The Impact of Carbon Trading Policy on Breakthrough Low-Carbon Technological Innovation

Abstract

This paper studies the impact of carbon trading policies on breakthrough low-carbon technological innovation through a difference-in-differences model based on the panel data of 30 provinces in China. Through descriptive statistics, regression analysis, a robustness test and heterogeneity analysis of the data, the results show that: (1) carbon trading policy has a significant effect on overall low-carbon technological innovation and gradual low-carbon technological innovation, and carbon trading policy lag can significantly promote breakthrough low-carbon technological innovation. (2) The timeliness of carbon trading policy in promoting breakthrough low-carbon technological innovation is different in different regions. The effect can quickly be displayed in some provinces, but there is a lag effect in other provinces. (3) Lags in carbon trading policy have promoted a significant increase in renewable energy technology patents and CCS-related technology patents, and contributed more to the increase in renewable energy technology patents.

1. Introduction

Global warming has become one of the most serious problems faced by the world and poses a great threat to our lives and development [1,2]. Therefore, all countries in the world should take effective measures to reduce emissions and mitigate climate change. While China’s economy has stepped into high-quality development, China’s low-carbon development has also entered a new transition period. In 2020, President Xi Jinping established a goal stating that “China’s carbon dioxide emissions will reach the peak value by 2030 and strive to achieve national carbon neutrality by 2060”. This is not only a solemn guarantee made by China to the world, but also an important strategic decision for China to promote its transformation to a low-carbon economy. Low-carbon technological innovation is the key to realizing a low-carbon economy [3]. Therefore, to achieve the goal of “double carbon”, it is necessary to attain low-carbon technological innovation. In the traditional theory of technological innovation, technological innovation is divided into two types: gradual and breakthrough. Breakthrough technological innovation refers to great changes in science and technology, and is the key to enterprises, industries and countries obtaining sustainable competitive advantages [4]. Breakthrough technological innovation is influential technological innovation, and its influence is reflected not only in technological breakthroughs, but also in the role of technological development in the market [5]. The innovation of breakthrough low-carbon technology is of great significance to the development of China’s “double carbon” strategy.

The existing low-carbon technologies can be divided into three types: carbon reduction, carbon-free and carbon removal technologies. So-called “carbon reduction” refers to technology that facilitates energy savings and emission reductions, clean energy utilization and the reduced use of oil and gas resources and coalbed methane mining in the field of high energy consumption and high emissions. Carbon-free technology refers to new sustainable energy technologies, such as nuclear energy, solar energy and other renewable energy technologies. Among the carbon removal technologies, the most representative one is carbon capture and storage (CCS). Renewable energy technology and carbon capture and storage technology are the most valuable low-carbon technologies [6]. Therefore, this paper takes renewable energy technology and carbon capture and storage technology as breakthrough low-carbon technologies. So, whether the carbon trading policy can stimulate China’s breakthrough low-carbon technological innovation is one of the most important standards of measurement.

This paper has the following important theoretical and practical significance for studying the impact of carbon trading policy on breakthrough low-carbon technological innovation:

Theoretical significance: At present, there is little research on breakthrough low-carbon technological innovation at home or abroad, especially in China, and there are few related documents in this field. Most scholars study low-carbon technology as a whole, and have not refined it into specific low-carbon technologies. This paper studies the innovative effect of carbon trading policy on breakthrough low-carbon technology, enriching the existing research. Additionally, this paper uses patent information to measure breakthrough low-carbon technological innovation, which was rare in the past.

Practical significance: By studying the impact of carbon trading policy on breakthrough low-carbon technological innovation, we hope that this paper can guide relevant industries and enterprises to carry out green transformation, and play a positive role in achieving the goal of “peak carbon dioxide emissions in 2030 and carbon neutrality in 2060” and promoting the green development of China’s economy.

2. Literature Review and Theoretical Analysis

2.1. Literature Review

(1) Research on Breakthrough Technological Innovation

Our research on breakthrough low-carbon technological innovation is divided into two parts: domestic research and foreign research. The first is related research abroad. Dismukes put forward that most of the innovations in the rising interval of an S-shaped curve are breakthrough low-carbon technological innovations, and the innovation process is divided into innovation prospects, design blueprints and standard designs [7]. Zhou et al. think that companies with a deep knowledge base are more capable of developing breakthrough innovation through market knowledge acquisition rather than internal knowledge sharing [8].

Foreign scholars’ research on low-carbon technological innovation is mostly based on the theory of technological innovation, and their scientific research results mainly focus on influencing factors and innovation models. According to the existing innovation theory, Hellstrom analyzed Schumpeter’s innovation types and models, and found that most of the current environmental innovations are gradual innovations [9]. Crawford et al. discussed the main influencing factors of technological innovation in energy savings and emission reductions from five angles: governments, enterprise organization, policies, regulations and technical analysis and design, and put forward corresponding improvement measures [10]. Park’s research states that China’s investment strategy for scientific and technological innovation often leads to technological dependence, thus reducing the scale of scientific and technological R&D investment, and thus, affecting the sustainable development of technology [11]. On the other hand, it is domestic. Compared with foreign scholars, Chinese scholars’ research in this field started late, and high enthusiasm has not been sustained. Chen Jin and others believe that high-tech industries are more suitable for breakthrough technological innovation [12]. Chen et al. took China Company as an example to conduct an empirical analysis from the perspective of strategy and organization. The results show that both organic organizational structure and explorer strategy are helpful in improving the level of breakthrough low-carbon technological innovation [13].

(2) Research on Carbon Trading Policy and Low-carbon Technological Innovation

Carbon trading policy is a kind of market-driven environmental regulation. As early as the early 1990s, Porter put forward the famous “Porter Hypothesis”, arguing that environmental regulation policy can effectively encourage enterprises to carry out technological innovation. Scholars’ research on the innovation effect of low-carbon technology in foreign carbon markets mainly focuses on the European market. Green and others have studied the manufacturing industry in Britain and verified the validity of the Porter Hypothesis [14]. Johnstone and Labonne studied OECD countries and proved that strict environmental control can drive enterprises’ innovative R&D activities [15]. Galeotti et al. took the data of 19 countries as samples, and their research results show that an increase of the strictness of environmental regulation policies will prompt enterprises to carry out more technological innovations [16]. Calel and Dechezlepretre’s research found that the influence of the EU’s carbon trading market on emission control enterprises increased year by year, and carbon trading policy did promote the low-carbon technological innovation of enterprises [17]. From the perspective of the “Narrow Porter Hypothesis”, Yuan and Zhang investigated the driving mechanism of flexible environmental regulation policies for sustainable development, and found that flexible environmental regulation had an obvious positive effect on technological innovation [18]. Cainelli et al. believe that environmental regulation policies have played an important role in promoting innovation in recycling, reducing waste and reducing the use of materials [19].

Wang et al. took seven provinces and cities with carbon emission trading pilots as their research object, measured low-carbon technological innovation by determining the number of patent applications and studied the impact of carbon trading on the level of low-carbon technological innovation using the comprehensive control method. Their results showed that the carbon trading pilots promoted the innovation of low-carbon technology as a whole [20]. Tan et al. explored how carbon trading policies affect the upgrading of industrial structure using the intermediary effect method, and found that carbon trading policies have a positive effect on promoting technological innovation through research [21]. Liao et al. studied the relationship and mechanism between carbon trading policy and green economy development, and found that policies can stimulate technological innovation and promote the development of enterprises [22]. Xiong et al. divided the technological innovation of enterprises into internal research and development and external introduction. Through empirical analysis, it was found that the carbon trading market plays an important role in promoting the technological innovation of industrial enterprises in China [23]. Meng et al. believe that a single carbon trading policy will not promote the low-carbon technological innovation of enterprises in the long run [24].

At the same time, the impact of carbon trading policy on low-carbon technological innovation shows heterogeneity. Wei et al. used the double difference model to discuss the impact of carbon trading on green technological innovation in China, and conducted a heterogeneity analysis according to the property rights and industries of enterprises. Their research shows that carbon emission trading has a significant promoting effect on the technological innovation of private and non-high-tech industries in China [25]. Ye et al. used the data of listed companies to empirically test the impact of carbon trading systems on enterprise innovation using the triple difference model. The empirical results show that carbon trading policy only has a significant positive effect on the innovation of large-scale enterprises, but not on small-scale enterprises [26]. Zhou et al. found that carbon trading pilot policies induced low-carbon technological innovation in the western region, but carbon trading policy did not induce low-carbon technological innovation in the eastern and central regions [27].

(3) Research on Influencing Factors of Breakthrough Low-Carbon Technological Innovation

By examining the existing research results, it was found that low-carbon technological innovation is influenced by many factors, such as carbon tax policy, environmental regulation, R&D expenses, R&D personnel investment, enterprise decision-making, etc. Zhao analyzed China’s industrial enterprises and thought that environmental regulation could promote the low-carbon technological innovation of enterprises [28]. By analyzing the power source framework of low-carbon technological innovation, Chen et al. found that China’s low-carbon technological innovation is influenced by the market, technology and policy support [29]. Based on 229 manufacturing enterprises, Li et al. found a relationship among IT technology progress, government IT policies, IT capabilities and process innovation power sources, and found an influence of IT power sources on process innovation [30]. Zhang et al. believe that the training of low-carbon technology R&D personnel also plays an important role in the development of low-carbon technology [31]. Through the empirical analysis of enterprise data, Shi found that China’s low-carbon technological innovation is mainly affected by R&D investment, technological R&D investment, personnel training, entrepreneurial leadership and low-carbon technological innovation strategies [32]. Xu et al. adopted a GMM estimation method, and the results show that China’s carbon tax policy has obviously promoted technological innovation, but its effect shows regional differences [33].

2.2. Theoretical Analysis

Carbon trading policies make carbon emission rights a commodity, allowing them to be traded in the market, and if a company’s carbon emissions exceed the carbon quota provided by the government, then the company must go to the carbon market to buy carbon quotas; if a company’s carbon dioxide emissions are lower than the government-provided quota, it can make more profit by selling carbon allowances. According to the Porter Hypothesis, carbon trading policies can encourage companies to carry out low-carbon technological innovation, offset some or all of their environmental costs, and produce innovation compensation effects. Carbon trading policies will encourage companies to make a trade-off between purchasing carbon allowances and technological innovation, and when the cost of purchasing carbon emission allowances is higher than the cost of low-carbon technological innovation, companies will choose to carry out low-carbon technological innovation. Due to the instability of carbon prices due to market fluctuations, in order to reduce risks, companies will be more inclined to carry out low-carbon technological innovation. At the same time, in order to obtain additional benefits, enterprises will also tend to carry out low-carbon technological innovation to reduce carbon emissions and sell carbon allowances in the carbon market. In summary, carbon trading policies can promote low-carbon technological innovation, and their impact mechanism is shown in Figure 1.

Compared with progressive low-carbon technologies, breakthrough technologies can establish new technologies and production methods that significantly reduce carbon dioxide emissions, thereby greatly reducing environmental costs and leading to additional profits. As a result, the assumptions in this article are as follows:

Hypothesis 1 (H1).

Carbon trading policies can promote breakthrough low-carbon technological innovation in pilot areas.

3. Model Building

The difference-in-differences method (DID) is an effective way to assess policy effects by evaluating the differences between a policy impact treatment group and a control group, thereby avoiding endogenous problems and yielding robust results. This paper took Guangdong and Shenzhen as a whole, and selected six provincial carbon trading pilots in Beijing, Shanghai, Tianjin, Chongqing, Guangdong and Hubei as the processing group, and the remaining 24 provinces as the control group (excluding Tibet, Hong Kong, Macao and Taiwan due to data loss). We constructed a model of the impact of carbon trading policies on China’s breakthrough low-carbon technological innovation:

$$Y_{it}={\beta }_0+{\beta }_{1}Trea{t}_i+{\beta }_{2}Pos{t}_t+{\beta }_{3}Trea{t}_i\times Pos{t}_t+\sum {\beta }_k{X^{\prime }}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

where $Y_{it}$ represents the breakthrough low-carbon technological innovation level of $i$ province in $t$ years. $Trea{t}_i$ refers to the virtual variables of the policy implementation object. If the province and city establish a carbon trading pilot, the value of the processing group is 1, and if there is no carbon trading pilot, the value of the control group is 0. $Pos{t}_t$ represents the time dummy variable, and the value of each province that opens a carbon trading pilot in subsequent years is 1, and the value in other years is 0. The coefficient of the cross-item $Trea{t}_i\times Pos{t}_t$ reflects the net effect of carbon trading policy on breakthrough low-carbon technological innovation, which is the main focus of this paper. ${X^{\prime }}_{it}$ represents individual control variables, ${\mu }_i$ individual fixed effects, $v_t$ time fixed effects, and ${\epsilon }_{it}$ random error terms.

In view of the lagging nature of society’s response to policies, this paper once again explores the role of lagging phase I carbon trading policies in breakthrough low-carbon technological innovation. The lag in carbon trading means that breakthrough low-carbon technological innovation may not immediately be promoted after the implementation of carbon trading policy, and it will take some time for the public to digest the policy; thus, an effect may only occur one or two years, or more, after the implementation of the policy.

4. Empirical Analysis of Carbon Trading Policies on Breakthrough Low-Carbon Technological Innovation

4.1. Variable Selection and Data Sources

4.1.1. Explanatory Variables

The explanatory variables in this paper are carbon trading policy dummies. The interaction terms of carbon trading pilot areas and time $Trea{t}_i\times Pos{t}_t$ are used as explanatory variables to indicate the net effect of breakthrough low-carbon technological innovation in the carbon market.

4.1.2. Interpreted Variables

Many scholars take the number of patent applications, R&D input, etc., as indicators to measure the ability of technological innovation, because an invention patent has a higher technical content. This paper views the number of invention patent applications (BTI) for provincial renewable energy technology and CCS technology as a measure of breakthrough low-carbon technological innovation. The number of invention patent applications was obtained by searching for international patent classification numbers (IPCs) through the patent search system of the State Intellectual Property Office. According to Wang Lanti’s research, the IPCs of renewable energy technologies were identified as F03D, F03G6, F24J2, H02N6, E04D13/18, F03G4, F24J3, F03G7/04, E02B 9/08, F03B13/10–26, F03G7/05, C10L1/02, C10L5/40, C12P7, and F02B43/08 [17]. The World Intellectual Property Organization has identified the following patent applications in CCS-related fields: IPCB63B35, C01B03, C01B31/20, C01B31/22, C02F01, C07C07/10, F01N03/10, F25J03/02, B01J20, B01D53, and B01D11. Therefore, the number of CCS-related invention patent applications was retrieved by combining the IPC number with the keywords “carbon capture, carbon capture, carbon recovery, carbon transport, carbon storage, carbon storage”.

4.1.3. Control Variables

According to the above research on the influencing factors of low-carbon technological innovation, this paper took the level of economic development, pollution control investment, industrial structure, openness to the outside world and R&D personnel investment as the main control variables. The level of economic development (PGDP) was measured using the per capita GDP of each province. Pollution control input (ER) was measured using the pollution investment control amount. Industrial structure (IS) was measured using the ratio of tertiary industry to secondary industry. The degree of opening up was measured using foreign direct investment (FDI). R&D personnel input (RD) was measured using the full-time equivalent of R&D personnel. The data were obtained from the China Statistical Yearbook, the China Energy Statistical Yearbook and the China Science and Technology Statistical Yearbook.

4.2. Empirical Results and Analysis

4.2.1. Variable Descriptive Statistics

This paper views the two pilots in Guangdong and Shenzhen as a whole, the six provincial carbon trading pilots as a whole treatment group, and the remaining provinces as the control group. In order to reduce heteroscedasticity, we chose four factors: per capita GDP, the amount of investment in pollution control, foreign direct investment, and the full-time equivalent of R&D personnel. We added 1 to each factor value, and finally, carried out logarithmic processing. The descriptive statistical results for the main variables of the processing group and the control group are shown in

Table 1. Descriptive statistics of the main variables of the control group.

Variable	Observations	Mean	Standard Deviation	Minimum	Maximum
BTI	144	92.76	142.0	0	786
IS	144	0.914	0.317	0.518	2.427
lnPGDP	144	10.61	0.353	9.706	11.48
lnER	144	5.355	0.853	3.096	6.860
lnFDI	144	5.235	1.630	0.690	7.722
lnRD	144	10.90	1.152	8.335	13.21

and

Table 2. Descriptive statistics of the main variables of the processing group.

Variable	Observations	Mean	Standard Deviation	Minimum	Maximum
BTI	36	190.9	140.8	19	602
IS	36	1.529	1.059	0.105	4.165
lnPGDP	36	11.13	0.397	10.44	11.68
lnER	36	5.493	0.458	4.279	6.502
lnFDI	36	6.536	0.643	5.228	7.424
lnRD	36	11.95	0.710	10.61	13.15

.

4.2.2. Basic Regression Results

The estimated results obtained by running the DID model are shown in

Table 3. Basic regression results.

	(1)	(2)	(3)	(4)
	BTI	GTI	Ti	BTI
$Trea{t}_i\times Pos{t}_t$	8.007	2694.484 **	2702.491 **
	(13.264)	(1189.473)	(1193.779)
$Trea{t}_i\times Pos{t}_{t-1}$				38.774 ***
				(13.530)
lnPGDP	3.407	1477.364	1480.771	39.001
	(19.857)	(2081.570)	(2096.867)	(61.160)
lnER	−13.821	499.284	485.463	−29.848 **
	(10.978)	(677.904)	(683.908)	(12.568)
IS	−1.331	−75.654	−76.984	−20.170
	(27.678)	(1782.298)	(1799.527)	(29.076)
lnFDI	5.176	380.964	386.140	3.799
	(8.893)	(544.671)	(550.793)	(9.484)
lnRD	59.468	5866.466 *	5925.935 *	27.508
	(55.721)	(3290.858)	(3330.643)	(68.349)
_cons	−538.425	−81,093.870 *	−81,632.295 *	−448.251
	(731.441)	(46,080.808)	(46,602.008)	(931.336)
N	180	180	180	150
r2	0.945	0.885	0.888	0.957
ar2	0.929	0.852	0.856	0.942

Note: ***, ** and * indicate significance levels of 1%, 5% and 10%.

. Among them, columns (1) to (3), respectively, indicate the impact of carbon trading policies on breakthrough low-carbon technological innovation, gradual low-carbon technological innovation and low-carbon technological innovation, and column (4), respectively indicates the impact of carbon trading lag on breakthrough low-carbon technological innovation. It can be seen from the table that the net effect of carbon trading policies on progressive low-carbon technological innovation and overall low-carbon technological innovation is positive, indicating that carbon trading policies have significantly improved the level of low-carbon technological innovation and progressive low-carbon technological innovation. The impact of carbon trading policies on breakthrough low-carbon technological innovation in the current period is not significant, and the net effect on the breakthrough low-carbon technological innovation of the lagging phase is significantly positive, indicating that the a carbon trading policy of lagging phase can significantly promote the breakthrough of low-carbon technological innovation. The possible reason is that compared with progressive low-carbon technological innovation, the risk of breakthrough low-carbon technological innovation is higher, the technical difficulty is greater, and enterprises are more inclined toward gradual low-carbon technological innovation in the early stage of policy implementation; moreover, enterprises need a certain cycle to carry out research and development on breakthrough low-carbon technological innovation, so there is a lag effect.

Furthermore, investment in R&D personnel can significantly promote the progression of low-carbon technological innovation and improve the overall level of low-carbon technological innovation. The impact of pollution control investment on the level of breakthrough low-carbon technological innovation is negative, which significantly inhibits breakthrough low-carbon technological innovation. This indicates that pollution control investment will increase environmental costs and have a “squeeze effect”, which is not conducive to breakthrough low-carbon technological innovation.

4.2.3. Robustness Testing

In order to ensure the reliability of the results, this paper conducted a robustness test through parallel trend testing and change variables, and the results, once again, prove that lagging carbon trading policy has a significant positive impact on breakthrough low-carbon technological innovation.

Parallel Trend Testing

Parallel trend testing is a prerequisite for the use of a double differential model, and if there is no systematic difference in the breakthrough low-carbon innovation levels between the processing group and the control group before carbon trading policy is implemented, the same time trend indicates that the double difference method is effective. In this paper, the dynamic effects of carbon trading policies are studied using the event research method. Figure 2 shows the interaction of term coefficients between carbon trading policies before and after implementation. It can be seen from the figure that before the implementation of the policy, the interaction term coefficient is not significantly different from 0, indicating that there is no significant difference in the level of breakthrough low-carbon technological innovation in China; this satisfies the parallel trend hypothesis. In addition, in the year of the implementation of carbon trading policy, the interaction coefficient is not significant, and the following two years are significantly greater than 0, indicating that carbon trading policy lagged behind in promoting breakthrough low-carbon technological innovation, which is in line with the above research results.

Change Variables

Because different data types and numerical distributions may have a greater impact on the robustness of the results, we chose to use the standardized distribution of the explained variables, and then, perform regression again for the robustness test. In this paper, the natural logarithm of the number of patents for breakthrough low-carbon technology (+1) was used as the dependent variable to re-examine the impact of carbon trading policies on breakthrough low-carbon technological innovation. The results are significantly positive, as shown in Column (1) of

Table 4. Robustness of the test results.

	(1)	(2)	(3)	(4)	(5)	(6)
	lnBTI	BTI	BTI	BTI	BTI	BTI
$Trea{t}_i\times Pos{t}_{t-1}$	0.203 *	40.040 ***	39.968 ***	37.164 **	37.229 **	37.230 **
	(0.112)	(13.251)	(13.299)	(14.834)	(15.145)	(14.773)
lnPGDP	−0.443	18.042	38.280	56.523	0.051
	(0.841)	(58.775)	(54.531)	(56.010)	(48.701)
lnER	−0.001	−25.962 **	−26.342 **	−25.101 **
	(0.124)	(11.340)	(11.349)	(10.543)
IS	−0.296	−22.335	−17.340
	(0.240)	(29.279)	(28.461)
lnFDI	−0.107	7.888
	(0.087)	(7.943)
lnRD	−0.178
	(0.537)
_cons	11.655	39.775	−137.914	−359.010	113.863	114.415 ***
	(8.399)	(622.854)	(582.449)	(574.403)	(521.505)	(3.697)
N	150	150	150	150	150	150
r2	0.958	0.957	0.957	0.956	0.955	0.955
ar2	0.943	0.942	0.942	0.943	0.941	0.942

Note: ***, ** and * indicate significance levels of 1%, 5% and 10%, respectively.

($Trea{t}_i\times Pos{t}_{t-1}$), and the results are consistent with the above conclusions. In addition, by gradually reducing the control variables, the coefficients of the results, as shown in columns (2) to (6) of Table 4, ($Trea{t}_i\times Pos{t}_{t-1}$) are still significantly positive, in line with the above conclusions. Therefore, the conclusions reached are stable and reliable.

4.2.4. Heterogeneity Analysis

Regional Heterogeneity

The above content analyzes the impact of carbon trading policies on breakthrough low-carbon technological innovation at the national level. In fact, the development of China’s provinces is uneven, and the implementation effect of policies is often heterogeneous. The article then analyzes the heterogeneity, dividing the country into eastern, central and western regions; according to this classification standard, Beijing, Tianjin, Shanghai and Guangdong belong to the eastern region, Hubei belongs to the central region, and Chongqing belongs to the western region. Through the DID model, the different impacts of China’s carbon trading policies on breakthrough low-carbon technological innovation are studied. As shown in

Table 5. Results of regression by region.

	(1)	(2)	(3)	(4)	(5)	(6)
	Four Provinces	Four Provinces	Hubei	Hubei	Chongqing	Chongqing
$Trea{t}_i\times Pos{t}_t$	−13.657		38.897 *		4.562
	(26.557)		(22.169)		(22.372)
$Trea{t}_i\times Pos{t}_{t-1}$		44.535 **		40.597 *		28.464
		(20.752)		(22.969)		(20.706)
lnPGDP	31.574	−40.830	−26.769	−65.592	113.187	167.766
	(51.279)	(136.322)	(17.297)	(141.875)	(81.838)	(111.160)
lnER	−2.850	−41.163 *	4.154	1.704	8.677	8.479
	(26.556)	(20.685)	(11.019)	(11.053)	(14.681)	(19.740)
IS	−45.994	−54.316	17.940	−13.635	80.566 *	102.022 **
	(71.033)	(67.891)	(25.644)	(22.015)	(46.713)	(49.121)
lnFDI	13.798	29.169	24.031	20.117	−14.389 **	−14.396 **
	(18.040)	(20.622)	(27.272)	(36.729)	(6.584)	(6.318)
lnRD	48.796	68.319	24.167	31.172	40.614	0.527
	(136.674)	(185.767)	(48.969)	(76.929)	(39.026)	(32.411)
_cons	−722.616	−25.738	−111.744	288.563	−1611.416 *	−1800.615
	(1731.578)	(2144.088)	(669.079)	(1286.238)	(915.681)	(1123.660)
N	72	60	54	45	54	45
r2	0.950	0.963	0.856	0.869	0.850	0.884
ar2	0.938	0.957	0.850	0.858	0.849	0.881

Note: ** and * indicate significance levels of 5% and 10%, respectively.

, it was found that the interaction term coefficient of the carbon trading policy in the four eastern provinces and cities is not significant, but the interaction coefficient of the lagging phase is significantly positive, indicating that the carbon trading policy can promote breakthrough low-carbon technological innovation in the four eastern provinces and cities; however, there is a lag effect. The interaction coefficients of the current period and the lagging period in Hubei Province are significantly positive, indicating that the carbon trading policy of Hubei Province has a significant role in promoting breakthrough low-carbon technological innovation. Regardless of whether Chongqing is current or lagging behind, the interaction items are not significant, indicating that the carbon trading policy has not had an impact on Chongqing’s breakthrough low-carbon technological innovation.

Technical Type Heterogeneity

This paper divides breakthrough low-carbon technological innovation into renewable energy technologies and CCS technologies. The impact of carbon trading policies on the two technologies will be discussed separately. According to

Table 6. Regression results by technical type.

	(1)	(2)	(3)	(4)
	CN	CCS	CN	CCS
$Trea{t}_i\times Pos{t}_t$	7.516	0.491
	(13.145)	(0.621)
$Trea{t}_i\times Pos{t}_{t-1}$			37.283 ***	1.491 *
			(13.481)	(0.754)
lnPGDP	3.994	−0.586	43.863	−4.862
	(20.018)	(0.692)	(61.989)	(3.123)
lnER	−13.766	−0.054	−29.594 **	−0.255
	(11.019)	(0.478)	(12.649)	(0.567)
IS	−2.477	1.146	−21.016	0.846
	(28.013)	(1.194)	(29.771)	(1.578)
lnFDI	5.533	−0.357	4.179	−0.381
	(8.877)	(0.308)	(9.462)	(0.286)
lnRD	58.473	0.995	24.705	2.803 *
	(55.769)	(1.267)	(68.249)	(1.551)
_cons	−535.680	−2.745	−472.785	24.534
	(733.785)	(18.856)	(938.252)	(38.038)
N	180	180	150	150
r2	0.944	0.588	0.956	0.595
ar2	0.928	0.469	0.940	0.451

Note: ***, ** and * indicate significance levels of 1%, 5% and 10%, respectively.

, carbon trading policy does not have a significant effect on current renewable energy technologies and CCS techniques, and the interaction term coefficients of the lag phase are significantly positive, which indicates that a carbon trading policy lag phase in renewable energy technologies and CCS technology has a significant role in their promotion, and the promotion of renewable energy technologies is even greater. A possible reason is that the development of renewable energy technology in China is relatively mature, and the risk of technological innovation related CCS technology is small compared to that related to renewable energy. In addition, pollution control investment has significantly inhibited the innovation of renewable energy technology, resulting in a ‘Squeeze effect’. R&D personnel investment in the right CCS technological innovation has a significant role in its promotion.

5. Conclusions and Research Prospects

5.1. Conclusions

Based on the panel data of 30 provinces in China from 2011 to 2016, this paper uses the double differential method to study the impact of carbon trading policies on breakthrough low-carbon technological innovation, and the results show that:

(1).

Carbon trading policies play a significant role in promoting overall low-carbon technological innovation and progressive low-carbon technological innovation, and a carbon trading policy lag phase can significantly promote breakthrough low-carbon technological innovation.

(2).

The effects of pilot policies show heterogeneity in different regions. Hubei Province responds quickly to the policies, while the other four provinces and cities, Beijing, Shanghai, Tianjin and Guangdong, lag behind in promoting breakthrough low-carbon technological innovation. Moreover, the carbon trading policies of Chongqing Municipality have no impact on breakthrough low-carbon technological innovation.

(3).

The carbon trading policy lag in the first phase has a significant effect on promoting both renewable energy technology and CCS technology, and the promotion effect on renewable energy technology is even greater.

5.2. Policy Suggestions

According to our research conclusions, we put forward the following three policy suggestions:

(1).

Breakthrough low-carbon technological innovation has the attributes of public goods. It is difficult to achieve innovation, and the income is uncertain, and therefore, it needs the protection of the government. First of all, we must strengthen institutional guarantees. In breakthrough low-carbon technological innovation, it is necessary to strengthen infrastructure and technical support, improve legal and regulatory mechanisms, and provide institutional guarantees for China’s breakthrough low-carbon technological innovation. Secondly, it is necessary to increase financial support, for example, by providing funds for research and development institutions working on breakthrough low-carbon technologies, and encourage enterprises and institutions to actively carry out breakthrough low-carbon technological innovation. At the same time, the state should formulate sound policies and regulations to ensure that financial subsidies can be invested in relevant technological research and development. They should make full use of carbon trading, a market-driven environmental regulation, to diversify the investment and financing channels of breakthrough low-carbon technological innovation and reduce the risk of innovation.

(2).

Different regions in China have different levels of economic development, industrial structure and resource endowments. When formulating policies, we should also consider the differences between regions, and we should not take a “one size fits all” approach. We should formulate reasonable carbon emission reduction targets according to the different characteristics of each province and city. At the same time, different industries have different characteristics, and different industries have different responses under the same policy. Therefore, carbon trading policies should be implemented according to the characteristics of industries, and enterprises with high emission reduction costs should be given a certain time buffer. As the largest developing country, China’s future carbon emission trading market covers a wide range of emission subjects, so it is difficult to give consideration to fairness and efficiency in different regions and industries. Therefore, we can learn from the experience of the relatively mature EU carbon trading market. We should formulate flexible policies for industries in different regions to maximize regional coordinated development.

(3).

Talent input is the key to improving the level of breakthrough low-carbon technological innovation. To promote breakthrough low-carbon technological innovation, we must attract outstanding scientific and technological personnel at home and abroad and encourage universities, scientific research institutions and enterprises to carry out international cooperation. China is rich in human resources. We should make good use of these talents, strengthen education in these talents, strive to cultivate high-tech talents, and at the same time, increase support for those who exhibit these talents. In addition, we must have a sound scientific management system and fully develop the role of talent investment in breakthrough low-carbon technological innovation.

5.3. Research Prospects

Compared with ordinary low-carbon technological innovation, breakthrough low-carbon technological innovation has high costs, is capital-intensive and involves long research and development cycles, so it has the characteristics of high start-up capital, high risk and high financing difficulty. Traditional financing methods cannot meet the needs of breakthrough low-carbon technological innovation. The emergence of the carbon trading market provides a new financing method for breakthrough low-carbon technological innovation. The financing difficulty of the main body of breakthrough low-carbon technological innovation will be reduced, thus accelerating the development of breakthrough low-carbon technological innovation. However, as China’s carbon trading market has just started, there are still some problems. The use of patent measurement may not fully reflect the situation of breakthrough low-carbon technology. Perhaps future research needs to find a more scientific measure of breakthrough low-carbon technological innovation.