2.1. Characteristic Fact Analysis
Industrial capacity utilization (ICU) is the proportion of real industrial output to industrial capacity. Capacity utilization was first proposed by Cassels et al. (1937) [
18] as a direct indicator of overcapacity; it is generally believed that there is overcapacity if the capacity utilization is below 75%, and the lower the capacity utilization, the more serious the overcapacity contradiction. The common measurement methods in academic research are the cost function method, stochastic frontier approach (SFA), cointegration method, and data envelopment analysis (DEA). Different measurement methods have their own strengths and limitations: (1) The cost function method was proposed by Klein (1960) [
19] and Berndt et al. (1981) [
20] and can comprehensively consider various factor inputs in the production process, and the measurement results are more objective and accurate, but the measurement process implies many strong assumptions and requires data on intermediate product inputs and a series of parameters; however, these data cannot be obtained from public channels, leading to subjective bias in parameter settings., which is difficult in practice. (2) The stochastic frontier approach (SFA) is a method for estimating efficiency using the stochastic frontier production function, which was developed by Aigner et al. (1977) [
21] and Meeusen et al. (1977) [
22], each independently; it is a parametric approach, and the parametric approach method requires setting specific production functions, such as the Cobb–Douglas production function, the fixed substitution elastic production function, and translog production function, etc. However, it is not possible to determine ex ante which production function is precisely applicable to a particular study, which may lead to a large bias. (3) The cointegration method is proposed by Shaikh et al. (2004) [
23], which has no harsh prerequisite assumptions and avoids specific functional form setting, but the insufficient portrayal of overcapacity due to non-cyclical factors leads to the measurement results being affected by period selection. (4) Data envelopment analysis (DEA) is based on a linear programming approach to construct the optimal production frontier, and the measurement of industrial capacity utilization is achieved by comparing the distance between the actual production and the optimal production frontier. DEA is one of the most commonly used nonparametric frontier efficiency analysis methods, and was first proposed by Fare et al. (1989) [
24] and used by Dong et al. (2015) [
25] to measure the industrial capacity utilization at the industry level in China. This method does not need to set the form of production function in advance, nor does it need to give or calculate the input–output weights, but determines the weights through the optimization process, thus making the evaluation of the decision unit more objective. DEA has the advantages of both loose assumptions and measurement results not affected by cyclical factors, which is more applicable and wider than all the above methods. By comparing the above measurement methods, this study finally used data envelopment analysis (DEA) to measure the industrial capacity utilization (ICU).
For a given industrial fixed capital input K, industrial capacity can be expressed as Y (K). The extent to which industrial capacity is transformed into real industrial output is governed mainly by industrial variable inputs L and E, and technical efficiency TE, where L denotes industrial Labor input and E denotes industrial energy input, then the real industrial output function can be written as TE × Y(K,L,E). By definition, industrial capacity utilization is the ratio of real industrial output to industrial capacity. Kirkley et al. (2002) [
26] argued that capacity utilization should be obtained using the additional output generated by the increased capacity rather than by the increased efficiency. In addition, Fare et al. (1989) [
24] defined unbiased capacity utilization and biased capacity utilization, and considered that biased capacity utilization excluding the downward bias caused by technical inefficiency is unbiased capacity utilization. In summary, the unbiased industrial capacity utilization is chosen as the dependent variable in this study, and the derived equation is shown below.
where ICU
unbiased and ICU
biased denote unbiased and biased industrial capacity utilization, respectively. The two output functions, Y(K,L,E) and Y (K), are measured by the DEA method using the following equations.
where t is the period, i or j is the city, and n is the total number of cities; λ denotes the weight vector, and
denotes variable returns to scale (VRS). The measurement process of each variable is described as follows.
(1) Real industrial output y is measured by the total industrial output value above the scale and deflated by the industrial producer ex-factory price index in the province where it is located using 2003 as a benchmark; (2) industrial capital input K, in this measurement, is the industrial fixed capital stock, and based on data availability, fixed capital stock is chosen as a proxy variable, which is measured using the perpetual inventory method [
27] and is deflated by the fixed asset investment price index of the province in which it is located using 2003 as a benchmark; (3) industrial labor input L is obtained by subtracting the number of employees in the secondary industry from the number of employees in the construction industry; (4) industrial energy input E, which is usually measured using energy consumption, uses industrial electricity consumption as a proxy variable due to the unavailability of industrial energy consumption in prefecture-level municipalities [
28,
29].
A description of the variables used to measure industrial capacity utilization by the DEA method is shown in
Table 1.
Before calculating the industrial capacity utilization, to test whether the fixed capital stock can effectively evaluate the local industrial fixed capital stock in the same period, a panel cointegration test is used to test whether there is a stable long-term relationship between them based on provincial data from 2003 to 2019. First, panel unit root LLC, IPS, HT, Breitung, Fisher, and Hadri tests were conducted on provincial fixed capital investment and fixed assets of industrial enterprises above the scale, respectively, and both were found to be first-order single integers, which is not shown here due to space limitations; subsequently, panel cointegration Kao, Pedroni, and Westerlund tests were conducted on both. The results are shown in
Table 2, and the
p-values are less than 5%, indicating that the two have a stable cointegration relationship. Therefore, the inferred fixed capital stock can effectively evaluate the situation of industrial fixed capital stock in the same period.
Given that the BCC model is the most basic VRS model in the DEA method, it is more representative, so this study chose the BCC model as the benchmark measurement result. The industrial capacity utilization was measured based on the output perspective, with 1% and 99% tailoring to reduce the effect of extreme values or outliers. The final measurement results of the DEA method are shown in
Table 3. It should be noted that the mean value of these measured results is lower than the mean value of the results measured by the cost function method due to the selection of global reference score and the large sample size in this study, which leads to a larger variance of the measured results and a more dispersed distribution between (0,1). Nevertheless, the measured results can still fully and accurately reflect the actual situation of all individual cities and their annual dynamic changes in industrial capacity utilization in Chinese cities.
During the period 2003–2019, industrial capacity utilization in Chinese cities as a whole showed a downward trend, and the changes in the industrial capacity utilization can be roughly divided into three stages by combining China’s economic operation cycle: 2003 to 2008, 2009 to 2012, and after 2013. The stages of the change in the annual mean of the industrial capacity utilization in Chinese cities and its growth rate are shown in
Figure 1.
Industrial capacity utilization in Chinese cities rose steadily between 2003 and 2008. At the beginning of the 21st century, China gradually emerged from the Asian financial crisis, and its economy began to develop rapidly. In particular, China’s accession to the WTO in 2002 led to a steady increase in social investment rates and accelerated its integration into the economic globalization process. China’s State Council issued the Notice on Accelerating the Structural Adjustment of Overcapacity Industries in 2006, promoting governments at all levels to improve the capacity utilization of enterprises through restructuring, renovation, and elimination.
Industrial capacity utilization in Chinese cities declined in fluctuations during 2009–2012. In 2008, during the global economic crisis the global market was weak, thus, China experienced industrial product export difficulties, and whilst many industrial enterprises did not choose to go bankrupt, the economic stimulus policy at that time ignored the efficiency to continue to expand the scale of production, resulting in the overall industrial capacity utilization falling. In 2008–2010, the national industrial capacity utilization showed a sharp downward trend. In this context, China’s State Council issued the “Notice on Several Opinions on Suppressing Overcapacity and Duplicate Construction in Some Industries to Guide Healthy Industrial Development” and “Notice on Further Strengthening the Work of Eliminating Backward Production Capacity” in 2009 and 2010 to deal with the problem of overcapacity. Under the policy regulation, the industrial capacity utilization rebounded during 2010–2012, but compared with the first stage, still showed a decreasing trend.
After 2012, with the early withdrawal of the second phase of economic stimulus policy and the side effects of inefficient investment and zombie enterprises caused by the “four trillion” investment plan introduced in response to the global financial tsunami in 2008, the growth rate of industrial capacity utilization has gradually decreased, sharply in 2013 and 2014. According to previous years, it can be seen that the problem of overcapacity in China is mainly on the supply side. In 2015, the central government put forward the strategy of “supply-side structural reform” at the 11th meeting of the Leading Group of Finance and Economics, aiming to promote supply-side structural adjustment from the perspective of improving supply quality, correcting distortions in factor allocation and promoting industrial structure upgrades, so as to solve the problem at the source and improve capacity utilization to be more scientific and reasonable. In the same year, key national leader further proposed five major structural reform tasks at the Central Economic Work Conference; the task of “removing production capacity” was the first of the five major tasks. As a result, supply-side structural reforms caused a blizzard of industrial capacity utilization in 2015. After 2016, with the disappearance of China’s demographic dividend and the decline in economic growth, the industrial capacity utilization was on a downward trend but still rebounded compared with 2014.
In conclusion, from 2003 to 2019, the industrial capacity utilization in Chinese cities showed the characteristic fact that it was decreasing in fluctuation. Government regulation has an important impact on the change in industrial capacity utilization; so how does the environmental regulation represented by the low-carbon city policy affect the industrial capacity utilization in Chinese cities?
2.2. Theoretical Mechanisms
Low-carbon city policy is a kind of environmental regulation. Regulation refers to the government’s efforts to address market incompleteness, such as negative externalities arising from the behavior of economic agents, by setting standards and other means [
30], referring to a broader scope than policy so that low-carbon city policy is included in environmental regulation. Existing studies have proved that environmental regulation has a certain improvement effect on capacity utilization, which is usually elaborated by the following two channels: compliance cost effect and innovation compensation effect [
31,
32]. The “Notice on the Piloting of Low-Carbon Provinces, Regions, and Cities” requires pilot cities to implement an emission target responsibility system, use market mechanisms to promote the implementation of greenhouse gas emission control targets, promote the concept of low-carbon living, and promote widespread participation and conscious action by the whole population, from which it can be seen that low-carbon city policy is a comprehensive environmental regulation, which takes into account command-and-control, market incentives, and public participation. Therefore, this study analyzes the impact of low-carbon city policy on industrial capacity utilization from three dimensions based on the different participating subjects. First, from the command-and-control perspective, the government directly increases the compliance costs of enterprises in terms of environmental protection expenditures by formulating environmental protection regulations, allocating emission limits and other environmental protection control measures that are mandatory, forcing enterprises with low industrial capacity utilization to reduce environmental management costs through technological innovation or optimize resource allocation through bankruptcy and restructuring, and improving the overall industrial capacity utilization of cities under the role of technological innovation mechanisms and resource allocation mechanisms [
33,
34]. Second, from the perspective of market incentive, the market investment and production behavior of enterprises are indirectly guided through the levy of sewage charges and environmental protection taxes, which stimulate the transformation of enterprises to green production and stimulate the vitality of enterprise technological innovation, and promote the improvement in industrial capacity utilization through the mechanism of industrial structure upgrading and technological innovation [
35,
36]. Third, from the perspective of public participation, the public’s green life and green consumption behavior form the demand-side feedback, while the public’s monitoring of policy implementation performance forms an effective complementary force to government regulation of the market, promoting the increase in the proportion of tertiary industry investment and improving the industrial capacity utilization through the industrial structure upgrading mechanism [
37,
38]. Therefore, as a comprehensive environmental regulation, low-carbon city policy combines the combined characteristics and advantages of three types of environmental regulations and has a positive impact on industrial capacity utilization.
Hypothesis 1 (H1). LCC improves industrial capacity utilization.
The LCC increases the environmental management costs of industrial enterprises, leading to bankruptcy or lower fixed asset investment, and affects the overall industrial capacity utilization by alleviating the resource misallocation mechanism. The “Notice on the Piloting of Low-Carbon Provinces, Regions, and Cities” requires that pilot cities implement an emissions target responsibility system, and local governments will cascade these responsibility targets to local industrial enterprises through assessment and evaluation as a means of command and control. Under the premise of unchanged technical conditions, enterprises comply with the carbon emission responsibility target assessment requirements by alleviating resource misallocation, which in turn affects capacity utilization. On the one hand, the shrinking profit margin forces some enterprises with a small production scale and high pollution control costs to exit the market, and the reduction in the total number of enterprises will reduce the idle rate of equipment and alleviate the problem of resource misallocation, which in turn improves industrial capacity utilization. On the other hand, some enterprises reduce their investment plans and future production capacity due to the high cost of environmental management and improve the efficiency of resource allocation, which also leads to an increase in industrial capacity utilization.
Hypothesis 2 (H2). LCC improves industry capacity utilization through the mechanism of alleviating resource misallocation.
The LCC changes the cost–benefit relationship of enterprises, promotes the adjustment of industrial structure, and influences the industrial capacity utilization in Chinese cities through the mechanism of industrial structure transformation and upgrading. By imposing emissions taxes and environmental protection taxes on low-carbon pilot cities, industrial enterprises with different capacity utilization rates will face different environmental compliance costs, prompting the market mechanism to play its full role [
39]. To internalize the external cost of environmental pollution, enterprises will actively change the original factor input ratio, reduce the high pollution production chain and increase the proportion of clean energy and clean technology input, which will lead to the transformation and upgrading of the industrial structure under the competitive mechanism of superiority. In addition, during the implementation of the LCC, the public effectively monitors the environmental governance performance of the city, solves the information asymmetry between enterprises and the government, further increases the rectification of polluting enterprises, and promotes the exit of polluting enterprises from the market or transformation and upgrading; at the same time, the formation of the public’s low-carbon living concept will increase the green consumption demand, force the green development of enterprises, and promote the transformation and upgrading of industrial structure. The upgrading of industrial structure implies a relative decrease in ineffective industrial capacity, and industrial capacity utilization in Chinese cities can be improved.
Hypothesis 3 (H3). LCC improves industrial capacity utilization through the mechanism of industrial structure upgrading.
The LCC stimulates enterprises to pursue profit maximization under the new constraints and forces them to invest in technological innovation, which affects industrial capacity utilization in Chinese cities through the technological innovation mechanism. Compulsory targets or levying taxes and fees on environmental management raise the compliance cost of environmental management for enterprises. In the context of the market environment of improving technology level and the stimulation of enterprises’ goal of profit maximization, enterprises are motivated to increase investment in R&D and innovation and adopt advanced production technology to improve productivity. When the positive effect of technological innovation exceeds the negative effect of environmental management cost enhancement on the production scale, and when the positive effect of technological innovation outweighs the negative effect of increased environmental management costs on a production scale, it will fundamentally reduce production costs. At the same time, enterprises with strong R&D capabilities have a demonstrable effect on technological innovation, which stimulates the improvement in technological innovation at the whole industry level [
40]. Technological innovation fundamentally solves the problem of rising compliance costs faced by enterprises, promotes the green productivity of enterprises, and eliminates industrial enterprises with backward capacity through a competitive mechanism, leading to an increase in the overall industrial capacity utilization in Chinese cities [
41].
Hypothesis 4 (H4). LCC improves industrial capacity utilization through the mechanism of technological innovation.
Figure 2 shows the mechanism for LCC to improve industrial capacity utilization, clearly demonstrating the analytical process of the above hypothesis.