Green technology innovation is essential to promoting not only the construction of ecological civilization but also the fundamental means of achieving sustainable development. Taking research and development (R&D) investment, CO2 emissions, and other related factors into account, this study constructed an extended logarithmic mean Divisia index (LMDI) decomposition model for the change in the number of green technology patent applications to quantify the contribution of each driving factor based on green patent applications data in China from 2000 to 2017. The results indicated that economic scale, R&D efficiency, R&D reaction, and green patent share play positive roles in promoting green patent applications in China, among which R&D efficiency is the most significant contributor. By contrast, carbon intensity plays a dampening role. The conclusions of this study could provide a theoretical foundation for China to formulate targeted green technology innovation management policies, promotion measures, and related R&D strategies.
Climate change and CO2 emissions mitigation have drawn global attention in recent years. Human beings have made considerable efforts to mitigate the impact of climate change and environmental degradation [
Growth in energy markets slowed in line with weaker economic growth in 2019. China was the exception, with its energy consumption accelerating in 2019. China was by far the biggest driver of energy, accounting for more than three-quarters of net global growth in 2019 [
Figure
Trends of GDP growth rate and CO2 emissions in China in 2000–2019. Data source: China Statistical Yearbook (2020) and BP Statistical Review of World Energy (2020).
In fact, it was a weak sustainable development model until the 12th Five-Year-Plan period in China was released, which, for the first time, focused on the concept of strong sustainability. Green growth was popular afterwards including the advocacy of green innovation [
The purpose of this paper is to explore the determinants of green patent applications in China from 2000 to 2017 through the logarithmic mean Divisia index (LMDI) analysis framework. Our study not only examines the influencing factors of green patent applications in China but also evaluates the effects of CO2 emissions and the efficiency of R&D expenditure.
The remainder of this paper is arranged as follows. Section
Braun first proposed the concept of green technology [
The measurement of green technology innovation includes three primary indicators: the amount of R&D, number of green patents, and green total-factor productivity; these represent the input, output, and performance of green technology innovation, respectively. Since Lanjouw and Mody [
From the perspective of research methods, decomposition analysis has been widely utilized to quantify the variations in energy consumption and CO2 emissions. There are primarily two categories of the decomposition approach: structure decomposition analysis (SDA) and index decomposition analysis (IDA). SDA is widely used in revealing the role of structure change [
Although countless efforts have been made to explore the factors influencing green technology innovation, this study highlights a few gaps in the literature. First, the majority of previous studies have focused on environmental regulations; however, little attention has been paid to the CO2 emissions and R&D activities factors. Second, most of the previous literature has centered on the presentation of econometric results but has rarely applied the patent decomposition method. Most importantly, much of the previous research has investigated green technologies in developed countries but rarely discussed them in developing countries.
To fill in the previously mentioned gaps, this paper identifies the central drivers behind China’s green technology innovation and focuses on CO2 emissions and R&D activities based on an extended LMDI decomposition analysis. Moreover, this paper evaluates China’s underlying policies and implemented measures and discusses the role of R&D activities in green technology innovation.
Our study’s contributions are as follows. First, we extend the LMDI by introducing CO2 emissions and R&D activities into green technology patent decomposition study. Second, we discuss the decomposition results through two ways: time-series decomposition analysis and period-wise decomposition analysis, which may provide a lot of new information for policy makers. In this regard, this study can provide insights for formulating different policies.
The LMDI method can be used for either multiplicative or additive decompositions [
Through expanding the Kaya identity [
As opposed to previous studies, this paper takes carbon emission intensity, R&D response, and R&D efficiency as essential factors of the extended LMDI decomposition framework. There are no definite study conclusions about the time lag between environmental changes and R&D investment changes. To achieve the goals of the
In related studies, the indicators for measuring green technology innovation output included chiefly new product sales revenue and green patent quantity; however, new product sales revenue is more suitable for the enterprise level, and the impact of factors such as marketing strategies on sales revenue cannot be excluded. Therefore, this study uses the number of green patent applications as a measurement of green technology innovation. Patents are also closely related to R&D activities. Some studies have shown that a time lag of approximately one to two years exists between R&D investment and patent applications [
We applied a patent decomposition analysis approach to identify the driving factors associated with green patent applications. Based on the existing literature [
Subsequently, (
According to the LMDI additive decomposition, the changes in the number of green patent applications from the base year
Equation (
The LMDI decomposition method includes an additive version and a multiplicative version that produces similar results. In this paper, we applied the additive decomposition method for decomposing changes in the number of green patent applications. The calculation process is shown as follows:
Following the decomposition approach, we also apply contribution rate analysis to further study the changing impact of drivers of the green patent over time. The contribution rate of each driving force is calculated by the following equations:
To apply the previously mentioned additive form of the LMDI method, we collected the necessary data related to GDP, CO2 emissions, R&D expenditure, and patent data. Patent data usually include two types: patent applications and patent grants. Patent application data can reflect inventors’ R&D activities and R&D strategies, and patent grant data represent the number of qualifying patent applications that are primarily used to examine the diffusion of technologies. Because patent grants are often granted long after the submission of the patent application, patent grant data can easily cause information distortion and are subject to human factors, such as differences between patent agencies in various countries, which causes uncertainty. Therefore, we use patent application data as the measurement of green technology innovation. Green patent application data come from the China National Intellectual Property Administration (CNIPA) patent search and analysis system. According to the international patent classification number corresponding to the World Intellectual Property Organization’s (WIPO) list of green technologies, China’s green patent invention and green utility model patents are counted to obtain the data on green patent applications from 2000 to 2017. China’s R&D investment data and the total number of patent applications are taken from the China Statistical Yearbook on Science and Technology (2001–2018). To correspond to the green patent statistics, total patent data include the number of patent applications for green patent invention and green utility model patents. The R&D investment data include R&D expenditures of R&D institutions, universities, and large- and medium-sized industrial enterprises. The CO2 emission data were compiled using the BP Statistical Review of World Energy (2001–2018), and the GDP data come from the China Statistical Yearbook (2001–2018). R&D expenditures and GDP were deflated based on the 2000 price to eliminate the influence of price fluctuations and make the yearly indicators comparable.
As previously mentioned, to reflect the time lag, however, the GDP and CO2 emissions values used were from 1999 to 2016. The data used, including the R&D expenditure and related patent application data, referred to the interval from 2000 to 2017.
Figure
Trends of green technology patent application in China in 2000–2017. Data source: China and Global Patent Examination Inquiry from the CNIPA.
In general, there are two types of decomposition analysis mode: the period-wise manner and the time-series manner. The period-wise manner compares indices between the first and last years of a given period without considering the details during different periods. Time-series analysis can compare the indices on an annual basis. To better analyze the influencing factors of green technology innovations, we employed the period-wise and time-series forms to study the driving factors that can change the number of green patent applications in China.
We analyzed the five determinants of green patent applications from 2000 to 2017. Following the five-year plan in China, we subdivided the study period into four intervals of five years and one period of one year, 2016-2017. The determinants of green patent applications and their relative contribution value and contribution rate are presented in Table
Period-wise decomposition results of green patent applications in China in 2000–2017.
Effect | 5-year intervals | Full period | |||
---|---|---|---|---|---|
2000–2005 | 2006–2010 | 2011–2015 | 2016-2017 | 2000–2017 | |
Δ | 3320.52 | 11955.35 | 31372.04 | 14054.50 | 91669.06 |
Δ | 320.23 | −5516.17 | −18759.84 | −15373.43 | −30566.62 |
Δ | −121.15 | 4998.94 | 16346.27 | 15779.27 | 29674.96 |
Δ | 3300.80 | 13544.22 | 45270.81 | 5080.47 | 115686.95 |
Δ | 205.59 | 6419.66 | 14046.73 | 31136.19 | 32008.65 |
Δ | 7026 | 31402 | 88276 | 50677 | 238473 |
The contribution rate of each factor in the changes in the number of green patent applications in China during different periods.
As shown in Table
Differences exist in various intervals. We applied the contribution rate to investigate each factor’s contribution degree. As shown in Figure
Compared with the period-wise decomposition, using the time-series mode to decompose the change factors of green technology patent applications year by year can provide more detailed information. Table
Year-by-year decomposition analysis results of green patent applications in China in 2000–2017.
Year | Economic activity | CO2 emission intensity | R&D reaction | R&D efficiency | Green patent share | Total | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
Δ | ΔPint | Δ | Δ | Δ | ΔGreen | ||||||
2000-2001 | 391.4 | 85.1 | −293.4 | −63.8 | 284.5 | 61.9 | 351.4 | 76.4 | −274.0 | −59.6 | 460 |
2001-2002 | 458.6 | 30.5 | −187.4 | −12.5 | 229.8 | 15.3 | 582.5 | 38.7 | 420.6 | 28.0 | 1504 |
2002-2003 | 630.2 | 43.5 | −0.7 | −0.1 | 60.1 | 4.2 | 909.4 | 62.8 | −151.1 | −10.4 | 1448 |
2003-2004 | 797.1 | 103.4 | 581.2 | 75.4 | −573.6 | −74.4 | −180.7 | −23.4 | 146.9 | 19.1 | 771 |
2004-2005 | 972.8 | 34.2 | 674.9 | 23.7 | −556.3 | −19.6 | 1604.7 | 56.4 | 146.9 | 5.2 | 2843 |
2005-2006 | 1414.1 | 45.3 | 335.0 | 10.7 | −183.1 | −5.9 | 1029.3 | 32.9 | 529.7 | 17.0 | 3125 |
2006-2007 | 1982.0 | 51.4 | −480.2 | −12.5 | 699.3 | 18.1 | 539.0 | 14.0 | 1117.8 | 29.0 | 3858 |
2007-2008 | 2876.2 | 44.4 | −1124.5 | −17.3 | 253.7 | 3.9 | 2942.4 | 45.4 | 1536.2 | 23.7 | 6484 |
2008-2009 | 2748.5 | 28.1 | −2187.2 | −22.4 | 2105.9 | 21.5 | 4785.9 | 48.9 | 2329.9 | 23.8 | 9783 |
2009-2010 | 3613.7 | 32.0 | −1852.2 | −16.4 | 2291.0 | 20.3 | 6555.8 | 58.1 | 668.7 | 5.9 | 11277 |
2010-2011 | 5438.6 | 32.9 | −2532.3 | −15.3 | 1992.7 | 12.0 | 14176.3 | 85.6 | −2517.3 | −15.2 | 16558 |
2011-2012 | 6541.9 | 33.2 | −832.2 | −4.2 | −228.8 | −1.2 | 11941.1 | 60.6 | 2274.1 | 11.6 | 19696 |
2012-2013 | 6801.9 | 46.7 | −4935.6 | −33.9 | 4852.7 | 33.4 | 13410.4 | 92.2 | −5578.3 | −38.3 | 14551 |
2013-2014 | 7835.2 | 51.7 | −5016.6 | −33.1 | 4531.6 | 29.9 | −2728.9 | −18.0 | 10523.8 | 69.5 | 15145 |
2014-2015 | 9198.6 | 23.7 | −9396.5 | −24.2 | 8908.9 | 22.9 | 21062.4 | 54.2 | 9110.7 | 23.4 | 38884 |
2015-2016 | 11398.3 | 27.5 | −12310.2 | −29.7 | 11990.3 | 29.0 | 31135.7 | 75.2 | −805.1 | −1.9 | 41409 |
2016-2017 | 14054.5 | 27.7 | −15373.4 | −30.3 | 15779.3 | 31.1 | 5080.5 | 10.0 | 31136.2 | 61.4 | 50677 |
The results show that all influencing factors contributed differently to the change in green patent applications in various years and grew involved in largely disparate influence with respect to time series. Based on the results in Table
The trend of contribution rate of economic scale and carbon emissions intensity factors to China’s green patent applications increase from 2000 to 2017.
Notably, R&D effects (i.e., R&D reaction effect and R&D efficiency effect) played a dominant role in the increased number of green patent applications. Figure
The trend of contribution rate of R&D activities factors to China’s green technology innovation output from 2000 to 2017.
This study has examined the main influencing factors contributing to green patent applications in China from 2000 to 2017. We developed the extended LMDI method to decompose and analyze the contributions of the main influencing factors, which include economic scale, carbon emission intensity, R&D reaction, R&D efficiency, and green patent share factors. The main conclusions are as follows.
First, taking the number of green patent applications as an indicator for green innovation output and based on the statistical data from 2000 to 2017, the number of green patent applications in China showed an upward trend—especially after 2010, maintaining rapid growth.
Second, the results of the LMDI additive decomposition based on the period-wise manner indicate that all of the factors, except the carbon emission intensity effect, are positive factors on the growth of China’s green patent applications. Of these positive factors, the R&D efficiency effect and economic scale effect contribute the most to the growth of China’s green technology patents. Among the positive and negative effects, R&D efficiency and carbon emission intensity effect are the most prominent, which indirectly reflects modern climate change and environmental deterioration; that is, improving R&D efficiency and reducing carbon emissions are important measures to promote China’s green technology innovation.
Third, the year-by-year decomposition analysis results show that all influencing factors contributed differently to the change in green patent applications in various years and grew involved in largely disparate influence with respect to time series. In most years, carbon emission intensity has a negative impact on green patent applications. Notably, the contribution rate of R&D reaction effect and R&D efficiency effect shows approximately the same trend, and the two effects played a dominant role in the increased number of green patent application.
From the previously mentioned findings, we can better understand the trend of China’s green technology innovation and clarify the importance of carbon emissions, R&D efficiency, economic development, and other factors for green technology innovation. We can then formulate targeted management policies, governance measures, and R&D strategies. Based on these conclusions, we propose the following policy recommendations.
First, China must accelerate the construction of a market-oriented green technology innovation system and promote the development of green technology innovation activities. With increasingly prominent domestic environmental problems and the ongoing pressure of international climate negotiations (e.g.,
Second, China should increase investment in green technology R&D to improve R&D efficiency. This study demonstrates that R&D factors, especially R&D efficiency, are important factors affecting the output of green technology innovation. Although China’s R&D investment has been increasing in recent years due to its large-scale market, the investment is too scattered, and most enterprises’ R&D expenditure is relatively low. Therefore, the government should increase R&D investment related to the environment and designate relevant incentive mechanisms so that enterprises can actively become the source of R&D funds and the main body of implementation.
Third, China needs to improve environmental regulatory policies and accelerate innovation in environmental regulatory tools. As opposed to previous studies, which mainly considered the impact of environmental regulation on green technology innovation, this study included carbon emissions in the decomposition framework of innovation output in the green technology department. The study found the intensity of carbon emissions inhibited the output of green technology innovation. Therefore, China still must strengthen environmental regulation, reduce carbon emission intensity, and balance environmental protection and economic development. Enterprises should be encouraged to carry out green technology innovation by further improving environmental regulation policies, innovating environmental regulation tools, and using diversified environmental regulation methods. At the same time, China should pay attention to strengthening the cooperation between environmental regulation policy tools and relevant policies (such as finance and innovation policies).
Finally, China must strengthen the protection of intellectual property rights. The primary objectives are to take the strictest measures to protect intellectual property rights, reduce the risk of infringement on enterprises’ R&D investment, maintain and protect the achievements and benefits of enterprise green technology innovation, and fully mobilize enterprise enthusiasm for green technology innovation.
We believe the novel decomposition analysis applied in this study is useful for understanding changes in green patent application activities in China. Additionally, a comparison of carbon intensity effect, R&D reaction effect, and R&D intensity effect are helpful for understanding the influencing factors of green technology innovation. A limitation of this study is the difficulty of clarifying the effects of policies and subsidies on green technology innovation activities. Therefore, further research is needed to develop a research framework to consider the previously mentioned factors. In addition, the green patent includes numerous types of green technologies; thus, further studies are needed to consider various green patent classifications.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that there are no conflicts of interest regarding the publication of this paper.