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Green certification is failing real-world sustainability

by Simon Foulds

Despite decades of policy, certification schemes, and investment, many buildings labelled as sustainable continue to underperform once operational. The Royal Institution of Chartered Surveyors (RICS) 2025 Sustainability Report highlights a growing gap between sustainability intentions and actual building performance. Demand for sustainable real estate is weakening, projects are stalling, and measurement practices remain inconsistent across the construction and real estate sectors.

According to RICS, global demand for sustainable buildings has declined from 41 per cent to 30 per cent. This trend is not driven by a lack of ambition, but by unresolved structural issues. Developers, investors, and asset owners increasingly question whether sustainability investments deliver measurable operational and financial returns. Between 35 and 46 per cent of respondents cite uncertain return on investment, payback periods, and operational savings as the primary barriers to further investment in green buildings.

Limited performance evidence

This uncertainty is compounded by limited performance evidence. Certification frameworks primarily assess design intent rather than operational outcomes, leaving stakeholders without assurance that certified assets will achieve energy efficiency, carbon reduction, or cost savings in practice. As a result, sustainable construction is frequently perceived as a cost premium rather than a value driver.

Energy-efficiency specialists argue that the sector is constrained by three systemic failures: stalled demand, unclear financial value, and weak operational follow-through. Without addressing all three, sustainability targets remain theoretical. Donatas Karčiauskas, Chief Executive Officer of Exergio, notes that buildings often achieve compliance on paper while failing to meet real-world performance expectations due to limited operational optimisation.

Misalignment

RICS data also reveals a misalignment between investor priorities and occupier expectations. Occupiers increasingly focus on building performance, with 94 per cent prioritising indoor environmental quality and 88 per cent emphasising energy efficiency. Investors, however, continue to prioritise sustainability certifications and resilience features, often without equivalent scrutiny of operational outcomes. This disconnect contributes to buildings that satisfy reporting requirements while delivering inconsistent user experience and higher-than-expected energy consumption.

Measurement practices remain a critical weakness. Nearly half of construction professionals do not measure embodied carbon, and only 16 per cent report that carbon assessments influence design decisions. Knowledge gaps persist across the industry, with just 17 per cent of respondents believing there is sufficient sustainability expertise, and only 10 per cent reporting strong familiarity with whole-life carbon assessment methods.

Central challenge

These findings underline a central challenge within digital construction: sustainability cannot be managed without continuous, reliable data. When carbon and energy decisions are based on assumptions rather than measured performance, optimisation becomes reactive and limited. Manual monitoring approaches lack scalability, particularly across large commercial portfolios and complex building systems.

Artificial intelligence is increasingly viewed as a mechanism to address these limitations. AI-driven building optimisation platforms can integrate data from existing building management systems, analyse operational patterns, and adjust heating, ventilation, and cooling systems in real time. This enables continuous performance improvement without reliance on scarce specialist expertise.

Operational settings

In operational settings, AI-based optimisation has demonstrated the ability to reduce energy consumption in large commercial buildings by up to 30 per cent, delivering substantial operational savings. These measurable outcomes directly address investor concerns around return on investment and provide evidence-based justification for sustainable construction strategies. Crucially, this approach focuses on improving existing assets, where the majority of emissions reductions must occur to meet climate targets.

AI alignment

AI also supports alignment between investors and occupiers by shifting the sustainability conversation from labels to outcomes. Continuous performance monitoring provides transparent data on energy efficiency, indoor environmental quality, and carbon emissions. This allows sustainability credentials to reflect how buildings operate daily, rather than how they were designed or certified at completion.

From a digital construction perspective, AI-enabled optimisation represents a shift towards performance-led asset management. It integrates sustainability, cost efficiency, and occupant experience into a single operational framework. As regulatory requirements tighten and reporting expectations increase, automated measurement and optimisation offer a scalable pathway to compliance and value creation.

Losing credibility

The RICS report suggests that without improved measurement and operational accountability, sustainability initiatives risk losing credibility. Certification alone cannot deliver carbon reduction or energy efficiency if buildings are not managed effectively post-handover. Embedding AI-driven performance management into digital construction workflows enables continuous improvement throughout the asset lifecycle.

As the industry faces rising energy costs, decarbonisation targets, and investor scrutiny, the ability to demonstrate verified operational performance will increasingly determine asset viability. Sustainable buildings must perform as intended, not simply comply with design-stage benchmarks.

Without measurement, financial clarity, and AI-driven optimisation, digital construction will continue delivering compliant buildings that underperform against sustainability, energy efficiency, carbon reduction, and long-term asset value expectations.


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