Skip to main content
SpringerLink
Log in

Governance Variety in the Energy Service Contracting Market

  • Chapter
  • First Online:
Sustainability Innovations in the Electricity Sector

Part of the book series: Sustainability and Innovation ((SUSTAINABILITY))

  • 1057 Accesses

Abstract

Earlier versions of this paper were presented at the DIME Workshop “The Changing Governance of Network Industries”, Naples, 29–30 April 2010, at the 1st DIME Scientific Conference “Knowledge in space and time: economic and policy implications of the knowledge-based economy” in Strasbourg, 7–9 April 2008 and at the 9th IAEE European Energy Conference “Energy Markets and Sustainability in a Larger Europe” in Florence, 10–13 June 2007.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    A chi-square probability of 0.05 or less is commonly interpreted by social scientists as justification for rejecting the null hypothesis that the row variable is unrelated – that is, only randomly related - to the column variable (http://www2.chass.ncsu.edu/garson/pa765/chisq.htm, 19.02.2007)

  2. 2.

    This model is generally suitable for analysing the relationship between a categorical outcome and the independent variables.

  3. 3.

    A common technical solution is that the CHP plant provides heat at a low temperature level that can be used all the year through, e. g. for supplying warm water. An additional central heating boiler is installed to provide additional energy during cold days.

  4. 4.

    Office buildings are omitted to avoid multicollinearity.

  5. 5.

    The logistic coefficients of the regression results depicting directions, positive or negative, of the different effects on contract choice are presented in Appendix 2, Table A.

  6. 6.

    For their mathematical definition see Appendix 1.

  7. 7.

    Base outcome: specialised contractors; Reference group for type of building (that is not included because of collinearity): commerce. For more statistical details see Appendix 2, Table B.

  8. 8.

    See Appendix 2, Table A.

  9. 9.

    Stata Base Reference Manual (2005), p. 211

References

  • E&M (Energie&Management) (2000) Contracting bleibt ein Wachstumsmarkt. In: Energie & Management, No. 7/2000 dated 01.04.2000

    Google Scholar 

  • Ménard C (1996) Of clusters, hybrids and other strange forms – The case of the French poultry industry. In: Journal of Institutional and Theoretical Economics, 152 (March), pp. 154–183

    Google Scholar 

  • Ménard C, Saussier S (2000) Contractual choice and performance: the case of water supply in France. Rev. ’Économie Ind 92(2/3):385–404

    Article  Google Scholar 

  • Ostertag K (2003) No-regret potentials in energy conservation: an analysis of their relevance, size and determinants. Heidelberg (Physica-Verlag), Technology, Innovation and policy, series of the Fraunhofer ISI, vol 15

    Google Scholar 

  • Perry MK (1989) Vertical integration: determinants and effects. In: Schmalensee R, Willig RD (eds) Handbook of industrial organization, vol 1. Elsevier, Amsterdam, pp 183–260

    Google Scholar 

  • Saussier S (2000a) Transaction costs and contractual incompleteness: the case of Électricité de France. J Econ Behav Organ 42(2):189–206

    Article  Google Scholar 

  • Saussier S (2000b) When incomplete contract theory meets transaction cost economics: a test. In: Ménard C (ed) Institutions, contracts and organisations. Perspectives from new institutional economics. Edward Elgar, Cheltenham, pp 376–398

    Google Scholar 

  • Shelanski HA, Klein PG (1995) Empirical research in transaction cost economics: a review and assessment. J Law Econ Organisation 11(2):335–361

    Google Scholar 

  • Stata Base Reference Manual (2005), -Release 9, Stata Press, vol 2, K-Q

    Google Scholar 

  • Williamson OE (1983) Credible commitments: using hostages to support exchange. Am Econ Rev 73(4):519–540

    Google Scholar 

  • Williamson OE (1985) The economic institutions of capitalism. Free Press, New York

    Google Scholar 

Download references

Acknowledgements

This paper is an outcome of the research project “Diffusion of innovations in energy efficiency and in climate change mitigation in the public and private sector”. We wish to thank the Volkswagen Foundation for the financial support of this project, the Verband für Wärmelieferung for the data provided and our colleagues Krisztina Kis-Katos and Joachim Schleich for helpful comments on earlier versions of this paper. The authors are solely responsible for remaining mistakes and weaknesses.

Author information

Authors and Affiliations

  1. Fraunhofer Institute for Systems and Innovation Research, Breslauer Str. 48, Karlsruhe, Germany

    Katrin Ostertag

  2. Mobil. Tum, Arcisstr. 21, 80333, München, Germany

    Friederike Hülsmann

Authors
  1. Katrin Ostertag
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Friederike Hülsmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Sciences, Chair of Sociology of Organisation, German University of Administrative, Freiherr-vom-Stein-Straße 2, Speyer, 67346, Rheinland-Pfalz, Germany

    Dorothea Jansen

  2. , Sustainability and Infrastructure System, Fraunhofer Institute for Systems and Inn, Breslauer Strasse 48, Karlsruhe, 76139, Baden-Württemberg, Germany

    Katrin Ostertag

  3. and Innovation Research, Sustainability and Infrastructure, Fraunhofer Institute for Systems, Breslauer Str. 48, Karlsruhe, 76139, Germany

    Rainer Walz

Appendix

Appendix

1.1 A.1 Applying a Multinomial Logistic Model

For each outcome (\( y \)) a set of coefficients, β1 (specialized contractors), β2 (municipal utilities), β3 (real estate enterprises) and β4 (equipment manufactures and consulting engineers) is estimated.

Probability of y = 1 (specialized contractor):

$$ {\hbox{Pr(}}y = 1) = \frac{{{e^{{X{\beta^1}}}}}}{{{e^{{X{\beta^1}}}} + {e^{{X{\beta^2}}}} + {e^{{X{\beta^3}}}} + {e^{{X{\beta^4}}}}}} $$

Probability of y = 2 (municipal utility):

$$ {\hbox{Pr(}}y{ = 2) = }\frac{{{e^{{X\beta }}}^{{^2}}}}{{{e^{{X\beta }}}^{{^1}} + {e^{{X\beta }}}^{{^2}} + {e^{{X\beta }}}^{{^3}} + {e^{{X\beta }}}^{{^4}}}} $$

Probability of y = 3 (real estate enterprises):

$$ {\hbox{Pr(}}y{ = 3) = }\frac{{{e^{{X\beta }}}^{{^3}}}}{{{e^{{X\beta }}}^{{^1}} + {e^{{X\beta }}}^{{^2}} + {e^{{X\beta }}}^{{^3}} + {e^{{X\beta }}}^{{^4}}}} $$

Probability of y = 4 (others):

$$ {\hbox{Pr(}}y{ = 4) = }\frac{{{e^{{X\beta }}}^{{^4}}}}{{{e^{{X\beta }}}^{{^1}} + {e^{{X\beta }}}^{{^2}} + {e^{{X\beta }}}^{{^3}} + {e^{{X\beta }}}^{{^4}}}} $$

In order to identify the model β1 is set to zero. Therefore, the other coefficients will measure the change relative to the base outcome, specialized contractor.

$$ {\hbox{Pr(}}y{ = 1) = }\frac{1}{{1 + {e^{{X\beta }}}^{{^{{(2)}}}} + {e^{{X\beta }}}^{{^{{(3)}}}} + {e^{{X\beta }}}^{{^{{(4)}}}}}} $$
$$ {\hbox{Pr(}}y{ = 2) = }\frac{{{e^{{X\beta }}}^{{^{{(2)}}}}}}{{1 + {e^{{X\beta }}}^{{^{{(2)}}}} + {e^{{X\beta }}}^{{^{{(3)}}}} + {e^{{X\beta }}}^{{^{{(4)}}}}}} $$
$$ {\hbox{Pr(}}y{ = 3) = }\frac{{{e^{{X\beta }}}^{{^{{(3)}}}}}}{{1 + {e^{{X\beta }}}^{{^{{(2)}}}} + {e^{{X\beta }}}^{{^{{(3)}}}} + {e^{{X\beta }}}^{{^{{(4)}}}}}} $$
$$ {\hbox{Pr(}}y{ = 4) = }\frac{{{e^{{X\beta }}}^{{^{{(4)}}}}}}{{1 + {e^{{X\beta }}}^{{^{{(2)}}}} + {e^{{X\beta }}}^{{^{{(3)}}}} + {e^{{X\beta }}}^{{^{{(4)}}}}}} $$

In order to detect the strength of the coefficient’s effect, relative risk ratios can be applied. The risk of choosing a municipal utility relative to choosing a specialized contractor is the relative probability of \( y = 2 \) to the base outcome:

$$ { }\frac{{{\text{Pr(}}y{ = 2)}}}{{{\hbox{Pr(}}y{ = 1)}}}{ = }{e^{{X\beta }}}^{{^2}} $$

The ratio of the relative risk for a one-unit change in xi, e.g. in size, is then

$$ \frac{{{{\beta_1}^2 {x_1}+ \cdots \ \cdots+ {\beta_k}^2{x_k}}}}{{{\beta_1}^2{x_1} + \cdots \ \cdots + {\beta_k}^2{x_k}}} =e^{{\beta_{1}}^{^{\!\!\!2}}}$$

Thus the exponential value of a coefficient is the relative-risk ratio for a one-unit change in the corresponding variable.Footnote 9 Relative risk ratios represent the risk of choosing a municipal utility as a contractor relative to choosing a specialized contractor for each one-unit change in, for example, the SIZE or FREQ measure, holding all other variables constant.

1.2 A.2 Regression Results for a Multinomial Logistic Model of Contract Choice

Table A Logistic coefficients
Full size table
Table B Relative risk ratios
Full size table

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Physica-Verlag HD

About this chapter

Cite this chapter

Ostertag, K., Hülsmann, F. (2012). Governance Variety in the Energy Service Contracting Market. In: Jansen, D., Ostertag, K., Walz, R. (eds) Sustainability Innovations in the Electricity Sector. Sustainability and Innovation. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2730-9_3

Download citation

Publish with us

Policies and ethics

Search

Navigation