Research on Industrial Ecological Efficiency Evaluation and Improvement Countermeasures Based on Data-Driven Evaluations from 30 Provinces and Cities in China

Abstract

Improving industrial ecological efficiency is important in promoting the industry’s sustainable development. However, the economy, resources, the environment, and other factors should be considered. This paper proposes a data-driven evaluation and promotion method for improving industrial ecological efficiency. Based on industrial input and output data, the super-efficiency slack-based model containing an unexpected output was used to measure industrial ecological efficiency. The kernel density estimation method was employed to analyze the time-series characteristics of industrial ecological efficiency. Using data from 30 provinces and cities in China, this study demonstrated the implementation of a data-driven method. The results show that China’s overall industrial ecological efficiency is increasing, and industrial ecological efficiency in the western region is rapidly improving. Differences exist between provinces and cities; the characteristics of polarization are significant, and there are short boards in the eastern, central, and western regions. Based on this, suggestions are made to improve the industrial ecological efficiency of the central region, narrow the gaps between the regions, and promote each region to develop its strengths and mitigate its weaknesses. This provides a basis for formulating policies related to ecological environment protection and industrial pollution control.

1. Introduction

The sustainable development of the industry is the key driving force of national sustainable development. However, the global industrialization process also faces multiple pressures, such as ecological destruction, resource shortages, and global warming [1]. The Paris Agreement suggests that efforts should be made to limit the temperature rise to 1.5 °C above the pre-industrial level. The World Wildlife Fund (WWF) clearly pointed out in the 2020 medium- and long-term emission reduction action of international cities to cope with climate change that controlling the temperature rise at 1.5 °C means that before 2030, global annual emissions need to be reduced by 45%, based on 2010’s emissions. In the climate change context, industrial development’s carbon constraints are gradually emerging. Promoting the green and low-carbon transformation of industry and reducing the environmental load of industrial development are inevitably needed in order to achieve sustainable industrial development. The eighth goal of the United Nations 2030 agenda for sustainable development is “to promote sustained, inclusive, and sustainable economic growth”. The world has set its sights on sustainable industrial development to enable the industry to play a key role in realizing sustainable development [2]. For example, Germany has implemented the Industry 4.0 strategy, the United States government has promoted the development of clean industry, and China has issued the “14th five-year plan” for industrial green development to comprehensively improve the level of green manufacturing. Ecological efficiency, which links environmental impact and economic performance, is the main standard of measurement of the level of sustainable development [3]. Against the background of countries around the world that have successively put forward goals for “carbon peaking” and “carbon neutralization”, a major global challenge is how to continuously reduce resource input and environmental load while increasing the industrial economic output [4,5]. Therefore, according to regional industrial input and output data, we objectively evaluate the levels of regional industrial ecological efficiency and put forward targeted policy recommendations to improve industrial ecological efficiency, according to the spatial and temporal characteristics of regional industrial ecological efficiency. These recommendations are of great significance in promoting the sustainable development of the regional industry.

The remainder of the paper is structured as follows. Section 2 is a literature review, including the status, deficiencies, and theoretical and practical significance of extant studies; Section 3 covers the study method, including data collection, modeling, analysis, and application; Section 4 reports on the case study, including the background and results; this is followed by the discussion, management implications, and policy recommendations; finally, Section 6 is the conclusion.

2. Literature Review

Research on industrial ecological efficiency is mainly conducted on the following aspects:

Definition of ecological efficiency. Ecological efficiency, first proposed by Schaltegger and Sturm, is the ratio of economic to environmental load added value [6], which reflects how to obtain the ideal economic output based on reduced resource consumption and ecological damage. Ecological efficiency takes into account social, economic, and environmental benefits. It can effectively evaluate the impact of economic activities on the ecological environment and analyze the development level of ecological efficiency in different countries, regions, or industries [7]. Scholars continue to extend the concept of ecological efficiency to governments and enterprises [8].

Measurement of industrial ecological efficiency. Studies have included industrial labor, capital, and environmental inputs, expected outputs (mainly economic output), and other indicators in the industrial ecological efficiency evaluation index system [9]. As research deepens, scholars have incorporated unexpected outputs (environmental pollution accompanying economic growth) into the evaluation index system of industrial ecological efficiency [10], including greenhouse gas emissions [11], industrial solid waste, and industrial wastewater [12]. The measurement methods include life cycles, ecological footprint, emergy analysis, intuitionistic fuzzy, random frontier, and data envelopment analysis (DEA) methods [13]. The DEA method has been widely used, and evaluates the efficiency of multi-objective decision-making units with multiple inputs and outputs, including three-stage DEA [14] and super-efficiency slack-based modeling (SBM) [15]. For example, Zhong et al. used the super efficiency SBM to measure China’s innovation efficiency, and showed that the model provided a stable efficiency evaluation tool [16].

Evaluation of industrial ecological efficiency. Studies frequently evaluate industrial ecological efficiency from static and dynamic perspectives [17]. The static evaluation combines DEA and the Malmquist index to evaluate regional differences in industrial ecological efficiency [18]. The dynamic evaluation uses methods such as nuclear density estimation and the Moran index [19] to evaluate the spatial–temporal evolution pattern of industrial ecological efficiency. The nuclear density estimation method analyzes the dynamic change characteristics of ecological efficiency from the perspectives of location, shape, and peak value [20]. For example, Zhang and other scholars have demonstrated through the nuclear density curve that the level of China’s industrial ecological efficiency shows a downward trend, and that polarization still exists but is weakening [21]. The spatial distribution characteristics of ecological efficiency are mainly analyzed from the perspectives of a region [22] and a city [23]. For example, Li pointed out that industrial ecological efficiency at the regional level of 30 provinces and cities in China had prominent agglomeration characteristics, and that the nearest neighbor effect is significant [24]. The above research showed the time and space characteristics of industrial ecological efficiency.

Promotion of industrial ecological efficiency. Based on the factors influencing ecological efficiency measurement, evaluation, and identification [25], studies have suggested methods to improve industrial ecological efficiency and, in turn, improve the coordination of the economy, environment, and resources [26]. They include introducing technologies, improving independent innovation, vigorously promoting the construction of ecological civilization, strengthening national supervision, transforming the mode of economic development, advocating for low-carbon skills, strengthening regional cooperation, and standardizing the manufacturing process [27,28]. The proposed suggestions (or paths) are mainly applied to enterprises, industries, governments, and other fields [29].

The results of domestic and foreign studies on industrial ecological efficiency provide important references for this study. There are several aspects worth further development:

• Owing to the differences in the natural endowments of the study areas, scholars have constructed different evaluation index systems for industrial ecological efficiency. However, regarding industrial technology change and “carbon peak”, it is necessary to consider indicators such as industrial technology research and development and industrial waste emissions when constructing an index system. How to build a more comprehensive evaluation index system for industrial ecological efficiency is a difficult problem and challenge to be solved in this study.
• The evaluation of industrial ecological efficiency involves multiple indicators and multi-source data, such as energy, resource input, and expected and unexpected outputs. The accurate measurement of industrial ecological efficiency is urgently required in order to improve the efficiency of industrial resource utilization.
• Industrial ecological efficiency shows different temporal characteristics and spatial distribution patterns in different periods. Therefore, targeted suggestions on improving regional ecological efficiency are urgently needed to promote the sustainable development of the regional industry. Such suggestions are also significant for achieving “carbon peaking” and “carbon neutralization” goals.

To solve the above research challenges, this study references the research ideas of previous studies [30,31,32] and proposes a data-driven evaluation method for industrial ecological efficiency. The study constructs an evaluation index system for industrial ecological efficiency, measures its regional efficiency, analyzes its temporal and spatial characteristics, and suggests ways to improve industrial ecological efficiency. The theoretical contribution and practical significance of this study are as follows:

Regarding theoretical significance, firstly, indicators such as research and development (R&D) investment, industrial wastewater, and waste gas emissions are included in the evaluation index system, which enriches the industrial ecological efficiency index system. Secondly, a data-driven method is proposed to form a closed loop of data collection, models, analyses, and optimized decision-making, which provides support for more objective measurements of regional industrial ecological efficiency. Thirdly, the research content of industrial ecological efficiency is improved by analyzing the temporal and spatial characteristics of industrial ecological efficiency in different regions.

Regarding practical significance, firstly, from a data-driven perspective, it provides an intelligent decision-making model for the green transformation and upgrades of industrial enterprises. Secondly, it suggests a feasible path for the industry’s sustainable development, and provides decision-making references for local governments. Thirdly, it provides new research ideas for the field of ecological efficiency.

3. Materials and Methods

3.1. Method and Process

This study aimed to promote sustainable industrial development. The path to improving industrial ecological efficiency was proposed from the perspective of overall optimization. However, the industrial ecosystem is a complex, comprehensive system, with large differences in natural endowments among regions and multiple industrial input–output data. When the value of regional industrial ecological efficiency (calculated by the traditional DEA method) was one, it was impossible to distinguish and compare across periods. Therefore, this study proposed a data-driven evaluation and promotion method for industrial ecological efficiency to solve the research challenge. The data was mainly input–output data, and the data model employed the super-efficiency SBM to realize the distinction and cross-period comparison when the regional industrial ecological efficiency values were all one. Data analysis was performed from two aspects: temporal and spatial characteristics. The data application proposed a targeted path to improve industrial ecological efficiency according to the evaluation results. The method flow is shown in Figure 1.

3.2. Data Collection and Index Construction

Based on data availability and scientificity, and referring to the existing literature [33], the research indicators for regional industrial ecological efficiency were divided into two levels: input and output (

Table 1. Index system for evaluating industrial ecological efficiency.

Primary	Secondary Index	Tertiary Indicators	Unit	Reference
Input	Capital	X1: Total assets of industrial enterprises above the designated size	10,000 RMB	[34]
	Labor force	X2: Number of employees in the secondary industry	10,000 people	[35]
	Environment	X3: Completed investment in industrial pollution control	10,000 RMB	[36]
	Research and development	X4: R&D funds for industrial enterprises above the designated size	10,000 RMB	[37]
	Resources	X5: Industrial water	100 million m3	[38]
	Energy	X6: Total industrial energy consumption	10,000 tons of standard coal	[39]
Output	Expected output	Y1: Industrial output	100 million RMB	[40]
	Unexpected output	Y2: Chemical oxygen demand and discharge of industrial wastewater	Ton	[41]
		Y3: Output of general industrial solid waste	10,000 tons	[42]
		Y4: Industrial sulfur dioxide emissions	Ton	[43]

). The indicators used in the present study were from the China statistical yearbook, China environmental statistical yearbook, and China energy statistical yearbook. Industrial wastewater was characterized by chemical oxygen demand emissions, and industrial waste gas was characterized by industrial sulfur dioxide emissions.

3.3. Data Model

The data models included the super-efficiency SBM (for measuring industrial ecological efficiency) and the kernel density estimation method (for analyzing the time-series characteristics of ecological efficiency).

3.3.1. Super-Efficiency SBM

DEA was used to calculate multi-input and multi-output data. It is a common method for measuring ecological efficiency. The traditional DEA model does not consider the relaxation variable in the objective function, resulting in the measurement results at the forefront and the efficiency value being high. The super-efficiency SBM solves the problem of input–output relaxation, and considers the impact of unexpected output on the industrial ecological efficiency [44]. We assumed that there were B decision-making units (B = 1, 2, 3, …, B), and each decision-making unit comprised D inputs and M outputs. $x\in R^D$ and defined $X=\left[x_1,x_2,\dots x_n\right]\in R^{B*D},x_i>0$; M included M₁ expected outputs, $y^g\in R^{M_1}$, and M₂ unexpected outputs, $y^b\in R^{M_2}$, and defined $Y^g=\left[y_{g1},y_{g2},\dots y_{gB}\right]\in R^{M_1*B},Y^b=\left[y_{b1},y_{b2},\dots y_{bB}\right]\in R^{M_2*B}$, $y_{gj}>0,y_{bi}>0$. If ρ was the industrial ecological efficiency value, then the super-efficiency SBM was expressed as

$$\rho =min\frac{1-\frac{1}{D}{{\displaystyle \sum }}_{i=1}^D\frac{S_{i=1}^{-}}{x_{i0}}}{1+\frac{1}{M_1+M_2}{\sum }_{i=1}^{M_1}\frac{S_i^g}{y_{i0}^g}+{\sum }_{i=1}^{M_2}\frac{S_i^b}{y_{i0}^b}}$$

(1)

$$\mathrm{s}.\mathrm{t}.\{\begin{array}{c}{x}_0=X\lambda +S^{-}\\ y_0^g=Y\lambda -S^g\\ y_0^b=Y^B\lambda +S^b\\ \lambda ,S^{-},S^g,S^b\ge 0\end{array}$$

(2)

In Equations (1) and (2), $S^{-}$, $S^g$, and $S^b$ were the relaxation variables of input, expected output, and unexpected output, respectively, and λ represented the weight vector. When $\rho >1$, the decision-making unit was efficient; when $\rho <1$, the decision-making unit was invalid, indicating that the input–output ratio of the decision-making unit required improvement [45].

3.3.2. Kernel Density Estimation Method

The kernel density estimation is a non-parametric method for estimating the probability density function of random variables. By plotting the probability density maps of multiple years in a coordinate system, the time-series evolution law of the research object can be analyzed without preset functions [46]. The formula for nuclear density estimation is as follows:

$$f\left(x\right)\frac{1}{nh}{\displaystyle \sum }_{i=1}^{n}K(\frac{X_i-x}{h}).$$

(3)

In Equation (3), n was the number of observation samples, h was the bandwidth, $X_i$ was the value of industrial ecological efficiency in i province and city, and K was the kernel function. This study used the Gaussian function to analyze the spatial–temporal evolution law of regional industrial ecological efficiency. The Gaussian function formula is as follows:

$$K\left(x\right)=\frac{1}{\sqrt{2\pi }}\exp \left[-\frac{x^2}{2}\right]$$

(4)

3.4. Data Application

This study used data driven methods to accurately evaluate and improve regional industrial ecological efficiency and provide a decision-making basis for regional industrial practitioners and managers. The data application process is shown in Figure 2.

Firstly, data was collected on industrial energy consumption, and a list databases of industrial ecological efficiency data was built. According to the scientificity and availability of data, the evaluation index system of industrial ecological efficiency was established by selecting indicators, such as capital, labor, environment, R&D, resources, energy, industrial added value, and industrial “three wastes” emission.

Secondly, we used the super-efficiency SBM to evaluate the current situation regarding regional industrial ecological efficiency. The kernel density estimation method was used to analyze the time-series characteristics of industrial ecological efficiency. The natural discontinuity method of the arcgis 10.2 software was employed to create the spatial distribution map of industrial ecological efficiency.

Thirdly, we suggested promotion paths for regional industrial ecological efficiency. Based on the evaluation results, this paper offers targeted measures to improve industrial ecological efficiency.

4. Case Study

4.1. Case Study Background

China has established the most complete industrial chain in the world, and its industrial output value accounts for one-quarter of that of the world. According to the China Bureau of Statistics, there were differences in industrial added value among provinces and cities in China in 2021 (as shown in Figure 3). The industrial sector is the key to China’s response to climate change and the realization of China’s carbon peak and neutrality goals. The 14th five-year plan for industrial green development, issued by the Ministry of industry and information technology of China, proposes that by 2025, the energy consumption per unit of the added value of industries above the designated size will reduce by 13.5%, the comprehensive utilization rate of bulk industrial solid wastes will reach 57%, and the water consumption per unit of industrial added value will reduce by 16%. Therefore, China’s industrial development is facing the challenge of green and low-carbon transformation. Regarding industrial production input–output, it is practically significant to measure China’s industrial ecological efficiency and offer targeted improvement suggestions to promote China’s industrial sustainable development.

4.2. Results

4.2.1. Measurement Results of Industrial Ecological Efficiency in China

The industrial eco-efficiency values of 30 provinces and cities in China were calculated using Equations (1) and (2) (

Table 2. Measurement results of industrial ecological efficiency in 30 provinces and cities in China.

No.	Region	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	Average	Ranking
1	Anhui	0.650	0.654	0.606	0.661	0.538	0.451	0.454	0.457	0.440	0.433	0.534	27
2	Beijing	1.186	1.157	1.163	1.173	1.232	1.201	1.262	1.269	1.290	1.268	1.220	1
3	Fujian	1.038	1.039	1.047	1.051	1.046	1.038	1.030	1.037	1.044	1.059	1.043	8
4	Gansu	0.586	0.479	0.521	0.499	0.484	0.393	0.407	0.474	0.613	0.736	0.519	28
5	Guangdong	1.122	1.085	1.097	1.107	1.132	1.129	1.092	1.076	1.079	1.075	1.099	2
6	Guangxi	0.524	0.505	0.476	0.491	1.006	1.028	1.036	1.049	1.049	1.028	0.819	21
7	Guizhou	0.441	0.519	0.708	1.000	1.052	1.094	1.072	1.046	1.066	1.056	0.905	16
8	Hainan	1.053	1.058	1.016	0.500	1.017	1.090	1.115	1.086	1.081	1.151	1.017	11
9	Hebei	1.030	1.029	0.527	0.435	0.614	1.011	1.001	0.450	0.562	1.011	0.767	23
10	Henan	1.054	1.031	0.822	0.763	1.017	1.043	1.062	1.045	1.050	1.042	0.993	13
11	Heilongjiang	1.040	1.047	1.008	0.777	0.565	0.495	0.457	0.685	1.020	0.454	0.755	24
12	Hubei	1.030	1.021	1.019	1.019	1.038	1.041	1.033	1.039	1.055	0.622	0.992	14
13	Hunan	1.016	1.025	1.034	1.036	1.042	1.045	1.048	1.045	1.066	1.111	1.047	7
14	Jilin	0.462	0.530	0.549	0.463	0.555	0.579	0.573	1.055	1.024	1.099	0.689	25
15	Jiangsu	1.023	0.709	0.669	0.708	0.766	0.743	1.015	0.613	1.005	1.000	0.825	20
16	Jiangxi	1.071	1.067	1.071	1.064	1.044	1.026	1.024	0.496	0.488	0.627	0.898	17
17	Liaoning	1.021	1.021	0.564	0.453	0.602	0.494	0.501	1.017	0.571	0.544	0.679	26
18	Neimenggu	0.348	0.351	0.297	0.295	0.357	0.349	0.392	0.474	0.538	1.058	0.446	29
19	Ningxia	0.446	0.343	0.300	0.273	0.339	0.282	0.300	0.269	0.317	0.355	0.322	30
20	Qinghai	0.294	0.330	0.361	0.317	1.008	1.057	1.056	1.096	1.061	1.091	0.767	22
21	Shandong	1.157	1.035	1.028	1.024	1.056	1.037	1.022	0.580	1.009	0.599	0.955	15
22	Shanxi	1.113	0.635	0.482	0.477	1.007	0.710	1.026	1.003	1.019	1.070	0.854	19
23	Shaanxi	1.074	1.112	1.118	1.114	1.102	1.100	1.103	1.102	1.090	1.055	1.097	3
24	Shanghai	1.085	1.074	1.123	1.054	1.081	1.063	1.113	1.120	1.125	1.130	1.097	4
25	Sichuan	1.033	1.055	1.072	1.053	1.076	1.057	1.032	0.805	1.037	1.007	1.023	9
26	Tianjin	1.047	1.044	1.046	1.042	1.030	1.072	1.080	1.074	1.075	1.068	1.058	6
27	Xinjiang	1.099	1.084	1.054	1.066	1.056	1.055	1.084	1.075	1.098	1.131	1.080	5
28	Yunnan	0.696	1.016	1.032	1.033	1.022	1.003	1.000	0.551	0.680	0.627	0.866	18
29	Zhejiang	1.025	1.013	1.014	1.011	1.018	1.030	1.021	1.023	1.033	1.023	1.021	10
30	Chongqing	0.694	1.033	1.029	1.080	1.022	1.059	1.016	1.000	1.019	1.013	0.996	12
Eastern mean		1.071	1.024	0.936	0.869	0.963	0.991	1.023	0.940	0.989	0.994	0.980	-
Central mean		0.930	0.876	0.824	0.783	0.851	0.799	0.835	0.853	0.895	0.807	0.845	-
Western mean		0.658	0.711	0.725	0.747	0.866	0.862	0.863	0.813	0.870	0.923	0.804	-
National average		0.882	0.870	0.828	0.801	0.897	0.893	0.914	0.870	0.920	0.918	0.879	-

). According to the National Bureau of Statistics standards, China is divided into three regions: eastern, central, and western. The eastern region includes Beijing, Fujian, Guangdong, Hainan, Hebei, Jiangsu, Liaoning, Shandong, Shanghai, Tianjin, and Zhejiang. The central region includes Anhui, Henan, Heilongjiang, Hubei, Hunan, Jilin, Jiangxi, and Shanxi. The western region includes Gansu, Guangxi, Guizhou, Inner Mongolia, Ningxia, Qinghai, Shaanxi, Sichuan, Xinjiang, Yunnan, and Chongqing.

At the national level (as seen in Table 2), from 2011 to 2020, the average industrial ecological efficiency of 30 provinces and cities in China was in the range of 0.801–0.920, showing a slight fluctuation and a steady upward trend, reaching the highest value (0.920) in 2019. The average industrial ecological efficiency of 30 provinces and cities in China has exceeded the 10-year average level (0.879) since 2015. This indicates that China’s industrial ecological efficiency has steadily improved, and that the original extensive development mode of high pollution, high energy consumption, and high emissions is gradually transforming into a green development mode. This is closely related to China’s implementation of the construction of ecological civilization and the “2015 special action for industrial green development”. This promoted the clean and efficient utilization of coal in the industrial field, established a national industrial energy conservation monitoring and analysis platform, and realized the dynamic monitoring and early warning prediction of industrial energy consumption data.

The industrial ecological efficiency values of China’s eastern, central, and western regions are represented in a column chart (Figure 4). The average industrial ecological efficiency values of the eastern, central, and western regions from 2011 to 2020 were 0.980, 0.845, and 0.804, respectively. The average industrial ecological efficiency of the eastern region was higher than the national average, which was mainly due to the advanced energy-saving technology, equipment, and clean energy utilization capacity in the eastern region. However, the industrial ecological efficiency of the western region has exhibited an increasing trend since 2019 and surpassed that of the central region in 2020. This indicates that the western region, rich in renewable energy, possesses the conditions and potential for industrial green transformation and development.

At the urban level, significant differences exist in the industrial ecological efficiency between provinces and cities (as shown in Table 2). For example, from 2011 to 2020 the average industrial ecological efficiency of 11 provinces and cities exceeded 1, which was at the forefront, including Beijing, Fujian, Guangdong, Hainan, Hunan, Shaanxi, Shanghai, Sichuan, Tianjin, Xinjiang, and Zhejiang. This indicated that these provinces and cities had a high level of coordinated development of their industrial economy and ecological environment. The other 19 provinces and cities demonstrated different degrees of inefficiency. The average industrial ecological efficiency of Guangxi, Guizhou, Hebei, Henan, Heilongjiang, Hubei, Jiangsu, Jiangxi, Qinghai, Shandong, Shanxi, Yunnan, and Chongqing ranged between 0.7 and 1. This indicated that these cities attached more importance to industrial green transformation, industrial pollution and carbon reduction, and the efficient utilization of energy resources, while promoting industrial economic development. The average industrial ecological efficiency of Anhui, Gansu, Jilin, Liaoning, Neimenggu, and Ningxia ranged between 0.3 and 0.7, indicating that the task of industrial green transformation in these cities was arduous. These cities are the main battlefield for China’s industrial energy conservation and carbon reduction, and are the key to promoting the comprehensive green and low-carbon transformation of China’s industrial economy.

In addition, taking 2020 as an example, the slack movement values of the industrial input and output in China’s provinces and cities with an industrial ecological efficiency of less than 1 are calculated, and the improvement objectives of industrial input and output in China’s provinces and cities are defined to provide a basis for local government decision-making. Details are shown in

Table 3. Slack movement values of input–output indicators of China’s industrial eco-efficiency in 2020.

DMU	Score	X1	X2	X3	X4	X5	X6	Y1	Y2	Y3	Y4
01	0.433	−13,630	−503	−117,081	−1,595,708	−51	−3152	0	−2262	−9664	−60,544
04	0.736	−1352	−95	0	0	0	−2285	0	0	−1190	−23,144
11	0.454	−1820	−76	−33,616	−82,324	−10	−2097	0	−12,884	−2600	−42,990
12	0.622	−6118	−208	−62,046	0	−44	−1566	0	−6307	−4377	−6501
16	0.627	0	−327	−7500	0	−26	−402	0	−6766	−6774	−30,174
17	0.544	−13,060	0	−17,100	0	0	−9896	0	−5009	−20,180	−115,284
19	0.355	−4800	−16	−31,315	−52,279	−3	−6862	0	−1726	−4880	−58,297
21	0.599	0	−419	−176,929	−2,774,281	0	−6704	0	−28,569	−10,341	−84,207
28	0.627	0	−162	−69,250	0	−2	−3554	0	0	−9686	−62,148

below:

It can be seen from Table 3 that there is a certain redundancy in the industrial input and output of Chinese provinces and cities where the industrial ecological efficiency value is less than 1. For example, in Anhui Province in 2020, the improvement direction of total industrial water input (×5) was to reduce the input of 5.1 billion cubic meters; the improvement direction of the three unexpected outputs was that Y2 decreased by 2262 tons, Y3 decreased by 96.64 million tons, and Y4 decreased by 60,544 tons.

4.2.2. Time-Series Characteristics of Industrial Eco-Efficiency in China

Using the equal difference method, 2011, 2014, 2017, and 2020 were selected as the time nodes, and the Stata software was used to estimate the kernel of the measurement results (Table 2). The nuclear density curve of China’s industrial ecological efficiency was drawn (Figure 5) to understand the evolution trend of China’s industrial ecological efficiency level at different time points.

As shown in Figure 5: regarding location, compared with 2011, the center of the nuclear density curve in 2014 and 2020 moved to the right, and the left tail continued to shorten toward the middle. This indicates that the number of provinces and cities in low-value areas of industrial ecological efficiency decreased, and China’s overall industrial ecological efficiency improved. Regarding shape, the nuclear density curves of 2011, 2014, and 2020 show a bimodal “M-shape” with high–low differences. The low values were concentrated at 0.5 and the high value was concentrated at around 0.9, indicating that the polarization of China’s industrial ecological efficiency was significant. Regarding the peak changes, in 2017, the gap between the two peaks was relatively wide, indicating that the input and output of industrial development in various provinces and cities in China differed, and that the two-level differentiation trend of the industrial ecological efficiency was severe. The main reason is that from 2013 to 2016, the Chinese government issued the “ten articles on water”, “ten articles on air”, and “ten articles on soil”, comprehensively strengthening the prevention and control of water, air, and soil pollution, and began to implement the newly revised environmental protection law. In addition, in 2017, China stopped levying sewage charges and began to levy environmental taxes. By 2017, a series of policies implemented by the Chinese government had shown results: by strengthening environmental supervision, promoting the transformation of industrial development, and improving resource utilization efficiency, the industrial ecological efficiency of some provinces and cities has been greatly improved. In 2020, the value of the right peak reduced, indicating that the polarization trend of industrial ecological efficiency in various provinces and cities in China slowed down after 2017. Thus, provinces and cities with low industrial ecological efficiency should improve their level of industrial green development and narrow the regional gap.

4.2.3. Spatial Characteristics of Industrial Eco-Efficiency in China

The values of China’s industrial ecological efficiency in 2011, 2014, 2017, and 2020 were selected, and the natural discontinuity method [47,48] of the ArcGis10.2 software was used to depict the spatial distribution map of China’s industrial ecological efficiency (Figure 6).

As shown in Figure 6, during the study period, the industrial ecological efficiency of Beijing, Shaanxi, Xinjiang, and Guangdong has always been at a high level. Compared with 2011, Shandong, Jiangxi, and other provinces withdrew from high-efficiency status in 2020, and the new industrial ecological high-efficiency provinces included Qinghai, Neimenggu, Liaoning, Guizhou, Hunan, Fujian, and Hainan. Although the high-efficiency regions changed, the proportion of high-efficiency regions increased overall.

Regarding spatial distribution, in 2011, except for in Xinjiang, where the industrial ecological efficiency was relatively stable, China’s industrial ecological efficiency exhibited a spatial distribution pattern where it gradually increased from west to east. In 2014, China’s industrial ecological efficiency exhibited a concentrated and contiguous “regional block” distribution. Industrial ecological efficiency showed a spatial distribution pattern of gradual increase from north to south. The regional differences in South China were significant, and formed a low-efficiency “collapse area” in Guangxi and Hainan. In 2017, the overall industrial ecological efficiency of Yunnan, Sichuan, Chongqing, Hunan, Jiangxi, Fujian, Zhejiang, and other places in central and southern China slightly declined. This is due to the fact that in 2017, China strengthened the construction of ecological civilization, built a national ecological civilization pilot area (Fujian and other places), and conducted full-coverage environmental protection supervision. In 2020, China’s industrial ecological efficiency improved, but the centralized and contiguous “regional block” spatial pattern had been broken. In 2020, the industrial ecological efficiency of Qinghai, Xinjiang, and Neimenggu in western China, Shanxi, Jilin, and Hunan in central China, and Fujian in eastern China were improved. Ningxia, Yunnan, and Gansu in the western region; Anhui, Heilongjiang, Hubei, and Jiangxi in the central region; and Shandong and Liaoning in the eastern region became weak areas in China’s industrial ecological efficiency.

5. Discussion and Policy Recommendations

5.1. Discussion

Compared with the existing literature [30,31], this study presented the following advantages: firstly, a more comprehensive evaluation index system of industrial ecological efficiency was established. Capital, labor, environment, resources, energy input, R&D input, and unexpected outputs were considered. Secondly, the consensus with the literature [49] is that it is urgent and challenging to promote industrial green transformation and improve industrial ecological efficiency, and a more objective, data-driven method for measuring industrial ecological efficiency was adopted. The super-efficiency SBM solved the problem of input–output relaxation and considered the impact of unexpected outputs on industrial ecological efficiency. Thirdly, targeted suggestions are proposed to improve industrial ecological efficiency. Compared with the literature [18], this study not only measured the level of industrial ecological efficiency but also analyzes the temporal and spatial characteristics of industrial ecological efficiency. According to the results of the industrial ecological efficiency measurement and the temporal and spatial analysis, suggestions are proposed to promote the overall improvement of China’s industrial ecological efficiency. The following management implications can be drawn:

Industrial ecological efficiency has an important impact on the sustainable development of industries in various countries. To promote the systematic improvement of industrial ecological efficiency, the key is accelerating green industrial transformation in regions with low ecological efficiency.

The data-driven method formed a closed loop of data acquisition, modeling, analysis, and application. Applying a data-driven approach to industrial ecological efficiency management can guide intelligent decision-making for improving industrial ecological efficiency.

Carbon peaking in the industrial sector is vital to promoting the sustainable development of the global economy. Despite the environmental resource constraints, exploring industrial low-carbon development from the perspective of industrial ecological efficiency is a subject worthy of in-depth study.

5.2. Policy Recommendations

Based on the above discussion results, we put forward the policy recommendations listed below.

5.2.1. Improving Industrial Ecological Efficiency in Central China

The results show that from 2011 to 2020, China’s industrial ecological efficiency steadily rose in a pattern of “east > middle > west”, which was similar to reported results [50]. However, the industrial ecological efficiency in the west rapidly improved. We should tap into the potential for industrial energy conservation and carbon reduction in the western region. The average ranking of industrial ecological efficiency in Central China in the last ten years is: Hunan (7), Henan (13), Hubei (14), Jiangxi (17), Shanxi (19), Heilongjiang (24), Jilin (25), and Anhui (27). Therefore, Hunan Province should be exemplary in industrial transformation and development. Anhui, Jilin, Heilongjiang, and other provinces with low ecological efficiency should accurately control resource input and environmental pollution according to the relaxation variable value (Table 3) and accelerate the improvement of their industrial economic output.

5.2.2. Narrowing the Gap between Regions

The time-series characteristics show that the number of provinces and cities in the low-value areas of China’s industrial ecological efficiency decreased and that polarization was significant. The industrial green and low-carbon scientific and technological revolution should be accelerated in the central and western regions, new drivers of industrial green development should be cultivated and expanded, the industrial transfer should be accelerated in the eastern region, and the polarization of industrial ecological efficiency should be alleviated.

5.2.3. Promote Each Region to Develop Strengths and Mitigate Weaknesses

The spatial characteristics show that the proportion of China’s industrial ecological high-efficiency regions increased, but weaknesses existed in the eastern, central, western, and other regions. We should encourage the development of high-efficiency regional industries for high-end industrial structures and low-carbon energy consumption. Additionally, the western region should improve the industrial ecological efficiency of Ningxia, Yunnan, and Gansu, the central region should improve the industrial ecological efficiency of Anhui, Heilongjiang, Hubei, and Jiangxi, and the eastern region should improve the industrial ecological efficiency of Shandong and Liaoning.

6. Conclusions

Owing to increasingly serious environmental problems, the scientific improvement of industrial ecological efficiency is a crucial requirement to promote the industry’s sustainable development. Therefore, it is necessary to measure and evaluate industrial ecological efficiency to promote the green transformation of industry.

This study proposes a data-driven evaluation and promotion method for industrial ecological efficiency. The innovations are as follows: it enriches the identification and evaluation index system of industrial ecological efficiency, considering the unexpected output of the industry. The study proposes a data-driven optimization mechanism of industrial ecological efficiency and analyses the spatial–temporal distribution pattern of industrial ecological efficiency, which provides a quantitative decision-making basis for promoting the sustainable development of the regional industry.

However, compared with the existing literature [51], the limitation of this paper is the lack of spillover analysis of the spatial effect of industrial ecological efficiency and analysis of external factors. A future research direction is to strengthen the spatial correlation and influencing factor analysis of industrial ecological efficiency.