Is economic growth compatible with a reduction in CO2 emissions? Empirical analysis of the United States

Resources, Conservation & Recycling 151 (2019) 104443

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Is economic growth compatible with a reduction in CO2 emissions? Empirical analysis of the United States

T

⁎

Qiang Wanga,b, , Xue-ting Jianga,b,c,d,e, Shuting Gea,b, Rui Jianga,b a

School of Economic and Management, China University of Petroleum (East China), Qingdao, Shandong 266580, People’s Republic of China Institute for Energy Economics and Policy, China University of Petroleum (East China), Qingdao, Shandong 266580, People’s Republic of China c State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, People’s Republic of China d CAS Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, 830011, People’s Republic of China e College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Decoupling Decomposition techniques Economic growth CO2 emission The United States

Energy-related carbon dioxide (CO2) emissions dropped 12% between 2007 and 2016 in the United States (U.S.), while the gross domestic product (GDP) increased by 19%. empirical data related to the decoupling of carbon emissions from economic growth in the U.S. provides a useful study pilot opportunity and serves as a good example for other countries to learn about CO2 emission mitigation. This study identified the relationship between CO2 emissions and economic growth in the U.S. The goal was to determine if the rigid link between the two can be changed, and identify the potential drivers of this trend. We combined the Cobb–Douglas (C-D) production function and the extended Kaya equation to develop decomposition and decoupling techniques to quantify six potential effects. The results show that the investment effect and economy structure effect played the most important roles in increasing carbon dioxide emissions. In contrast, the energy intensity effect cut carbon emissions in most of the years studied. Strong decoupling and weak decoupling are the main states; the energy intensity effect accelerated the decoupling process. In contrast, the investment effect and labor effect decelerated the decoupling process in recent decades. The study concludes that emission mitigation and decoupling policies should emphasize energy efficiency, investment patterns, and improvements in labor force quality.

1. Introduction Since the landmark Paris Agreement was signed within the United Nations Framework Convention on Climate Change (UNFCCC) in 2015, policymakers have asked whether and how economic growth can be decoupled from carbon emissions. If the rigid link between economic growth and carbon emissions cannot be changed, or if stunting economic growth is the only way to curb carbon dioxide emissions, national governments may fear economic loss and hesitate to develop assertive Intended Nationally Determined Contributions (INDCs). The climate actions derived from INDCs determine whether the world overall meets the Paris Agreement’s goals. Previous empirical studies have found a close relationship between economic growth and the rise in carbon emissions (Holtz-Eakin and Selden, 1995; Panayotou, 2016; Soytas and Sari, 2009). These studies suggest that policies that reduce carbon emissions may also increase risks of economic loss. Other studies, using hypotheses or modeling scenarios, have proposed sacrificing

short-term economic benefits to avoid long-term climate challenges that incur much larger economic losses. For example, Nicholas Stern, the President of the British Academy, estimated that achieving carbon dioxide stabilization at levels between 500 and 550 ppm would cost 1–2% of gross domestic product (GDP) (Stern, 2007). Previous studies that found a close correlation between the economic growth and the increase in carbon emissions were based on theoretical hypotheses. To build on such studies, this study explored a specific example of decoupling economic growth from carbon emission growth, further explores the decoupling status, and identifies the factors that drive emissions. The specific example comes from the United States (U.S.), the world’s second largest carbon emitter. The U.S. has achieved many years of economic growth in ways that are compatible with carbon reduction. According to the U.S. Energy Information Administration (EIA), carbon dioxide emissions from fuel combustion in the U.S. were reduced by 14% (from 6.00 to 5.17 billion metric tons) between 2007 – 2016 (EIA, 2017). During the same time period, the

⁎ Corresponding author at: School of Economics and Management, China University of Petroleum (East China), Qingdao, Shandong 266580, People’s Republic of China. E-mail address: [email protected] (Q. Wang).

https://doi.org/10.1016/j.resconrec.2019.104443 Received 14 November 2018; Received in revised form 15 July 2019; Accepted 6 August 2019 Available online 17 September 2019 0921-3449/ © 2019 Published by Elsevier B.V.

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AMDI technique, Ang and Choi developed a refined Divsia index technique by using logarithmic mean weight in 1997 (Ang and Choi, 1997). This was an early version of the LMDI technique. Ang et al. subsequently improved the LMDI technique to solve the residual and “zero-value” problems and to implement more perfect decomposition approaches (Ang, 2005, 2015a; Ang and Zhang, 2000). The LMDI has been a widely used decomposition techniques (Wang et al., 2017; Wang and Li, 2016) because of its ability to perform perfect decomposition and other features. Wu and Zeng decomposed carbon dioxide emission into four effects: industry structure effect, industrial energy intensity effect, energy structure effect, and emission coefficient effect. These four effects were used to analyze the factors influencing cargo transport consumption in China for 2000–2012, using Logic Mean Divisia Index (LMDI) (Wu and Zeng, 2013). Ren and Yin adopted the LMDI method, based on the extended Kaya identity, to explore the impact of the five factors on total carbon dioxide emissions from China's manufacturing industry from 1996–2010: industry structure, economic output, energy structure, energy intensity, and emission factors (Ren et al., 2014). Achour and Belloumi applied LMDI to study transportation energy consumption in Tunisia and identified the associated driving mechanisms of the following factors: energy intensity, transportation structure effect, transportation intensity effect, economic output, and population scale effects (Achour and Belloumi, 2016). Jeong applied the LMDI method to decompose the Korea’s industrial manufacturing greenhouse gas (GHG) emissions into overall industrial activity (activity effect), industrial activity mix (structure effect), sectoral energy intensity (intensity effect), sectoral energy mix (energy-mix effect), and CO2 emission factors (emission-factor effect) (Jeong and Kim, 2013). Moutinho et al. used LMDI method to decompose CO2 emissions in top 23 countries group on renewable energies into six effects which are carbon trade intensity, trade of fossil fuels effect, fossil fuels intensity, renewable sources productivity, electricity financial power effect, the financial development effect (Moutinho et al., 2018). Lopez et al. proposed a novel identity function to explore the contributions of electricity imports and exports to changes in carbon emissions in the European region (Lopez et al., 2018). Zheng et al. applied LMDI method to estimate seven socioeconomic drivers which are emission intensity effect, energy mix effect, energy industrial structure effect, energy efficiency effect, regional structure effect, economic growth effect and population effect of the changes in CO2 emissions in China (Zheng et al., 2019). Ma et al. employed LMDI method to decompose Chinese commercial building carbon emissions into the reciprocal of GDP per capita of Tertiary Industry in China, the Chinese commercial building carbon emissions intensity, gross floor area of existing commercial buildings in China, gross floor area per capita of existing commercial buildings in China and GDP of Tertiary Industry in China (Ma and Cai, 2018). As the world's second-largest carbon emitter, the United States has achieved a reduction in carbon emissions in the period of 2007–2014 while maintaining economic growth. Investigating the influencing factors of U.S. carbon emissions can provide a reference for other countries to achieve emission reduction targets. Various studies have been conducted on detecting key drivers of United States, Table 1 listed some researches which were carried out via decomposition method. This demonstrates the mostly discussed factors and the key driver they found through the extended Kaya equation and decomposition method. By reviewing the related researches, whether it is the decomposition of carbon emissions in the United States or the decomposition of carbon emissions in other countries, the five effects are most widely discussed: CO2 emission coefficient of fossil fuels, energy mix, energy intensity, GDP per capita and population. However, labor and investments can have influence to economic growth, which can potentially change the direction and efficiency of carbon emissions. As a result, the influence of investment and labor factors should be considered when conducting the carbon emission or decoupling analysis. In general, since total carbon emission change is constant between

U.S. GDP grew by 12% (from $15.8 to $17.0 trillion) while the U.S. dollar remained stable in 2010, according to the World Bank Data (The_World_Bank, 2017). Labor effect and investment effect are important to influence overall economic growth, it is considered there is a link between carbon emission and economic growth, however, no countries will voluntarily to obey the mitigation rules if cutting down carbon emission must sacrifice the economy development. Consequently, seeking for innovative strategies to cut the so-called link between CO2 emission and economic growth can be of vital importance. Also, when developing feasible measures suggestion for policy makers, most previous studies just considered economic growth as a potential factor without analyzing the internal mechanism, the problem to identify the actual driver from the internal economic mechanism is not well addressed. In general, there are two problems we are aiming to solve: what are the key drivers of carbon emission changes? Whether it is possible to control CO2 emission increase without hindering economic growth (decouple CO2 emission from economic growth) and if it is possible, how to achieve that goal? Better understanding the decoupling of economic growth from carbon emissions and the drivers behind this decoupling in the U.S. can inform more effective policies and assist in supporting economic growth that is compatible with CO2 reductions. This understanding of decoupling processes and drivers also has policy implications for the rest of the world, as countries develop mitigation strategies and move towards de-carbonization. This study explored the changing trends in total decoupling and identifies the causes of the decoupling process and the internal drivers of CO2 emission changes. The rest of this paper is structured as follows. Section 2 provides a literature review to demonstrate existing research gaps. Section 3 presents the methodology and data. Section 4 offers results and discussion. Conclusion and policy implications are included in Section 5. 2. Literature review 2.1. Decomposition methods Two decomposition techniques are widely used to decompose carbon emissions: SDA (Structural Decomposition Analysis) and IDA (Index Decomposition Analysis). The SDA method is based on inputoutput analysis (Rose and Casler, 1996; Rose and Chen, 1991). Many scholars have used input-output tables, studying specific time periods, to decompose energy consumption changes (Alises and Vassallo, 2015; Su and Ang, 2016; Xu et al., 2017). In some empirical analyses, the energy decomposition can only be performed additively, because the SDA method heavily relies on input-output tables (Ang, 2004; Wang et al., 2019). In general, the IDA technique is used more widely than the SDA technique to quantify the impact of different factors on changes in energy consumption and related CO2 emissions. The IDA technique is a time-series analysis. It implements yearly decomposition using timeseries data, demonstrating the changing impact of pre-defined explanatory factors over a defined period (Ang and Zhang, 2000). The IDA technique includes two more specific techniques: the Laspeyres and Divisia index techniques. The traditional Laspeyres index technique uses either a relatively simple decomposition formula to produce decomposition results with a residual, or a very complicated decomposition formula to settle the residual problem (Ang, 2004). The Divisia index technique was first proposed by Boyd et al. (Boyd et al., 1987), and was then extended and refined by other scholars (Ang, 2005, 2015a; Ang and Choi, 1997; Ang and Lee, 1994; Ang and Zhang, 2000). The Divisia index technique has developed into two branches of decomposition techniques: AMDI (the arithmetic mean Divisia index) and LMDI (log mean Divisia index). The AMDI technique applies the arithmetic mean weight function; the LMDI applies the log mean weight function. To address the residual and non zero-value problems of the 2

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LMDI method 2000-2014 the carbon emissions in USA Wang and Wang (2019), Wang et al. (2018b)

LMDI method the CO2 emissions in USA Shahiduzzaman and Layton (2017)

1974-2014

the CO2 emissions in USA Jiang et al. (2016)

the multilevel LMDI method

yearly data back to 1949 and monthly data from 1973 the CO2 emissions, CO2 intensity and CO2 emissions per capita in USA Shahiduzzaman and Layton (2015)

1990-2014

1990-2016 Jiang et al. (2018)

two years, the contribution rate result of potential factors can be different because of the change of factor selection technique. Compared with most previous literatures just analyzed the impact of economic output to carbon emissions, the human economic activities will be further decomposed in this research, which can be the basis of the possibility analysis of decoupling carbon emission change from economic growth. Since economic output effect can be significant when influencing annual CO2 emission change. Unlike the analysis of the direct impact of human economic activities factors (such as GDP per capita) on carbon emission changes, few scholars took labor and investment effects into consideration, which revealing indirect impacts of these factors to the decoupling process (the possibility of controlling energy-related CO2 emission without hindering economic growth). The impacts from investment and labor on CO2 emission are important and need to be further clarified. The aging process can influence greenhouse gas (GHG) emissions and economic development in the US (Wei et al., 2018), labor force has direct link with the share of elderly in population, aging process and labor effect can cause impacts to carbon emission changes (Dalton et al., 2008; Hassan, 2015). With regard to the investment effect, foreign direct investment increases carbon emissions and hamper environmental quality (Shahbaz et al., 2019). In the context of the continuous decline of renewable energy in the US, investment in renewables is now cost competitive. Also, continuing investment in technological innovations have direct relationship with CO2 emission change because solid energy efficiency improvement can control the carbon emission growth (Li et al., 2019). Both labor effect and investment effect can have influence on economic development, economic growth then pose impacts to carbon emission changes (Dean and Hoeller, 1992), in the context of the relationship of their influencing mechanism, labor effect, investment effect may have impacts on carbon emission change, so this issue need to be further clarified. Building on previous studies, this work combined the Cobb–Douglas (CD) production function and the extended Kaya equation to develop an improved decomposition technique to fill the gap, considering both the labor effect and investment effect when analyzing the potential influencing factors of carbon dioxide emission changes. Additionally, after combining the extended decomposition and the Cobb–Douglas (C-D) production function, we estimated the value of the parameters from C-D production function using an econometric tool to identify the carbon emission changes from the two factors.

CO2 emissions (fuel mix, CO2 intensity of energy use, energy intensity, GDP per capita, population) CO2 intensity (fuel mix effect, CO2 intensity of energy use, energy intensity) CO2 emissions per capita (fuel mix, CO2 intensity of energy use, energy intensity, GDP per capita) the effect of population, the effect of human economic activities effect, the effect of energy intensity, the effect of energy mix, the effect of carbon dioxide emission factor carbon intensity of energy use, energy intensity of real GDP, sectoral composition, GDP per capita and population population, income growth, energy intensity, energy mix, and carbon intensity

the emission coefficients, value added coefficients effect, the input–output structure effect, the domestic scale effect and the foreign scale effect. GDP per capita, population, gas share, energy intensity, coal share, oil share

the generalized logarithmic mean Divisia index decomposition Geographical Detector tool and LMDI method LMDI method 1995-2009

the emissions per value added gaps between China and USA the carbon emission in USA Zhao et al. (2017a,b)

Decomposition Method Study period Research object Authors

Table 1 U.S. carbon emissions research by decomposition method via decomposition methods.

Influencing factors

Q. Wang, et al.

2.2. Decoupling techniques In general, two widely used measuring indexes are the decoupling index and decoupling elasticity; however, there are many ways to measure the decoupling of economic growth and energy consumption (Acs et al., 2010; Andreoni and Galmarini, 2012; Chang and Li, 2017; Diakoulaki and Mandaraka, 2007; Dong et al., 2016; Ren et al., 2014; Tapio, 2005; Yu et al., 2017; Zhao et al., 2017a,b; Zhao et al., 2016). Andreoni and Galmarini assessed the progress made in decoupling Italian economic growth from CO2 emissions. During their study period, the Italian economy did not achieve absolute decoupling in terms of energy consumption and carbon dioxide emissions. They also found that economic growth and energy intensity were the largest contributors to the increase in CO2 emissions (Andreoni and Galmarini, 2012). Chang explored the issue of decoupling the lock-in effect between economic growth and CO2 emissions by adjusting the structure from a final demand perspective. This involved identifying the most relevant productive linkages and the most sensitive components of final demand in terms of CO2 emissions (Chang and Li, 2017). Diakoulaki and Mandaraka applied decomposition analysis to explain changes in industrial CO2 emissions. They also compared and evaluated the progress made by 14 EU countries to decouple emissions from industrial growth. These studies assessed the efforts made by each country and their effectiveness in dissociating the economic and environmental dimensions of development. They found that most 3

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European Union (EU) countries made a considerable, but not always sufficient, decoupling effort; however, no significant acceleration was observed in the post-Kyoto period (Diakoulaki and Mandaraka, 2007). Wang et al applied decoupling analysis and decomposition analysis to explore the decoupling relationship between economic growth and carbon emissions in China and India (Wang et al., 2018a). The decoupling status can be detected by calculating decoupling elasticity. This elasticity can be further decomposed into six influencing factors: investment effect, labor effect, energy intensity effect, economy structure effect, energy mix effect, and emission coefficient effect. Investment and labor are closely correlated with economic growth; as such this study investigated whether investment and labor can influence the decoupling process. However, most previous studies have focused on the impact from the stated perspective; few of them determine the effects derived from labor and investment-related issues and to what extent they can accelerate the decoupling process. This study extended and modified the original model to fill this gap. The study analyzed the impact of different effects using the decomposition method, reporting the decoupling states in the years studied, and investigated the contributions of different effects on the decoupling process. In addition, we extended the original LMDI technique to the potential factors of the decoupling elasticity coefficient. This allowed the identification of the impact of the fixed asset investment and labor input factor when conducting the decoupling analysis, offering a deeper exploration of the contribution of individual sub-sectors and energy consumption. Labor effect and investment effect can influence economic growth, possibly posing indirect impacts to energy-related carbon emission change. Meanwhile, the phenomenon of population aging is closely related to the level of labor supply. For a representative developed country like the United States, relevant studies have shown that the occurrence and degree of aging will lead to changes in carbon dioxide emissions, aging (Dalton et al., 2008), labor levels and carbon emissions are closely related. Investment in clean energy can promote research on clean energy and become a guarantee for further energy efficiency. Under the current conditions, clean energy still has a higher cost of use than traditional fossil fuels. Since building a low-carbon technology system need pragmatic actions. If the government can pour more budget to renewable fuels, it will play a positive role in further energy replacement and upgrading. The increase in investment will reduce the cost of plot energy use, and the decline in US clean energy use costs will continue to promote energy substitution of carbon dioxide emissions control and thus control climate change in the future in the United States by gradually replacing the energy-using structure from carbonintensive fuels to carbon-free fuels.

Ct =

i, j

=

∑A

i, j

×(K t )α

Eijt Cijt Qit Et × it × t × t t Q Qi Ei Eij

× (Lt ) β × Sit × Iit × Mijt × F

i, j

t ij

(2)

Cijt

In this expression, C t and denote the total emissions and the emissions of industry i, fuel j in year t; and Eijt and Eit represent the energy consumption of industry i, fuel j and the aggregate energy consumption of industry in year t, respectively. The variables Qit and Qt denote the GDP of industry i and the whole country. The variables A, α , β are unknown constant parameters; K t and Lt denote the fixed asset investment amount and labor input in year t, respectively; Sit and Iit refer to energy intensity of GDP and economic product share of industry i in year t; and Mijt and Fijt represent the energy consumption mix in year t The carbon emission changes brought from each factor can be calculated using the Logarithmic Mean Divisia Index (LMDI) technique when decomposing CO2 emissions and energy consumption. The specific technique is shown as follows:

3. Methodologies and data 3.1. Extended decomposition model The Cobb–Douglas (C-D) production function is widely used to test the economic development change created by the amounts of two or more inputs, particularly physical capital and labor and the associated amount of output. The function is shown in Eq. (1):

Qt = A (K t )α (Lt ) β

∑ Cijt = ∑ Qt ×

(1)

Combining the Cobb–Douglas (C-D) production function and traditional decomposition technique allows for the investigation of the impact of investment effect and labor effect on carbon dioxide emission changes. Additional new effects based on the Cobb–Douglas (C-D) production function and the influencing factors from the extended Kaya equation are obtained using Eq. (2):

Cijt − Cij0 (K t )α ⎧ × LN ( 0 α ), Cijt ≠ Cij0 and Cijt Cij0 ≠ 0 ∑ ⎪ 0 t ( K ) − ln ln C C ij ij ⎪ ij t α ΔCK = ⎨ ∑ Cij0 × LN ( (K ) ), Cijt = Cij0 (K 0)α ⎪ ij ⎪ t 0 0, Cij Cij ≠ 0 ⎩

(3a)

Cijt − Cij0 (Lt )α ⎧ 0 t t 0 ⎪ ∑ lnC t − lnC 0 × LN ( (L0 )α ), Cij ≠ Cij and Cij Cij ≠ 0 ij ij ij ⎪ ⎪ ΔCL = (Lt )α ⎨ ∑ Cij0 × LN ( 0 α ), Cijt = Cij0 (L ) ⎪ ij ⎪ 0 t ⎪ 0, Cij Cij ≠ 0 ⎩

(3b)

Cijt − Cij0 Sit ⎧ 0 t t 0 ⎪ ∑ lnC t − lnC 0 × LN ( S 0 ), Cij ≠ Cij and Cij Cij ≠ 0 ij ij i ij ⎪ ⎪ ΔCS = St ⎨ ∑ Cij0 × LN ( i0 ), Cijt = Cij0 Si ⎪ ij ⎪ 0 t ⎪ 0, Cij Cij ≠ 0 ⎩

(3c)

Cijt − Cij0 Iit ⎧ 0 t t 0 ⎪ ∑ lnC t − lnC 0 × LN ( I 0 ), Cij ≠ Cij and Cij Cij ≠ 0 ij ij i ij ⎪ ⎪ ΔCI = It ⎨ ∑ Cij0 × LN ( i0 ), Cijt = Cij0 I ⎪ ij i ⎪ ⎪ 0, Cijt Cij0 ≠ 0 ⎩

(3d)

Cijt − Cij0 Mijt ⎧ t 0 t 0 ⎪ ∑ lnC t − lnC 0 × LN ( M 0 ), Cij ≠ Cij and Cij Cij ≠ 0 ij ij ij ⎪ ij ⎪ Mijt ΔCM = ⎨ ∑ Cij0 × LN ( 0 ), Cijt = Cij0 Mij ⎪ ij ⎪ ⎪ 0, Cijt Cij0 ≠ 0 ⎩

(3e)

Cijt − Cij0 Fijt ⎧ t 0 t 0 ⎪ ∑ lnC t − lnC 0 × LN ( F 0 ), Cij ≠ Cij and Cij Cij ≠ 0 ij ij ij ⎪ ij ⎪ Fijt ΔCF = ⎨ ∑ Cij0 × LN ( 0 ), Cijt = Cij0 Fij ⎪ ij ⎪ t 0 ⎪ 0, Cij Cij ≠ 0 ⎩

(3f)

The influence of constant parameter A is zero. As such, we discuss six effects: investment effect, labor force effect, economy structure effect, energy intensity, fuel mix effect, and emission coefficient effect. 4

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Combining the LMDI technique and C-D production function is a novel way to analyze the factors influencing energy-related carbon dioxide emissions. To obtain the value of parameters of α and β, both sides of the equation are taken logarithms simultaneously

lgQt

= lgA +

αlgK t

+

βlgLt

Table 2 Classification standards for decoupling. Classification standards for decoupling

ΔQ

ΔC

δt

Decoupling

>0 >0 <0 >0

>0 <0 <0 >0

0 < δ t < 0.8 δt < 0 δ t > 1.2 δ t > 1.2

<0

>0

δt < 0

<0

<0

0 < δ t < 0.8

>0 <0

>0 <0

0.8 < δ t < 1.2 0.8 < δ t < 1.2

(4) Negative decoupling Coupling

Because α + β=1(Romer, 2001), the equation can be also expressed as:

Qt Kt lg t = lgA + αlg t L L

(5)

Assume the dependent variable y and independent variable x, Qt Kt then y=lg Lt , x=lg Lt , γ = lgA In this case, the equation can be simplified as:

y = αx+γ

the decoupling elasticity is divided into five variables to show the specific contribution of each known effect. Eq. (10) expresses the decoupling indicator:

(6)

Then the value of α and γ can be calculated using the ordinary least square (OLS) method; t is the study time and the year t, Eqs. (7) and (8) are then used to estimate the parameters:

αˆ =

27 ∑t = 1

(x t − x¯)(yt − y¯) 27

∑t = 1 (x t − x¯)2

=

Cov (x , y ) Var (x )

γ¯ = y¯ − aˆx¯

Q0 ΔCK + ΔCL + ΔCM + ΔCS + ΔCF × C0 ΔQ Q0 ΔCK Q0 ΔCL Q ΔCS Q ΔCI ΔCM = × + × + 0 × + 0 × + C0 ΔQ C0 ΔQ C0 ΔQ C0 ΔQ ΔQ Q0 Q0 ΔCF + × × C0 C0 ΔQ t =δKt + δLt + δSt + δIt + δM + δFt

δt =

(7) (8)

The variables αˆ and γ¯ are the estimated values using the technique; Cov (x , y ) and Var (x ) are the covariance and variance of x and y, respectively.

The decoupling elasticity indicator, presented by Tapio (2005), reveals the relationship between energy consumption and economy development. The indicator is provided in Eq. (9)

ΔC / C0 Q C − C0 = = 0 × t ΔQ/ Q0 C0 Qt − Q0

(10)

The relationship between energy consumption and economic growth was tested using the decoupling elasticity indicator. We combined the Cobb–Douglas (C-D) production function and the widely used decomposition technique to advance the decoupling analysis and identify its drivers. We then decomposed the decoupling indicator into the following determinant factors:

3.2. Advanced decoupling model

δt

Weak decoupling Strong decoupling Recessive decoupling Expansive negative decoupling Strong negative decoupling Weak negative decoupling Expansive coupling Recessive coupling

(i) The investment effect (denoted by δKt ) indicates changes in the fixed asset investment, measured using 2010 constant prices (ii) The labor effect (denoted by δLt ) indicates the changes in labor input (iii) The economic industry structure effect (denoted by δSt ) indicates the consequences of the changing share of the economic industry (iv) Energy intensity effect (denoted by δIt ) reflects the decoupling influence created by the energy intensity of GDP. t (v) Energy consumption mix effect (denoted by δM ) reflects the decoupling contribution of the changes in fuel consumption (vi) Emission coefficient factor effect (denoted by δFt ) reflects the decoupling process created by the carbon dioxide emissions coefficient

(9)

δt

In this expression, represents the decoupling elasticity in year t; and Q0 and C0 are the GDP and energy-related emissions in the base year, respectively. Tapio also defined three major decoupling states and eight sub-states. In addition, he proposed a 20% variation to avoid a slight change appearing to be a significant one (Tapio, 2005). To be more specific, in the decoupling states, when CO2 emission and GDP both grow (0 < decoupling elasticity < 0.8), the sub-state is defined as one of weak decoupling. However, if the economy grows while the carbon emissions are cut, strong decoupling is defined (decoupling elasticity < 0). This is the most powerful and expected state hoped for by policy makers. When economic growth and CO2 both decrease in the studied period (decoupling elasticity > 1.2), it is defined as being recessive decoupling. Negative decoupling also contains three sub-states. (1) Weak negative decoupling reveals a decrease in both GDP and the carbon emissions while 0 < elasticity < 0.8. (2) In the case of expansive negative decoupling, these variables all increase when the decoupling elasticity > 1.2. (3) In the strong negative decoupling state, economic output and carbon emissions increase (elasticity < 0). Similarly, coupling is defined as a state that does not fall between negative decoupling and decoupling, which can also be viewed as a moderate state between these two states. Coupling also consists of three sub-states: weak negative decoupling, expansive coupling, and recessive coupling. Table 2 shows the decoupling status classifications; these classifications reflect the relationship between energy consumption and economy growth tested using decoupling elasticity. The decoupling model can be advanced based on the decomposition technique, combining the extended Kaya equation and the Cobb–Douglas production function. Using the decomposition method,

3.3. Data definitions Research data used in this research was from 1990 to 2016, and was mainly collected from the U.S. Energy Information Administration (EIA) (EIA, 2017), the U.S. Bureau of Economic Analysis (BEA), and the World Bank (The_World_Bank, 2017). Energy and carbon dioxide emission data came from EIA; the value added and investment in fixed asset data were collected from BEA; and the labor force data were from the World Bank. All economic data were converted into 2010 constant dollars. 4. Results and discussion 4.1. The trajectory of CO2 emissions The U.S. GDP increased between 1990 and 2016 at an overall annual rate of 2.42%. The trend in carbon dioxide emissions varied from year to year. The total CO2 emissions increased in the first decade 5

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Fig. 1. Change of energy-related CO2 emissions and GDP(1990–2016).

(1990–2000); levels fluctuated between 2001 and 2004; then, emissions decreased until 2007 at a rate of 1.45% per year. There was an overall decline in emissions from 2007 to 2016, though some fluctuations occurred (Fig. 1).

and further relationship between the stated economy-related factors and overall carbon emission change can be revealed. During the periods of 2001–2002 and 2008–2010, the emissions changed by the labor effect became a negative factor in increasing carbon dioxide emissions. This switch of its role in this period mainly resulted from the decrease in labor force numbers in the corresponding years. Fig. 3 shows that the labor input subsequently increased after 2010, with an average growth rate of 1.33% per year. The investment effect shifted from being a positive to a negative factor in increasing the CO2 emissions after 2007. However, the fixed investment amount dropped from 3319.38 to 2683.10 billion dollars between 2007 and 2010, at an annual rate of 6.85%. In particular, in 2009, 59.00% of the energy-related carbon dioxide emission can be attributed to the investment effect. After 2010, the investment effect was associated with an increasing trend of total CO2 emissions; however, the decrease in labor input after 2010 accounted for most of the changes in emission trends, with fixed assets investment dropping by 10.33% per year. Fig. 4 shows that the share of the industrial sector decreased from 24.56% to 16.80%; in contrast, commercial and other sectors increased from 72.74% to 80.50%. This helped cut carbon dioxide emissions to optimize the economic structure and development patterns. As an important curbing effect for CO2 emission, the energy intensity effect played a positive role in cutting carbon dioxide emissions except in years 1991, 1995, 2002, 2007, and 2009. This factor contributed a decrease of 455.24 million metric tons of carbon dioxide between 2010 and 2016 because of the drop of energy intensity. Fig. 5 shows that the energy intensity of the transportation, industrial, commercial, and other sectors decreased by 1.53% yr−1, 1.04% yr−1, and 1.56% yr−1, respectively. Energy technology improvement and massive fuel switch from carbon-extensive fuels to less extensive fuels helped

4.2. Advanced decomposition analysis of CO2 emissions The OLS technique was used to obtain the following equation and parameters: y = 0.89x − 0.58, then, α= 0.89, β= 0.11. This facilitated the calculation of CO2 emissions from the investment effect and the labor effect. Fig. 2 shows that investment effect played a dominant role in changing carbon dioxide emissions, economic structure also contributed greatly to the annual carbon emission changes, however, energy intensity effect helped to control the growth of carbon emission changes in most years. In general, the energy intensity effect and energy mix effect dampened total carbon emission changes. The changes from labor effect and the economic industry structure effect varied from year to year. Since investment have direct impacts to economic growth, the change of investment can pose significant influence to total economic growth, unlike most previous similar studies, the further decomposition of economic growth reveals the changing mechanism from human economic system to CO2 emission changes. Also, the adjustment of economic industrial structure has corresponding influence to total economic output of the US, which consequently bring impacts to carbon emission change. Most of studies concerning the driver analysis of carbon emission changes concluded that economic output effect (GDP per capita) can be the most significant factor (Cansino et al., 2015; Chen et al., 2018; Feng et al., 2015; Hatzigeorgiou et al., 2008; Wang et al., 2005; Wang and Feng, 2017), by combining the decomposition technique and C-D production function, the internal influencing mechanism

Fig. 2. Contributions of different factors to changes in the US CO2 emissions. 6

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Fig. 3. The trajectory of the fixed asset investment amount and labor input.

structure effect contributed less in years with a strong decoupling trend (2004–2006, 2010–2012, and 2014–2016). In general, the investment effect slowed the speed of decoupling. The decrease of investment by 17.76% between 2005 and 2010 contributed to the decoupling process for this period. Meanwhile, labor effect did not contribute enough to the decoupling process in most study years, indicating that labor structure still needs to improve the upgraded. These results indicate that main efforts to curb carbon dioxide emissions should focus on improving energy efficiency, shifting the energy consumption mix, and searching for feasible ways to steadily advance employment and investment. Higher education can raise people’s awareness of the importance along with the urgency of fighting for environment degradation and climate change (Darby et al., 2016). As a consequence, people can be more likely adjust their behavior to reduce the carbon emission as much as they can to meet the significant climate change mitigation goal (Darby et al., 2016; Klein-Banai and Theis, 2013). Meanwhile, for the fact that most renewable fuels or energy-saving facilities are still very expensive because of some problems to be solved in the future, such as the large research investment and comparatively small market. Thus, living a greener life may cost more money with current technology level, not all individuals can afford some expensive energy-saving products or facilities with less carbon emissions. For example, hydrogen fuel cell vehicles (HFCVs), which run on hydrogen gas rather than gasoline and emit almost no CO2, are regarded as a promising travel tool to make contribution to climate change and the reduction of urban pollution (Chang et al., 2019). However, the HFCVs technology still has a long way to go to massively replacing the vehicles powered by gasoline because of the high price. Besides, according to the research from Georgetown University, people holding bachelor's degrees earn about $2.27 million over their lifetime, while those with master's, doctoral, and professional degrees earn $2.67 million, $3.25 million, and $3.65 million, respectively (Burnsed, 2011), in other words, people with higher education or professional degrees normally earn more money

decrease energy intensity in the U.S. The mix effect and emission coefficient effect had less impact on changes in carbon dioxide emissions. The energy mix effect tended to be the limit of CO2 emissions in most years, even though the impact of emission coefficient factor varied in different phases. In addition, the significant replacement of coal with oil and natural gas in the U.S. led to a decrease of the total carbon dioxide emissions (Feng et al., 2015). As carbon emission coefficients of oil and gas are smaller compared to coal (approximately 0.83 and 0.63 of coal emission coefficient, respectively) ( Zhu et al., 2014), fuel share adjustment, especially the substitution of coal with natural gas, can further decrease emissions. Additionally, energy efficiency can be improved and the energy system can be decarbonized.

4.3. Decoupling analysis Fig. 6 shows that expansive negative decoupling, expansive coupling, weak decoupling, and strong decoupling all occurred during the study period. More specifically, a weak decoupling status was observed in most years, indicating that GDP and emission volume both increased (0 < elasticity < 0.8). Strong decoupling (when GDP increases and emission volume decreases) and recessive decoupling (GDP and emission volume both decrease) were the main states after 2005. Data for 2005–2006, 2010–2012, and 2014–2016 indicated strong decoupling. The years 2007–2009 experienced recessive decoupling, consistent with the decrease in carbon dioxide emissions after 2005. In addition, with the exception of the 2007–2009 global economic crisis, economic growth was decoupled from increasing environmental problems in the (U.S) Table 3 provides the main factors contributing to decoupling and Table 4 demonstrates the changing trend for every five years. The energy intensity effect was the main factor driving the observed strong decoupling status; in addition, the energy mix effect and economy

Fig. 4. Economy structure of the US between 1990 and 2016. 7

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Fig. 5. Changes of energy intensity in the US between 1990 and 2016. Table 4 Decomposition factors of decoupling elasticity (every five years).

1990-1995 1995-2000 2000-2005 2005-2010 2010-2016

δL

δI

δS

δM

δF

0.5379 0.7510 0.3504 −2.9743 0.5989

−0.0545 −0.0777 0.0640 1.0521 −0.2383

−0.1985 −0.2699 −0.1530 0.2165 −0.4618

0.0236 −0.0890 −0.1272 −0.7747 0.1364

−0.0390 −0.0514 −0.0518 −0.2115 0.0152

−0.0228 −0.0421 0.0272 −0.0850 −0.0292

than those with less education. In this case, salaries may influence people’s choices of the energy-saving products and facilities. As a result, with the higher level of climate change mitigation awareness and ability to afford the expensive facilities and products, higher education programs can contribute to the carbon emission reduction. The recessive decoupling status between 2007 and 2009 primarily resulted from the global economic recession. After the economic recovery in 2010, the development pattern returned to expansive negative decoupling, with growth in both GDP and CO2 emissions. The strong decoupling trend occurred until the end of 2012. The modest impact of the fuel mix of energy after 2014 suggests that further increasing natural gas use may not significantly curb CO2 emissions and the decoupling process, but may improve energy efficiency.

Fig. 6. The degrees of coupling and decoupling of CO2 emission growth from economic growth.

Table 3 Decomposition factors of decoupling elasticity.

1990-1991 1991-1992 1992-1993 1993-1994 1994-1995 1995-1996 1996-1997 1997-1998 1998-1999 1999-2000 2000-2001 2001-2002 2002-2003 2003-2004 2004-2005 2005-2006 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 2012-2013 2013-2014 2014-2015 2015-2016

δK

5. Conclusions and policy implications

δK

δL

δI

δS

δM

δF

−1.1372 0.1812 0.5562 0.5515 0.6828 0.7156 0.5081 0.7814 0.6719 0.5073 −0.3436 −0.5063 0.3785 0.5891 0.6197 0.3224 −0.1860 −1.9092 4.5041 −0.0276 0.5349 0.8490 0.5034 0.7538 0.5563 0.0457

0.4632 0.1398 −0.0992 −0.1285 −0.1084 −0.0287 −0.0841 −0.1003 −0.0602 −0.0484 0.3827 0.4439 0.0595 −0.0798 −0.0778 −0.1016 0.0142 0.9910 −1.5940 0.0755 −0.1412 −0.1678 −0.1943 −0.3101 −0.2996 −0.1928

0.3598 −0.0953 −0.3075 −0.3314 0.0680 −0.0780 −0.1953 −0.5266 −0.1303 −0.1881 −0.0996 0.6586 −0.3988 −0.2162 −0.2799 −0.4063 0.2485 −1.0012 −0.6670 −0.2810 −0.6436 −0.6168 −0.1873 −0.2606 −0.6490 −0.1729

−0.5837 −0.0785 0.0775 0.1920 −0.1733 −0.0627 −0.1532 0.1027 −0.1870 −0.0677 −0.6230 −0.7550 0.0116 0.1572 −0.0550 0.1978 −0.4406 −0.4676 1.3480 0.3772 0.2590 0.0368 0.2444 0.0238 0.2457 −0.0205

−0.0715 −0.0040 −0.0630 −0.0447 −0.0357 0.0135 −0.0393 −0.1185 −0.0656 0.0076 0.0518 −0.0695 −0.0139 −0.0384 −0.1053 −0.0918 −0.0714 −0.2950 0.1704 −0.0195 −0.0797 −0.0289 0.1392 0.0455 0.0463 −0.0543

−0.1275 0.0468 −0.0515 −0.0457 −0.0411 0.0202 −0.0511 −0.0487 −0.0897 0.0075 0.1598 −0.0614 0.0366 0.0035 0.0465 0.0066 0.0261 −0.2461 0.1365 −0.0380 −0.0160 0.0008 −0.1344 −0.0437 0.0155 −0.0177

5.1. Conclusion This study explored the connection between economic growth and carbon reduction using an empirical study (U.S. GDP and carbon emissions during 2007–2016); this approach differed from using the previous hypotheses-focused approaches. We combined the Cobb–Douglas (C-D) production function and the extended Kaya equation to develop a new decomposition technique. The decoupling status was calculated using the decoupling elasticity, which can be further decomposed into six influencing factors: investment effect, labor effect, energy intensity effect, economy structure effect, energy mix effect, and the emission coefficient effect. The main conclusions are as follows: (i) Investment effect played a dominant role in the growth of carbon dioxide emissions for most years between 1990 and 2016. In most cases, the energy intensity effect helped cut CO2 emissions, especially since 2010. Energy intensity was a core contributor to reducing CO2 emissions. (ii) Five decoupling statuses were detected: strong decoupling, weak decoupling, expansive coupling, expansive negative decoupling, and recessive decoupling. Strong decoupling and weak decoupling were the most common states. After 2005, strong decoupling and 8

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recessive decoupling occurred more frequently. This demonstrated a decarbonization of economic growth, with a total 738.14 million metric tons of CO2 reduction between 2005 and 2016. (iii) The investment effect was the most significant factor hindering decoupling, followed by the labor input effect and economy structure effect. The investment effect, labor input effect, and economy structure effect reached their decoupling ceiling after the year 2008. The energy intensity effect was the force driving decoupling and the main reason for the observed strong decoupling status. The energy mix effect and economy structure effect also contributed to a “strong decoupling” state. After the global economic crisis in 2007 and 2009, the investment effect and economy structure effect negatively influenced the decoupling trend; energy intensity was a core contributor to the entire decoupling process. The impact of energy mix effect and emission coefficient effect was small, indicating that a shift in energy mix and energy replacement did not significantly change the decoupling trend.

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5.2. Policy implications Governments may hesitate to develop aggressive plans to cut emissions if those cuts cause economic and job losses. But since strong decoupling state has been detected in the U.S., the economy can continue to expand without bringing more GHG emissions. Based on the research conclusions, this research has highlighted ways to curb carbon emissions without economic losses. (i) Since the investment effect and economy structure were vital factors influencing total CO2 emissions, developing low-carbon economies and adjusting industry structures is key direction to go to achieve climate change mitigation goal. More modern energysaving initiatives contributing to the switch from traditional industrial development pattern to advanced economy development with frontier technologies which can less carbon emission should be further emphasized (Chen et al., 2019; Cui and Huang, 2018; Cui et al., 2019). Also, more research concerning reducing the dependence of economic development pattern on energy consumption in consideration of energy costs should be conducted. (ii) (ii)This paper confirms that improving the quality of the labor force is critical, because the labor input effect significantly impacts carbon dioxide emission growth. Educational programs may help curb total CO2 emissions because education can raise people’s awareness of the importance along with the urgency of fighting for environment degradation and climate change (Darby et al., 2016; Klein-Banai and Theis, 2013). (iii) Energy efficiency improvements should be advanced to accelerate decoupling. The energy mix and economy structure did not significantly influence the decoupling trend; as a result, efforts should focus on improving energy efficiency or emission-free technologies improvement when burning the carbon-intensive fuels. Acknowledgement The authors would like to thank the editor and these anonymous reviewers for their thoughtful comments and constructive suggestions, which greatly helped us to improve the manuscript. This work is supported by National Natural Science Foundation of China (Grant No. 71874203), Humanities and Social Science Fund of Ministry of Education of China (Grant No. 18YJA790081), and Natural Science Foundation of Shandong Province, China (Grant No. ZR2018MG016). References Achour, H., Belloumi, M., 2016. Decomposing the influencing factors of energy consumption in Tunisian transportation sector using the LMDI method. Transp. Policy (Oxf) 52, 64–71.

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