Green innovation and economic complexity drive resource efficiency in G20

The study reveals the negative relationship between financial inclusion (FIN) and resource efficiency (RCE). Contrary to the prevailing assumption that increased access to financial services supports green investment, the analysis revealed that in G20 nations, greater financial development often correlates with unsustainable consumption patterns.

A new study delivers fresh insights into how G20 economies can accelerate sustainable development through resource efficiency. The study, titled “Sustainable Transition Through Resource Efficiency: The Synergistic Role of Green Innovation, Education, Financial Inclusion, Economic Complexity and Natural Resources,” is published in Sustainability.

Based on data from 2000 to 2022, the researchers applied advanced panel econometric techniques, including CS-ARDL, AMG, and CCEMG models, to quantify both short-term and long-term relationships between five critical drivers and resource consumption efficiency (RCE). These drivers include natural resources (NRS), educational quality (EDQ), financial inclusion (FIN), green innovation (GRI), and economic complexity (ECC). The results challenge conventional wisdom, particularly with regard to the roles of education and financial inclusion in shaping sustainable outcomes.

Why are some traditional drivers falling short of expectations?

The study reveals the negative relationship between financial inclusion (FIN) and resource efficiency (RCE). Contrary to the prevailing assumption that increased access to financial services supports green investment, the analysis revealed that in G20 nations, greater financial development often correlates with unsustainable consumption patterns.

The negative coefficients for FIN were consistent and statistically significant across all three econometric models. The authors argue that this outcome may stem from misalignment between financial flows and environmental goals. Instead of funding sustainable infrastructure or green technologies, financial access may be driving high-consumption lifestyles or investments in polluting sectors. The findings call for a reconfiguration of financial frameworks to better align with sustainability objectives, including the use of green bonds, conditional lending mechanisms, and sustainability-linked credit instruments.

Educational quality (EDQ) also displayed an unexpected inverse relationship with RCE. The study found that improvements in education have not translated into gains in resource efficiency, potentially due to a lack of sustainability-centered curricula. While education enhances skills and human capital, its impact on RCE depends heavily on the integration of environmental literacy, critical thinking, and systemic problem-solving in formal education systems. Without these elements, higher educational attainment may inadvertently fuel increased consumption and waste.

What is driving positive change in resource efficiency?

In contrast to FIN and EDQ, three other variables emerged as robust and consistent enhancers of RCE: natural resources (NRS), green innovation (GRI), and economic complexity (ECC).

Natural Resources (NRS) were found to have a statistically significant and positive impact on RCE, defying the long-standing “resource curse” narrative. The findings suggest that with proper governance and investment in sustainable resource management, natural capital can become an asset rather than a liability. Countries such as Canada, Brazil, and Saudi Arabia were highlighted as examples where resource wealth has translated into gains in efficiency.

Green Innovation (GRI) stood out as a particularly powerful driver of sustainable transformation. GRI, measured through patents in environmentally friendly technologies, was positively associated with RCE across all tested models. The authors point to this as evidence that innovation in clean technologies leads directly to more efficient use of energy and materials, reducing waste and environmental degradation. Saudi Arabia and Argentina were identified as leaders in this area, driven by strong national strategies such as Saudi Vision 2030.

Economic Complexity (ECC) also plays a pivotal role. Countries with more diverse and technologically sophisticated economies tend to exhibit higher resource efficiency. This supports the Economic Complexity Theory, which links advanced industrial structures to efficient resource use and innovation adoption. The study highlights Japan and Germany as prime examples of high ECC translating into superior sustainability performance.

How can policymakers leverage these insights?

Based on their findings, the authors recommend a multidimensional policy approach tailored to the unique conditions of each G20 economy. Key recommendations include:

• Redirecting financial flows: Regulatory bodies should mandate that a larger share of domestic credit be channeled toward sustainability-aligned investments. This may include tax incentives, subsidies for green lending, and integration of ESG metrics into financial assessments.

• Reorienting education systems: Policymakers should embed sustainability into curricula at all levels of education, emphasizing systems thinking, environmental stewardship, and green skill development in STEM and vocational training programs.

• Scaling green innovation: Governments should offer stronger incentives for research and development in eco-friendly technologies. Public-private partnerships, intellectual property support, and fast-track commercialization mechanisms can enhance innovation uptake.

• Optimizing natural resource management: The positive role of NRS implies that with improved governance, such as stricter environmental regulations and reinvestment of resource revenues, resource-rich countries can boost RCE significantly.

• Promoting economic complexity: Structural reforms that foster diversification and technological upgrading in domestic industries should be prioritized. Supporting value-added exports and high-tech sectors can further reinforce RCE and resilience.

The study confirms the long-run equilibrium relationships between all five variables and RCE using Westerlund cointegration tests, with results reinforced by both AMG and CCEMG estimators. The high R-squared value of 0.95 in the CS-ARDL model underlines the robustness and predictive accuracy of the chosen variables.