1. Introduction
In today’s world, climate warming has become a global challenge, causing harms to the environment, health, and sustainable development, threatening people’s social development [
1,
2]. To address this global challenge, 175 parties signed the Paris Agreement in April 2016, and China joined it in September 2016. In September 2020, China proposed to “achieve carbon peaking by 2030 and carbon neutrality by 2060” in order to demonstrate its determination to achieve ecologically sustainable development and its commitment to building a community of human destiny. In addition, the European Union is planning that by 2030,
emissions will be 40% lower in comparison to 1990, while the United States has proposed a 50–52% reduction from 2005 levels. Reducing
emissions during economic development has become an urgent issue that needs to be addressed by China and the global community.
Since its reform and opening-up in 1978, China has relied on resource-intensive and polluting forms of economic development to drive rapid economic growth. [
3]. Due to limitations of technical levels, the high energy consumption per unit of GDP leads to high
emissions and increasingly serious environmental pollution [
4]. By 2006, China had become the world’s largest emitter of
. Furthermore, in 2019, China’s
emissions reached 9.83 billion tons, accounting for 29.76% of the global
emissions. Even though the state has repeatedly emphasized paying attention to environmental protection, China is still in the process of industrialization and urbanization characterized by high energy consumption [
5], so the demand for traditional energy sources is still growing. In 2020, coal consumption in China still made up a significant portion of total energy consumption at 56.8%. This highlights the pressing need for significant
emissions reduction in the country [
6]. One key step for China to achieve its carbon reduction target is to reduce energy consumption and improve the energy structure [
7,
8]. To reduce
emissions, we can leverage technological innovation to enhance the efficient utilization of traditional energy sources [
9] and also increase the adoption of clean energy alternatives to traditional energy sources [
10,
11]. Furthermore, research has shown that the development of the tertiary industry can drive down
emissions [
12], while the secondary industry increases
emissions [
13]. Therefore, we can also reduce
emissions by changing the industrial structure and increasing the proportion of tertiary industry, such as by using internet technology to upgrade and transform industries and improve carbon emissions [
14].
emissions mainly originate from the consumption of traditional energy sources such as coal and oil. Much research has pointing out that
emissions have a significant positive correlation with energy input [
15,
16]; that is, increasing energy input leads to an increase in
emissions [
17,
18,
19], especially in key industries, such as road transportation and construction [
20]. However, there have been few studies on the changes in marginal
emissions from energy input. Building on the existing literature, this study aims to examine the scale economic effects of energy input on
emissions. Our empirical analysis shows that there is an “inverted-U” relationship between energy input and
emissions. When energy input reaches a certain level, the scale economies of energy input lead to a decrease in the marginal
emissions resulting from additional energy input. The larger the scale of energy input, the more pronounced this decrease becomes.
At the same time, China’s resource misallocation is also a very important problem. The misallocation of energy inputs significantly affects the energy efficiency of the entire industry [
21,
22]. Chen and Chen [
23] found that among the distortion of factors affecting the efficiency of China’s resource allocation, the distortion of energy gradually exceeded the distortion of capital. This widens the gap between industries and hinders the improvement of economic efficiency [
24]. At the same time, the misallocation of energy can inhibit the effective flow of production factors and upgrading of industrial structure, leading to lower efficiency in terms of carbon emissions [
25]. The degree of influence is restricted by other regional factors, such as the interference of regional corruption [
26]. Yang et al. [
27] found that in a poorly allocated energy market, changes in pure energy and production efficiency could lead to an increase in carbon emissions. This paper further confirms that areas with excessive energy input have higher
emissions compared with areas with insufficient energy input, and the more severe the excess, the more
emissions there are.
This paper demonstrated, through calculation of the relevant coefficient, that areas with excessive energy input tend to have a larger amount of energy input as well. Excess energy input contributes to higher emissions; however, at the same time, the scale economies resulting from increased energy input can reduce marginal emissions. Should areas with excessive energy input (often areas with large amounts of energy input) focus on energy conservation and emission reduction, or should areas without scale economic effects (often areas with small amounts of energy input) prioritize energy conservation and emission reduction? This paper compares the perspectives of energy misallocation and scale economics and uses empirical evidence from Chinese city data to show that in areas with relatively small energy input scales, the negative effect of excess energy input on emissions is greater than the positive effect of scale economies on reducing emissions. Therefore, these areas should prioritize energy conservation and emission reduction. In contrast, in areas with relatively large energy input scales, the positive effect of scale economies on reducing emissions is greater than the negative effect of excess energy input on increasing emissions. Therefore, these areas should prioritize energy conservation and emission reduction.
In summary, the innovation of this paper lies in the fact that it differentiates the impact of energy input and the misallocation of emissions from the perspective of previous scholars and analyzes the impact of emissions from three angles: energy misallocation, energy scale economic effects, and the combined impact of energy input misallocation and scale economics. (1) From the perspective of energy scale economics, the increase in energy input brings about scale economics that reduce the marginal emissions from energy input, and the larger the scale of energy input, the more obvious the decrease in marginal emissions. (2) From the perspective of energy misallocation, areas with excessive energy input have higher emissions than areas with insufficient energy input, and the more severe the excess of energy input, the greater the emissions. (3) Considering both scale economics and energy misallocation, for areas with relatively small energy input scales, the negative impact of excess energy input on emissions is greater, so these areas should focus on reducing emissions from excess energy input. For areas with relatively large energy input scales, the positive impact of scale economic effects on reducing emissions is greater, so these areas should focus on reducing emissions from insufficient energy input.
The rest of the paper is organized as follows:
Section 2 measures the degree of energy misallocation in prefecture-level cities and China as a whole;
Section 3 is an empirical model design to examine the effects of energy input scale and energy allocation efficiency on
emissions;
Section 4 analyzes the empirical results and tests robustness;
Section 5 selects emission-saving cities based on the above results; and
Section 6 shows our conclusions and the implications of our study.
5. The Options of Energy-Saving Cities
Based on the above results, this paper divides Chinese cities into four groups. The cities in Group (1) are those with insufficient input and energy input greater than 0.5625. The cities in Group (2) are those with insufficient input and energy input less than 0.5625. The cities in Group (3) are those with excessive input and energy input greater than 0.5625. The cities in Group (4) are those with excessive input and energy input less than 0.5625. The results are shown in
Table 12.
Based on
Figure 3, it can be seen that for areas with energy input less than 0.5625, the
emissions of Group (2) are significantly better than those of Group (4). That is, in areas with less energy input, the under-input cities have less
emissions, and the over-input cities have more
emissions. However, for areas with energy input more than 0.5625, the
emissions of Group (3) are significantly better than those of Group (1). That is, in areas with more energy input, the over-input cities have less
emissions, and the under-input cities have more
emissions.
Table 12 shows that most of the cities are concentrated in Group (2), and almost all of these cities belong to Tier 3, 4, and 5 cities (Some are Tier 2 cities), which is consistent with the regional differences in energy consumption reported by Liu [
40]. Referring to the China City Statistical Yearbook and China Energy Statistical Yearbook, these cities have a more backward level of economic development, technology, and infrastructure, and their own energy output is not high. These conditions will increase a lot of costs to the energy inputs of firms, such as the cost of purchasing energy from other places and the cost of transportation, while the lack of technology and related talents will lead to low production efficiency, resulting in less output and revenue. In this case, the location of these areas does not attract a large number of energy-dependent firms, and the firms surviving in these areas may be some local firms with relatively high technical levels. At the same time, because there are not many competitors, firms can have additional funds to control
emissions, resulting in Group (2) emitting less
than Group (4) under the same energy input.
For Group (4), referring to the China Energy Statistical Yearbook, it can be seen that the energy output of these cities is extremely low (except for Datong, Luzhou, Mianyang, Guangyuan, Leshan, and Dazhou). Therefore, the energy used in these areas needs to be purchased from other places, resulting in a small scale of energy input in Group (4). However, most of the cities in Group (4) are concentrated around Shanxi Province. Shanxi Province is a major coal province and energy base in China, which drives the energy input of the surrounding areas. So, the surrounding areas of Shanxi Province have a larger energy input compared with the areas in Group (2). Similarly, these cities are poor in economic development, infrastructure, and technical personnel, creating low demand for energy input and allowing these areas to reach the optimal level. However, the energy input driven by Shanxi Province exceeds the optimal energy input level, resulting in the phenomenon of excessive energy input in these areas. In addition, according to the China City Statistical Yearbook, most of the cities in Group (4) have a large number of industrial firms, which exceeds the optimal number of firms. Due to the limitations in talent and technology, the competition between firms is more about price, which leads to insufficient R&D investment and no extra funds for -emission-reduction research. Firms are more likely to choose to sacrifice the environment for maximum productivity in response to fierce competition. This competition makes Group (4) have higher emissions than Group (2) for the same energy input.
The cities in Group (1) are concentrated in China’s four major industrial zones (Liao-Zhong-Nan Industrial Base, Jing-Jin-Tang Industrial Base, Hu-Ning-Hang Industrial Base, and Pearl River Delta Industrial Base), which have large amounts of energy inputs. Due to the economic agglomeration effect of industrial zones, these areas are good at industrial development conditions and total factor productivity, and have high optimal energy input value. However, compared with Group (3), the cities in Group (1) are relatively less attractive to capital, firms, and technical talent due to the differences in geographic location, infrastructure, and economic levels. Referring to the China City Statistical Yearbook, the number of industrial cities and the amount of energy input in those cities is small, and most of the energy input is concentrated around 0.5625. The advantages brought by the industrial zone are not fully utilized, and the technical level is not improved, resulting in higher emissions compared with Group (3) at the same energy input.
The cities in Group (3) are not only concentrated in China’s four major industrial zones with superior industrial production conditions but are also mostly provincial capitals and cities with a high level of economic development, making them very attractive to capital, firms, and technical talents. This makes these areas have high total-factor productivity and optimal energy input value; however, the actual energy input is too large and still exceeds the optimal energy input value. There is a problem of excessive energy input. Referring to the China City Statistical Yearbook and China Energy Statistical Yearbook, we can find that the number of industrial firms in this group of areas is large and the energy input is significantly higher than 0.5625. At the same time, the industrial production technology and energy and environmental efficiency in such areas are higher. Therefore, the competition between firms is no longer a simple “bottom-up competition” but more likely to be a competition between technology and productivity. In this environment, the technological level of firms in the area is increasing, and the emissions from energy output are decreasing. The emissions generated by the same energy input are smaller than those in Group (1).
Based on the above analysis, it can be concluded that for areas with small energy inputs scale, the impact of excessive energy input on the increase in emissions is greater than the impact of the scale effect of energy input on the decrease in emissions, so the over-input areas will produce greater emissions under the same energy input. However, for areas with large energy inputs, the impact of the scale effect of energy input on the decrease in emissions is greater than the impact of excessive energy input on the increase in emissions, so the over-input areas will produce less emissions under the same energy input. The main reason for this difference is that the scale effect of energy input in areas with small energy input is not significant. Those areas do not have the infrastructure and technology advantages brought by economies of scale. However, it may lead to the “bottom-up competition” of firms to aggravate emissions. On the contrary, the scale effect of energy input in areas with excessive input scale is significant. Those areas have perfect infrastructure, excellent talents, and advanced technology brought by economies of scale, which can guide firms to reduce emissions while developing healthy competition. Then, for Group (2) and (4) where energy input is less than 0.5625, whether from the perspective of resource allocation efficiency or the perspective of reducing emissions, the energy input of Group (4) should be reduced, and the energy input of Group (2) should be increased, so as to improve the efficiency of resource allocation and reduce emissions, achieving a win–win situation of efficiency and green development. However, for Groups (1) and (3), which have energy inputs greater than 0.5625, from the perspective of reducing emissions, the energy input of Group (1) should be reduced, while the energy input of Group (3) should be increased.
6. Conclusions and Implications
To summarize, for cities with high energy inputs, those with low energy inputs require more energy conservation and emission reduction, as the scale effect of energy input on emissions is more significant than the effect of energy input misallocation. On the other hand, for cities with low energy inputs, those with high energy inputs require more energy-saving and emission-reducing measures, as the scale effect of energy input on emissions is less significant than the impact of energy input misallocation.
To address this issue, we can implement stricter environmental regulations, which have a strong “corrective effect” on energy misallocation and can help eliminate inefficient firms and promote the growth of more efficient ones through strict environmental compliance. However, the research found that the mitigating effect of environmental regulation on energy misallocation is more pronounced in areas with advanced emission-reduction technology compared with areas with limited emission-reduction technology. In other words, for areas with high energy investment and excessive energy input, strengthening environmental regulation can have a significant impact in promoting businesses to adopt green technology innovations and reduce emissions. However, in areas where energy input is small but still excessive, environmental regulation may have less of an impact on energy misallocation. In such areas, it may be more effective to eliminate industries with resource-intensive production processes, provide incentives for green innovation, increase environmental awareness among local consumers, and encourage them to engage in eco-friendly consumption behaviors to support the implementation of environmental regulations and reduce emissions.
Improving the emissions trading system can also help reduce emissions. Our findings indicate that the marginal emissions from energy inputs are relatively small in areas with high energy inputs and excessive energy inputs, so the impact of reducing emissions by reducing energy inputs may not be significant. However, this is more important for areas where energy input is low but still excessive. The emissions trading system can effectively address this issue. On the one hand, Tier 1 cities and municipalities directly under the central government have better conditions for technological innovation, industrial adjustment, and resource allocation. The emission trading system can significantly encourage technological innovation among businesses in these cities. However, due to the limitations of human resources and funds, the emission trading mechanism does not promote the technological level of relatively backward areas; however, it can also encourage them to trade emission indicators to developed areas, which have economies of scale, thereby reducing energy input and emissions. In conclusion, establishing a robust carbon trading system and increasing the transparency and availability of carbon market information can be effective in promoting the use of low-carbon technologies and management practices, ultimately leading to a reduction in emissions.
Additionally, the government can implement policy measures to incentivize the adoption of low-carbon technologies and the transition to low-carbon practices in key areas mentioned above by offering financial subsidies or tax incentives. Encouraging the use of renewable energy and the development of related industries is a viable option as well. At the same time, effectively attracting foreign investment and integrating business development and innovation is also an important measure. In China, areas with more open international trade generally have larger energy inputs and more severe excess input. Foreign direct investment in China is primarily concentrated in traditional manufacturing industries, which often have low technology levels and prioritize economic effects over environmental impacts when foreign investment is attracted. Therefore, China should regulate and conduct strict reviews of foreign investment, including environmental indicators in the review criteria. Additionally, China should strategically plan for foreign investment and encourage the upgrading of environmental protection technologies through foreign investment. This can aid high-pollution, high-energy-consumption enterprises in transitioning to more sustainable practices and ultimately reduce emissions.