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The adverse effects of political instability on innovation systems: The case of Mexico's wind and solar sector

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  • Aguiar-Hernandez, Carlos
  • Breetz, Hanna L.

Abstract

The global transition to clean energy requires policies to support innovation in the energy sector. However, the stability of energy policies is vulnerable to political disruptions and policy reversals. This paper aims to understand how innovation systems respond to periods of political instability. Specifically, it investigates the impact of policy retrenchment under Mexico's President Andrés Manuel López Obrador (AMLO) on the sectoral innovation system (SIS) for renewable energy. Employing the innovation system framework, we examine how the structure and functions of the SIS were affected by political changes under the AMLO Administration. Notable policy changes include canceling long-term auctions and strategic transmission projects, reducing science funding, and fostering uncertainty. These alterations significantly impact entrepreneurial experimentation, knowledge development, market formation, guidance of the search, resource mobilization, and the creation of legitimacy in the SIS for wind and solar technologies. On the positive side, we find that non-governmental organizations (NGOs) and local governments took action to compensate for the lack of federal leadership, and distributed generation continued as a niche of innovation. This research contributes to the innovation literature with an empirical study of innovation systems in Mexico. More generally, it highlights the adaptability of innovation systems during political transitions.

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  • Aguiar-Hernandez, Carlos & Breetz, Hanna L., 2024. "The adverse effects of political instability on innovation systems: The case of Mexico's wind and solar sector," Technovation, Elsevier, vol. 136(C).
    • Handle: RePEc:eee:techno:v:136:y:2024:i:c:s0166497224001330
    DOI: 10.1016/j.technovation.2024.103083
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    3. Leogrande, Angelo & Drago, Carlo & Mallardi, Giulio & Costantiello, Alberto & Magaletti, Nicola, 2024. "Patenting Propensity in Italy: A Machine Learning Approach to Regional Clustering," MPRA Paper 123081, University Library of Munich, Germany.

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