How do companies differ in their investment behaviour for energy efficiency? Comparing the iron & steel and cement sectors with survey results

Transcription

1 How do companies differ in their investment behaviour for energy efficiency? Comparing the iron & steel and cement sectors with survey results ENERDAY 2016, Dresden April 8 th, 2016 Thomas Ketelaer a (t.ketelaer@fz-juelich.de), Russell McKenna b, Wolf Fichtner b, Wilhelm Kuckshinrichs a a: Forschungszentrum Jülich GmbH, Institute of Energy and Climate Research Systems Analysis and Technology Evaluation (IEK-STE) b: Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP) Chair of Energy Economics

2 Outline 1. Research topic 2. Methods and results 1. Expert interviews, company survey 2. Data analysis (regression) 3. Discussion 3. Summary and outlook 2

3 1.1 Energy use in German industry data: [Destatis 2015] Industry: 29 % of end energy use (2013) [AG Energiebilanzen 2015] data: [Destatis 2015] Future goals Germany (not industry specific): Primary energy use: 2020: -20% : -50% (vs. 2008) GHG emissions: 2020: -20% : -80% (vs. 1990) [BMWi & BMU 2011] 3

4 1.2 Energy efficiency gap Barriers: Lack of information Lack of time/high working effort Responsibility in energy questions not clarified No high significance of energy costs Lack of capital Lack of motivation of employees General investment uncertainty Drivers: Positive environmental orientation Expected cost savings Positive image Public funding Use of energy indicators Expected rising energy prices Support of management [DeCanio 1993, de Groot et al. 2001, Cagno et al. 2013, Sorrell et al. 2011, Thollander, Palm 2013] 4

5 1.3 Selected literature overview Study Country Sector Focus Method Size Trianni et al Mai et al Brunke et al Trianni et al Fleiter et al Schleich 2009 Thollander, Ottosson 2008 Rhodin et al degroot et al IT SMEs Barriers & drivers, company characteristics DE SMEs Transaction costs as barrier SE FI, FR, DE, IT, PL, ES, SE Iron & steel Foundry Barriers & drivers, management related Comparison of barriers in the European countries DE SMEs Barriers & drivers, energy audit program DE SMEs Barriers & drivers in small sub sectors SE Pulp & paper Ranking of barriers & drivers SE Foundry Ranking of barriers & drivers NL 9 ind. sectors Barriers & drivers in different sectors, ranking Descriptive analysis, cluster analysis Descriptive analysis, regression analysis Mann-Whitney U test 23 Descriptive analysis, correlation analysis Fractional logic model Logic and probit regression model >2000 Descriptive analysis 40 Descriptive analysis 28 OLS, ordered probit regression 135 [Based on Trianni et al. 2016] 5 April 8th 2016

6 2.1 Expert interviews Interviews with one representative of industry associations and one representative of a company (six sectors) 12 interviews performed between August 2015 and November 2015 Investment criteria: 1: monetary/risk, 2: monetary/risk + image, 3: monetary/risk + quality, 4: environment + monetary, 5: monetary/risk + quality + environment Influence of client/consumer: 1: very high,, 5: very low Load management: 1: very high potential,, 5: very low potential Attitude towards climate aspects: 1: very positive,, 5: very negative Future of industry: 1: very positive,, 5: very negative 6

7 2.1 Details of company survey Method: Computer assisted telephone interviews (CATI) Duration: Fieldwork: November 16th 2015 January 5th 2016 Interview time: mean time 18.5 minutes # according to WZ 2008 [Destatis 2015] Questions: Aluminium Casting of metals Cement 12 screening questions about company and interviewee characteristics Ranking of energy efficiency in general Energy management, load management Investment behaviour factors, barriers and drivers for investments in energy efficiency Iron & steel Glass Pulp & paper ,211 # interviewed

8 2.1 Interviewee and company characteristics 8

9 2.2 Method: Ordinary least squares regression (OLS) General approach: Yi = α + b 1 X i,1 + + b p X i,p with α = intercept b j = regression coefficient. Objective function of linear multiple regression: K e 2 K k=1 k = k=1 [yk (b 0 + b 1 x 1k + b 1 x 2k + + b j x jk + + b j x Jk )] 2 min with e k = values of residual (1, 2,, K) y k = values of dependent variables (k = 1, 2,, K) b 0 = constant b j = regression coefficient (j = 1, 2,, J) x jk = values of independent variables (j = 1, 2,, J; k = 1, 2,, K) J = # independent variables K = # observations 9

10 2.2 Ordinary least squares regression (OLS) Influence of barriers and drivers were asked with Likert-scale items: Likert-scale item: 1: very important,, 4: neutral,, 7: not important at all Using the mean (equal weighting) of the Likert-item responses to get factors for OLS Barriers factor composed of lack of information, lack of time/high working effort, responsibility in energy questions not clarified, no high significance of energy costs, lack of capital, lack of motivation of employees, general investment uncertainty Drivers factor composed of positive environmental orientation, expected cost savings, positive image, public funding, use of energy indicators, expected rising energy prices, support of management Hypothesis: barriers and drivers are influenced by the following items: Sector, number of employees, company s turnover Proportion of energy costs Energy efficiency measures implemented in the last three years Implementation of a certified energy management system Existence of an own power supply plant Full-time energy manager Implementation of load management Type of production process: iron & steel (oxygen steel, electro steel, others), cement (integrated (clinker and cement), only cement 10

11 2.2 OLS regression, barriers constant SCR1 sector SCR3 number of employees SCR4 company s turnover f1 proportion of energy costs f3xa1 energy efficiency measures implemented in the last three years f5 certified energy management system f6 own power supply f7 full-time energy manager Model 1: all sectors Model 2: cement Model 3: iron & steel regression coefficient (standard errors) 3.552** 5.046** (0.424) (2.005) (1.077) (0.036) (0.130) (0.837) (0.276) * (0.064) (0.282) (0.122) ** ** (0.038) (0.272) (0.096) (0.203) (0.087) (0.106) (0.108) (0.097) (0.753) (1.001).252 (0.532) (0.912) (0.464) ** (0.506) (0.224) (0.262) (0.233) ** (0.215) (0.118) f28 load transfer/demand side management oxygen steel, electro steel, others integrated plant or cement.199 only (0.592) R n ** Significance < 0.05, * Significance < 0.1 Statistics of indicator barriers Standard deviation Min Max Mean Model Model Model No significant model for all sectors Cement (significant variables): Company s turnover Proportion of energy costs Iron & steel (significant variables): Proportion of energy costs Energy efficiency measures implemented in the last three years Load transfer/demand side management 11

12 2.2 OLS regression, drivers constant SCR1 sector SCR3 number of employees SCR4 company s turnover f1 proportion of energy costs f3xa1 energy efficiency measures implemented in the last three years f5 certified energy management system f6 own power supply f7 full-time energy manager Model 1: all sectors Model 2: cement Model 3: iron & steel regression coefficient (standard error) (0.257) (1.143) (0.712) (0.022) (0.079) (0.477) (0.178) ** (0.039) (0.161) (0.079) (0.023) (0.155) (0.080) (0.123) (0.053) (0.064) (0.065) (0.059) (0.429) (0.571) (0.304) (0.520) (0.265) 1.009** (0.320) (0.145) (0.167) (0.150) (0.141) (0.075) f28 load transfer/demand side management oxygen steel, electro steel, others integrated plant or cement only (0.337) R n ** Significance < 0.05, * Significance < 0.1 Statistics of indicator drivers No significant model for all sectors Cement (significant variables): Company s turnover Iron & steel (significant variables): Energy efficiency measures implemented in the last three years Standard deviation Min Max Mean Model Model Model

13 2.3 Discussion No significant influence of any variable for all sectors and less influence of dependent variables in all models (e.g. number of employees, full-time energy manager) than expected Choice of dependent and independent variables Equal weighting of independent variables Alternative methods for the factor calculation (investment behaviour, barriers and drivers) and the general statistical analysis (factor analysis, cluster analysis, logistic regression, ) 13

14 3 Summary and Outlook Summary Attitude towards barriers and drivers do change depending on structure of the sector and/or company Studies about energy efficiency potentials in industry need to take into account certain company characteristics Object Sector EMAS Power supply Energy costs EEM (three years) Load transfer/dms Barriers Iron & steel X X X Turnover Cement X X Drivers Iron & steel X Cement X Outlook Perform data analysis for the other four investigated sectors Use of the results to characterize company agents for an agent-based simulation 14

15 Thank you very much for your attention! Contact: April 8th

16 References [BMWI & BMU 2011] [Brunke et al. 2014] [Cagno et al. 2013] BMWI & BMU (2011) Energiekonzept für eine umweltschonende, zuverlässige und bezahlbare Energieversorgung. Berlin BRUNKE, J.-C., et al. (2014). "Empirical investigation of barriers and drivers to the adoption of energy conservation measures, energy management practices and energy services in the Swedish iron and steel industry." Journal of Cleaner Production 84(0): CAGNO, E., et al. (2013). "A novel approach for barriers to industrial energy efficiency." Renewable and Sustainable Energy Reviews 19: decanio 1993] DE CANIO, S. J. (1993). "Barriers within firms to energy-efficient investments." Energy Policy 21(9): [degroot et al. 2001] [Destatis 2015a] [Destatis 2015b] [Fleiter er al. 2012] [Jochem 2000] [Mai et al. 2014] DE GROOT, H. L. F., et al. (2001). "Energy saving by firms: decision-making, barriers and policies." Energy Economics 23(6): DESTATIS (2015) Tabellen zu den Umweltökonomischen Gesamtrechnungen, Teil 2: Vorbericht Energie, Berichtszeitraum Statistisches Bundesamt, Wiesbaden. DESTATIS (2015) Statistisches Unternehmensregister Unternehmen nach Wirtschaftsgruppen und Größenklassen der sozialversicherungspflichtig Beschäftigten im Berichtsjahr 2012, Registerstand: , Statistisches Bundesamt, Wiesbaden. FLEITER, T., et al. (2012). "Adoption of energy-efficiency measures in SMEs An empirical analysis based on energy audit data from Germany." Energy Policy 51(0): JOCHEM, E. (2000) The World Energy Assessment Report: Energy and the Challenge of Sustainability, Chapter 6. Energy End-Use Efficiency, UNDP, New York MAI, M., et al. (2014). "Transaction costs of energy efficiency investments in companies." Zeitschrift für Energiewirtschaft 38(4): [Rhodin et al. 2007] RHODIN P., et al. (2007). "Barriers to and drivers for energy efficiency in the Swedish foundry industry." Energy Policy 35(1): [Schleich 2009] [Sorrell et al. 2011] SCHLEICH, J. (2009). "Barriers to energy efficiency: A comparison across the German commercial and services sector." Ecological Economics 68(7): SORRELL, S., et al. (2011). Barriers to industrial energy efficiency: A literature review. Working Paper. Vienna, United Nations Industrial Development Organization. [Thollander, Ottoson 2008] [Thollander, Palm 2013] [Trianni et al. 2016] THOLLANDER, P. and M. OTTOSON (2008). "An energy efficient Swedish pulp and paper industry exploring barriers to and driving forces for cost-effective energy efficiency investments." Energy Efficiency 1(1): THOLLANDER, P., PALM J. (2013). Improving Energy Efficiency in Industrial Energy Systems - An Interdisciplinary Perspective on Barriers, Energy Audits, Energy Management, Policies, and Programs. London, Springer-Verlag. TRIANNI, A., et al. (2016). "Barriers, drivers and decision-making process for industrial energy efficiency: A broad study among manufacturing small and medium-sized enterprises." Applied Energy 162: [Trianni et al. 2013] TRIANNI, A., et al. (2013). "Barriers to industrial energy efficiency in foundries: a European comparison." Journal of Cleaner Production 40(0):