Supporting energy technology deployment while avoiding unintended technological lock-in: a policy design perspective

Technology deployment policies can play a key role in bringing early-stage energy technologies to the market and reducing their cost along their learning curves. Yet deployment policies may drive unintended and premature lock-in of currently leading technologies. Here we develop an empirically calibrated agent-based model to analyse how deployment policy design in fl uences which technologies are selected by investors. We focus on Germany ’ s solar photovoltaics feed-in tariff policy between 2003 and 2011 and analyse two design features, technology speci fi city and application speci fi city. Our results show that both features are highly important in technology selection and that spillover effects between applications exist. Policies that fail to consider these effects can unintendedly lock in or lock out technologies. To avoid this, policymakers can leverage the fact that different technologies are competitive in different applications and, by

[1]  J. Sweeney,et al.  Learning-by-Doing and the Optimal Solar Policy in California , 2008 .

[2]  V. Dinica.  Support systems for the diffusion of renewable energy technologies—an investor perspective , 2006 .

[3]  Varun Rai,et al.  Agent-based modelling of consumer energy choices , 2016 .

[4]  Gregory F. Nemet,et al.  Inter-technology knowledge spillovers for energy technologies , 2012 .

[5]  G. Barbose,et al.  Assessing the costs and benefits of US renewable portfolio standards , 2017 .

[6]  T. Schmidt,et al.  Technology Life-Cycles in the Energy Sector – Technological Characteristics and the Role of Deployment for Innovation , 2015 .

[7]  M. Howlett,et al.  From tools to toolkits in policy design studies: The new design orientation towards policy formulation research , 2015 .

[8]  Gregory C. Unruh.  Escaping carbon lock-in , 2002 .

[9]  T. Schmidt,et al.  Internal or external spillovers—Which kind of knowledge is more likely to flow within or across technologies , 2016 .

[10]  J. Bergh.  Optimal Diversity: Increasing Returns versus Recombinant Innovation , 2008 .

[11]  Varun Rai,et al.  Agent-Based Modeling of Energy Technology Adoption: Empirical Integration of Social, Behavioral, Economic, and Environmental Factors , 2014, Environ. Model. Softw..

[12]  Towhidul Islam,et al.  Household level innovation diffusion model of photo-voltaic (PV) solar cells from stated preference data , 2014 .

[13]  Santiago Arango,et al.  Renewable energy technology diffusion: an analysis of photovoltaic-system support schemes in Medellín, Colombia , 2015 .

[14]  Judith Lipp,et al.  Lessons for effective renewable electricity policy from Denmark, Germany and the United Kingdom , 2007 .

[15]  Elmar Kiesling,et al.  Agent-based simulation of innovation diffusion: a review , 2011, Central European Journal of Operations Research.

[16]  Stephen Davies,et al.  The patterns of induced diffusion: Evidence from the international diffusion of wind energy , 2011 .

[17]  P. Erickson,et al.  Assessing carbon lock-in , 2015 .

[18]  V.V.N. Kishore,et al.  Wind power technology diffusion analysis in selected states of India , 2009 .

[19]  M. Guidolin,et al.  Cross-country diffusion of photovoltaic systems: Modelling choices and forecasts for national adoption patterns , 2010 .

[20]  Björn A. Sandén,et al.  Near-term technology policies for long-term climate targets—economy wide versus technology specific approaches , 2005 .

[21]  T. Schmidt,et al.  Cost-Efficient Demand-Pull Policies for Multi-Purpose Technologies – The Case of Stationary Electricity Storage , 2015 .

[22]  James McNerney,et al.  Technology Improvement and Emissions Reductions as Mutually Reinforcing Efforts: Observations from the Global Development of Solar and Wind Energy , 2015 .

[23]  V. Hoffmann,et al.  The impact of technology-push and demand-pull policies on technical change – Does the locus of policies matter? , 2012 .

[24]  Elmar Kriegler,et al.  Complementing carbon prices with technology policies to keep climate targets within reach , 2015 .

[25]  Benedikt Battke,et al.  Use cases for stationary battery technologies: A review of the literature and existing projects , 2016 .

[26]  T. Schmidt,et al.  Do deployment policies pick technologies by (not) picking applications?—A simulation of investment decisions in technologies with multiple applications , 2016 .

[27]  Staffan Jacobsson,et al.  The politics and economics of constructing, contesting and restricting socio-political space for renewables - the German Renewable Energy Act , 2016 .

[28]  Sanya Carley.  State Renewable Energy Electricity Policies: An Empirical Evaluation of Effectiveness , 2009 .

[29]  B. Girod,et al.  Compulsive policy-making—The evolution of the German feed-in tariff system for solar photovoltaic power , 2014 .

[30]  Patrik Söderholm,et al.  Rationales for technology-specific RES support and their relevance for German policy , 2017 .

[31]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[32]  Jeroen C.J.M. van den Bergh,et al.  Environmental and climate innovation: Limitations, policies and prices , 2013 .

[33]  Felix Groba,et al.  Assessing the Strength and Effectiveness of Renewable Electricity Feed-In Tariffs in European Union Countries , 2011 .

[34]  D. Jacobs.  Renewable Energy Policy Convergence in the EU: The Evolution of Feed-in Tariffs in Germany, Spain and France , 2012 .

[35]  B. Truffer,et al.  Market Formation in Technological Innovation Systems—Diffusion of Photovoltaic Applications in Germany , 2011 .

[36]  Andrew Stirling,et al.  Multicriteria diversity analysis: A novel heuristic framework for appraising energy portfolios , 2010 .

[37]  Yves Gagnon,et al.  Energy Storage: Technology Applications and Policy Options , 2015 .

[38]  Björn A. Sandén,et al.  The elusive quest for technology-neutral policies , 2011 .

[39]  O. Edenhofer,et al.  Learning or Lock-In: Optimal Technology Policies to Support Mitigation , 2011, SSRN Electronic Journal.

[40]  F. Bass,et al.  A diffusion theory model of adoption and substitution for successive generations of high-technology products , 1987 .

[41]  F. Creutzig,et al.  The underestimated potential of solar energy to mitigate climate change , 2017, Nature Energy.

[42]  Y. Gagnon,et al.  An analysis of feed-in tariff remuneration models: Implications for renewable energy investment , 2010 .

[43]  T. Schmidt,et al.  Technology Life-Cycles in the Energy Sector – Technological Characteristics and the Role of Deployment for Innovation , 2015 .

[44]  T. Johansson,et al.  European renewable energy policy at crossroads--Focus on electricity support mechanisms , 2008 .

[45]  Peter H. Janssen,et al.  Exploring domestic micro-cogeneration in the Netherlands: An agent-based demand model for technology diffusion , 2010 .

[46]  Paul Lehmann,et al.  Why should support schemes for renewable electricity complement the EU emissions trading scheme , 2011 .

[47]  V. Hoffmann,et al.  The two faces of market support—How deployment policies affect technological exploration and exploitation in the solar photovoltaic industry , 2013 .

[48]  Albert Faber,et al.  Survival of the greenest: evolutionary economics and policies for energy innovation , 2006 .

[49]  Bertha Maya Sopha,et al.  Exploring policy options for a transition to sustainable heating system diffusion using an agent-based simulation , 2011 .

[50]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[51]  D. North.  Competing Technologies , Increasing Returns , and Lock-In by Historical Events , 1994 .

[52]  Paolo Zeppini,et al.  Competing Recombinant Technologies for Environmental Innovation: Extending Arthur's Model of Lock-In , 2011 .

[53]  René Kemp,et al.  The innovation effects of environmental policy instruments — A typical case of the blind men and the elephant? , 2011 .

[54]  W. Arthur,et al.  INCREASING RETURNS AND LOCK-IN BY HISTORICAL EVENTS , 1989 .

[55]  F. Bass.  A new product growth model for consumer durables , 1976 .

[56]  Chihiro Watanabe,et al.  Towards a local learning (innovation) model of solar photovoltaic deployment , 2008 .

[57]  P. David.  Clio and the Economics of QWERTY , 1985 .

[58]  Survival of the Greenest , 2008 .

[59]  T. Schmidt,et al.  Measuring the temporal dynamics of policy mixes – An empirical analysis of renewable energy policy mixes’ balance and design features in nine countries , 2019 .

[60]  Inês L. Azevedo,et al.  Effects of government incentives on wind innovation in the United States , 2013 .

[61]  G. Dosi.  Technological Paradigms and Technological Trajectories: A Suggested Interpretation of the Determinants and Directions of Technical Change , 1982 .

[62]  Varun Rai,et al.  Determinants of Spatio-Temporal Patterns of Energy Technology Adoption: An Agent-Based Modeling Approach , 2014 .

[63]  V. Rai,et al.  Effective information channels for reducing costs of environmentally- friendly technologies: evidence from residential PV markets , 2013 .

[64]  Pablo del Río González,et al.  Policy implications of potential conflicts between short-term and long-term efficiency in CO2 emissions abatement , 2008 .

[65]  Jessika E Trancik,et al.  Renewable energy: Back the renewables boom , 2014, Nature.

[66]  T. Schmidt,et al.  Limiting the public cost of stationary battery deployment by combining applications , 2016, Nature Energy.

[67]  Gregory C. Unruh.  Understanding carbon lock-in , 2000 .