*4.4. Regional Convergence Analysis of Green Development Efficiency of the Chemical Industry in the Yangtze River Economic Belt*

#### 4.4.1. σ-Convergence Test

The σ-convergence test method is used to calculate the σ-convergence coefficient of the whole Yangtze River Economic Belt and the upper, middle, and lower reaches, as shown in Figure 5. From 2002 to 2004, the global σ-convergence coefficient of the Economic Belt increased and showed a divergent state. From 2006 to 2016, the global σ-convergence coefficient generally showed a downward trend, indicating that the global σ-convergence occurred. This means that the regional differences in the green development efficiency of the chemical industry in the Yangtze River Economic Belt are shrinking.


**Table 3.** Regional differences in green development efficiency of the chemical industry in the Yangtze River Economic Belt.

**Figure 5.** Absolute convergence graph.

From different regions, the σ-convergence coefficient of downstream areas showed a downward trend from 2002 to 2016 and σ-convergence, indicating that the regional difference in green development efficiency of the chemical industry in downstream areas had narrowed. From 2002 to 2012, the σ-convergence coefficient in the middle reaches increased and then decreased, and after 2012, it continued to decline, showing σ-convergence. After 2012, the regional difference of green development efficiency of the chemical industry in the middle reaches was reduced. From 2002 to 2016, the σ-convergence coefficient in the upstream region basically showed an upward trend and no σ-convergence, indicating that the difference in green development efficiency of the chemical industry in the upstream region was expanding.

Overall, the green development efficiency of the chemical industry in the whole region and the downstream areas of the Economic Belt has σ-convergence. After 2012 in the middle reaches, the green development efficiency of the chemical industry also had σ-convergence. There is no σ-convergence in the upstream region, and the regional imbalance of green development efficiency of the chemical industry intensified, which is basically consistent with the analysis results of Gini coefficient.

#### 4.4.2. Absolute Convergence of β

The Hausman test shows that the panel data model with time and individual double fixed effects is more appropriate, and so the β absolute convergence mechanism was tested. The results show that the β absolute convergence coefficients in the whole region and the upper, middle, and lower reaches of the Yangtze River Economic Belt are negative, indicating that the green development efficiency of its chemical industry exists in β absolute convergence. Among them, the significance of the whole region, the upstream region, and the middle reaches region passed the significance test of 1%, 5%, and 5%, respectively. These results showed that the growth rate of green development efficiency of the chemical industry in the whole region, the upstream region, and the middle reaches region of the Yangtze River Economic Belt converged, while the significance of the downstream region did not pass the test. In terms of convergence speed, the upstream region is the fastest, followed by the midstream region (Table 4).


**Table 4.** β Absolute convergence table.

Note: \*\* and \*\*\*, respectively, represent significance at the confidence levels of 5% and 1%, and T statistics are in brackets. "-" means empty.

Conditional β convergence does not require different regions to have the same basic characteristics, i.e., different regions can be at different growth paths and steady-state levels. If conditional β convergence exists, they eventually converge to their respective steady states by virtue of their own characteristics. In this paper, the green development efficiency of chemical industry in the Yangtze River Economic Belt is examined in four aspects, namely environmental regulation, industrial structure, foreign investment intensity, and scientific and technological progress, to investigate which factors contribute to the green development efficiency of chemical industry in Yangtze River Economic Belt to reach the conditional convergence. After controlling the control variables, such as environmental regulation, industrial structure, foreign capital intensity, and scientific and technological progress, the β absolute convergence coefficient in the whole region and the upper, middle, and lower reaches of the Yangtze River Economic Belt was still negative. In addition, the significance of the whole region and the middle reaches passed the significance test at 1% and 5%, respectively. This shows that the green development efficiency of the abovementioned regional chemical industry follows the trend of β absolute convergence, which is under the consideration of environmental regulation, industrial structure, foreign capital intensity, and scientific and technological progress. In terms of convergence rate, the convergence rate in the middle reaches is faster.

In the panel data regression model of the whole region and the upper, middle, and lower reaches of the Yangtze River Economic Belt, the regression coefficients of the control variable environmental regulation are negative, and the whole region and the upper reaches pass the 5% and 10% significance tests, respectively. This finding showed that the environmental regulation of the whole region and the upper reaches restricts the reduction in regional difference in the green development efficiency of the chemical industry. The regression coefficients of industrial structure in the whole region, the middle reaches, and the downstream regions are positive, while those of the upstream regions are negative, but they all fail to pass the significance test. The regression coefficient of foreign capital intensity in the whole region, the upstream region, and the downstream region is positive, while that of the middle reach region is negative, but only the upstream region passes the significance test. The results showed that the foreign capital intensity helps reduce the regional difference of green development efficiency of the chemical industry in the upstream region. The regression coefficients of scientific and technological progress in the whole region, upstream, midstream, and downstream regions are positive, but only the whole region passes the significance test. The outcome means that the improvement

of scientific and technological level helps reduces the difference in green development efficiency of the chemical industry in these regions but is not the main reason (Table 5).


**Table 5.** β Conditional convergence table.

Note: \*, \*\*, and \*\*\*, respectively, represent significance at the confidence levels of 10%, 5%, and 1%, and T statistics are in brackets. "-" means empty.

#### **5. Discussion**

First, the problem of water ecological environment in the Yangtze River Economic Belt has attracted increasing research attention. Grey water footprint has been widely recognized as an indicator of pollution intensity [39,40]. The industrial grey water footprint index can better reflect the water pollution of industrial production activities than the wastewater pollutant discharge index can [23]. In this study, grey water footprint is incorporated into the evaluation framework of green development efficiency of the chemical industry. It can better reflect the impact of chemical industry production activities on the water environment and provide new research ideas for the calculation of green development efficiency of the chemical industry. In recent years, the green development, transformation, and upgrading of the chemical industry in the Economic Belt has achieved remarkable results. Nonetheless, in the future, we should still focus on the dynamic change trend of pollutant discharge in chemical industry wastewater, further strengthen the water pollution control of the chemical industry, optimize the industrial scale, reduce the grey water footprint of the chemical industry, and improve the efficiency of grey water footprint of the chemical industry. On the basis of this calculation model, provinces and cities can also build a measurement model of green development efficiency of the chemical industry based on grey water footprint, monitor the green development of the chemical industry, and put forward governance strategies. However, the water consumption and grey water footprint data of the chemical industry used in this paper were estimated using the provincial and industrial data in the *Yearbook of China Economic Census* (2008) and reference [38], and there is a certain error with the actual value. In future research, it is still necessary to improve the accuracy of data estimation or expand the channels for obtaining water environment data of the chemical industry.

Second, this study showed that the green development efficiency of the chemical industry in the Yangtze River Economic Belt increased significantly from 2002 to 2016 and showed a development and evolution trend of first declining and then rising. Yijun Zhang (2020) found that the overall green development performance of China's chemical industry showed a significant improvement trend from 2007 to 2017 [6]. Yeh Jiahuey (2019) studied the green development performance of China's chemical industry from 1980 to 2013 and found that it showed an evolution law of first declining and then rising from 2002 to 2013 [17]. These results are basically consistent with the results of the present research. In recent years, the Chinese government has strengthened the construction of ecological

civilization and actively promoted the green transformation and upgrading of the chemical industry with remarkable results. In particular, the 18th Congress of the Communist Party of China in 2012 proposed to give prominence to the construction of ecological civilization and build a "Beautiful China." According to the data of the China Development and Reform Commission from 2016 to 2020, there were more than 8000 chemical enterprises along the Yangtze River. Furthermore, the proportion of excellent water environment sections in the Yangtze River Basin increased from 82.3% in 2016 to 91.7% in 2019 and further increased to 96.3% from January to November in 2020, and the proportion of inferior class V water quality in the Yangtze River Basin decreased from 3.5% to 0.6% during 2016 and 2019. The elimination of inferior class V water bodies would be realized for the first time in 2020, which showed that the green development of the chemical industry in the Economic Belt has achieved remarkable results, thus supporting the conclusions of this paper from the practical level.

Third, the regional heterogeneity of green development efficiency of the chemical industry in the Yangtze River Economic Belt is very obvious. Affected by the natural geographical environment and socioeconomic conditions, there are obvious gaps in the socioeconomic development level, industrialization level, and scientific and technological innovation ability in the upper, middle, and lower reaches of the Yangtze River Economic Belt. The zonality of industrial ecological efficiency [41] and green development level [42] is significant. This study found that the green development efficiency of the chemical industry in the Yangtze River Economic Belt was the highest in the lower reaches from 2002 to 2016, followed by the middle reaches, and the lowest in the upper reaches. The overall regional difference and interregional difference tended to narrow, and the intraregional difference expanded. Our result is similar to the research conclusions of Yunbo Xiang (2021) on the spatial differences of green development efficiency of the chemical industry in the Yangtze River Economic Belt [28], but there are some differences in specific values, which may be caused by different measurement indicators and used models. According to the above research results, in the future, the focus of the chemical industry governance in the Yangtze River Economic Belt would still be to accelerate the green development, transformation, and upgrading of the chemical industry in the middle and upper reaches, improve the green development efficiency of the chemical industry, and reduce regional differences. At the same time, we should pay attention to controlling regional differences and preventing their expansion. The middle and upper reaches should improve the green development efficiency of the chemical industry by means of technological innovation, optimizing the industrial scale, adjusting industrial structure, and strengthening environmental regulation. At the same time, we should promote the diffusion of technology, capital, and talents in the lower reaches to the middle and upper reaches. We should promote the green and coordinated development of the chemical industry in the Yangtze River Economic Belt.

#### **6. Conclusions**

This research studied the regional differences, influencing factors, and convergence of green development efficiency of the chemical industry in the Yangtze River Economic Belt by using the super efficiency SBM model, Dagum Gini coefficient, coefficient of variation method, and panel data regression model. The green development efficiency measurement model of the chemical industry constructed in this paper can more objectively and comprehensively reflect the impact of the chemical industry on the water environment than in previous studies and is very consistent with the industrial characteristics of the chemical industry and the regional water environment problems of the Economic Belt. Accurately measuring the green development efficiency of the chemical industry in the Economic Belt can provide theoretical support for chemical water treatment and green development in the area. Analyzing the difference, influence, and convergence of green development efficiency of the chemical industry in the Yangtze River Economic Belt can provide reference for formulating strategies to improve the green development efficiency of the chemical industry by region and classification.

The main conclusions of this paper are as follows.

First, from 2002 to 2016, the total grey water footprint of the chemical industry in the Yangtze River Economic Belt showed a downward trend, while the green development efficiency of the chemical industry showed an upward trend as a whole. This finding means that remarkable achievements have been made in environmental governance and green development of the chemical industry in the Yangtze River Economic Belt in recent years.

Second, there are significant regional differences in the green development efficiency of the chemical industry in the Yangtze River Economic Belt. From the perspective of space, the green development efficiency of the chemical industry in the lower reaches is high, while that in the middle and upper reaches is low. In terms of time, the overall regional differences and interregional differences tend to narrow, and the intraregional differences expand. From the source of difference, regional difference is the main source of regional difference in green development efficiency of the chemical industry in the Yangtze River Economic Belt.

Third, there is σ-convergence in the green development efficiency of the chemical industry in the whole region, the middle reaches, and the lower reaches of the Yangtze River Economic Belt, while there is no σ-convergence in the upper reaches. There are β absolute convergence and β conditional convergence in the green development efficiency of the chemical industry in the whole region and the upper, middle, and lower reaches of the Yangtze River Economic Belt.

Fourth, there is spatial heterogeneity in the impact of environmental regulation, industrial structure, foreign capital intensity, and scientific and technological progress on the green development efficiency of the chemical industry in the Yangtze River Economic Belt. This spatial heterogeneity suggests that in the governance of the chemical industry in the Yangtze River Economic Belt, common but differentiated policy measures should be formulated and precisely applied according to the characteristics of the region, by region and by industry. The middle and upper reaches should strengthen environmental regulation, adjust the structure of chemical industry, strengthen supervision and management of foreign investment, and improve environmental access standards. The lower reaches should focus on scientific and technological innovation, promote the upgrading of chemical industry value chain, and enhance the resilience of chemical industry.

It should be noted that, due to the limitation of statistical data, the paper uses the proportion of COD and ammonia nitrogen (NH<sup>+</sup> <sup>4</sup> -N) emissions in industrial wastewater of the chemical industry by industry nationwide to estimate the COD and ammonia nitrogen (NH<sup>+</sup> <sup>4</sup> -N) emissions in wastewater of the chemical industry by industry in each province when calculating the gray water footprint, without distinguishing the proportion of COD and ammonia nitrogen (NH<sup>+</sup> <sup>4</sup> -N) emissions in wastewater between provinces and regions differences. To some extent, this underestimates the differences in gray water footprints of chemical industries in the Yangtze River Economic Belt among provinces and cities. Meanwhile, the study of the differences in the overall green development efficiency of the chemical industry in the Yangtze River Economic Belt and its convergence can help to grasp the green development efficiency of the chemical industry in provinces and cities in general, but in-depth studies on the gray water footprint and green development efficiency of five subsectors are needed in the future to reveal the differences between industries and to develop more refined governance strategies for the chemical industry.

**Author Contributions:** Conceptualization, Y.X. and S.W.; methodology, Y.X., S.W., and Y.Z. (Yaxin Zhang); data curation, W.S. and Y.Z. (Yong Zhang); writing, Y.X., S.W., and W.S.; super-vision S.W.; funding Acquisition, Y.X. and S.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by regional funds of the National Natural Science Foundation of China (42061026), projects of Jiangxi Social Science Foundation in 2021 (21JL01) and the Humanities and Social Sciences Research Project of the Ministry of Education (20 YJA 790071).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

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