Gender equality and social integration shape clean energy progress

A new study suggests that the success of clean energy innovation may depend just as heavily on who participates in the economy and how inclusive its institutions are. Gender equality and social integration, the research finds, are not peripheral considerations but structural components of energy transformation.

Published in Sustainability, the study Do Investments in Women's Education and Social Integration Matter for Clean Energy Technologies? applies advanced time-frequency analysis to U.S. data from 2000 to 2024. The study concludes that renewable innovation is shaped by the dynamic interplay of women's education, democracy, financial development, globalization, and economic growth.

Clean energy and gender equality move together over time

Do investments in women's education and social integration meaningfully influence clean energy innovation? To answer it, the authors measure women's education and skills development through the share of women aged 25 and older who have completed at least upper secondary education. Clean energy technologies are proxied by the total number of renewable energy technology patents, capturing innovation capacity rather than deployment.

Wavelet power spectrum analysis reveals that clean energy technologies, economic growth, and financial development display strong volatility at medium- to long-term horizons, especially during major systemic disruptions such as the 2008–2012 global financial crisis and the COVID-19 shock between 2020 and 2022. These fluctuations occur primarily at cycles ranging from roughly two to eight years, suggesting that clean energy innovation is shaped by multi-year investment and policy adjustments rather than short-lived disturbances.

On the other hand, women's education evolves gradually. Its strongest variance appears at longer horizons, reflecting structural shifts in human capital formation and labor market participation. This slower-moving pattern aligns with cohort-based education dynamics and long-term skill accumulation rather than rapid cyclical swings.

When the authors examine co-movement through wavelet coherence analysis, they find that clean energy technologies and women's education display significant positive alignment at medium horizons of approximately one to two years, particularly in the mid-2000s and again between 2013 and 2018. During some episodes, clean energy innovation appears to precede improvements in women's educational participation. In others, women's education leads innovation activity.

This alternating leadership suggests a feedback process. Human capital expansion supports innovation outcomes, while growth in clean energy industries may increase demand for skilled female labor, reinforcing educational incentives. The relationship is not constant but episodic and scale-dependent.

The findings reinforce a broader argument advanced by the authors: clean energy transitions are not gender-neutral. The extent to which innovation systems integrate women's education and skills formation influences technological development. Investments in women's empowerment operate as enabling mechanisms within energy innovation ecosystems.

Institutions and finance shape the clean energy cycle

Beyond gender equality, the study examines the roles of democratic governance and financial development in shaping clean energy technologies.

Democracy is measured through an electoral democracy index, capturing institutional transparency, accountability, and participatory governance. Financial development is proxied by domestic credit to the private sector as a share of GDP. Social globalization reflects cross-border flows of information, culture, and people using the KOF Social Globalization Index.

Wavelet power spectrum results show that financial development experiences episodic volatility spikes around systemic crises, particularly during the financial crash and pandemic period. Like clean energy innovation, financial cycles unfold over multi-year horizons, consistent with long leverage and credit cycles.

Democratic governance and social globalization, however, evolve more gradually, displaying dominant variance at longer frequencies. These structural variables change slowly through institutional reform and social transformation.

Wavelet coherence results reveal that the relationship between clean energy technologies and financial development shifts across time. In the early 2000s, the association is weak or occasionally negative at certain horizons. After 2013, coherence strengthens and becomes predominantly positive at business-cycle horizons of roughly three to eight quarters.

This transition reflects the maturation of green finance mechanisms, including the expansion of green bond markets and sustainable finance instruments that channel capital into renewable innovation. Financial development becomes more supportive of clean energy technologies once institutional frameworks align with sustainability objectives.

Democratic governance displays a similar time-varying pattern. Significant positive coherence appears during the mid-2010s at short- to medium-term horizons. Institutional quality during this period appears to reinforce clean energy innovation, possibly through regulatory stability and policy credibility. Earlier periods show fragmented or negative associations, underscoring that democracy's impact depends on contextual and temporal factors.

Social globalization exhibits episodic positive coherence with clean energy technologies, particularly between 2014 and 2019. Information flows and cultural exchange appear to align with renewable innovation during this period. However, earlier phases show weaker or even negative alignment, indicating that globalization can both accelerate and complicate technological diffusion depending on conditions.

Multiple wavelet coherence analysis further demonstrates that clean energy technologies are most strongly synchronized with combinations of women's education, democracy, financial development, social globalization, and economic growth at medium horizons. Clean energy innovation emerges as the product of joint socio-institutional forces rather than isolated variables.

From short-term adjustment to long-term co-evolution

To assess directionality, the authors apply wavelet-based Granger causality, a method capable of identifying time- and scale-specific predictive relationships.

In the short run, financial development tends to adjust to movements in clean energy technologies and other variables rather than drive them. Finance absorbs shocks at business-cycle horizons. Women's education and social globalization show predictive power at certain short and medium frequencies, while democratic governance exerts more noticeable influence at intermediate horizons.

At medium and long-term horizons, however, the system becomes densely interconnected. Bidirectional causality dominates. Clean energy technologies and their determinants increasingly co-determine one another.

Over multi-year cycles, improvements in women's education, institutional quality, financial depth, globalization, and economic growth both influence and are influenced by renewable innovation. Clean energy technologies become embedded within a broader socio-economic system characterized by feedback loops.

This long-run co-evolution suggests that energy transitions cannot be sustained through technological policy alone. Structural factors such as gender empowerment, democratic participation, and financial maturity become integral components of innovation systems.

Policy implications follow directly from these findings.

• Counter-cyclical clean energy financing mechanisms are necessary to prevent innovation collapse during economic downturns.
• Deepening green finance markets and standardizing sustainable taxonomies can strengthen the positive finance–innovation link.
• Expanding women's participation in STEM education and energy-sector leadership can enhance innovation spillovers.
• Strengthening democratic institutions can increase policy stability and investor confidence.

The authors warn that their analysis relies on aggregate U.S. data and that wavelet methods, while powerful, remain correlational. Future research incorporating firm-level evidence and cross-country comparisons could clarify causal pathways.