decade, the development model of the provinces and municipalities is more extensive, running along a single track of pursuing socioeconomic development, rather than also considering the environment which leads to ecological deterioration, more frequent disasters, and the need for the stabilization of social order.
4.3.2. Result Analysis of Malmquist Total Factor Productivity Index of Spatial Cross Section
Super-SBM-Undesirable evaluation model is used to evaluate and analyze the relative level of sustainable development of provinces and municipalities in the YERB at a certain time cross-section. However, it cannot reflect the internal factors behind the annual changes in the level of sustainable development of provinces and municipalities from the perspective of their own comparison. Therefore, with the help of Malmquist productivity index evaluation model based on DEA, this paper makes a deeper evaluation analysis of the level of sustainable development of provinces and municipalities from the dynamic spatial cross-section and finds out the key factors that restrict the sustainable development of provinces and municipalities.
(1) overall analysis of the YERB
As shown in
Table 5 and
Figure 2, it can be found that excluding PTEC, SEC, EC, TC and TFPC greatly fluctuate. Firstly, except for a few years, where EC is concerned, values are basically less than 1. This shows that the overall technical efficiency of the YERB is declining, which is mutually corroborated with the results of the Super-SBM-Undesirable model evaluation above. Secondly, SEC and EC show a trend of synchronous change and are generally less than 1, showing that efficiency has an inhibitory effect on the promotion of comprehensive technical efficiency. Continuing, it also reflects that the YERB has not been in the optimal production scale in the past development process. At the same time, with the rapid development of society and economy, investment and financing for development has gradually increased, there are too many duplicate development investments, thus resulting in the decline of comprehensive technical efficiency. We can also see that PTEC is generally larger than 1, and is increasing year by year, displaying that the internal management system of the YERB is constantly optimized and the management levels have continuously improved, thus promoting the growth of comprehensive technical efficiency. From the perspective of TC, the fluctuation of TC and TFPC is large, and the trend is synchronous. During, and due to, the 2008 international financial crisis, TC and TFPC showed a large decline. It is indicated that the influence of TC is the dominant factor affecting the TFPC fluctuation in the YERB. In other words, the main reason why TFPC in the YERB has fluctuated greatly over the past decade is that the contribution of comprehensive technical efficiency is far lower than that of technological progress, which plays a vital role in the development process.
(2) Result analysis of 11 provinces and municipalities
As shown in
Table 6, TFPC in the provinces and municipalities of the YERB in addition to its decomposition shows large degrees of fluctuation over the 2006–2015 period. Only Guizhou, Shanghai and Sichuan have an EC average of more than 1. This is consistent with the conclusion that only Guizhou, Shanghai and Sichuan have improved their comprehensive technical efficiency, while the other provinces and municipalities have shown a decreasing trend. Also, in
Table 6, we can see that only Jiangxi, Yunnan and Chongqing are in decline because of SEC. This shows that those three provinces have failed to make optimum use of resources, which results in the severe problem of resource redundancy during the past development process. Only Zhejiang province has been affected by a diminishing PTEC value. This shows that the internal management structure of Zhejiang does not match the actual management needs. It is recommended that Zhejiang improve its management mechanisms and management level. As total factor productivities throughout the studied regions are mostly less than 1, this then indicates a downward trend. Using mean TFPC, it is Jiangsu, Shanghai and Yunnan have values greater than 1. From the perspective of technical efficiency and technological progress, TFP in Hubei, Jiangxi, Zhejiang, Chongqing, Anhui and Hunan decreased because of the influence of technology, while Anhui and Hunan are also affected by TC. By contrast, Guizhou and Sichuan TFP declined only by TC.
In summary, the provinces with TFPC less than 1 are mainly affected by the change of technological efficiency. At the same time, it also shows that technological progress plays a vital role in improving the total factor productivity of provinces and municipalities. In the future development practice, we should adhere to the technology-oriented, optimize the management structure, make full use of production resources and take the road of sustainable development.