New framework uses AI to automate governance of sustainable rural projects

A newly published study outlines a comprehensive engineering-based governance framework aimed at modernizing rural regions through smart systems, value engineering, and the integration of AI-driven tools. The research, titled “Smart Agri-Region and Value Engineering” and published in the journal Systems (2025, 13, 430), introduces the SAVE model, a systems-of-systems (SoS) approach designed to address the dual challenge of rural vulnerability and ecological transition by leveraging engineering, digital technologies, and programmatic oversight.

Developed by a multidisciplinary team from Spanish universities and innovation centers, the framework centers on the rural region of Extremadura as a test case. It explores how Model-Based Systems Engineering (MBSE), Artificial Intelligence (AI), and Natural Language Processing (NLP) can be used to support smart program governance, sustainability, and employment across five key engineering domains: construction, solar energy, aeronautics, communications, and circular economy.

How can engineering drive rural sustainability?

The SAVE model begins with a diagnosis of the challenges facing rural regions, chief among them population decline, underutilized ecosystems, and regulatory complexity. Using Extremadura, a region with a predominantly rural population and rich biodiversity, the study maps economic dependence on agriculture, tourism, and forest preservation. The model proposes an engineering-led transformation via coordinated development of sustainable agroindustry, mobility, fire prevention, and eco-tourism paradigms.

To facilitate this transition, the SAVE framework introduces high-level value engineering principles grounded in ISO 25000 quality metrics and ASTM value engineering standards. These metrics emphasize functional suitability, performance efficiency, usability, safety, and return on investment - all linked to sustainability indicators such as carbon footprint, water use, and circular economy participation.

Central to the model is the deployment of Key Enabling Technologies (KETs) to support engineering design, execution, and evaluation. By combining MBSE with AI and NLP, engineers can translate complex systems into actionable work products and reports that inform governance and enable funding. This automation also reduces technical overhead, particularly for low-resourced municipalities or rural cooperatives.

What role do technologies like MBSE, AI, and NLP play in rural development?

The research highlights three distinct but complementary technological advances, MBSE, AI, and NLP, that support automation, traceability, and risk reduction in engineering projects.

MBSE enables engineers to design, verify, and validate systems throughout their lifecycle, helping manage interdependencies within the SoS structure that defines rural environments. AI contributes decision-support capabilities, particularly in areas such as predictive maintenance, safety risk detection, and configuration management. NLP bridges the human-machine gap, translating natural language project descriptions into formal models, contracts, and system configurations. This is particularly relevant for reporting obligations in grant-funded or publicly monitored programs.

The study’s findings from bibliometric and R&D project reviews show that while MBSE-AI-NLP convergence is nascent in program governance, promising examples exist. These include drone-supported inspection systems, AI-driven construction risk analysis, and NLP-powered regulatory compliance tools. However, governance applications remain underrepresented in engineering literature, indicating a significant gap in smart public administration adoption.

The authors conclude that for smart agri-regions to succeed, governance frameworks must integrate these tools to ensure data quality, verification, and stakeholder alignment. The automation of engineering documentation also reduces the costs of supervision, enabling more funds to go toward implementation.

What are the practical implications for policy and replication?

The SAVE model not only identifies gaps in the current use of solar energy and aeronautics in rural circular economy projects, but also provides a roadmap for replicable innovation. Case studies and database analysis from Horizon Europe-funded projects reveal limited rural-specific applications of drone technology in construction or solar energy in industrial bio-processes. Despite this, the technology foundations are available, and the framework encourages more targeted investments and pilot deployments.

One key proposal is the use of public procurement of innovation (PPI) to stimulate demand for MBSE-AI-NLP tools tailored to rural governance. Through PPI, regional authorities could acquire platforms that help assess sustainability metrics, align engineering output with job creation mandates, and certify systems before funding is released.

In regions like Extremadura, where solar irradiance is among the highest in Europe and traditional construction methods dominate, the SAVE model supports dual goals: ecological performance and social impact. It offers standards for rehabilitating rural buildings using green materials, deploying modular PV and thermal systems, and enabling hybrid microgrids. The model also advocates for modular air transport using unmanned vehicles to support medical logistics, fire detection, and tourism in low-density areas.

To ensure successful implementation, the framework emphasizes the use of common ontologies, semantic models, and digital twins. This shared knowledge infrastructure allows for interoperability across systems and disciplines and supports transparent data exchange between stakeholders.