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Sustainability 2014, 6(2), 1037-1045; doi:10.3390/su6021037

Communication
Understanding the Causality between Carbon Dioxide Emission, Fossil Energy Consumption and Economic Growth in Developed Countries: An Empirical Study
Bing Xue 1,*, Yong Geng 1,, Katrin Müller 2,, Chengpeng Lu 1, and Wanxia Ren 1,
1
Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; E-Mails: gengyong@iae.ac.cn (Y.G.); luchp.lzu@gmail.com (C.L.); renwanxia@iae.ac.cn (W.R.)
2
Institute for Applied Material Flow Management, University of Applied Sciences Trier, Birkenfeld D-55768, Germany; E-Mail: katrin.mueller@student. unisg.ch
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed; E-Mail: xuebing@iae.ac.cn; Tel.: +86-24-8397-0433; Fax: +86-24-8397-0371.
Received: 30 January 2014; in revised form: 12 February 2014 / Accepted: 13 February 2014 /
Published: 21 February 2014

Abstract

: Issues on climate change have been recognized as serious challenges for regional sustainable development both at a global and local level. Given the background that most of the artificial carbon emissions are resulted from the energy consumption sector and the energy is also the key element resource for economic development, this paper investigated the relationship between CO2 emission, fossil energy consumption, and economic growth in the period 1970–2008 of nine European countries, based on the approach of Granger Causality Test, followed by the risk analysis on impacts of CO2 reduction to local economic growth classified by the indicator of causality degree. The results show that there are various feedback causal relationships between carbon emission, energy consumption and economic growth, with both unidirectional and dual-directional Granger causality. The impact of reducing CO2 emission to economic growth varies between countries as well.
Keywords:
climate change; carbon reduction; regional disparity; causality test; European countries

1. Introduction

Global climate change has been a hot topic since the end of the 20th century (see, for instance, [1,2,3]), and it is currently also a major concern and challenge confronting countries in the world [4], for example, the report of IPCC4 pointed that it was very likely (more than 90% of probability) that global warming was related to the increasing of greenhouse gases (GHG) over the past 50 years [5]. Concerning climate change mitigation and adaption, the Kyoto Protocol is a very important step for reducing GHG emissions, and, moreover, broader participation and deeper cuts in GHG emissions are essential for any post-Kyoto agreement [6]. However, the common recognition on reducing GHG emissions from the past several Conferences of Parties (COP) to the United Nations Framework Convention on Climate Change, such as the COP 15 in 2009 of Copenhagen, COP 17 in 2011 of Duban and COP19 in 2013 of Warsaw are not sufficient enough. The key issue for reaching a consensus on GHG emission reduction is the concern on its impact to local economic development [7], either in developed regions or in developing and emerging countries. For example, van Renssen explored the worries voiced within industry and argued that Europe must come up with effective climate, environment and energy policies that do not jeopardize economic competitiveness [8], Wang et al. [9] stated that China’s economic growth and export trade had significantly promoted its carbon emissions, and the rapid economic growth is the main determinant that causes an increase in carbon emissions [10]. Major emitters seem unwilling to accept binding emissions reductions targets as their economies have stagnated [11]. Given the common background that most of the artificial carbon emission results from the energy consumption sector and that energy is also the key element resource for economic development [12], the causal relationship between carbon emission, energy consumption and economic growth should be investigated. This could offer better understanding on the interaction impacts to local sustainable development [13,14,15].

As for developing and emerging countries, such as China, India and Brazil, the question as to how to delink the energy consumption and carbon emission from the economic growth has been the key issue faced by policy makers and scientific communities. However, currently, in the face of international pressure to curb carbon releases as well as the tight domestic fossil-energy supply [16], relative indicators are usually applied for measuring carbon reduction target in order to avoid the possible negative impacts on local economic development [17]. For example, a case study for Brazil shows that the business-as-usual CO2 emissions in 2040 from energy use and industrial processes would be almost three times as high as those in 2010, while the current policy aims to lower emissions intensity of the overall economy by 36–39 percent by 2020 [18]. Another analysis on carbon reduction in China indicates that the region with a lower reduction benchmark and higher decoupling elasticity value has more difficulty in achieving the emission reduction targets without powerful relevant policy [19], and a provincial level case study illustrates that with the rapid economic development, CO2 emission in Jiangsu province rose from 1, 88 × 108 t in 1995 to 5, 20 × 104 t in 2009, with an average annual growth rate of 7.54% [20]. Even the Chinese central government has stated that in 2020 the carbon intensity will be reduced by 40%–45% compared to the 2005 level [21]. An early study that aimed to examine the long-run relationship between output and energy consumption during the period 1971–1999 in Malaysia indicated that energy use was positively related to output in the long-run, and offered strong support for causalities running from economic growth to energy consumption growth both in the short-run and long-run [22]. Research in the case of South Africa on the effects of economic growth and coal consumption on CO2 emissions over the period 1965–2008 also shows that a rise in economic growth increases energy emissions, and coal consumption makes a significant contribution to deteriorating the environment [23], which was also verified by Menyah and Wolde-Rufael’s research [24]. Under such circumstances, investigation and insights into the relationship between carbon emission, energy consumption and economic growth in developed countries could offer further understanding for developing countries.

As the typical representatives of developed and industrialized countries in this world, Europe plays a leading role in climate change mitigation and adaptation. Thus, investigating the causal relationship between the mentioned elements is significant and could have policy implications for developing countries. Actually, few related studies have been done: Ang investigated the dynamic causal relationships between pollutant emissions, energy consumption, and output for France by using co-integration and vector error-correction modeling techniques for the period 1960–2000, and the causality results support the argument that economic growth exerts a causal influence on growth of energy use and growth of pollution in the long run [25]. Marrero took the advantage of a panel data set of 24 European countries in the period of 1990–2006 and applied a Dynamic Panel Data framework to examine the relationship of GHG emissions, growth and the energy mix in Europe, and found evidence for the existence of conditional convergence in terms of GHG emissions among the EU27 countries [26]. The change in energy intensity of the main economic activities in period of 1991–2005 of the EU15 countries was also investigated by Marrrero and Ramos-Real in 2013, and the results revealed four different typologies for EU15 countries and showed the importance of identifying those economic activities which are key to reducing energy consumption [27].

Herein, based on the statistical data in the open database of World Bank, nine European countries, namely, Denmark, France, Greece, Italy, Netherlands, Portugal, Spain, Sweden and United Kingdom are selected to investigate the causal relationship between GDP growth, energy consumption and CO2 emissions in the period of 1970–2008, and followed by the evaluation on the possible impacts of CO2 emissions reduction to economic development by ranking the classes based on the causality degree results.

2. Methodologies and Results

Selected variables used in this study include: (1) GDP per capita (GDPP) which represents the local economic developmental level of studied countries, (2) CO2 emissions per capita (PCO2) which represents the contribution of economic activities to climate change, and (3) Energy Consumption Intensity (ECI) (fossil energy consumption per capita) which represents the fossil energy resources consumption affected by the type of the fossil energy mix and population.

2.1. Unit Root Test

Co-integration analysis introduces the idea that even if underlying time series are non-stationary, linear combinations of these series might be stationary. Therefore it is essential to verify that all variables are integrated of order one in levels before employing panel co-integration techniques. The main idea behind performing a unit root test while using ADF statistics is to ensure that the error term should be lagged independent [14]. In this method, the unit root test is carried out by means of the following formulation [15].

Yt = +ρYt-I + T+∑biYt-1 + εt, i = 1, 2, …, n

Together with the fixed term regressions with trend, ADF statistics and McKinnon’s critical values are derived. Where ∆Yt = YtYt-i, ⍺ is a drift term, and T is the time trend with the null hypothesis H0: ρ = 0 and H1 its alternative hypothesis H1: ρ ≠ 0, n is the number of lags necessary to obtain white noise, and ε is the error term. Failure in rejecting H0 implies that the time series is non-stationary. The unit root tests of all the three variables sets mentioned above for the nine countries are tested under differential levels of both first/second differences. From Table 1, we could conclude that most of the unit root hypothesizes have not been rejected when the variables are taken in various levels, but when the first difference was used, most of the hypothesizes of unit root non-stationary are rejected at the level 1% or level 5% of the significance, and few of the hypothesizes of unit root non-stationary are rejected under the second differences at level 1%.

Table Table 1. Results of the unit root test in first difference.

Click here to display table

Table 1. Results of the unit root test in first difference.
CountriesVariables’ seriest-StatisticProbability1% level
FrancePCO2−7.15340.0000−3.6267
ECI−6.86730.0000−3.6267
GDPP−4.01210.0037−3.6267
United KingdomPCO2−7.37240.0000−3.6267
ECI−6.29710.0000−3.6267
GDPP−4.53100.0009−3.6267
ItalyPCO2−6.00260.0000−3.6267
ECI−5.98010.0000−3.6267
GDPP−4.25350.0019−3.6267
SpainPCO2−5.15320.0002−3.6267
ECI−4.21210.0021−3.6267
GDPP−3.30310.0221−2.9458 a
SwedenPCO2−7.58310.0000−3.6267
ECI−6.74910.0000−3.6267
GDPP−4.31460.0016−3.6267
PortugalPCO2−10.130 b0.0000−3.6394
ECI−5.72870.0000−3.6267
GDPP−3.85200.0056−3.6267
NetherlandsPCO2−6.00890.0000−3.6267
ECI−2.9170 c0.0530−3.6210
GDPP−3.44340.0158−3.6267
GreecePCO2−6.80680.0000−3.6267
ECI−5.76850.0000−3.6267
GDPP−5.4214 b0.0001−3.6463
DenmarkPCO2−2.6389 c0.0945−3.6210
ECI−3.41660.0166−3.6210
GDPP−3.72370.0078−3.6268

a at level 5%; b tested in level 10%; c tested in 2nd difference.

2.2. Co-Integration Test

The co-integration test was employed in order to determine whether there exists a co-integration between the studied series. The aim of co-integration analysis is to model and estimate the long term correlation-ship among non-stationary time series. Herein, the Johansen co-integration procedure was performed for determining whether the co-integration exists. The first step in Johansen procedure is to determine the lag order. In this study, the significant and right lag period is finally selected after the numbers test from lag 1 to lag 10 and followed by the trace statistic and the maximum Eigen-value statistic (see the supplementary materials). The result shows that causality exists among the selected variables series; however, the co-integration test cannot tell the direction of the causal relationship. Thus, the following multivariate Granger-causality test based on the VECM was carried out to investigate both short-run and long-run causality.

2.3. Granger Causality

If the series X and Y are individually I(1) and co-integrated as well, then the Granger causality tests could use I(1) data because of the super consistency properties of estimation, whereas ut and vt are zero-mean, serially uncorrelated, random disturbances. The optimum lag lengths m, n, q and r are determined on the basis of Schwarz Bayesian and/or log-likelihood ratio test criterion:

Sustainability 06 01037 i001

In the above equation, Y Granger causes X if H0: a21 = a22 = … a2n = 0 is rejected, and H1: at least on a2i ≠ 0, I = 1,2,3,…, n. and, correspondingly, in the following equation, X Granger causes Y if H0: b21 = b22 = … b2n = 0 is rejected, and H1: at least on b2i ≠ 0, i = 1,2,3,…, r:

Sustainability 06 01037 i002

Results of the Granger-causality test for the 9 countries show that there are various feedback causal relationships between carbon emission, energy consumption and economic growth, with both unidirectional and dual-directional causalities (Table 2, and supplementary materials). In conclusion, (1) Granger Causality between energy consumption and CO2 emissions: the null hypothesis of “ECI does not Granger Cause PCO2” and “PCO2 does not Granger Cause ECI” are rejected in countries of France, Spain and Sweden, but accepted in countries of United Kingdom, Italy, Portugal and Greece; while in Denmark, the null hypothesis of “ECI does not Granger Cause PCO2” is rejected but that of “PCO2 does not Granger Cause ECI” is accepted; (2) Granger Causality between economic growth and CO2 emissions, the null hypothesis of “GDPP does not Granger Cause PCO2” and “PCO2 does not Granger Cause GDPP” are all accepted for most of the countries includes France, Italy, Spain, Sweden, Portugal, Netherlands and Greece; while for United Kingdom, the null hypothesis of “GDPP does not Granger Cause PCO2” is rejected but that of “PCO2 does not Granger Cause GDPP” is accepted; however, the situation in Denmark is opposite than that in the United Kingdom.

Comparing the differences based on the long term, conclusions are summarized as follows: (1) for France, Spain and Sweden, the causality relationship between ECI and PCO2 is reciprocal relation, but has no causality relationship between GDPP and PCO2; (2) for the United Kingdom, there exist non-causality relationship between ECI and PCO2, and GDPP is the Granger causality to PCO2 while PCO2 is not the Granger causality to GDPP; (3) for Italy, Portugal and Greece, there no causality exists between ECI and PCO2, and between GDPP and PCO2. (4) for the Netherlands, ECI is not the Granger Cause to PCO2, but PCO2 is the granger cause to ECI; in addition, there exists a non-causality relationship between GDPP and PCO2; (5) for Denmark, ECI is the Granger Cause to PCO2, but PCO2 is not the granger cause to ECI; also the same situation exists between GDPP and PCO2.

Table Table 2. Granger-causality test results for nine countries from 1970–2008.

Click here to display table

Table 2. Granger-causality test results for nine countries from 1970–2008.
CountriesNull HypothesisObsLagF-StatisticProbabilityResult (1%)
FranceECI does not Granger Cause PCO23356.652010.00065Reject
PCO2 does not Granger Cause ECI2.828480.04043Reject
GDPP does not Granger Cause CO23350.923450.48451Accept
CO2 does not Granger Cause GDPP0.529340.75166Accept
United KingdomECI does not Granger Cause PCO23712.829770.10170Accept
PCO2 does not Granger Cause ECI0.010320.91968Accept
GDPP does not Granger Cause CO23717.009270.01220Reject
CO2 does not Granger Cause GDPP0.002920.95720Accept
ItalyECI does not Granger Cause PCO23530.912940.44730Accept
PCO2 does not Granger Cause ECI1.344150.28009Accept
GDPP does not Granger Cause CO23530.469910.70566Accept
CO2 does not Granger Cause GDPP1.290070.29716Accept
SpainECI does not Granger Cause PCO237110.42690.00275Reject
PCO2 does not Granger Cause ECI13.72060.00075Reject
GDPP does not Granger Cause CO23710.006930.93415Accept
CO2 does not Granger Cause GDPP0.019970.88845Accept
SwedenECI does not Granger Cause PCO23715.167070.02946Reject
PCO2 does not Granger Cause ECI7.326240.01055Reject
GDPP does not Granger Cause CO23712.809250.10290Accept
CO2 does not Granger Cause GDPP8.4 × 10−70.99928Accept
PortugalECI does not Granger Cause PCO23352.058560.10960Accept
PCO2 does not Granger Cause ECI1.508570.22773Accept
GDPP does not Granger Cause CO23351.997190.11887Accept
CO2 does not Granger Cause GDPP1.083210.39693Accept
NetherlandsECI does not Granger Cause PCO23710.475940.49495Accept
PCO2 does not Granger Cause ECI3.029770.09079Reject
GDPP does not Granger Cause CO23711.129330.29541Accept
CO2 does not Granger Cause GDPP0.295900.59001Accept
GreeceECI does not Granger Cause PCO23710.098390.75569Accept
PCO2 does not Granger Cause ECI0.777820.38400Accept
GDPP does not Granger Cause CO23710.002970.95684Accept
CO2 does not Granger Cause GDPP0.245990.62311Accept
DenmarkECI does not Granger Cause PCO23714.896810.03372Reject
PCO2 does not Granger Cause ECI0.092040.76345Accept
GDPP does not Granger Cause CO23716.990120.01231Reject
CO2 does not Granger Cause GDPP0.030040.86342Accept

Based on the long-run causality tests among PCO2, ECI and GDPP in the nine countries, we can establish an evaluation matrix to identify the differences of the possible impacts resulted from reducing CO2 emission to energy consumption and economic growth. Herein, we defined the risk as six levels: (1) Remote—Probability of less than 10%; (2) Highly Unlikely—Probability between 10% and 35%; (3) Possible-Probability between 36% and 50%; (4) Probable—Probability between 51% and 75%; (5) Highly Likely—Probability 76% and 90%; and (6) Certain—Probability above 90%. The assessment result is shown in Table 3. The results show that the impact of reducing CO2 emissions on economic growth for different countries varies. In general, reducing CO2 emission has more negative impacts on the economic growth of Italy, followed by Portugal, and has the lowest impacts on the United Kingdom and Sweden.

Table Table 3. Risk assessment based on the causality analysis.

Click here to display table

Table 3. Risk assessment based on the causality analysis.
CausalityRemote (<10%)Highly Unlikely (10%–35%)Possible (36%–50%)Probable (51%–75%)Highly Likely (76%–90%)Certain (>90%)
PCO2 does not Granger Cause ECIFrance; Spain; Sweden; DenmarkUnited Kingdom; Portugal;Italy; Netherlands Greece
ECI does not Granger Cause PCO2France; Spain; Sweden; NetherlandsItaly; Portugal;GreeceDenmark United Kingdom
GDPP does not Granger Cause PCO2DenmarkUnited Kingdom; Sweden; Portugal; NetherlandsFranceItaly Spain; Greece
PCO2 does not Granger Cause GDPP ItalyPortugalFrance; Netherlands; GreeceSpain;DenmarkUnited Kingdom; Sweden

3. Conclusions

Issues on climate change have been recognized as a serious challenge for addressing regional sustainable development at both a global and local level. This paper examined the relationship among CO2 emission, fossil energy consumption, and economic growth in period 1970–2008 of nine European countries, by employing the Granger Causality Test, followed by the impacts analysis of carbon reduction to local economic growth. The results show that there are both unidirectional and dual-directional Granger causality existing in the nine countries. However, given the background of the environmental industries’ development in European countries, reducing carbon emissions would have short-term throes to economic development, because, in the long-term scenario, the reduction of carbon emission also could accelerate the development of renewable energies, such as in Germany [28].

Acknowledgments

This research is supported by National Natural Science Foundation of China (41101126, 71033004, 71303230), the International Cooperation Project “Urban Co-benefits Research”, 100 Talents Program of the Chinese Academy of Sciences (2008-318), Ministry of Science and Technology of China (2011BAJ06B01), and the Alexander von Humboldt Foundation. Special thanks go to the anonymous reviewers for their valuable comments.

Supplementary Materials

Supplementary materials can be accessed at: http://www.mdpi.com/2071-1050/6/2/1037/s1.

Author Contributions

Bing Xue conducted the research, contributed to data analysis and paper-wrote; Yong Geng, Katrin Müller, Chengpeng Lu and Wanxia Ren contributed to data analysis and revision.

Conflicts of Interest

The authors declare no conflict of interest.

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