Clara Ines Pardo Martinez 2011

1. Estimating and Analyzing Energy Efficiency in German and Colombian Manufacturing Industries Using DEA and Data Panel Analysis Clara Ines Pardo Martinez 2011 KTH Energy and Climate Studies www.ecs.kth.se

2. CONTENTS Introduction Methodology Data Results and Discussion Conclusions KTH Energy and Climate Studies www.ecs.kth.se

3. 1. INTRODUCTION „To the advance of civilisation“ Energy is key „The evolution of human societies is dependent on the conversion of energy for human use“ Finding alternatives to expand the quality and quantity of energy services while simultaneously addressing the environmental impacts associated with energyuse Nowadays a challenge of humanity This study departs from the recognition that the reduction of environmental effects of the energy cycle is a priority and that energy efficiency plays a key role in the transition toward a sustainable energy system. KTH Energy and Climate Studies www.ecs.kth.se

4. 1. INTRODUCTION Fossil energy consumption Air pollution Slowing down anthropogenic climate change Energy efficiency The most common definition of energy efficiency is energy intensity, defined as the quantity of energy required per unit of output or activity. KTH Energy and Climate Studies www.ecs.kth.se

5. 1. INTRODUCTION Objectives To measure energy efficiency from a production theoretic framework using DEA in German and Colombian manufacturing industries. To explain variations in energy efficiency across manufacturing industries, using regression analysis with several key factors that might have influenced the energy efficiency in both countries. KTH Energy and Climate Studies www.ecs.kth.se

6. 1. INTRODUCTION Why Data Envelope Analysis (DEA)? Several studies have concluded that the energy index generated through DEA is superior to the commonly used index of the energy productivity ratio. Technical efficiency indexes under the DEA framework reflect the ability to obtain maximum output from given inputs or to reduce the input level without sacrificing output. KTH Energy and Climate Studies www.ecs.kth.se

7. a 2. METHODOLOGY G Data Envelope Analysis (DEA) c E DEA allows for the measurement of relative efficiency for a group of decision making units (DMUs) that use resources (inputs) to produce products (outputs). This methodology involves the use of linear programming methods to build a non-parametric piecewise frontier over data, so as to be able to calculate efficiencies relative to this frontier.  B e Input x2 A C D H F IA DMUA OA IB OB DMUB IC DMUC OC ID DMUD OD f d b In DMUn On Input x1 KTH Energy and Climate Studies www.ecs.kth.se

8. 2. METHODOLOGY The DEA model measures the potential reduction in energy use when maintaining output levels and without including additional amounts of other inputs (technical efficiency) (a) subject to (b) (c) (d) (e) (f) KTH Energy and Climate Studies www.ecs.kth.se

9. 3. DATA EISs (3-digits of aggregation level) German and Colombian manufacturing industries NEISs (2-digits of aggregation level) KTH Energy and Climate Studies www.ecs.kth.se

10. 4. RESULTS Averageresultsofenergyintensity and DEA model. EISs KTH Energy and Climate Studies www.ecs.kth.se

11. 4. RESULTS Averageresultsofenergyintensity and DEA model. NEISs KTH Energy and Climate Studies www.ecs.kth.se

12. 4. RESULTS Results from DEA analysis In both countries, the great majority of EISs improved on this index during the sample period, demonstrating that energy input is an important variable within production structure and a key element in technology development. In the Colombian case, we saw that the energy-efficiency indexes in both sectors were lower than in the German case, which might indicate that the Colombian manufacturing industry has an emerging and expanding industrial infrastructure with great potential to improve their energy efficiency. The results demonstrate that, in the Colombian case, the technology level is still moderate in comparison with state-of-the-art technology, generating opportunities in the adoption of new technology, where firms can aim to improve efficiency and productivity while paying attention to sustainability. KTH Energy and Climate Studies www.ecs.kth.se

13. 4. RESULTS To explain the observed variation in energy efficiency across manufacturing industries regression analysis is used with panel data techniques. where: KTH Energy and Climate Studies www.ecs.kth.se

14. 4. RESULTS Data Panel Techniques What data panel model? Hausman test: Fixed effects vs. Random effect F-test: OLS vs. Fixed effects Breusch and Pagan test: OLS vs. Random effect The estimations are robust? The Pesaran cross sectional dependence test. Testing for heteroskedasticity. The Wooldridge test for serial autocorrelation. The Lagrange multiplier test for random effects. KTH Energy and Climate Studies www.ecs.kth.se

15. 4. RESULTS Resultsof regressions for explaningenergyefficiency. EISs t-statistics in parentheses. *, **, *** imply significance at the 10%, 5%, and 1% levels, respectively. aIf Prob > chi2 < 0.05, reject random effects. KTH Energy and Climate Studies www.ecs.kth.se

16. 4. RESULTS Resultsof regressions for explaningenergyefficiency. EISs In German and Colombian EISs, the coefficient of enterprise size has a positive influence on energy efficiency, implying that a higher output in medium and large enterprises leads to higher energy efficiency. Labour productivity has a positive and statistically significant coefficient across EISs, implying that sectors with a higher quality of labour have higher energy efficiency. This suggests that growth in energy and labour productivity are complementary rather than substitutable. The coefficients of electricity are positive in both countries, indicating that higher electricity consumption is associated with improvements in energy efficiency which demonstrates that energy efficiency improvements could be the result of a shift in the structure of energy sources. The results of EISs could indicate that the main factor behind improvement in energy efficiency is the technological change. KTH Energy and Climate Studies www.ecs.kth.se

17. 4. RESULTS Resultsof regressions for explaningenergyefficiency. NEISs t-statistics in parentheses. *, **, *** imply significance at the 10%, 5%, and 1% levels, respectively. aIf Prob > chi2 < 0.05, reject random effects. KTH Energy and Climate Studies www.ecs.kth.se

18. 4. RESULTS Resultsof regressions for explaningenergyefficiency. NEISs The effect of capital input (KL) show difference in the sign of the coefficients suggesting that this variable could be substitutable or complementary in the production framework in the NEISs. The results of investments indicate that this variable have probably not been tailored to improving energy efficiency, as the coefficient is statistically insignificant. Hence, energy savings in the NEISs are a side effect of investments in labour savings and technological changes in general. The results show that only labour productivity has influenced energy efficiency performance, probably because energy consumption is lower than other inputs in terms of production costs. KTH Energy and Climate Studies www.ecs.kth.se

19. 5. CONCLUSIONS The results show that the great majority of EISs improved on this index, demonstrating that energy input is an important variable within production structure and a key element in technology development. The results show that several NEISs decreased energy efficiency index during the sample period, indicating that energy savings for this sector are not a priority in terms of improvements of production processes, due to the fact that energy is a minor cost item in relation to labour and the fact that capital is instead used to save other production costs. In German EISs, labour productive, size of enterprise and investments have played an important role in the improvement of energy efficiency, whereas in Colombian EISs, labour productivity and capital input are positively associated with energy efficiency. KTH Energy and Climate Studies www.ecs.kth.se

20. 5. CONCLUSIONS In German and Colombian NEISs, energy efficiency is positively associated with labour productivity, size of enterprise and electricity. From methodology used, the test of cross-sectional dependence, heteroskedasticity and serial correlation demonstrated that both DEA scores and the data panel techniques are adequate to analyse energy efficiency. It should encourage the importance of energy efficiency, especially among SMEs and developing countries, increase the application of energy-efficient best practices, technologies, and innovations, and motivate investments related to energy conservation in manufacturing industries. KTH Energy and Climate Studies www.ecs.kth.se

21. Thank you very much for your attention Any questions? Email: cipmusa@yahoo.com “Engineering consultants shoulder the responsibility to promote energy-efficient and eco-friendly technologies to meet the challenge of energy over-consumption and environmental deterioration” ZengPeyan KTH www.ecs.kth.se