Digital and Energy Transitions in the Agro-Industry: An Economic Analysis of Technology Diffusion and Sustainable Innovation Pathways

Elvira Rustenova (), Aizhan Ibyzhanova (), Aizhamal Aidaraliyeva (), Sergey Barykin (), Lyudmila Gennadievna Rudenko (), Asiat Bahauovna Mottaeva () and Olga Voronova ()
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Elvira Rustenova: Institute of Economics, IT and Vocational Training, West Kazakhstan Agrarian and Technical University named after Zhangir Khan, 090000 Uralsk, Kazakhstan,
Aizhan Ibyzhanova: Institute of Economics, IT and Vocational Training, West Kazakhstan Agrarian and Technical University named after Zhangir Khan, 090000 Uralsk, Kazakhstan,
Aizhamal Aidaraliyeva: Institute of Economics, IT and Vocational Training, West Kazakhstan Agrarian and Technical University named after Zhangir Khan, 090000 Uralsk, Kazakhstan,
Sergey Barykin: Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia,
Lyudmila Gennadievna Rudenko: Department of Economic Theory, Financial University under the Government of the Russian Federation, 125167, Moscow, Russia,
Asiat Bahauovna Mottaeva: Department of General and Project Management, Financial University under the Government of the Russian Federation, 125167, Moscow, Russia.
Olga Voronova: Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia,

International Journal of Energy Economics and Policy, 2026, vol. 16, issue 2, 383-391

Abstract: The global agro-industry is changing, with a shift toward high-tech, data-driven systems to tackle environmental challenges and resource shortages. This study examines how digital and energy transitions are occurring together within the agricultural innovation ecosystem, driven by global demand for sustainability. Using a dataset of more than 2 million patents filed from 2000 to 2019, the research maps how technology spreads with Main Path Analysis (MPA) and explores the factors behind this process using an Exponential Random Graph Model (ERGM). The MPA identifies 12 main technology paths, starting with GPS-based management and progressing to advanced, energy-efficient uses of AI, IoT, and blockchain. ERGM results show that the innovation network forms selective, trust-based clusters and follows a "Matthew Effect," where leading technologies attract more investment. The analysis finds that aligning policy frameworks is the most important factor in building these networks, even more than traditional R&D spending. Environmental sustainability also has a strong positive effect, showing that the ecosystem favors technologies that lower carbon emissions and energy costs. While individual farmers do not yet have much influence on the larger innovation network, including them is important for fair technology sharing and system resilience. These results provide policymakers and agribusinesses with a strategic guide, showing that a successful digital and energy transition requires a balanced approach that combines market forces, robust regulations, and inclusive innovation.

Keywords: Digital Agriculture; Energy Transition; Patent Network Analysis; ERGM; Innovation Ecosystem; Sustainable Development (search for similar items in EconPapers)
Date: 2026
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DOI: 10.32479/ijeep.22489

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