2. Five Digital Technologies That Can Support Environmental Performance

Internet of Things: Making Resource Consumption Visible

The Internet of Things (IoT) connects physical equipment to sensors and communication networks. In industrial facilities, smart buildings, and energy systems, these devices can collect information about electricity use, temperature, equipment performance, water flows, and operating conditions.

The sustainability value of IoT comes from turning measurements into operational decisions. For example, facility managers may use sensor data to identify equipment that continues consuming energy outside operating hours, detect abnormal water use, or adjust heating and cooling systems according to actual occupancy.

However, installing sensors is not itself an environmental achievement. Organizations need to establish a baseline, verify whether consumption has changed, and account for the energy and materials required to operate and replace the devices.

Artificial Intelligence: Improving Decisions, With an Energy Cost

Artificial intelligence can analyze operational data, identify patterns, forecast demand, and support decisions that would be difficult to make through manual monitoring alone. Potential applications include industrial process optimization, renewable-energy forecasting, building management, and predictive maintenance.

One documented example comes from Google DeepMind. The company reported that a machine-learning system reduced the energy used for cooling in a tested Google data centre by 40 percent. The company also reported a 15 percent reduction in overall power usage effectiveness overhead after accounting for other inefficiencies. This is a specific company-reported result from a particular deployment, not a universal performance guarantee for AI systems.

AI also creates an important sustainability trade-off. Training and operating models require computing infrastructure, and data centres consume electricity and may require water for cooling. The International Energy Agency (IEA) examines both sides of this relationship: AI can help optimize energy systems, but its expanding use also increases demand for electricity and data-centre capacity.

A responsible AI strategy should therefore assess whether a model produces measurable operational savings that justify its computational requirements. Using a larger model for a task that could be handled by a simpler system may increase costs without delivering meaningful environmental value.

Blockchain: Improving Traceability, Not Automatically Proving Sustainability

Blockchain can provide a shared record of transactions and product information across organizations. This can be useful when multiple suppliers, distributors, and retailers need to access a consistent traceability record.

A frequently cited example is Walmart's food-traceability work with IBM using Hyperledger Fabric. According to a case study published by LF Decentralized Trust, the system reduced the time needed to trace the provenance of a mango in a proof-of-concept exercise from nearly seven days to 2.2 seconds.

The example demonstrates the potential value of faster information retrieval in food traceability. It does not, by itself, prove that blockchain reduces emissions, eliminates food fraud, or verifies every environmental claim. The reliability of a digital record still depends on the accuracy of the information entered into the system and the quality of the verification process.

Cloud Computing: Efficiency Depends on How Infrastructure Is Used

Cloud computing allows organizations to access computing and storage resources through shared infrastructure rather than maintaining every server locally. For some organizations, this can reduce the need for underused on-site equipment and make computing capacity easier to scale.

Nevertheless, moving workloads to the cloud does not automatically reduce environmental impact. The result depends on server utilization, data-centre efficiency, electricity sources, cooling systems, network requirements, and whether the migration genuinely replaces existing infrastructure.

Organizations should compare the environmental footprint of the previous system with the new arrangement. They should also consider data retention policies: storing unnecessary information indefinitely can increase demand for storage and processing resources.

Digital Twins: Testing Operational Changes Before Physical Implementation

A digital twin is a digital representation of a physical asset, process, or system that can be updated using operational data. Depending on its design, it can help engineers examine performance, test scenarios, and identify possible problems before making changes to physical equipment.

In manufacturing, energy, and infrastructure projects, this approach may help teams compare operating conditions, evaluate maintenance strategies, and investigate potential efficiency improvements. Its value is strongest when the underlying model is validated against real-world measurements.

A digital twin should not be treated as proof of environmental improvement simply because it produces a simulation. Any claimed reduction in energy, materials, or emissions must be checked against actual operational results.

3. The Environmental Cost of Digital Transformation

A balanced sustainability strategy must account for the environmental footprint of digital infrastructure itself. This includes more than electricity used by computers. It can involve water consumption, mineral extraction, manufacturing emissions, transportation, equipment replacement, and electronic waste.

The United Nations Conference on Trade and Development (UNCTAD), in its Digital Economy Report 2024, examines the environmental consequences of digitalization across devices, networks, and data centres. The report highlights the importance of looking beyond the benefits of digital services to consider resource use and impacts across the digital lifecycle.

Electronic waste is another significant concern. The Global E-waste Monitor 2024 reported that the world generated 62 billion kilograms of electronic waste in 2022. This figure illustrates why frequent hardware replacement and short device lifecycles should be part of any discussion about green digital transformation.

These issues create a practical test for organizations: does a new digital system reduce total resource consumption, or does it simply move environmental costs from one part of the operation to another?

Three Questions Before Calling a Digital Project Sustainable

1. What measurable physical resource will be reduced? Identify the expected change in electricity, fuel, water, raw materials, waste, or emissions. A general claim of improved efficiency is not enough.
2. What additional digital resources will the project require? Account for devices, networks, data storage, computing workloads, cooling, maintenance, and eventual equipment disposal.
3. Can the improvement be verified after implementation? Compare actual results with a documented baseline and account for changes in production volume, weather, operating hours, and other relevant factors.

4. From Technology Adoption to Measurable Sustainability

The main weakness of many digital sustainability initiatives is not a lack of technology. It is the absence of a clear measurement framework. Organizations may report the number of sensors installed, dashboards created, or processes automated without showing whether those activities have reduced environmental impacts.

A stronger approach connects each digital project to a specific operational problem and a measurable environmental outcome.

These indicators are proposed measurement options, not universal industry benchmarks. Each organization should select metrics that reflect its operations, define the measurement boundary, and document the assumptions behind its calculations.

5. A Practical Roadmap for Responsible Digital Innovation

Step 1: Establish an Environmental Baseline

Before purchasing new technology, document current energy consumption, water use, material losses, waste generation, and relevant emissions. A baseline provides the reference point needed to determine whether a project has delivered an actual improvement.

Step 2: Prioritize Problems, Not Technology Trends

Select projects according to their potential to address a clearly defined environmental or operational problem. A facility with high cooling demand may benefit from better monitoring and controls, while a business with limited supply-chain visibility may need stronger data collection and supplier verification.

Step 3: Evaluate the Full Lifecycle

Assess the equipment, software, electricity, networks, maintenance, and disposal requirements of the proposed system. Consider whether existing devices can be repaired, upgraded, or reused instead of replaced.

Step 4: Run a Controlled Pilot

Test the solution in a limited operational setting. Record its performance, compare the results with the baseline, and identify unintended consequences before expanding the project.

Step 5: Publish Verifiable Results

Report the measurement period, system boundary, methodology, and limitations of the results. Distinguish between estimated savings and measured reductions. Where possible, use independent assurance for material environmental claims.

6. The Next Phase: More Accountable Green Digital Systems

The next phase of digital sustainability will depend less on adopting the newest technology and more on making digital systems accountable for their resource requirements and outcomes.

AI-assisted energy management may help balance electricity demand and support renewable-energy integration. More detailed product records may improve supply-chain accountability. Connected infrastructure may help cities and industries detect waste earlier. However, these possibilities depend on reliable data, suitable infrastructure, transparent governance, and measurable results.

There is also a growing need to make digital infrastructure more sustainable. This includes improving data-centre energy performance, managing water use, extending hardware lifecycles, reducing unnecessary computation, and improving the collection and recycling of electronic waste.

For policymakers and businesses, the challenge is therefore two-sided: use digital innovation to improve environmental performance while reducing the environmental burden created by the digital economy itself.

Conclusion: Sustainability Must Be Demonstrated, Not Assumed

Digital innovation can strengthen sustainability efforts by improving measurement, operational efficiency, supply-chain visibility, and planning. IoT, AI, blockchain, cloud computing, and digital twins each offer useful capabilities, but none guarantees an environmental benefit on its own.

The most credible projects begin with a defined problem, establish a baseline, evaluate the full lifecycle of the technology, and verify results after implementation. They also acknowledge trade-offs, including electricity demand, water use, material extraction, and electronic waste.

Ultimately, the contribution of digital innovation should be judged not by how advanced a system appears, but by whether it produces a measurable and durable improvement in environmental performance. Technology becomes a meaningful sustainability tool only when its benefits are demonstrated and its own costs are responsibly managed.