The Potential of Agrivoltaics in the Mekong Delta Region of Vietnam

A GIS Analysis

Authors

DOI:

https://doi.org/10.52825/agripv.v4i.2822

Keywords:

Agrivoltaics, Geographic Information System, Analytical Hierarchy Process

Abstract

The Mekong Delta, a key agricultural hub of Vietnam, faces mounting challenges from climate change, including sea level rise, salinity intrusion, and erratic rainfall. Simultaneously, Vietnam's transition to a green economy necessitates renewable electricity generation expansion. Agrivoltaics, which integrates solar electricity generation with agriculture, presents a promising solution to address these challenges by optimizing land use and enhancing climate resilience. This study employs a GIS-based Analytical Hierarchy Process (AHP) to assess the suitability of agrivoltaics deployment in the region, considering solar irradiation, slope, aspect, and proximity to transmission networks. The results indicate that over 99% of the Mekong Delta is suitable for small- and medium-scale agrivoltaics systems, while more than 90% is viable for large-scale systems. However, a sensitivity analysis reveals that the feasibility of large-scale agrivoltaics is significantly influenced by proximity to transmission infrastructure. Key barriers include Vietnam’s constrained transmission grid, high initial investment costs, and regulatory challenges. To unlock agrivoltaics' full potential, policy recommendations include grid upgrades, financial incentives, and public-private partnerships. This study provides the first comprehensive assessment of agrivoltaics in the Mekong Delta, offering critical insights for policymakers, investors, and researchers to advance sustainable energy-agriculture integration in Vietnam.

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Published

2026-07-30

How to Cite

Van Nguyen, L., & Brent, A. (2026). The Potential of Agrivoltaics in the Mekong Delta Region of Vietnam: A GIS Analysis. AgriVoltaics Conference Proceedings, 4. https://doi.org/10.52825/agripv.v4i.2822

Conference Proceedings Volume

Section

Environmental Modeling
Received 2025-07-03
Accepted 2026-06-22
Published 2026-07-30