key: cord-0887368-uliqro2a
authors: Li, Rongrong; Jiang, Rui
title: Does increase in R & D investment reduce environmental pressures? Empirical research on the global top six carbon emitters
date: 2020-06-08
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2020.140053
sha: c1566d91a2fadf409094c3ac25b625f5b7649ccb
doc_id: 887368
cord_uid: uliqro2a

This work is aimed to investigate the effect of research and development (R&D) on reduce in environmental pressures through an empirical analysis of the top six global carbon emitters (the C6: China, USA, India, Russia, Japan, and Germany). This work is valuable toward carbon reduction within C6 countries and the world (C6 emit roughly 60% of the global carbon emissions). Moreover, it is also meaningful for exploring the decoupling of economic development from carbon emissions in other areas (both developing and developed countries). The main findings displayed that the decoupling status in developed countries (i.e., USA, Japan, and Germany) were better and more stable than in developing countries (i.e., China, India, and Russia). Germany performed best among the developed countries, and China performed most stable among the developing countries. The effect of the per capita R&D expenditure was main resistance to decoupling carbon emissions from economic development in C6 countries. However, the energy intensity effect and R&D efficiency effect related to technological progress were the main driving forces for the decoupling process. Consequently, this study proposes that the improvement of technological progress should be prioritized.

In recent years, the greenhouse effect mainly caused by greenhouse gas emissions has continued to accumulate (Wang and Su, 2020) , further leading to rising temperatures and global warming, which has caused widespread concern in the international community (Mora et al., 2018; Steininger et al., 2018) . The 2015 Paris Agreement clearly stated a globally pursued goal, i.e., all parties will strengthen their capacity to address climate change challenges, command the increase of global average temperature to not more than 2 °C compared with pre-industrial levels, and try to control within 1.5 °C (King et al., 2017; Rogelj et al., 2016) . However, in the "Environment Emissions Gap Report 2019", the United Nations Environment Programme stated that by 2018, the global carbon emissions exceeded 55 billion tons, which indicates an average annual growth of 1.5% over the past decade. The program further explained that to accomplish the temperature command target of 1.5 °C, global carbon emissions will have to be reduced by 7.6% per year over the next 10 years, and the emission reduction targets of all countries must be increased by more than five times of the current level (Olhoff and Christensen, 2018) . The urgency of the emission reduction process has been acknowledged by various countries around the world.

However, in-depth explorations of the causes of carbon emissions indicated that the J o u r n a l P r e -p r o o f 5 the extended factor decomposition method and the Tapio decoupling model to find out the mechanism by which the driving factors act on the decoupling states.

 In view of the important role of research and development in reducing carbon emissions and further achieving decoupling, this paper incorporates R&D efficiency effect and per capita R&D expenditure effect into the drivers of carbon emissions, and quantify the extent of their impact on decoupling of carbon emissions from economy by extending the Kaya formula.

The organization of the rest of this paper is below: Section 2 reviews the literature related to decoupling theory. Section 3 presents data source and model approach which includes decoupling index and decomposition model. Section 4 introduces the main decoupling and decomposition results. Conclusions and policy recommendations are presented in Section 5.

Decoupling can expose the relationship between global or individual environmental pollution and economic development, can be used to indicate asynchronous changes in environmental pollution and economic growth. Such decoupling can achieve low-carbon development at a quantitative level, and was first utilized in the environmental field by Zhang in 2000 (Zhang, 2000 . In 2002, the OECD first proposed the concept of decoupling indicators and began a quantitative analysis of decoupling (OECD, 2002) . Immediately thereafter, Juknys expanded the decoupling J o u r n a l P r e -p r o o f 6 indicators in the OECD decoupling model (Juknys, 2003) , and Vehmas et al. established a comprehensive decoupling framework in 2003 (Vehmas et al., 2003a; Vehmas et al., 2003b) . Based on this development of decoupling research, Tapio first proposed decoupling elasticity and expanded the decoupling index to 8 categories in 2005 (Tapio, 2005) . In 2007, Diakoulaki and Mandaraka established a decoupling effort model, which is based on the Tapio decoupling model and refined Laspeyres model (Diakoulaki and Mandaraka, 2007) . The continuous improvement of decoupling by many scholars has promoted its application at an unprecedented level.

Most decoupling studies focused on a single region, including the national level (García-Gusano et al., 2018; Martinico-Perez et al., 2018; Wang et al., 2019a) , the provincial level (Hu et al., 2019; Siping et al., 2019; Zhao and Li, 2018) , and the level of a specific city (Rao and Yang, 2018; Su et al., 2019; Wang and Zhou, 2019 The existing research shows that whether a single region or multiple regions are investigated, the Tapio decoupling model is the best choice for many scholars (Karakaya et al., 2019; Wenbo and Yan, 2018) . When the connection between carbon emissions and economy is investigated, scholars generally focus on a single region, and comparative analysis of multiple regions are rare. Moreover, when analyzing multiple regions, many scholars conduct comparative analysis mainly based on location or economic relationships, while few scholars focus on a country's carbon emissions rank in the world. However, the top six countries with regard to carbon emissions are the major contributors to the global carbon emissions; therefore, the realization of low-carbon economic development in the C6 countries is critical toward achieving global carbon emissions targets. in the field of input-output technology, but the application of this method needs to be built on the input-output table . Therefore, the lack of input-output table hinders the further promotion of the SDA method (Wang and Jiang, 2020 ).

On the contrary, the IDA method is widely used, and there are many derivative methods (Hoekstra and van den Bergh, 2003) . Among the derivative methods of IDA method, the most common methods can be divided into Laspeyres decomposition method and the Divisia decomposition method (Lyu et al., 2016) . The Laspeyres decomposition method can explore the percentage change of various factors during the study period. Because this method is easy to explain the results, it has been applied by institutions and scholars (Maqsood and Burney, 2017) . However, the decomposition results of the Laspeyres decomposition method have residual errors, and complex formulas are needed to solve the residual errors (Ang, 2004) . The Divisia decomposition method is based on the logarithmic change to study the change of the weight of each factor in the total amount, which was applied to the energy decomposition by Boyd in 1987 (Boyd et al., 1987 . Then, the Divisia decomposition method has been extended and improved by other scholars (Ang, 2005 ; Ang and Choi, J o u r n a l P r e -p r o o f 11 1997; Ang and Zhang, 2000) . Among them, because the LMDI decomposition method is simple to calculate and has no residual error, it has become the factor decomposition method preferred by many scholars Ma et al., 2019) . Carbon emission reduction is a common global goal, which has led scholars to J o u r n a l P r e -p r o o f 13 extensively study the mechanism underlying the growth of carbon emissions (Mousavi et al., 2017; Moutinho et al., 2015) . However, due to the special connection between carbon emissions and economic growth, we cannot blindly trigger an economic downturn just to reduce carbon emissions. However, due to the special connection between carbon emissions and economy, economic decline cannot be blindly triggered simply to achieve carbon emission reduction. Therefore, it is even more important to achieve between decoupling carbon emissions and economy by exploring the mechanism that impact the relationship between both ( 

The methodological flow diagram in this paper is shown in Fig. 1 , and the specific method description and derivation are shown in section 3.1 and 3.2. Table 2 . 

The CO 2 emissions can be decomposed via Eq.

(2), following (Kaya, 1990) :

Here, i represents the energy type (i = 1 represents coal, i=2 represents oil and i=3

represents natural gas). Table 3 shows the meaning of the symbols in Eq.

(2). According to the additive LMDI model (Ang, 2005) , the aggregated CO 2 emissions changes, are decomposed into six driving forces as shown in Eq. (3): emission coefficient effect, energy structure effect, energy intensity effect, R&D efficiency effect, per capita R&D expenditure effect, and population effect.

The effects of driving forces of CO 2 emission changes from period 0 to t are calculated using Eqs. (4)-(10). 

3 0 0 1 , ln t t i EC i i i i EC C L C C EC    (4)   3 0 0 1 , ln t t i EM i i i i EM C L C C EM    (5)   3 0 0 1 , ln t t i EI i i i i EI C L C C EI    (6)   3 0 0 1 , ln t t i RE i i i i RE C L C C RE    (7)   3 0 0 1 , ln t t i PR i i i i PR C L C C PR    (8)   3 0 0 1 , ln t t i P i i i i P C L C C P    (9)   0 0 0 0 00 , , ln ln , t t ii ii t t ii ii tt i i i i CC CC L C C CC C or C C C          (10)CC GG C C C C C C C GG CC C C C C C C C C C C G G G G G G G G G G G G                                               (11)

This study investigated the period from 1996 to 2014. The data for energy-related 

As illustrated in Fig. 2 

To further clarify the results, the long study period was divided into four 

Corresponding to the above decoupling analysis, the driving forces of C6 countries' decoupling were further quantified for the four phases defined in Section 4.2 (see Fig. 8 ).

During the first phase of 1996-2000: The per capita R&D expenditure effect was the main obstacle for the decoupling process in the C6 countries. Among them, the per capita R & D expenditure effect promotes the decoupling index of China, USA and India increased by more than 1, indicating that the growth rate of carbon emissions caused by this factor is greater than the economic growth rate. The effect of the energy intensity effect and R&D efficiency effect on the decoupling index of C6 countries is less than 1, which indicate that energy intensity effect and R&D efficiency effect are the two main determinants that promote the decoupling process of C6 countries. The former was the main favorable factor for the USA, Russia, and Germany, while the latter was the main favorable factor for China, India, and Japan. However, none of the three contributing factors had a lower effect than -1, indicating that the carbon emission reduction rate caused by these three factors was less than the economic growth rate. India had the worst decoupling status among all C6 countries, which is mainly because only the energy intensity effect promoted the decoupling process, while other effects inhibited the decoupling process with the effect value is greater than 0. China, Russia, and Japan were all characterized by weak decoupling;

however, for different reasons. For China, the per capita R&D expenditure effect had a strong inhibitory effect on the decoupling process with the effect value of 1.3955.

However, due to the positive impact of the energy intensity effect with the effect value J o u r n a l P r e -p r o o f 30 of -0.5180 and the R&D efficiency effect with the effect value of -0.5079, China stabilized at the weak decoupling state. The energy intensity effect had played the major driving force in Russia's decoupling process; however, it suppressed Japan's decoupling process. R&D efficiency effect was the main promoter of Japan's decoupling process but was the biggest obstacle for Russia's decoupling process. 

This papery compared the decoupling of economy from CO 2 emissions of the top six CO 2 emitting countries (China, USA, India, Russia, Japan, and Germany) during the period from 1996 to 2014. The extended LMDI model was employed to decompose the decoupling index into six drivers: carbon emission coefficient, energy mix, energy intensity, R&D efficiency, per capita R&D expenditure, and population. Germany performed best among the developed countries, and China was most stable among the developing countries.

The per capita R&D expenditure effect played the major resistance for the decoupling between carbon emissions and economy in C6 countries. The effect of energy intensity and R&D efficiency related to technological progress were the main driving forces of the decoupling process. Per capita R&D expenditure effect, energy intensity effect, and R&D efficiency effect dominated the decoupling of developing countries, thus leading to a difference in decoupling performance. Population effect played an important role for suppressing the decoupling process between the USA and India. However, it only had a relatively small impact on other countries and has even promoted the decoupling process between Russia and Japan. The effects of carbon emission coefficient effect and energy mix effect on the decoupling process were relatively weak and thus did not exert a decisive role in the decoupling states.

J o u r n a l P r e -p r o o f 32 CO 2 emissions from China and USA far exceeded those of the other C6 countries.

Therefore, China and the USA should actively assume the responsibility for emission reduction and increase their efforts to promote the decoupling process. To further promote the coordinate development of economic growth and environment, energy efficiency and R&D efficiency are supposed to improve as the basis of future decoupling efforts. Aggregate energy efficiency and R&D efficiency improved mainly as a result of technological progress (Voigt et al., 2014) . The overall technological levels of developed countries were better than those of developing countries.

Developing countries can thus learn from developed countries (especially from Germany) how to promote technological advancement. In addition, the energy structure should be further optimized to fully utilize its emission reduction potential by prioritizing cleaner energy sources. It is essential to transform the development pattern of domestic economy and maintain stable economic growth. The promotion of low-carbon awareness through publicity and education is also important in daily life. 

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Appendix A Table A1. CO 2 emissions in C6 countries

Uint: Million tonnes oil equivalent

Table A3. GDP in constant 2010 US$ in C6 countries

Population in C6 countries (Uint: million person) China

Research and development expenditure in C6 countries (Uint: % of GDP)

The current work is supported by -the Fundamental Research Funds for the Central Universities (18CX04009B)‖.