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Regional Environmental Change

, Volume 13, Issue 5, pp 979–988 | Cite as

CO2 emissions in German, Swedish and Colombian manufacturing industries

  • Alexander Cotte Poveda
  • Clara Inés Pardo Martínez
Open Access
Original Article

Abstract

This study evaluates and compares the trends in CO2 emissions for the manufacturing industries of three countries: two developed countries (Germany and Sweden) that have applied several measures to promote a shift towards a low-carbon economy and one developing country (Colombia) that has shown substantial improvements in the reduction of CO2 emissions. This analysis is conducted using panel data cointegration techniques to infer causality between CO2 emissions, production factors and energy sources. The results indicate a trend of producing more output with less pollution. The trends for these countries’ CO2 emissions depend on investment levels, energy sources and economic factors. Furthermore, the trends in CO2 emissions indicate that there are emission level differences between the two developed countries and the developing country. Moreover, the study confirms that it is possible to achieve economic growth and sustainable development while reducing greenhouse gas emissions, as Germany and Sweden demonstrate. In the case of Colombia, it is important to encourage a reduction in CO2 emissions through policies that combine technical and economic instruments and incentivise the application of new technologies that promote clean and environmentally friendly processes.

Keywords

CO2 emissions Manufacturing industries Panel data model 

Introduction

An increase in carbon emission levels is likely associated with an increased risk of adverse climate change and severe negative socio-economic effects in the long run. The relationship between climate change and energy is a key challenge for sustainable development, indicating the need to use energy more efficiently and reduce CO2 emissions (G8 2005; IEA 2007). Determining and analysing various energy policies and development paths matter for controlling emission levels because it has become necessary to mitigate the impacts of development on climate change and to keep the projected increase in atmospheric carbon dioxide levels within reasonable bounds1 (Hawksworth 2006; IPCC 2001).

In recent years, the manufacturing industry has accounted for, on average, 33 % of total energy consumption and 36 % of total global CO2 emissions. This sector has significant potential to decrease energy consumption and CO2 emissions through the application of technical and economic strategies (IEA 2007, 2008b). If policy makers want to decrease energy consumption and CO2 emissions, they need an understanding of the different mechanisms that lead to climate change. In addition, policy makers should encourage increased energy efficiency and lower CO2 emissions. Methods of regulating CO2 emissions include investments in new technologies, inter-fuel substitution and economic instruments, such as energy price controls and taxes. In this study, we analyse CO2 emissions and factors that influence these emissions in two developed countries, Germany and Sweden. Germany and Sweden were selected because they have significantly decreased their level of CO2 emissions through energy policies that have promoted technology change and clean production systems (Federal Ministry of Economics and Technology 2006; Speck 2008; Swedish institute 2011). We selected Colombia as an example of a developing country because Colombia has also reduced its CO2 emissions, and it is an environmental leader among countries with comparable incomes (GTZ 2003; WEC 2004; Kim 2010).

Several studies have analysed trends in CO2 emissions among manufacturing industries. For example, Ciais et al. (2010) analysed fossil-fuel CO2 emissions across different sectors in 25 European countries using emission inventories from energy-use statistics. They found that an adequate definition of system boundaries is fundamental for studying CO2 emissions. Another finding was that the uncertainty of fossil-fuel CO2 fluxes in the atmosphere can be reduced through the use of transport models. Schipper et al. (2001) analysed the trends in CO2 emissions across manufacturing industries in 13 developed countries by applying an adaptive weighting Divisia decomposition and compared emissions by country and subsector. This study revealed that emissions have been increasing since 1990. Output growth has been the main factor behind increased carbon emissions, while better energy efficiency has been the largest factor compensating for this growth. Hamilton and Turton (2002) studied the determinants of emission growth in Organisation for Economic Co-operation and Development (OECD) countries with a decomposition formula analysing the effects of economic growth, energy intensity, energy consumption, the share of fossil fuel and carbon intensity. They demonstrated that growth in emissions depended on how effectively energy use can change to offset the effects of economic growth. Improvements in energy efficiency and a declining share of fossil fuel decrease CO2 emissions.

Other studies have examined CO2 emissions in specific industrial sectors. For example, in the cement industry, Ali et al. (2011), Moya et al. (2011) and Oggioni et al. (2011) studied methods of decreasing CO2 emissions. They found that investments in new technologies and the use of alternative fuels and raw materials contribute to decreased CO2 emissions. Furthermore, they argued for developing policies to establish an environment combining support for technology change, development and deployment in the industrial sector. Takeda et al. (2011), Luengen et al. (2011), Johansson and Söderström (2011) and Shevelev (2010) analysed trends in carbon emissions and different technological strategies to decrease CO2 emissions in the steel industries in Japan, Germany, Sweden and Russia. Lindmark et al. (2011) analysed CO2 emissions in the paper industry and determined that the main drivers for decreasing these emissions are energy substitution and the application of new technologies. The OECD (2001) studied carbon emissions in chemical industries and found various possibilities for decreasing emissions in this sector. Audsley et al. (2009) analysed CO2 emissions in the food industry and proposed several scenarios to determine the best strategy for reducing these emissions from the value chain. However, because these studies focused on detailed aspects of CO2 emissions, especially on technology change rather than the entire manufacturing industry, the current understanding of the effects of several variables, such as production factors and energy price, on CO2 is quite limited.

From an empirical perspective, the relationship between carbon dioxide emissions, energy consumption and economic growth has been investigated. For example, Niu et al. (2011) studied this relationship in eight Asia–Pacific countries using panel data. They found long-run equilibrium relationships between CO2 emissions, energy consumption and economic growth. In addition, in developing countries, they found that is important to improve economic growth while reducing energy consumption and emissions. Haggar (2011) evaluated the long run and the causal relationships between greenhouse gas emissions, energy consumption and economic growth for Canadian industrial sectors by applying a panel data framework based on the environmental Kuznets curve. This study determined that energy consumption had a positive and statistically significant impact on greenhouse gas emissions, whereas a non-linear relationship existed between greenhouse gas emissions and economic growth. Wang (2011) applied an empirical test to explore the relationship between CO2 emissions and economic growth, which was stable in the long run. This study found that countries with high economic growth and high CO2 emissions should develop an energy policy for controlling global warming. Pao and Tsai (2011) analysed the impact of both economic growth and financial development on environmental degradation using a panel cointegration technique for BRIC countries and found a strong, bi-directional causality between emissions and FDI and a strong, unidirectional causality from output to FDI. They contended that these countries should encourage investments in energy supply and energy efficiency to reduce CO2 emissions without affecting competitiveness. All of these studies have analysed the relationship between CO2 emissions, energy consumption and economic growth without including other factors that could affect this relationship. Thus, comparisons between developed and developing countries are limited.

The main contribution of the present study is an analysis and comparison of the trends in CO2 emissions in the manufacturing industries of two developed countries (Germany and Sweden) and one developing country (Colombia) by applying several indicators and econometric techniques. We focus on the causal relationship between CO2 emissions and production factors and other variables. The purpose of this study is to determine the effects of several variables, such as fossil fuel consumption, investments, energy price and taxes, on carbon dioxide emissions. The research questions that guide this study are as follows: (1) What are the trends in CO2 emissions in the manufacturing industries of the developed countries Germany and Sweden and the developing country Colombia? (2) What factors determine CO2 emissions trends and differences between the countries?

To answer these questions, we examine the manufacturing industries of the three countries between 1995 and 2008, and we use panel data cointegration techniques. This paper is structured as follows: In section “Methodology and data”, a description of the methodology and data used in this study is presented. Section “Manufacturing industry: trends and developments in Germany, Sweden and Colombia” shows the trends in CO2 emissions and the activity indicators of the manufacturing industries in the three countries. In section “Results and discussion”, we analyse and discuss the results of this study, and the main conclusions are presented in section “Conclusions and policy implications”.

Methodology and data

Dataset

The time period for this study was determined by the availability of consistent and disaggregated CO2 emission and energy data. Hence, for the three countries selected, the analysis covers the period 1995–2008 at the 2-digit level of disaggregation of the International Standard Industrial Classification (ISIC—Rev. 3.1) for the 19 manufacturing industries. The data were obtained from the following statistical offices and energy agencies: Statistisches Bundesamt and DENA (Germany), SCB and the Swedish Energy Agency (Sweden), DANE and UPME (Colombia) and the database of the OECD in the module industry. In all three countries, all monetary variables were standardised to the 2005 euro values (see Table 1).
Table 1

Summary statistics of variables used in this study

 

CO2

Fossil fuels

Value added

Capital

Investments

Productivity

Energy taxes

Sweden

 Obs.

266

266

266

266

266

266

266

 Mean

11.85

7.49

7.16

8.70

5.24

2.99

13.30

 Std. Dev.

2.05

1.91

1.24

1.58

1.48

0.68

1.37

 Min.

7.88

3.61

3.59

4.50

1.10

1.25

10.00

 Max.

15.67

10.68

8.99

11.22

7.28

5.51

15.76

Germany

 Obs.

266

266

266

266

266

266

266

 Mean

15.67

9.35

16.35

24.29

14.20

5.21

2.18

 Std. Dev.

1.42

1.45

1.05

0.90

1.22

0.32

2.01

 Min.

11.59

6.12

13.42

21.94

10.54

4.63

3.95

 Max.

18.58

13.64

18.13

25.72

16.39

6.10

6.49

Colombia

 Obs.

266

266

266

266

266

266

266

 Mean

11.84

6.22

12.70

13.03

10.33

3.70

1.72

 Std. Dev.

1.95

1.96

1.28

1.42

1.65

0.58

0.78

 Min.

7.36

1.09

9.18

8.89

4.94

1.82

0.01

 Max.

15.47

10.23

15.98

15.98

14.48

5.20

4.05

CO2 is measured as tonnes of carbon dioxide, fossil fuels are measured in terajoules, value added is measured in euros, capital is measured as the capital stock in euros, Investments are measured in euros, labour productivity is measured as gross production per worker and energy prices are measured in euros. All monetary units were indexed and linked to the consumer price index and data using a natural logarithm

Model

Following the empirical literature in energy economics, we develop a long-run relationship between CO2 emissions and other variables (OV) as energy sources, output and production factors, energy prices and investments in a natural logarithm as follows (Ang 2007; Apergis and Payne 2009; Pao and Tsai 2010):
$$ {\text{LCO}}_{it} = \beta_{0} + \beta_{1} {\text{LFF}}_{it} + \beta_{2} {\text{LVA}}_{it} + \beta_{3} {\text{LK}}_{it} + \beta_{4} {\text{LINV}}_{it} + \beta_{5} {\text{LPROD}}_{it} + \beta_{6} {\text{LEP}}_{it} + u_{it} $$
(1)
where i stands for the manufacturing for every country and i = 1,…, N; t denotes the time, t = 1,…, T and u it is assumed to be a serially uncorrelated error term. The variable LCO represents the logarithm of CO2 emissions (measured as tonnes of carbon dioxide), and LFF represents fossil fuels (measured in terajoules). The variable LVA denotes value added (measured in euros), LK measures capital (measured as the capital stock in euros) and LINV represents investments (measured in euros). Finally, LPROD denotes labour productivity (measured as gross production per worker) and LEP denotes energy prices (measured in euros).

The empirical strategy

In this analysis, the model is estimated using the dynamic OLS (DOLS) panel cointegration technique proposed by Stock and Watson (1993) and analysed later by Kao and Chiang (2000) and Pedroni (2001). In this model, the causality among CO2 emissions, production factors and energy sources is explored using several test and panel data cointegration methods that are explained in this section.

Panel unit root test

Before proceeding with the cointegration techniques, we need to verify that all of the variables are integrated to the same order. The panel unit root test is established on the following autoregressive specification (Dickey and Fuller 1979; Mahadevan and Asafu-Adjaye 2007):
$$ y_{it} = \rho_{i} y_{it - 1} + \Updelta_{i} x_{it} + u_{it} $$
(2)
where i = 1,2,…,N represents every manufacturing industry by country observed over periods, t = 1,2,..,T, x it are exogenous variables in the model comprising any fixed effects or individual trends, and ρ i is the autoregressive coefficient. If ρ i  < 1, y i is weakly trend-stationary. Conversely, if ρ i  = 1, then y i contains a unit root. u it is the stationary error terms.

This analysis employs the tests proposed by Im–Pesaran–Shin (2003) (IPS). This test method is integrated in Eq. (2).

Cointegration techniques

To establish that all variables are integrated at an order of one, a cointegration analysis to determine whether a long-run relationship exists among the variables is performed by applying the Pedroni (1999) heterogeneous panel cointegration test, which allows for cross-section inter-dependence with different individual effects. The empirical model for this test is the following equation:
$$ y_{it} = \alpha_{i} + \gamma_{i} t + \beta x_{it} + \varepsilon_{it} $$
(3)
where α and γ are manufacturing industries by country and time fixed effects, respectively, and ε is the estimated residual representing deviations from the long-run equilibrium relationship.

Pedroni proposed two types of tests. The first type is based on the within-dimension approach and includes the panel PP-statistic and the panel ADF t-statistic. The second test proposed by Pedroni (1999) is based on the between-dimension approach and includes the group PP-statistic and the group ADF-statistic.

The Kao (1999) Cointegration Tests are tests with the null hypothesis of no cointegration for panel data. These tests follow the same basic approach as the Pedroni tests, but they specify cross-section specific intercepts and homogeneous coefficients on the first-stage regressors.

DOLS estimators

To estimate a long-run relationship between the variables in the panel model in the presence of cointegration, several estimators have been suggested: dynamic OLS (DOLS), Pool Mean Group (PMG), OLS and Fully Modified OLS (FMOLS). In this paper, we apply the estimators with the dynamic OLS (DOLS) error correction because Kao and Chiang (1997, 2000) demonstrated that both the OLS and FMOLS showed small sample bias and that the DOLS estimator outperformed both of these estimators.2

The dynamic OLS (DOLS) methodology was proposed by Kao and Chiang (2000) to estimate the long-run cointegration vector for non-stationary panels. These estimators allow for the correction of the serial correlation and endogeneity of regressors that are normally present in a long-run relationship. The DOLS estimator proposed by Kao and Chiang (1997, 2000) is an extension of Stock and Watson’s (1993) estimator. To obtain an unbiased estimator of the long-run parameters, the DOLS estimator applies a parametric adjustment to the errors by including the past and future values of the differenced I(1) regressors.

Manufacturing industry: trends and developments in Germany, Sweden and Colombia

To provide a background on CO2 emissions in the manufacturing industries, we examine the economic and energy contexts. In these three countries, the manufacturing industry is one of the most important economic activities, as evidenced by its contribution to the gross domestic product, employment, development and innovation. German manufacturing industries are global leaders in diverse sectors due to their advanced technological applications, human capital intensity and quality of goods (Wagner 2010). Swedish manufacturing industries have been among the highest overall R&D expenditures in the world and are major developers of innovative and knowledge products (Åström et al. 2006).

Colombian manufacturing industries are growing and developing and are regional leaders in several sectors, including agribusiness and chemicals, among others (Pardo Martínez 2009; Cotte Poveda and Pardo Martinez 2011). Figure 1 shows the trends in CO2 emissions, energy, production value and value added in the manufacturing industries of the three countries between 1995 and 2008. In both of the developed countries, these indicators show similar trends: an increase in economic indicators and a decrease in energy and CO2 emissions. Alternatively, the trends in Colombia show an increase in economic indicators and a decrease in CO2 emissions. Energy use is lower in Colombia than it is in Sweden and Germany, indicating that in Colombia, it is important to encourage a reduction in CO2 emissions. Moreover, in the three countries, the trend is to produce greater output with less pollution.
Fig. 1

CO2 emissions and economic and energy indicators in the Swedish, German and Colombian manufacturing industries, 1995–2008

The main energy sources of all three countries are fossil fuels, electricity and natural gas. However, Germany and Sweden have increased their electricity and bio-fuel consumption and decreased their use of fossil fuels. In Colombia, the consumption of electricity and natural gas has increased, while fossil fuel consumption has decreased. The inter-fuel substitution from low efficiency or high polluting fuels, such as petroleum products, to cleaner and more efficient fuels, such as electricity, natural gas and biofuels, has led to a decrease in energy consumption and CO2 emissions, which concurs with the UNEP (1976)and with Pardo Martinez’s (2011) theories on the manufacturing industries.

Results and discussion

The results of the application of panel cointegration techniques to determine the interrelationships among CO2 emissions, energy sources, output and production factors, energy prices and investments are described herein.

Results of panel unit root tests

The results of the panel unit root test for each country are displayed in Tables 2, 3 and 4.3 The IPS test statistic is calculated for each variable. This test assumes that there are individual unit root processes across the cross sections. The null hypothesis is that there exists a unit root, and the alternative hypothesis is that some cross sections do not have a unit root. Tables 2, 3 and 4 report the results of the IPS panel unit root tests, which include an intercept and trend term. The panel unit root tests indicate that all the variables are integrated at an order of one.
Table 2

Results of the panel unit root tests for Sweden—individual intercept and trend

Test

CO2

Fossil fuel

Value added

Capital

Investments

Productivity

Energy taxes

Im, Pesaran and Shin

       

 Level

−0.417

−0.585

3.007

−1.846

−3.006a

−1.865

−1.309

 1st difference

−3.939a

−4.008a

−7.439a

−2.839a

−2.571b

−10.483a

−3.686a

 Decision

I(1)

I(1)

I(1)

I(1)

I(1)

I(1)

I(1)

aSignificance at the 1 % level

bSignificance at the 5 % level

Table 3

Results of panel unit root tests for Germany—individual intercept and trend

Test

CO2

Fossil fuel

Value added

Capital

Investments

Productivity

Energy prices

Im, Pesaran and Shin

       

 Level

−3.840a

−0.782

−1.504

−1.975

−2.005

−0.189

−1.258

 1st difference

−5.246a

−6.148a

−3.138a

−6.951a

−2.914a

−16.369a

−3.708a

 Decision

I(1)

I(1)

I(1)

I(1)

I(1)

I(1)

I(1)

aSignificance at the 1 % level

bSignificance at the 5 % level

Table 4

Results of panel unit root tests for Colombia—individual intercept and trend

Test

CO2

Fossil fuel

Value added

Capital

Investments

Productivity

Energy prices

Im, Pesaran and Shin

       

 Level

1.548

−1.282

1.443

0.477

−0.600

0.536

−5.295a

 1st difference

−5.468a

−5.339a

−4.927a

−3.970a

−5.432a

−4.972a

−9.756a

 Decision

I(1)

I(1)

I(1)

I(1)

I(1)

I(1)

I(1)

aSignificance at the 1 % level

bSignificance at the 5 % level

Results of the panel cointegration test

Based on the above results, we calculated cointegration statistics to test the models selected in this study. We used the panel cointegration proposed by Pedroni (1999) and Kao (1999). Panel-PP and panel-ADF are based on pooling along the “within-dimension”, and the group-PP and group-ADF are based on averaging along the “between-dimension”. All the statistics are based on the null hypothesis of no cointegration. Table 5 summarises the results of the Kao and Pedroni cointegration test for the three countries. The results indicate a rejection of the null hypothesis of no cointegration in the model (see Table 6 for a description of the variables in the model), implying that there exists a long-run relationship. This fact allows us to estimate the panel data cointegration relationships.
Table 5

Results of the panel cointegration tests for Sweden, Germany and Colombia

 

Sweden

Germany

Colombia

Pedroni panel cointegration test

   

Panel cointegration test

   

 Panel PP-statistic

−34.09a

−8.686a

−8.566a

 Panel ADF-statistic

−10.62a

−6.646a

−8.077a

Group mean cointegration test

   

 Group PP-statistic

−36.80a

−8.571a

−8.438a

 Group ADF-statistic

−10.72a

−6.304a

−7.895a

Kao panel cointegration test

   

 ADF t-statistics

−9.110a

−15.951a

−22.71a

aSignificance at the 1 % level

Table 6

DOLS estimates for Sweden, Germany and Colombia (CO2 emissions dependent variable)

Parameter

Colombia

Germany

Sweden

Fossil fuel

0.284a

(5.21)

0.558a

(14.76)

1.164a

(39.08)

Value added

0.210c

(1.88)

0.329c

(1.97)

0.097a

(17.05)

Capital

1.048a

(8.69)

0.321

(0.87)

0.026a

(3.25)

Investments

−0.120b

(2.59)

−0.633a

(4.67)

−0.155a

(10.18)

Productivity

−0.022

(0.19)

−0.062

(0.42)

−0.019a

(13.09)

Energy prices

−0.111

(1.56)

−0.240a

(6.95)

 

Energy taxes

  

−0.094a

(4.94)

Obs.

266

266

266

The value in parentheses denotes the t-statistic

a, b, c The statistical significance at the 1, 5 and 10 % levels, respectively

Results of estimating the panel model using DOLS estimator

After confirming that the variables are cointegrated, we estimate the cointegrating vector using the DOLS estimator. We consider a model for every country. Table 6 shows the estimates for each country’s model. In general, we expect that higher energy prices, energy taxes, investments and productivity decrease CO2 emissions, and higher economic activity and fossil fuels increase CO2 emissions.

For the three countries analysed, a decrease in fossil fuel consumption leads to lower CO2 emissions. In the last decades, switching to lower carbon energy in the industrial sector has expanded the use of higher environmental quality fuels while maintaining production standards (Ramos and Ortege 2003; Homma et al. 2008).

Value added and capital have a positive and significant effect, indicating a direct relationship between these variables and CO2 emissions. Therefore, higher economic activity generates a higher level of CO2 emissions. This finding is consistent with the results of Pao and Tsai (2010, 2011) in the context of the BRIC countries, with the results of Niu et al. (2011) in the Asia–Pacific region and with the results of Wang (2011) in developed and developing countries.

Energy prices are a key instrument for energy policy, especially in the manufacturing industries, because higher energy prices should encourage more rapid adoption of energy saving, low-carbon technologies (Pardo Martinez, 2010). In Germany and Colombia, energy prices have a negative coefficient. In Germany, however, the coefficient is significant, indicating that higher energy prices generate lower CO2 emissions in the industrial sector of this country. Higher fossil fuel consumption increases CO2 emissions in the countries analysed. These results are important in the design and application of an energy price policy that encourages energy savings and lowers CO2 emissions through new technologies and production standards while maintaining productivity and promoting sustainable development.

In Sweden, energy taxes have a negative and significant coefficient, indicating that an increase in this variable leads to a decrease in CO2 emissions. These results demonstrate the importance of combining energy prices and energy taxes in the policy instrument for reducing CO2 emissions. Moreover, the Swedish energy taxation system is one of the most innovative and effective schemes. Consequently, this instrument has been combined with several instruments and mechanisms to ensure their effectiveness in decreasing CO2 emissions while maintaining the competitiveness of the manufacturing industries (Speck 2008; Johansson 2006). In Germany, the results indicate that the level of energy prices and investments leads to lower CO2 emissions, thus indicating the interdependent relationship between energy prices and investments. Therefore, when energy prices increase from a cost minimisation perspective, manufacturing industries invest in improvements in technology and processes designed to decrease production costs and increase environmental performance (Mukherjee 2008). In the Colombian case, energy prices have a negative coefficient, indicating the importance of designing adequate energy price instruments that encourage a low-carbon economy and sustainable development.

In all three countries, investments have a negative and significant coefficient, indicating that higher investments decrease CO2 emissions. However, in Sweden and Germany, the coefficients are statistically significant at the 1 % level, while in Colombia, the statistical significance is only 5 %. In the developed countries, many investments seek to improve environmental performance through energy savings and low-carbon technologies. However, in Colombia, the main objective of the investments is to reduce production costs and increase productivity through investments in machinery and equipment and in production plants that indirectly improve environmental performance (Pardo Martinez 2010; Pardo Martinez and Cotte Poveda 2011; Hendricks 2000).

In Sweden, investments and CO2 taxes have negative and significant coefficients, indicating that policy instruments that combine taxation and encourage technological change are important for decreasing CO2 emissions. The Swedish government has developed several instruments, such as an energy and CO2 tax index, that link to the consumer price index emissions-reduction subsidies; climate investment programmes (Klimp) that increase investments in clean technologies, mainly renewable electricity production; regulations that encourage the use of biofuels; and techniques that increase energy efficiency and decrease CO2 emissions without decreasing the productivity or the competitiveness of Swedish manufacturing industries (Swedish Energy Agency 2009).

In Germany, increases in investments and energy prices have led to reduced CO2 emissions. These results are consistent with several public and voluntary instruments developed in this country. Grants and loans within the environmental program provide capital for investments in environmental protection activities, and low-interest loans to SMEs can be used to supplement the European Recovery Programme’s Environment and Energy Saving Program. Additionally, Germany’s Declaration of German Industry on Global Warming Prevention was strengthened by the agreement of the industries regarding climate protection, which generated diverse strategies across industries to decrease CO2 emissions (Price 2005; Eichhammer et al. 2006).

The findings of this study have important implications for policy makers, especially in Colombia, where policy makers must design adequate policy instruments that combine fiscal instruments and technological progress to reduce CO2 emissions while promoting economic growth and development. In designing their policy instruments, Colombians should consider the experiences of Germany and Sweden. These two countries are recognised for developing innovative, effective and successful policy strategies that led to decreased CO2 emissions while maintaining economic growth and competitiveness.

Conclusions and policy implications

This paper evaluated and compared trends in CO2 emissions with their main determinants in the manufacturing industries of two developed countries (Germany and Sweden) and one developing country (Colombia) using annual data from 1995 through 2008. Panel data cointegration techniques were applied to estimate the causality among CO2 emissions, production factors and energy sources through the DOLS estimator.

The empirical findings reported in the paper reveal that, in general, higher energy prices, energy taxes, labour productivity and investments decrease CO2 emissions, while higher economic activity and fossil fuel consumption increase CO2 emissions. The model has several implications. First, a decline in fossil fuel consumption results in lower CO2 emissions. Second, higher economic activity should generate higher levels of CO2 emissions. Third, higher energy prices result in lower CO2 emissions. Fourth, manufacturing sectors with higher levels of investment decrease their CO2 emissions more than sectors with lower levels of investment.

Germany and Sweden show similar trends regarding increases in economic indicators and decreases in CO2 emissions. These trends have been led by policy instruments that have combined fiscal instruments, such as energy taxes and prices, technological changes through switching to lower carbon energy, investments in energy saving technologies and new production standards that led to economic growth and sustainable development while reducing greenhouse gas emissions.

In Colombia, a developing country, CO2 emissions have not decreased as much as in the two developed countries studied. Colombia has great potential to become a low-carbon economy. Therefore, policy makers must develop energy policies that combine technical and economic instruments to reduce CO2 emissions through the application of new technologies and the promotion of clean and environmentally friendly processes.

Footnotes

  1. 1.

    Reasonable bounds refer to 2 °C as the “acceptable” level of risk or the approximate level of global warming above which the scientific analysis suggests severe adverse impacts from climate change (IPCC 2001; IEA 2008a).

  2. 2.

    For more details on the advantages of these estimators, see Kao and Chiang (1997, 2000).

  3. 3.

    In this study, the following unit root tests were additionally applied: Levin et al. (2002), Breitung (2000) and the Fisher-Type test using ADF and PP-test. Levin et al. (2002) allows for individual effects, time effects and a time trend, though it does not allow for heterogeneity in the autoregressive coefficient under the null hypothesis of stationarity. Breitung (2000) tests for a null hypothesis of a unit root against the alternative of no unit root. The Fisher-Type test using an ADF and a PP test (Maddala and Wu 1999; Choi 2001) tests the null hypothesis of a unit root against the alternative hypothesis of some individuals without unit roots. These tests give similar results, confirming that all series are I(1) and integrated of the same order.

Notes

Acknowledgments

The authors gratefully acknowledge the editors of Regional Environmental Change for their support and the anonymous reviewers for the helpful suggestions and comments. The data from this research can be shared by the authors upon request.

References

  1. Ali M, Saidur R, Hossain M (2011) A review on emission analysis in cement industries. Renew Sustain Energy Rev 15:2252–2261CrossRefGoogle Scholar
  2. Ang JB (2007) CO2 emissions, energy consumption and output in France. Energy Policy 35:4772–4778CrossRefGoogle Scholar
  3. Apergis N, Payne J (2009) CO2 emissions, energy usage and output in Central America. Energy Policy 37:3282–3286CrossRefGoogle Scholar
  4. Åström T, Eduards K, Varga H, Segerpalm H (2006) Strategic evaluation on innovation and the knowledge based economy in relation to the structural and cohesion funds, for the programming period 2007–2013. The European Commission, CE.16.0.AT.015Google Scholar
  5. Audsley E, Brander M, Chatterton J, Murphy-Bokern D, Webster C, Williams A (2009) How low can we go? An assessment of greenhouse gas emissions from the UK food system and the scope to reduce them by 2050. FCRN-WWF-UKGoogle Scholar
  6. Breitung J (2000) The local power of some unit root tests for panel data. In: Baltagi BH (ed) Advances in Econometrics, vol 15., Non-stationary panels, panel cointegration, and dynamic panelsJAY Press, Amsterdam, pp 161–178Google Scholar
  7. Choi I (2001) Unit root tests for panel data. J Intern Money and Finance 20:249-272Google Scholar
  8. Ciais P, Paris J, Marlandw G, Peylin P, Piao S, Levin I, Pregger T, Scholz Y, Friedrich R, Rivier L, Houwellingk S, Schulze E (2010) The European carbon balance. Part 1: fossil fuel emissions. Glob Change Biol 16:1395–1408CrossRefGoogle Scholar
  9. Cotte Poveda A, Pardo Martinez C (2011) Trends in economic growth, poverty and energy in Colombia: long-run and short-run effects. Energy Syst 2:281–298CrossRefGoogle Scholar
  10. Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) (2003) Relationship between energy efficiency and economic development. (In Spanish)Google Scholar
  11. Dickey D, Fuller W (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Soc 75:427–431Google Scholar
  12. Eichhammer W, Schlomann B, Kling N (2006) Energy efficiency policies and measures in Germany 2006. Monitoring of energy efficiency in EU 15 and Norway (ODYSSEEMURE). Fraunhofer Institute for Systems and Innovation Research (Fraunhofer ISI).Google Scholar
  13. Federal Ministry of economics and technology (BMWI) (2006) Energy efficiency policy in the face of Germany’s upcoming EU presidency in the first half of 2007. Annual meeting of the energy efficiency initiative of dena. 2Hhttp://www.bmwi.de
  14. Haggar M (2011) Greenhouse gas emissions, energy consumption and economic growth: a panel cointegration analysis from Canadian industrial sector perspective. Energy Econ 34:358–364. doi: 10.1016/j.eneco.2011.06.005
  15. Hamilton C, Turton H (2002) Determinants of emissions growth in OECD countries. Energy Policy 30:63–71CrossRefGoogle Scholar
  16. Hawksworth J (2006) The World in 2050. Implications of global growth for carbon emissions and climate change policy. http://www.pwc.com/gx/en/world-2050/pdf/world2050carbon.pdf
  17. Hendricks L (2000) Equipment investment and growth in developing Countries. J Dev Econ 61:335–364CrossRefGoogle Scholar
  18. Homma T, Mori S, Akimoto K, Tomoda T, Murota Y (2008) A study of fuel substitution flexibility effects on economic impacts of carbon emission constraints using an energy-multi-sector-economy model. www.iioa.org/pdf/16th%20Conf/Papers/HOMMA.pdf
  19. Im K, Pesaran M, Shin Y (2003) Testing for unit roots in heterogeneous panels. J Econom 115:53–74CrossRefGoogle Scholar
  20. Intergovernmental Panel on Climate Change (IPPC) (2001) Mitigation climate change: summary for policymakers. Third assessment report of the IPPC. Cambridge University PressGoogle Scholar
  21. International Energy Agency (IEA) (2007) Tracking industrial energy efficiency and CO2 emissions. In support of the G8 plan of action. Energy indicators. http://www.iea.org/textbase/nppdf/free/2007/tracking_emissions.pdf
  22. International Energy Agency (IEA) (2008a) Energy technology perspectives: scenarios and strategies to 2050. http://www.iea.org/textbase/nppdf/free/2008/etp2008.pdf
  23. International Energy Agency (IEA) (2008b) Worldwide trends in energy use and efficiency. Key insights from IEA indicator analysis. http://www.iea.org/papers/2008/indicators_2008.pdf
  24. Johansson B (2006) Climate policy instruments and industry—effects and potential responses in the Swedish context. Energy Policy 34:2344–2360CrossRefGoogle Scholar
  25. Johansson M, Söderström M (2011) Options for the Swedish steel industry—energy efficiency measures and fuel conversion. Energy 36:191–198CrossRefGoogle Scholar
  26. Kao C (1999) Spurious regression and residual-based tests for cointegration in panel data. J Econom 90:1–44CrossRefGoogle Scholar
  27. Kao C, Chiang M (1997) On the estimation and inference of a cointegrated regression in panel data. Econ 9703001, EconWPAGoogle Scholar
  28. Kao C, Chiang M (2000) On the estimation and inference of a cointegrated regression in panel data. Adv Econom 15:179–222CrossRefGoogle Scholar
  29. Kim C (2010) Environmental performance index (EPI). http://epi.yale.edu/
  30. Levin A, Lin C, Chu C (2002) Unit root tests in panel data: asymptotic and finite-sample properties. J Econom 108:1–24CrossRefGoogle Scholar
  31. Lindmark M, Bergquist A, Andersson L (2011) Energy transition, carbon dioxide reduction and output growth in the Swedish pulp and paper industry: 1973–2006. Energy Policy 39:5449–5456CrossRefGoogle Scholar
  32. Luengen H, Endemann G, Schmöle P (2011) Measures to reduce CO2 and other emissions in the steel industry in Germany and Europe. In: AISTech—Iron and steel technology conference proceedings.Google Scholar
  33. Maddala G, Wu S (1999) Cross-country growth regressions: problems of heterogeneity, stability and interpretation. Appl Econo 32:635–642Google Scholar
  34. Mahadevan R, Asafu-Adjaye J (2007) Energy consumption, economic growth and prices: a reassessment using panel VECM for developed and developing countries. Energy Policy 35:2481–2490CrossRefGoogle Scholar
  35. Moya J, Pardo N, Mercier A (2011) The potential for improvements in energy efficiency and CO2 emissions in the EU27 cement industry and the relationship with the capital budgeting decision criteria. J Clean Prod 19:1207–1215CrossRefGoogle Scholar
  36. Mukherjee K (2008) Energy use efficiency in U.S. manufacturing: a nonparametric analysis. Energy Econ 30:76–96CrossRefGoogle Scholar
  37. Niu S, Ding Y, Niu Y, Li Y, Luo G (2011) Economic growth, energy conservation and emissions reduction: a comparative analysis based on panel data for 8 Asian-Pacific countries. Energy policy 39:2121–2131CrossRefGoogle Scholar
  38. Oggioni G, Riccardi R, Toninelli R (2011) Eco-efficiency of the world cement industry: a data envelopment analysis. Energy Policy 39:2842–2854CrossRefGoogle Scholar
  39. Organisation for Economic Co-Operation and Development (OECD) (2001) Environmental outlook for the chemicals industry. http://www.oecd.org/dataoecd/7/45/2375538.pdf
  40. Pao H, Tsai C (2010) CO2 emissions, energy consumption and economic growth in BRIC countries. Energy Policy 38:7850–7860CrossRefGoogle Scholar
  41. Pao H, Tsai C (2011) Multivariate Granger causality between CO2 emissions, energy consumption, FDI (foreign direct investment) and GDP (gross domestic product): evidence from a panel of BRIC (Brazil, Russian Federation, India, and China) countries. Energy 36:685–693CrossRefGoogle Scholar
  42. Pardo Martínez C (2009) Energy efficiency developments in the manufacturing industries of Germany and Colombia, 1998–2005. Energy Sustain Dev 13:189–201Google Scholar
  43. Pardo Martinez C (2010) Investments and energy efficiency in Colombian manufacturing industries. Energy Environ 21:545–562CrossRefGoogle Scholar
  44. Pardo Martinez C (2011) Energy efficiency development in German and Colombian non-energy-intensive sectors: a non-parametric analysis. Energ Effic 4:115–131CrossRefGoogle Scholar
  45. Pardo Martinez C, Cotte Poveda A (2011) Analysis of energy efficiency in the Colombian manufacturing industries: an estimation with data envelopment analysis—DEA and data panel. Economía Gestión y Desarrollo 11:39–58Google Scholar
  46. Pedroni P (1999) Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economic and Statistics 61(Special Issue Nov.):653–678Google Scholar
  47. Pedroni P (2001) Purchasing power parity tests in cointegrated panels. Rev Econ Stat 83:727–731CrossRefGoogle Scholar
  48. Price L (2005) Voluntary agreements for energy efficiency or GHG emissions reduction in industry: an assessment of programs around the World. Ernest Orlando Lawrence Berkeley National Laboratory. Environmental Energy Technologies Division LBNL-58138Google Scholar
  49. Ramos J, Ortege M (2003) Non-linear relationship between energy intensity and economic growth. ESEE conference frontiers 2Google Scholar
  50. Schipper L, Murtishaw S, Khrushch M, Ting M, Karbuz S, Unander F (2001) Carbon emissions from manufacturing energy use in 13 IEA countries: long-term trends through 1995. Energy Policy 29:667–688CrossRefGoogle Scholar
  51. Shevelev L (2010) A review of greenhouse gas emissions in the Russian iron and steel industry. Steel Times Int 34:33–34Google Scholar
  52. Speck S (2008) The design of carbon and broad-based energy taxes in European countries. In the reality of carbon taxes. In the 21st century. A Joint Project of the Environmental Tax Policy Institute and the Vermont Journal of Environmental Law Vermont Law SchoolGoogle Scholar
  53. Stock J, Watson M (1993) A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica 61:783–820CrossRefGoogle Scholar
  54. Swedish Energy Agency (2009) Energy efficiency policies and measures in Sweden. Monitoring of energy efficiency in EU 27 (ODYSSEE-MURE). www.odyssee-indicators.org/publications/PDF/sweden_nr.pdf
  55. Swedish Institute (2011) Energy: generating power for a sustainable future. http://www.sweden.se/upload/Sweden_se/english/factsheets/SI/SI_FS3_Energy/FS3-Energy-low-resolution.pdf
  56. Takeda K, Anyashiki T, Sato T, Oyama N, Watakabe S, Sato M (2011) Recent developments and mid- and long-term CO2 mitigation projects in ironmaking. Steel Res Int 82:512–520CrossRefGoogle Scholar
  57. United Nations (UNEP) (1976) Increased energy economy and efficiency. A study on measures taken or which might be taken to achieve increased energy efficiencyGoogle Scholar
  58. Wang K (2011) The relationship between carbon dioxide emissions and economic growth: quantile panel-type analysis. Qual Quant. doi: 10.1007/s11135-011-9594-y
  59. Wagner J (2010) Exports and firm characteristics in German manufacturing industries. Discussion Paper No. 5244. Institute for the Study of Labor (IZA)Google Scholar
  60. World Energy Council (WEC) (2004) Energy Efficiency: A Worldwide Report. Indicators, Policies, Evaluation. www.worldenergy.com (In Spanish)

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Authors and Affiliations

  • Alexander Cotte Poveda
    • 1
    • 2
  • Clara Inés Pardo Martínez
    • 3
    • 4
    • 5
  1. 1.Department of EconomicsUniversity of GöttingenGöttingenGermany
  2. 2.Faculty of Accounting and AdministrationUniversity of La SalleBogotáColombia
  3. 3.Department of Energy TechnologyRoyal Institute of Technology (KTH)StockholmSweden
  4. 4.Faculty of Environmental EngineeringUniversity of La SalleBogotáColombia
  5. 5.Faculty of AdministrationUniversidad del RosarioBogotáColombia

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