Distribution of Spatial and Temporal Heterogeneity of Green Total-Factor Productivity in the Chinese Manufacturing Industry, and the Influencing Factors

Abstract

This paper considers GTFP of energy consumption and environmental pollution from a sustainable perspective as a measure of the evolutionary efficiency of manufacturing industries. It uses the super-efficiency SBM model to calculate the GTFP efficiency values of manufacturing industries in 30 Chinese provinces from 2011 to 2019. Moran’s index and the GTWR model were used to study the spatial correlation and impact factors of GTFP. The results found that the following. (1) The overall level of GTFP in China’s manufacturing industry from 2011 to 2019 rose progressively, and the level of GTFP between different regions gradually reduced. (2) The spatial correlation of GTFP in China’s manufacturing industry is significantly positive, with a positive spatial spillover effect. (3) The level of manufacturing GTFP is affected by economic development, investment, and other factors. (4) There is spatiotemporal heterogeneity in the impact factors of manufacturing GTFP. According to empirical research focusing on the goal of sustainable development, it is proposed to increase the use of clean energy and reduce environmental pollution. To carry out green development according to local conditions, the eastern region will strengthen the development of new energy manufacturing and continue to increase investment in innovation, and the central and western regions will strengthen environmental supervision, accelerate industrial transformation, and attract more foreign investment.

1. Introduction

Since the reform and opening up, manufacturing as a pillar industry has been thriving. China’s neglect of environmental protection in the process of development of negative externalities seriously affects the needs of the majority of people for a better life, while restricting the sustainable and healthy development of the economy of various regions. “First pollution, then treatment”, through high input to obtain the high output of the traditional development model, is challenging to sustain. Green manufacturing is the only way to achieve manufacturing transformation and upgrading and high-quality development [1,2]. The implementation of various environmental regulations is also to achieve green development, and the most fundamental thing in green development is to improve green total-factor productivity (GTFP) [3,4,5]. Manufacturing pollution emissions account for a large proportion of all pollution emissions, and the improvement of GTFP is of great significance to achieve green development. By taking into industrial account benefits and ecological benefits, the GTFP of the manufacturing industry comprehensively considers industrial and ecological benefits and comprehensively evaluates the overall effect of economy, resources, and environment, which is a centralized embodiment of the goal of high-quality development and an essential basis for measuring the level of green development. Therefore, scientific evaluation of China’s manufacturing industry GTFP and exploration of key driving factors are of great significance for guiding industries to transform, upgrade, and achieve high-quality economic growth.

Therefore, this paper attempts to innovate from the following aspects. (1) Based on the super-efficiency SBM model, sulfur dioxide, and carbon dioxide are used as undesired outputs, and carbon dioxide is measured from IPCC data sources. (2) Compared with the existing literature, which focuses on the impact of a single factor on manufacturing GTFP, this paper uses the GTWR model to analyze the temporal and spatial heterogeneity of economic development, fixed investment, technology investment, foreign investment, environmental regulation, and manufacturing development level on manufacturing GTFP.

The rest of the article is as follows. Section 2 reviews the GTFP methodology and the current state of development of GTFP in manufacturing. Section 3 describes the methodology and the sources of the data. Section 4 analyzes the empirical research results and interpretation of GTFP in the manufacturing industry. Finally, we present conclusions, policy recommendations, limitations, and future research in Section 5.

2. Literature Review

In the early days, China’s macroeconomic focus was on economic growth rather than economic development, and urban total-factor productivity (TFP) research also neglected the adverse outputs of the economic growth process. GTFP is a more scientific measure of the quality level of economic development than total-factor productivity [6,7]. From the research dimension, the evaluation of total-factor productivity involves micro and macro levels. The early research on GTFP was largely based on micro areas such as products and firms [8,9]. The research focuses on the measurement of GTFP, but micro-perspective analysis alone cannot effectively solve the externality impact. The research direction is gradually shifted from the micro to the macro level. It tends to the provincial [7,10], municipal [11,12], and other levels around the spatiotemporal evolution characteristics and impact factors of GTFP.

GTFP evaluation is achieved through two main methods. One is stochastic frontier analysis (SFA), and the other is data envelopment analysis (DEA). Zhang et al. [13] used the former method to assess the GTFP of 30 provinces in China. DEA is a flexible evaluation tool that does not require production functionality. Owing to its versatility, DEA has evolved into the dominant method for GTFP measurements. Liu et al. [14] have calculated GTFP and its disaggregation values based on the DEA–Malmquist productivity index. The directional distance function (DDF) and the Malmquist–Luenberger (ML) index combined with the slack-based measure (SBM) model are used to measure GTFP [15,16]. Some scholars also use the undesirable-SBM model [17] and Global Malmquist–Luenberger (GML) index combined with the super-SBM model [18].

In measuring GTFP, the environmental elements must be incorporated into the input–output productivity function. These elements provide an accurate estimate of the loss of resource efficiency [19,20,21]. Some scholars also measure the impact of environmental factors by separating the “good output” and “bad output” in resource output. Poor yields are typically measured using sulfur dioxide emissions as well as chemical oxygen demand (COD) [22] or CO₂ emissions [23].

From the perspective of research models, standard econometric models of manufacturing GTFP include GMM (generalized moment method) [24], panel threshold regression model [25], and SDM (spatial Durbin model) [26]. It is found that the impact of environmental regulation on manufacturing GTFP is “U-shaped” [16], and flexible regulation policies are conducive to the green development of manufacturing. Some scholars have studied that green industrial policies [27] and carbon emission trading policies [28] are conducive to the green transformation of China’s manufacturing industry. Foreign investment promotes manufacturing GTFP [29], while manufacturing agglomeration harms local GTFP and has a negative spatial spillover effect on surrounding areas [30]. There is a marginal diminishing effect of technological input on the progress of green technology in the manufacturing industry [31].

At present, the research on manufacturing GTFP has achieved rich results. However, most of them focus on the measurement of manufacturing GTFP and individual factors affecting GTFP. They lack research on the temporal and spatial heterogeneity and multi-factor synergy of manufacturing GTFP. This paper will measure the carbon dioxide emission data of provincial panels, focus more on the carbon emission reduction effect of manufacturing GTFP, and provide further discussion for achieving the “dual carbon” goal.

3. Methods and Data Resources

3.1. Super-Efficiency SBM Model

The super-efficiency SBM model comprehensively considers the nonlinear relationship between the input–output elements that affect the efficiency level of the decision-making unit, to determine the efficiency level of the decision-making unit. Compared with the traditional data envelopment model, the super-efficiency SBM model takes the undesired output elements into account in considering the efficiency of the decision-making unit. At the same time, the relaxation variable of input and output can be adjusted non-radially to maximize the efficiency value. Comprehensive comparison, a unique characteristic of the super-efficiency SBM model, is more in line with the GTFP measurement needs of the manufacturing industry. Therefore, this paper uses the super-efficiency SBM model to measure the GTFP of the forestry manufacturing industry and clearly distinguishes between various levels of efficiency in the determination unit. According to Tone et al. [21,32]:

$$\begin{gathered} min{\rho }_{SE}=min\frac{1-\frac{1}{m}{{\displaystyle \sum }}_{i = 1}^m\frac{s_i^{-}}{x_{i0}}}{1+\frac{1}{q_{1 + }{q}_2}({{\displaystyle \sum }}_{r = 1}^{q_1}\frac{s_r^g}{{y_{r0}}^g}+{{\displaystyle \sum }}_{r = 1}^{q_2}\frac{s_r^b}{{y_{r0}}^b})} \\ \begin{array}{c}s.t.\left\{\begin{array}{c}{x}_0=X\lambda +s^{-}\\ {y_0}^g=Y\lambda -s^g\\ {y_0}^b=Y\lambda +s^b\\ s^{-}\ge 0,s^g\ge 0,s^b\ge 0,\lambda \ge 0\end{array}\right.\end{array} \end{gathered}$$

(1)

As shown in the above equation, ${\rho }_{SE}$ means the value of the calculated manufacturing industry GTFP. X is the input vector, Y is the desired output vector, and b is the unexpected output vector. $s_i^{-}$, $s^g$, and $s^b$ represent input and output relaxation variables, λ represents the weight vector, n represents the number of DMUs (decision-making units), m means the number of entered indexes, and $q_1$ and $q_2$ represent the desired and unwanted outputs, respectively.

3.2. Spatial Correlation Analysis

3.2.1. Spatial Autocorrelation Analysis

To test the spatial pattern of the sample area and investigate whether the data are spatially related, global Moran’s I was used for the study. Its expression is:

$${\mathrm{Moran}}^{’}\mathrm{s} I=\frac{N{{\displaystyle \sum }}_{i = 1}^N{{\displaystyle \sum }}_{j = 1}^N{w}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{{{\displaystyle \sum }}_{i = 1}^N{(x_i-\overline{x})}^2{{\displaystyle \sum }}_{i = 1}^N{{\displaystyle \sum }}_{j = 1}^N{w}_{ij}}$$

(2)

In the above formula, $x_i$ represents the GTFP value; $\overline{x}$ represents the average GTFP; $w_{ij}$ represents a matrix of spatial weights, reflecting the strength of spatial correlations between regions. The closer the distance between spatial units, the more powerful the spatial spillover effect is, while the spatial spillover effect diminishes with an increase in distance, so spatial inverse distance weights will be used in this paper.

3.2.2. Local Moran’s I

Local Moran’s I explores the concentration of similar attribute values of manufacturing GTFP in each province and neighboring provinces, and its expression is:

$$I_{GTFP}=\frac{\left(x_i-\overline{x}\right){{\displaystyle \sum }}_{j = 1}^n{w}_{ij}\left(x_j-\overline{x}\right)}{\frac{1}{n}{{\displaystyle \sum }}_{i = 1}^n{(x_i-\overline{x})}^2}$$

(3)

3.3. Geographically and Temporally Weighted Regression Model

GTWR (geographically and temporally weighted regression) adds a time factor to GWR (geographically weighted regression), which not only solves the problem of a limited number of cross-sectional data samples [33], but also considers the non-stationary nature of time and space, and can effectively estimate factor parameters. The GTWR model was constructed by Huang et al. [34]:

$$Y_i={\beta }_0\left({\mu }_i,v_i,t_i\right)+{\displaystyle \sum }_{k = 1}^m{\beta }_k\left({\mu }_i,v_i,t_i\right)X_{ik}+{\epsilon }_i\left(i=1,2,\dots ,n\right)$$

(4)

where $Y_i$ is the observation, ${\mu }_i$ and $v_i$ are the latitude and longitude of the ith observation point, respectively, and $t_i$ is the time series of the i observation pointwhere. ${\beta }_0\left({\mu }_i,v_i,t_i\right)$ is the regression constant; $X_{ik}$ is the kth explanatory variable; ${\displaystyle \sum }_{k = 1}^m{\beta }_k\left({\mu }_i,v_i,t_i\right)$ is the regression coefficient of the kth independent variable at the ith observation point; ${\epsilon }_i$ is the error term.

3.4. Data Sources

In this study, 30 provinces in China were selected as research objects (Tibet, Hong Kong, Macao, and Taiwan were not included in the scope of the study due to difficulties in obtaining data). In order to ensure the timeliness of the conclusions, and due to the unavailability of some data (total regional energy consumption and consumption of major energy products are updated as of 2019), the period from 2011 to 2019 was used as the research period. The sample data were mainly derived from the China Statistical Yearbook, China Energy Statistical Yearbook, China Environment Statistical Yearbook, and China Science and Technology Statistical Yearbook.

4. Indicator Selection and Result Analysis

4.1. Construction of Manufacturing GTFP Evaluation Index System and Analysis of Results

Based on the principles of scientificity, objectivity, completeness, and comparability of index sets, this paper constructs an index system from three aspects: input, expected output, and unexpected output, based on the development status and connotations of China’s manufacturing industry. The specific indicators are described in

Table 1. Variables—Descriptive Statistics.

	Variable	Index	Unit
Input variable	Labor	Number of employees in the manufacturing industry	104 persons
	Capital	Investment in the manufacture industry	CNY 104 million
	Energy	Total energy consumption	104 tce
Output variable	Expected output	Industrial added value	CNY 104 million
	Unexpected output	SO2 emissions	104 tons
		CO2 emissions	104 tons

.

Considering input variables, first is labor input. Suppose the working time and education level of workers in a region lack statistical results. In that case, the number of employed people in a region is used as the indicator of labor input. The second is capital investment, taking the investment in fixed assets of a region’s manufacturing industry as the indicator of capital investment. The third is energy input. Some regions (Inner Mongolia, Heilongjiang, Jiangsu, and other provinces) lack data on total energy consumption of the manufacturing industry. This article refers to Li et al. [35], where the total energy consumption of an area (in units converted to 10⁴ tce) is used as the indicator of energy inputs.

Indicators for output variables include expected outputs and unexpected outputs: expected outputs. Taking the regional industrial added value [36] as the indicator of expected output, the industrial producer production index (2006=100) is used for unflattering treatment, as 2006 is the first year of the “11th Five-Year Plan”. The reduction in energy consumption per unit of GDP was started as a binding target. This was the first point in time when sustainable development was proposed, so it is used as a base period to deflate the data. It is an undesired output, and SO₂ [15] and CO₂ emissions, the primary pollutants of the environment, are used as the indicators of undesired output. Since CO₂ emission data are not given, and due to the lack of data on manufacturing energy product consumption in some regions (Inner Mongolia, Heilongjiang, Jiangsu, and other provinces) it is difficult to measure data on manufacturing CO₂ emissions. Using the study of Yang et al. [37], the conversion coefficient ξi and emission coefficient ψi of the primary energy source is used to convert the CO₂ emissions, as shown in

Table 2. The main energy sources and correlation coefficients.

Energy Sources	Coal	Coke	Crude Oil	Gasoline	Kerosene	Diesel Oil	Fuel Oil	Natural Gas
ξi	0.71	0.97	1.43	1.43	1.47	1.47	1.46	1.33
ψi	0.76	0.86	0.55	0.59	0.59	0.57	0.62	0.45

Data from publications—IPCC—TFI (iges.or.jp), Chapter 2: Stationary Combustion.

.

The carbon emission factor method is used to calculate this [37], and the specific formula is as follows:

$${\mathrm{CO}}_2={\displaystyle \sum }_{i = 1}^8{E}_i\times \mathsf{\xi }\mathrm{i}\times \mathsf{\psi }\mathrm{i}$$

(5)

4.2. Manufacturing GTFP Measurement Results and Analysis

MaxDEA8 software was used to process the data of 30 provinces to obtain the efficiency value of GTFP of manufacturing in 30 provinces from 2011 to 2019, which were then divided into eastern, central, and western regions. The average GTFP of the manufacturing industry was calculated, which intuitively reflected the GTFP of China’s three major regional manufacturing industries from 2011 to 2019. In order to directly reflect the GTFP of the manufacturing industry in the three regions of China, a line chart of GTFP values for the manufacturing industry in China from 2011 to 2019 is drawn.

As shown in Figure 1, the GTFP of 30 provinces in the eastern, central, and western regions from 2011 to 2019, as well as the overall average value from 2011 to 2019, and the efficiency values of GTFP in manufacturing, are depicted for different regions and the national average. During the research period, GTFP in China showed a trend of “stable and rising”. Before 2015, the GTFP of the manufacturing industry in the central region fluctuated slightly. At this time, economic development adopted the mode of pollution while pursuing an excessive pursuit of economic growth. This led to resource consumption and pollution emissions reaching new highs. Environmental factors have further strengthened the constraints on industrial development, and the overall manufacturing GTFP in the western region fluctuates due to relatively backward technology and low management efficiency. Due to the “13th Five-Year Plan” proposal, all localities actively responded to the call, so GTFP achieved growth after 2015. The overall steady increase in GTFP in the manufacturing industry is due to the gradual pursuit of high-quality social and economic development. Municipal governments have increased environmental regulations, restricted energy consumption and controlled pollution emissions, improved resource utilization, and promoted the improvement of GTFP in the manufacturing industry. From a regional point of view, the average GTFP of manufacturing in the eastern region is consistently above other regions and averages, and the gap between the central and western regions is narrowing year by year.

At the same time, the value of GTFP in the central region is higher than that in the western region because the central region is a transportation hub, and the western region is sparsely populated and lacks infrastructure. Hence, the development level of GTFP of manufacturing in the central region is generally higher than that in the western region. However, the difference between the western and central regions narrowed rapidly after 2017, and the GTFP of the western region rose at this stage, but the overall development of the central region is flat. This is because the new economic development model in the western region, supported by the service industry and science- and technology-based industries, has enabled stable and good economic development. The “13th Five-Year Plan” embodies the five major development concepts of innovation, coordination, green, openness, and sharing. After entering the “13th Five-Year Plan”, China’s three major regions, eastern, central, and western China, rely on their advantages, respond to national policies, and optimize the industrial structure and economic growth model of the manufacturing industry, put environmental governance first, and achieve substantial growth in GTFP in the manufacturing industry.

To further illustrate, we compare the GTFP efficiency values in 2011 with those in 2019 in Figure 2 and find that the regions with higher GTFP values in 2011 are concentrated in the Yangtze River Economic Belt, Guangdong, and Shanxi. Nevertheless, in 2019, the gap between regions narrowed significantly, and the GTFP of the manufacturing industry gradually developed in a balanced direction. However, by analyzing only the trend of GTFP in the manufacturing industry in eastern, central, and western China, it is impossible to deeply explore the intrinsic correlation of GTFP in the manufacturing industry of various provinces and cities. Therefore, it is necessary to conduct a spatial analysis of the interaction of GTFP in manufacturing in different regions.

4.3. Spatial Correlation Analysis of GTFP in Manufacturing

Using ArcGIS software, the inverse distance squared matrix is used as the spatially correlated weight matrix to calculate Moran’s I index value of China’s manufacturing GTFP from 2011 to 2019, as shown in

Table 3. Moran’s I and test results of GTFP in China’s manufacturing industry from 2011 to 2019.

Year	Moran’s I	z-Value	p-Value
2011	0.143	1.737	0.08
2012	0.081	2.745	0.006
2013	0.153	1.785	0.074
2014	0.135	2.014	0.044
2015	0.076	1.303	0.192
2016	0.148	1.787	0.074
2017	0.118	1.862	0.063
2018	0.046	2.028	0.043
2019	0.082	3.192	0.001

. The global Moran’s I index was positive for all years, indicating that China’s manufacturing GTFP has a positive spatial spillover effect. Moreover, from Moran’s I index chart in Figure 3, Moran’s I index fluctuates in the range of 0 to 0.2, indicating that China’s manufacturing GTFP has a positive spatial spillover effect.

The LISA map is calculated and plotted according to Equation (3) (as shown in Figure 4). From 2011 to 2019, the GTFP in the manufacturing industry of 30 provinces was mainly characterized by high–high (H–H) and low–low (L–L) agglomeration. H–H agglomeration areas are mainly distributed in the eastern and central areas. L–L agglomeration areas are mainly distributed in the northeast and northwest areas.

From the above results, it can be seen that the spatial characteristics of H–H aggregation and L–L aggregation are relatively stable, and are basically located in the central region, Xinjiang, Heilongjiang, and Jilin Province.

4.4. Research on the Influencing Factors of GTFP in China’s Manufacturing Industry

4.4.1. Research Design of the GTWR Model

In order to further explore the temporal and spatial heterogeneity of GTFP in manufacturing, this paper selects the level of economic development (ECO), social fixed-asset investment (INV), technology input (R&D), foreign direct investment (FDI), environmental regulation (ER), and manufacturing industry development level (MI) as the independent variables [36,38,39,40]. It establishes a GTWR model with the GTFP value as the dependent variable.

In this paper, the GDP per capita of the territory is adopted to determine the level of economic development of the territories, and the data are deflated using the GDP index for the 2006 base period. Social fixed-asset investment uses each province’s fixed asset investment and deflates the fixed-asset investment price index in 2006 as the base period. The level of external openness is expressed using the ratio of the total import and export volume of actual foreign capital investment to GDP, and the unit is converted into RMB through the exchange year’s exchange rate. Environmental regulation is a constraint imposed by local governments on production and other economic activities to improve environmental problems. The indicator is expressed using industrial pollution investment as a percentage of GDP. The development of the manufacturing industry has promoted the formation of a specialized division of labor in the manufacturing industry in the region, and positive externalities such as resource sharing and technical knowledge spillover have further improved resource utilization, reduced pollution emissions, and helped to improve GTFP. This index can be presented in terms of the positional entropy of the manufacturing industry. As with Xiao et al. [36], this paper calculates the development level of the manufacturing industry by the ratio of industrial value added to the region’s GDP, and the industrial added value is deflated using the industrial producer production index in the base period of 2006.

In this paper, an OLS model is first constructed to test the variance inflation factor (VIF) of the independent variable, and some of the results are shown in

Table 4. The regression results calculated by OLS.

Variables	2011			2016
	Coef.	p-Value	VIF	Coef.	p-Value	VIF
ECO	0.046	0.173	4.483	0.081	0.000	3.486
INV	−0.127	0.716	2.06	0.349	0.098	1.027
R&D	−4.396	0.000	3.446	−1.099	0.045	2.977
FDI	6.316	0.000	3.408	4.133	0.032	2.73
ER	0.022	0.494	2.172	−0.041	0.351	2.033
MI	2.474	0.000	2.193	0.505	0.359	2.715
R2	0.702			0.642
Adjusted R2	0.624			0.548
AICc	−9.168			−0.456

. The OLS regression in Table 4 shows the results of multiple cointegrations of factors affecting GTWR in manufacturing. The variance inflation factor for the independent variables was below 5, indicating little multicollinearity.

The GTWR models were constructed by ArcGIS, and the differences in the influencing factors of GTFP in manufacturing in different regions are analyzed. The results are shown in

Table 5. The coefficient results calculated by GTWR.

Variables	Min	Max	Mean
ECO	−1.05085	19.79946	1.420104
INV	−0.93486	4.456833	0.347339
R&D	−19.4245	16.15276	−0.47923
FDI	−214.548	295.6555	1.730573
ER	−4.78307	0.944149	0.063692
MI	−3.17551	2.098178	0.441178

. The regression R² and Adjusted R² for GTWR are 0.804 and 0.799, respectively. The GTWR model also has the smallest AICc value of −190.8. Therefore, the GTWR model can explain well the spatiotemporal heterogeneity of GTFP in manufacturing.

4.4.2. Temporal and Spatial Heterogeneity Analysis of Influencing Factors of GTFP in China’s Manufacturing Industry

The coefficients of GTFP and independent variables of China’s manufacturing industry from 2011 to 2019 are shown in Figure 5.

Figure 5a shows that the impact coefficient of economic development on the GTFP of the local manufacturing industry is positive. The more developed the economy, the more favorable it is to increase the level of GTFP in manufacturing. From 2011 to 2019, the economy will have a more significant impact on the GTFP in the West than in the East. The reason is that the manufacturing base in the western region is weak, started late, and has flourished in recent years due to the level of economic development keeping up. In contrast, manufacturing in the eastern region started earlier. As the utility of scale decreases, the efficiency of capital utilization gradually declines. Therefore, the impact of economic development on the GTFP in the western region is more significant than that in the eastern. As shown in Figure 5b, from 2011 to 2019, the impact of social fixed-asset investment gradually shifted from the northeast to the southeast. The possible explanation is that the industrial development scale in the early northeast region was larger and absorbed more fixed-asset investment. However, with the concept of sustainable development and green development, the manufacturing industry has ushered in a transformation, so the focus of manufacturing development has shifted from the northeast region to the southeast coastal area.

In addition, the impact of technology investment on GTFP is shown in Figure 5c. It can be seen that the impact of R&D investment on manufacturing GTFP in southeast China is higher than that in other regions, especially in Guangdong, Guangxi, and Hainan, with coefficients of more than 0.5. From 2011 to 2019, the impact coefficient of national R&D investment on manufacturing GTFP increased from −1.25 to 0.12, and the impact on manufacturing GTFP turned from negative to positive. This is due to the proposal of the “13th Five-Year Plan”, and the emphasis of the 18th National Congress of the Communist Party of China on innovation and green as the development concept and provinces are paying more and more attention to innovation-driven green economic development.

As shown in Figure 5d, in 2011, the FDI regression coefficient was positive in all regions except Xinjiang. After 2011, with China’s emphasis on environmental protection and sustainable development, the government began to rectify foreign-funded, pollution-intensive enterprises, with the Beijing–Tianjin–Hebei urban agglomeration as the core, and the quality of foreign direct investment continued to improve, thus providing various necessary support for the green development of industrial manufacturing.

From 2011 to 2019, the impact coefficient of ER on manufacturing GTFP decreased from 0.14 to −0.1 and then increased to 0.1, showing a positive “U” relationship. Figure 5e shows the distribution of GTFPsER regression coefficients in various provinces, and it can be seen that the Chinese mainland coefficient is high, and the regression coefficient in the southeast region is low because the manufacturing industry in the southeast region is developing rapidly and the environmental pollution is relatively severe. At the same time, the cost of environmental pollution control is high, and the efficiency is low. As a result, GTFP in manufacturing has been weakened. However, the development of the inland industry is slow, the environmental pollution is slight, and the pollution control cost of input is relatively low, so the regression coefficient is relatively high.

From 2011 to 2019, the impact coefficient of the national manufacturing development level on the manufacturing GTFP decreased from 0.81 to −0.12. This is because, in the early days, China relied on the development of manufacturing to accelerate economic development. However, after the concept of green development was proposed, China began industrial transformation, optimized the industrial structure, reducing heavy industry, strengthened environmental regulations, and turned to new energy manufacturing and innovative technology-based industries. As shown in Figure 5f, the development level of the manufacturing industry in the western region has a negative impact on GTFP. This is because the manufacturing base in the western region is relatively weak compared to other regions. In addition, because the Sichuan–Chongqing Economic Circle has entered a new economic development model relying on emerging industries in science and technology, the industrial transformation is very rapid.

5. Conclusions and Discussions

5.1. Conclusions

In this paper, the super-SBM model is used to calculate the GTFP of the manufacturing industry. Then, the spatial heterogeneity of the GTFP of manufacturing in 30 provinces in China from 2011 to 2019 is measured by Moran’s I and the GTWR model, and the temporal and spatial changes and influencing factors of the GTFP of manufacturing in different times and regions are analyzed. The following conclusions are drawn:

China’s manufacturing GTFP has been continuously improved on the whole. From a regional point of view, the average GTFP of manufacturing in the eastern region is consistently above other regions and averages, and the gap between the central and western regions is narrowing year by year. The GTFP of the manufacturing industry is gradually becoming balanced.

From the viewpoint of the spatial spread, Moran’s I is all positive, indicating that the GTFP of China’s manufacturing industry has a positive spatial spillover effect. In the distribution map of GTFP in 30 provinces, the green development level of central and western China is gradually approaching that of the eastern region, showing a balanced situation in space.

China’s manufacturing GTFP is affected by various factors, and the intensity and trend of each influencing factor vary from region to region. The impact on manufacturing GTFP has evident temporal and spatial heterogeneity. In addition, the level of economic development and social fixed-asset investment has a positive impacted GTFP. The impact of technology investment on GTFP turned from negative to positive, and the regression coefficient in the southeast coastal area was high. Foreign investment has a positive impact on GTFPs in most regions. The impact of environmental regulation on the green development of the local manufacturing industry is in a positive “U” shape. The level of development of the manufacturing industry has positively impacted on the GTFP of the local manufacturing industry. However, its influence has decreased as the industry has been transformed.

5.2. Policy Implications

Based on the above conclusions, the following targeted suggestions are put forward. First of all, the uncontrolled development of manufacturing has led to an increase in environmental pollution and other harmful gases, so it is necessary to firmly follow the green development route, reduce the consumption of energy such as coal and gasoline, and use cleaner energy. The second is to enhance the level of opening up further, attract more foreign investment in the local market, and increase the competitiveness of local enterprises. Third, the influencing factors driving the development of manufacturing GTFP in different provinces are different, and the green development of manufacturing should be combined with local characteristics. The eastern region will strengthen the development of new energy manufacturing and continue to increase investment in innovation. The central and western regions will strengthen environmental regulation to accelerate industrial transformation and attract more foreign investment.

5.3. Limitations and Future Research

Although this paper delves into the spatiotemporal distribution and influence of GTFP in China’s manufacturing industry, there are still the following shortcomings in the research. (1) Owing to the challenges of data collection, some local data are incomplete and cannot be measured, and there are certain deficiencies in the selection of indicators, such as energy input, sulfur dioxide, and carbon dioxide emissions using regional totals rather than regional manufacturing totals, so there will be an impact on the experimental results. (2) This paper only measures the GTFP of the manufacturing industry, does not consider technological efficiency changes and technological progress, and only one-sidedly analyzes the impact of driving factors on GTFP.

Future research can be carried out from the following levels. (1) Manufacturing includes 26 sub-industries, and there are also differences between industries, and the temporal and spatial changes of GTFP in one or more sub-industries can be studied. (2) The establishment of research models can consider adding more indicators to make the model results more accurate and closer to reality. (3) This paper only analyzes the provincial panel and can study and interpret the manufacturing GTFP in core areas such as metropolitan areas and economic belts in the future.