1. Introduction
Global warming caused by carbon emissions has caused severe damage to the world’s ecological environment [
1]. China’s carbon emissions have exceeded that of the United States in 2007, and has become the world’s largest CO
2 emission emitter [
2]. With the rapid development of China’s economy and increasing improvement of people’s living standards, a large amount of energy will inevitably be consumed, resulting in a large number of carbon emissions [
3]. China has made a lot of efforts to reduce carbon emissions. The Chinese government has committed that by 2030, China’s carbon emission intensity will be reduced by 60%–65% compared with 2005 [
4], and the proportion of non-fossil fuels in primary energy consumption will be increased to 20% [
5], striving to reach peak carbon emission by 2030 [
6]. Therefore, China’s carbon emission actions are increasingly concerned by the world [
7]. Moreover, China will still be in the process of industrialization for a long time in the future [
8], and the industrial sector is the main source of China’s energy consumption and carbon dioxide emissions. Therefore, it is of great significance to understand the driving factors of carbon dioxide emission in China’s major industries.
As the engine of China’s industrialization and the pillar of China’s national economy, the manufacturing industry has maintained rapid development since the 1990s. It is an industry with high energy consumption and emissions, and one of the largest contributors to the growth of China’s CO
2 emission [
9]. Especially with the further development of China’s reform and opening-up, China is becoming a “world factory” [
10], and the sales revenue of manufacturing industry has increased from 459 billion yuan in 1990 to 98,793.9 billion yuan in 2015, with an average annual growth of 24% [
11]. However, the rapid development of China’s manufacturing industry has also accelerated environmental damage, especially coal will continue to be China’s primary energy source for a long time in the future. Due to the backward technology level, the energy consumption of China’s manufacturing industry accounts for about 60% of China’s total energy consumption and more than 50% of its total CO
2 emission [
12]. China’s manufacturing industry will continue to expand, which will also lead to an increase in carbon dioxide emissions of China’s manufacturing industry [
13]. Therefore, it is of great significance to explore the influencing factors of carbon emission in China’s major manufacturing industries.
The three most important and discussed variables related to carbon emissions are economic growth, foreign direct investment and energy efficiency [
14,
15,
16,
17]. Due to different analysis samples, different selection variables and different estimation methods, although a large number of studies have discussed the relationship between economic growth and carbon emissions, the existing research has not found consistent evidence about the impact of economic growth on CO
2 emission. The main reason is that the heterogeneity of distribution has been neglected in the past [
18].
With the increasing importance of foreign direct investment (FDI), many researchers believe that only better practice and excellent knowledge can make transnational corporations gain a competitive advantage in foreign land, which is the reason for the productivity spillover and environmental spillover of FDI [
19]. Many studies focused on the relationship between economic growth, environmental pollution and FDI inflows, but few studies discussed the relationship between CO
2 emission and FDI inflows [
20], especially in China’s manufacturing industry. Economic growth depends on more FDI inflows, which in turn may lead to increased CO
2 emissions. In fact, will increasing FDI investment from developing countries have an impact on the environment [
21]? Who should be responsible for greenhouse gas emissions, the producer or consumer? These problems have aroused broad and intense debate all over the world. Furthermore, to attract a large number of foreign investments, developing countries often neglect environmental issues through loose regulatory mechanisms, which leads to the emergence of the pollution avoidance hypothesis. In particular, the relaxation of environmental regulations and standards may promote CO
2 emission caused by foreign direct investment [
22]. However, when advanced technology and management concepts are introduced by foreign capital, or foreign capital flows to the tertiary industry, the overall carbon emissions will decrease, which leads to the emergence of the halo effect hypothesis. Therefore, it is necessary to study the impact of FDI on China’s manufacturing carbon emissions.
Moreover, compared with total carbon emissions and per capita carbon emissions, energy intensity is a better indicator of a country’s energy and economic performance, and an important indicator of China’s international emission reduction commitment [
23]. However, the impact of energy intensity on overall carbon emissions is also controversial. Especially in the context of sustainable development, carbon emission reduction and economic development are significant for China. Therefore, the target of energy intensity reduction means reducing CO
2 emissions without damaging economic growth.
Against this background, many scholars have conducted in-depth investigation and research on the main factors affecting carbon emissions and their relationship from three perspectives: The relationship between carbon emissions and economic development, the relationship between carbon emissions and foreign direct investment and the relationship between carbon emissions and energy intensity.
A large number of studies have explored the relationship between carbon emissions and economic development. The environmental Kuznets curve (EKC) theory and decoupling analysis model are the hot spots of this kind of research [
24]. EKC theory reflects the inverted U-shaped relationship between economic growth and income; that is, at the initial stage of economic development, environmental degradation will be stimulated, and then environmental quality will be improved with economic growth. Due to global warming, the existence of EKC theory has attracted great attention from scholars. For example, Narayan and Narayan [
25] studied the relationship between economic growth and carbon emissions in 43 developing countries and found that EKC curves do not exist in all countries and regions. Dong et al. [
26], based on panel data of carbon emission levels related to natural gas consumption in 30 provinces of China, checked whether the EKC curve exists. Shuai et al. [
27] used EKC theory to judge the inflexion point of an EKC curve of 164 countries and regions and proved that the relationship between economic development and carbon emissions of 123 countries conforms to EKC theory. Decoupling analysis is another method to study the relationship between carbon emissions and economic growth, which originates from physics and is defined by the OECD as the relationship between economic growth and environmental degradation [
28]. Compared with EKC theory, decoupling analysis has the advantages of simple calculation [
29], easier understanding and operation [
30], as well as effective identification of the real-time dynamic relationship between economic development and environmental degradation [
31]. Many studies explored the relationship between carbon emissions and economic growth through decoupling analysis [
32,
33,
34], especially the research on China’s industrial sub-industries [
35,
36,
37]. For example, Hardt et al. [
34] conducted a study on the economic growth and carbon emissions of the UK’s production sector from 1997 to 2013 using decoupling analysis, proving that the UK’s economic growth and carbon emissions have been successfully decoupled.
For the relationship between carbon emission and foreign direct investment, from previous studies, FDI has a two-way impact on carbon emissions; that is, the pollution haven hypothesis and halo effect hypothesis [
18,
38,
39,
40]. For example, Ren et al. [
38] tested the impact of FDI, trade opening, export, import and per capita income on CO
2 emissions through a GMM method based on industrial panel data, and the results proved the existence of the pollution paradise hypothesis. Hao and Liu [
39] used a two-equation model to explore the relationship between FDI, foreign trade and China’s CO
2 emission. The results confirmed the existence of the halo effect hypothesis.
From the perspective of the relationship between carbon emissions and energy efficiency, the International Energy Agency (IEA), the United Nations Intergovernmental Panel on Climate Change (IPCC), some countries and many scholars believe that energy efficiency is an effective strategy to reduce energy consumption and carbon emissions [
41], while other scholars believe that the improvement of energy efficiency will lead to the increase of CO
2 emissions, which is the rebound effect of energy efficiency. The improvement of energy efficiency reduces the effective price of energy use and services, which may increase the demand for energy and its services, leading to an increase in total emissions [
42]. For example, Wang and Wei [
15] evaluated China’s energy and emission efficiency based on the DEA method and measured its energy conservation and emission reduction potential. Yao et al. [
43] discussed carbon emission efficiency, energy efficiency and emission reduction potential from a regional perspective, and found that there was significant group heterogeneity between carbon emission efficiency and energy efficiency in various regions of China. Lin and Zhao [
44], based on the Morishima alternative elasticity (MES) model, through asymmetric energy price, cross logarithmic cost function and other methods, established a research framework to measure the rebound effect of China’s textile industry. The results show that improving energy efficiency is not the only way for China’s textile industry to achieve energy conservation and emission reduction. Zhang et al. [
42], based on the annual data of 1994–2012, studied the energy rebound effect of the industrial sector through an index decomposition model and panel data model. The results show that the energy rebound effect does exist in China’s industrial industry, and the energy rebound effect of industrial industry and manufacturing industry shows an overall downward trend over time.
Although the above research has a great contribution to understanding the main factors affecting carbon dioxide emissions of China’s manufacturing industry, it also has its limitations. First, the mitigation potential of China’s total carbon dioxide emissions from manufacturing is still unclear at this stage [
45]. Moreover, the analysis of the relationship between China’s carbon emissions and economic growth, FDI and energy intensity is still lacking. Given this, it is necessary to clarify the factors that affect the carbon dioxide emissions of China’s manufacturing industry, and make an effective and comprehensive analysis of the driving factors of the carbon emissions of China’s manufacturing industry, so as to make up for the research gap of the relationship between the carbon emissions of China’s manufacturing industry and China’s economic development, FDI and energy intensity, and strive to enrich the research results of China’s low-carbon economy at the industry level.
Compared with the traditional OLS method, the panel quantile method may provide more complete results, and prove the possible heterogeneity at the same time [
46]. Meanwhile, many scholars employ the panel quantile method to study the relationship between carbon emissions and its influencing factors [
9,
18,
46,
47,
48,
49]. Besides, each quantile can fully describe the distribution characteristics of the carbon emissions of China’s manufacturing industry. That is, the high quantile represents the provinces with high carbon emissions from the manufacturing industry, while the low quantile represents the provinces with low carbon emissions from the manufacturing industry. Therefore, based on the improved STIRPAT model and the panel quantile regression model with a two-way fixed effect, this paper uses the panel data of 2000–2013 to study the impact of FDI, economic growth and energy intensity on China’s manufacturing carbon emissions. Each quantile can fully describe the distribution characteristics of carbon emissions of China’s manufacturing industry. That is, the high quantile represents the provinces with high carbon emissions from the manufacturing industry, while the low quantile represents the provinces with low carbon emissions from the manufacturing industry. The results show that the impact of economic growth, foreign direct investment and energy intensity on the carbon emissions of the manufacturing industry is different under different levels of carbon emissions from the manufacturing industry and different regions, with obvious heterogeneity, and economic growth plays a decisive role in the carbon emissions of the manufacturing industry. Among them, economic growth has a positive impact on the carbon emissions of the manufacturing industry, and the higher the impact of the economic growth of high emission provinces on the carbon emissions of the manufacturing industry is, the more significant the impact of foreign direct investment is on the carbon emissions of the manufacturing industry and on regional heterogeneity. The impact is also more significant in high emission provinces and supports the hypothesis that there is a pollution paradise in China’s manufacturing industry, but there is no halo effect hypothesis. In addition, the reduction of energy intensity does not have a positive effect on the reduction of carbon emissions. The higher the impact of energy intensity on the carbon emissions of the manufacturing industry in high emission provinces, the higher the impact of energy intensity on the carbon emissions of the manufacturing industry, indicating that there is an energy rebound effect in China’s manufacturing industry. Finally, we have proven that China’s manufacturing industry has considerable space for emission reduction. The novelties of this paper are fourfold: (1) This paper focuses on the carbon emissions of China’s manufacturing industry. As reducing manufacturing’s carbon emissions plays a crucial role in China’s response to climate change, studying the impact of economic development, foreign direct investment and energy efficiency on China’s manufacturing carbon emissions will help us better understand the importance of industry emission reduction and provide a new perspective for policymakers to reduce overall carbon emissions. (2) We thoroughly study the determinants of CO
2 emission of the Chinese manufacturing industry with distribution heterogeneity. This is mainly because to effectively achieve reducing manufacturing’s emissions will require full consideration of the spatial differences in different regions and the differential effects of various variables in different periods [
47,
50]. China is currently facing economic transformation, with substantial regional differences. In this context, if cross-regional heterogeneity is not considered, the calculation results of energy and carbon emission variables may be biased. Specifically, China has many provinces, and the level of economic development, natural resources, technology and human capital of each province are different. (3) Fixed-effect panel quantile regression model can provide more information and data, which provides a new perspective on how these factors affect the carbon emissions of China’s manufacturing industry, and then helps decision-makers to make more strict environmental protection policies. The regression coefficients of different quantiles are often different; that is, the impact of explanatory variables on carbon emissions of the manufacturing industry in different quantiles is different. The quantile regression model can describe the full conditional distribution of dependent variables. Therefore, it can help us to understand more comprehensively the factors related to China’s manufacturing industry’s carbon emissions, especially in extreme distribution conditions. (4) Our model contains some related control variables, which can solve the problem of variable deviation ignored in previous studies [
51].
4. Discussion
According to the above empirical results, there are some interesting phenomena.
In terms of economic growth, we can find that the impact of economic growth on carbon emissions of the manufacturing industry is not heterogeneous. The GDP coefficients of all quantiles are very significant (at the level of 1%), and their coefficients show a trend of decreasing first and then increasing. These results indicate that economic growth has a positive impact on carbon emissions of the manufacturing industry, which is consistent with the research conclusions of Xu et al. [
50] and Lin and Xu [
9]. The higher the impact of economic growth on the manufacturing industry’s carbon emissions of high emission provinces, the stronger the impact of economic growth on manufacturing’s carbon emissions of provinces in the 95th quantile than other quantile provinces. The reason may be that, at present, China mainly relies on fixed investment to promote economic growth, and the manufacturing industry, as an essential industry, plays a vital role in fixed investment. At the same time, the manufacturing industry is a high emission industry, and there is a significant demand for many products. The manufacturing industry not only promotes the economy but also produces a lot of emissions. Besides, China’s production emissions are greater than its consumption emissions. Under the existing climate policy and international trade rules, carbon leakage occurs [
78]. Although foreign trade is one of the power sources of China’s economic growth, due to the low level of technology in China at this stage, most of the products exported are energy-intensive products. Therefore, while foreign trade causes economic growth, it also leads to an increase in carbon emissions in the manufacturing industry.
On the contrary, for FDI, we can observe that the FDI coefficient is positive at all quantile points (especially the impact is significantly positive in high emission provinces), but it is not significant at the 10% level except for the high quantile (i.e., 95th quantile, which is significant at 10%). These results support the hypothesis that the manufacturing industry is a pollution heaven in China, but not the halo effect hypothesis. The inflow of FDI will lead to an increase in carbon emissions in big emission provinces, but the impact on the low quantile point is not significant, which means that most FDI investment in small emission provinces may be located in the less-polluting manufacturing industry, and the environmental laws and regulations of low emission provinces may be relatively perfect and strict. Alternatively, with the help of its advanced production technology and management experience, it has a positive impact on the emissions of the manufacturing industry.
Similarly, in terms of energy intensity, we can find that the impact of energy intensity on the carbon emissions of the manufacturing industry is not heterogeneous. The
ENE coefficient of all quantile points is very significant; that is, it is significant at the 1% level, and its coefficient shows a monotonous increasing trend. Compared with the low emission provinces, the energy intensity of the high emission provinces has a higher impact on the carbon emissions of their manufacturing industries. In particular, the impact of energy intensity on carbon emissions of the 95th quantile provinces is higher than that of other quantile provinces. Consistent with Du et al. [
79] and Lin and Xu [
9], the reduction of energy intensity does not have a positive effect on the reduction of carbon emissions. The research results show that there is a rebound effect of energy intensity in China’s manufacturing industry, and our research results are consistent with the previous empirical study Zhang et al. [
42], which also confirms that there is indeed an energy rebound effect in China’s manufacturing industry. Through the above studies, we find that for China’s manufacturing industry, the current stage is too single to pursue the macro energy intensity goal, while ignoring the overall control of carbon emissions, which directly affects the energy-saving effect of the manufacturing industry, causing damage to the whole environment.
In addition, regardless whether in regions with high carbon emission or low carbon emission, the impact of economic growth on carbon emission of the manufacturing industry is far higher than that of foreign direct investment and energy intensity, which plays a decisive role, followed by energy intensity, because the ENE coefficient in each quantile is greater than the FDI coefficient. With the gradual improvement of carbon emission levels of the manufacturing industry, the impact of economic growth, foreign direct investment and energy intensity on the carbon emission of the manufacturing industry is gradually increasing. In addition, China’s manufacturing industry has a huge space for emission reduction. Because with the gradual improvement of the carbon emission level of the manufacturing industry, economic growth, foreign direct investment and energy intensity increase promote an increase in carbon emissions in the manufacturing industry (especially the sum of coefficients of the 70th, 80th, 90th and 95th percentiles are greater than 1). Therefore, we should adequately deal with the relationship between economic growth and carbon emissions, pay attention to the changes in total carbon emissions while focusing on foreign direct investment and energy intensity improvement, especially making good use of the larger emission reduction space in high emission areas, and reduce the total emissions of China’s manufacturing industry through a reasonable combination of economic growth, foreign direct investment and energy intensity.
The empirical results of the control variables included in the model also provide reference information. First, we can observe the impact of urbanization rate on carbon emissions. At the low quantiles (20th and 30th), the coefficient of urbanization rate is significant; at other quantiles, it is not significant. All coefficients of URB are positive, which means that higher urbanization rate will lead to higher carbon emissions of the manufacturing industry. In comparison, the impact of urbanization in low emission areas on manufacturing carbon emissions is greater than that in high emission areas, because its coefficient elasticity is greater. The reason may be that the increase in urbanization rate not only causes the increase of urban population, but also leads to the rise in demand for high emission products such as vehicles and real estate, and then causes the increase of carbon emissions in the manufacturing industry. Second, the coefficient of dependence on foreign trade is not significant in all quantiles. All coefficients are positive, but the elasticity is very weak; that is, the dependence on foreign trade will have a positive impact on the carbon emissions of China’s manufacturing industry, which shows that China’s manufacturing industry has a weak carbon leakage phenomenon. Third, the impact of total population on carbon emissions; we can see that the impact of total population on carbon emissions is obvious heterogeneity. At the 5th, 20th, 30th and 40th quantiles, the pop coefficient was positive, but not significant at the 10% level. In the 50th, 60th, 70th, 80th, 90th and 95th quantiles, the POP coefficient was negative, but not significant at the level of 10%. In low emission areas, population size has a positive impact on carbon emissions of manufacturing industry, while in high emission areas, population size hurts carbon emissions of manufacturing industry. This shows that population size is not an important factor affecting carbon emissions of the manufacturing industry in these regions. In addition, in the low quantile (5th and 20th quantiles), the energy structure coefficient is not significant, but the high quantile (30th and 40th quantiles), the 50th, 60th, 70th, and 80th quantiles, are at the level of 5%, whereas at the 90th and 95th quantiles they are significant at the level of 1%; the energy structure coefficient is significant. All these results above proved that it is necessary to further optimize the energy structure for low emission areas.