Emerging Tech for SDG Data

The United Nations' Sustainable Development Goals (SDGs) face a critical challenge: data gaps. A staggering 68% of environmental indicators lack sufficient data, making it harder to track progress on issues like climate change, biodiversity loss, and resource depletion. Traditional data collection methods are too slow and unreliable to meet the growing demands of SDG monitoring.

Emerging technologies - Artificial Intelligence (AI), Internet of Things (IoT), and blockchain - are transforming how we gather, process, and secure data. Here's how each contributes:

• AI: Speeds up data analysis, identifies trends, and predicts outcomes, but faces challenges like regulatory hurdles and high computational demands.

• IoT: Enables real-time data collection with high accuracy, supported by advancements like 5G, though scaling remains difficult due to infrastructure and data overload.

• Blockchain: Guarantees tamper-proof records and transparency in reporting but struggles with speed, scalability, and energy consumption.

These technologies, when combined, address the limitations of traditional methods and enable more reliable SDG reporting. However, challenges like regulatory compliance, integration costs, and energy usage need to be managed carefully.

Key takeaway: Integrating AI, IoT, and blockchain can revolutionise SDG data collection and reporting, but organisations must balance their strengths and limitations to achieve meaningful results.

"Convergence Tech and SDGs: Unlocking Digital Transformation" | GBBC's Blockchain Central UNGA 2023

1. AI (Artificial Intelligence)

AI (Artificial Intelligence) is now a big help for gathering data for the Sustainable Development Goal (SDG) by making tasks fast, seeing trends, and guessing outcomes. It works with big data faster than old ways, catching hard trends and giving fast views.

Data Rightness and Quick Work

A top thing about AI is that it makes SDG data gathering more right and fast. It finds wrong things, fills empty spots, and looks at data from many places. One key example is the UNEP AI-based SDG Meter site, using a brain-like network that deals with text to sort it by the 17 SDGs. It got things right 98% of the time when checked on 500 texts, putting 490 right.

This sharp rightness is much better than old ways, which often have ups and downs and need many manual checks. AI can sort out thousands of texts and data sets in minutes, a job that would take weeks by normal ways. This speed lets us use AI for many more SDG parts.

Using AI in Real SDG Parts

AI helps a lot in moving forward 134 goals (79%) in all SDGs, but also may bring bad sides, harming 59 goals (35%). For example, in Spain’s Valencian Area, AI joins nature and money data to solve issues like dry land, ground breaking down, and smaller towns. It helps in smart farming (SDG 2), managing water (SDG 6), and saving nature (SDG 15). Also, AI makes danger checks better and uses resources well.

Further, AI helps by guessing dangers and making early warning setups. It backs acts like less pollution, better recycling, and less trash, key for going round economy aims.

Making it Big and Hard Parts in Setting it Up

While AI is great for making SDG data gathering big, putting it to use is not easy. Groups face troubles with data safe-keeping, needing lots of computer power, and rules that keep changing. Fitting AI into old setups can be hard and take a long time.

Rules and Leading

AI gives tools that are helpful for groups trying to handle hard reports on staying green. Yet, sticking to rules is still hard. The rules for AI differ a lot by place and are still changing. With no strong rules, AI might by chance make things like power use unequal.

Places like Clarity AI are facing these problems. Their site helps money groups understand and share results, like how they line up with SDGs and what effects they have, by making complex data sets simple. To use AI fully in SDG data work, dealing with rules and fitting challenges is key.

Dealing With Hard Parts in Setting Up

To make AI work well, it's key for governments, businesses, and study groups to work together. They should make rules on being fair, make the AI's workings clear, make data safety strong, and fix the lack of skills by giving the right training. They should also look at how AI affects things and put money into clean tech to meet green goals.

AI is great for linking nature study, rules, and work, and so is key for modern ways to gather SDG data. Yet, people need to be careful with what AI puts out. They should check and study more to make sure the findings fit what their group needs.

2. IoT (Internet of Things)

IoT tools are changing how we get info for the Sustainable Development Goals (SDGs). With their way to watch all the time and give facts right now, these tools let us see things with sharp detail and speed that old ways just can't.

Real-Time Data Get and Accuracy

One big plus of IoT is its skill to pick up info non-stop with no need for people to help. This always-on watch makes it more right by cutting the chance of mistakes from hand checks. Over the years, the trust in SDG-linked info has grown a lot. Back in 2016, only about a third of SDG signs had trusty info. Now, that number is almost two-thirds at 68%. Plus, since 2020, all 231 SDG signs are backed by known ways.

No-Wires Link and the Part of 5G

IoT sends depend on no-wire techs like Wi‑Fi, Bluetooth, Zigbee, and cell nets to pick up and share info at once. The start of 5G has pushed this further. With quicker speeds, less delay, and better links, 5G lets for more sharp watch in many places, making IoT systems work even better.

Auto Working and Rules Fit

IoT setups make rules fit easy by doing the info get auto, cutting the need for hand checks. For groups in ISSB reporting, IoT tools give real-time work info to meet rule needs. These setups are great for sticking to sets like the CSRD and ISSB, as they help get info needed for set tellings under the ESRS, like greenhouse gas (GHG) facts and value link hits. Plus, IoT backs climate risk looks and chances as per the TCFD rules, key to both CSRD and ISSB needs.

Hard Parts in Making Bigger and Data Handling

While IoT brings many good things, making these setups bigger for SDG watch has hard parts. Right now, about 95% of IoT work is small to mid in size, held back by things like working together, mixed up IT setups, and little money. The big load of info is another block. In 2019, IoT tools made about 18.3 zettabytes of info, and this may grow to between 73.1 and 79.4 zettabytes by 2025.

Steps to Make It Work Well

To use IoT well for SDG data gathering, groups must put security first. Things like all-through code protection and safe talking ways are key. They also need to think big, which comes from using cloud and cutting-edge tech fixes. Making data shapes and talk ways the same is another key move to make sure things work well together. This really matters because the European rules on how to report on keeping things going (ESRS) ask for over 1,100 data bits.

"Tracking progress on the SDGs will be key in the coming years, as it will highlight where societies will need to focus their efforts." – UN Resident Coordinator Matilde Mordt

3. Blockchain

Blockchain technology secures data for Sustainable Development Goals (SDGs) through an unchangeable distributed ledger. By ensuring records cannot be altered once entered, blockchain enhances trust in sustainability reporting. Instead of relying on a single storage location, data is distributed across multiple computers, making unauthorised tampering significantly more challenging.

Transparency and Immutable Records

One of blockchain's standout features is its immutability. Once data is added, it becomes nearly impossible to modify or delete. This addresses long-standing concerns about the integrity of sustainability reporting, where data manipulation can undermine trust. Blockchain ensures that all stakeholders - whether auditors, regulators, or the public - can access and verify information with confidence. For organisations adhering to ISSB standards, this means providing a secure, unalterable record of their sustainability efforts.

Real-World Applications in Sustainability

Blockchain is already making waves in sustainability. For example:

• Everledger: Tracks the journey of ethically sourced diamonds, creating a permanent record for each stone.

• IBM Food Trust: Secures data on food provenance, allowing companies to monitor their supply chains effectively.

• BanQu: Empowers vulnerable communities by offering digital identities and secure access to financial services.

The impact extends beyond tracking. Power Ledger has facilitated over 250 GWh of green energy trading, preventing approximately 180,000 tonnes of CO₂ emissions. Similarly, the Toucan Protocol has tokenised more than 25 million tonnes of carbon credits, showcasing blockchain's capacity to handle large-scale environmental data.

Smart Contracts and Automated Compliance

Blockchain also simplifies compliance through smart contracts. These self-executing agreements automate processes like verifying adherence to ESG standards. By reducing manual intervention and errors, smart contracts not only save time but also enhance accountability through decentralised governance.

Processing Speed and Scalability Challenges

However, blockchain isn't without its challenges. Processing speed remains a concern, especially with Proof of Work systems, which require significant computational effort and are slower than Proof of Stake mechanisms. Scalability is another issue, though solutions like Layer-1 and Layer-2 technologies are advancing. For now, permissioned blockchains tend to offer better scalability and performance compared to public networks.

Regulatory Compliance Complexities

Navigating blockchain's regulatory landscape can be tricky. The lack of consistent regulations across regions complicates its use in SDG reporting [4.1]. Additionally, the immutable nature of blockchain can clash with laws requiring data to be editable or deletable [4.2]. Another challenge lies in blockchain's anonymity, which can hinder Know Your Customer (KYC) and Anti-Money Laundering (AML) processes [4.2]. These hurdles underscore the need for robust systems like neoeco to ensure compliance in sustainability reporting.

Energy Consumption Concerns

Blockchain's energy usage, particularly with Proof of Work systems, has raised eyebrows. While newer, more efficient consensus mechanisms are emerging, the paradox remains: using a technology with high energy demands to monitor environmental goals. Yet, as blockchain evolves, its energy efficiency improves. The global blockchain market is forecast to grow from £17.57 billion in 2023 to £825.93 billion by 2032. A study by Deloitte found that 81% of executives believe blockchain has reached mainstream adoption, driven by its transparency and unchangeable records.

Balancing Trade-offs for SDG Implementation

For organisations exploring blockchain for SDG data, the challenge lies in balancing speed, security, and decentralisation. Choosing the right blockchain network and integrating compliance measures tailored to specific needs is critical. For example, combining blockchain with AI and IoT has enabled greater supply chain transparency while cutting costs. By carefully managing these trade-offs, blockchain can strengthen reliable SDG reporting while complementing technologies like AI and IoT.

Technology Comparison: Advantages and Disadvantages

When exploring AI, IoT, and blockchain for Sustainable Development Goal (SDG) data, it's clear that each technology has its own strengths and challenges. Knowing these trade-offs is essential for organisations aiming to build effective systems for sustainability monitoring.

Data Accuracy and Reliability

IoT devices enhance accuracy by minimising the errors common in manual data collection. However, they aren't without flaws - data authenticity can be compromised by machine faults or even artificial manipulation.

Blockchain, on the other hand, ensures data authenticity and consistency once the information is uploaded. Its unchangeable ledger is a core feature, but verifying the accuracy of data during its collection remains a hurdle.

AI plays a critical role in assessing data authenticity by analysing the logic of events and the interplay between physical and digital systems. Its predictive capabilities are especially useful for spotting inconsistencies or potential quality issues before they affect reporting.

But accuracy alone isn't enough - timely data collection is equally vital for SDG reporting.

Timeliness and Real-Time Capabilities

When it comes to speed, these technologies vary significantly. IoT stands out for real-time monitoring and transmitting environmental data, which is often easier to quantify and standardise than social or governance metrics.

AI can process enormous datasets quickly, delivering near real-time insights. However, its heavy computational demands can slow down the analysis of complex datasets.

Blockchain, while excellent for maintaining data integrity, struggles with speed. Systems using Proof of Work (PoW) are particularly slow due to their high computational requirements, which can create delays in time-sensitive reporting. Proof of Stake (PoS) systems are faster but still have limitations.

Scalability Considerations

Scalability is another area where these technologies differ significantly. Blockchain's adoption is expected to grow, with public blockchains offering high security and openness, while private blockchains focus on privacy and efficiency.

IoT scalability depends largely on the strength of network infrastructure and the ability to manage growing data volumes. Expanding sensor networks requires organisations to invest in robust data processing systems.

AI has the ability to scale complex data analysis, but its scalability hinges on available computational resources and the complexity of the datasets. If not implemented thoughtfully, it could also worsen inequalities.

Regulatory Compliance and Standards Alignment

Each technology faces unique regulatory challenges. With ESG and sustainability reporting becoming mandatory for many organisations from 2025, AI tools are increasingly being adopted to structure and streamline data collection. However, the EU AI Act adds layers of complexity by introducing restrictions on certain AI applications.

Blockchain encounters hurdles due to inconsistent regulations across different regions, complicating its use in SDG reporting.

IoT raises concerns about data security and privacy, given the sensitive nature of much of the environmental and operational data it collects.

These regulatory factors directly influence costs and the challenges organisations face during implementation.

Cost Implications and Implementation Challenges

Costs can vary widely depending on the technology. Traditional ESG reporting often relies on manual data collection, which is prone to errors and data loss, undermining the quality of disclosures. In contrast, tech-driven approaches using IoT, blockchain, and AI offer more precise and timely information.

One global food company successfully used a combination of blockchain, IoT sensors, and AI to track products, cutting emissions and reducing costs.

For companies aiming to meet ISSB reporting standards, the choice of technology often depends on specific needs and existing infrastructure. Combining multiple technologies can often provide a more effective solution than relying on just one.

Making the Right Technology Choice

The best technology depends on an organisation's goals and reporting needs. AI's transparent practices can boost stakeholder trust and engagement by up to 20%, making it a strong option for those prioritising clear communication.

IoT is ideal for real-time environmental data collection, while blockchain shines in scenarios requiring secure data integrity and reliable audit trails. AI is particularly useful for analysing complex datasets and generating predictive insights.

Conclusion

AI, IoT, and blockchain each bring unique strengths to the table - real-time monitoring, data integrity, and advanced analytics - that, when combined thoughtfully, address the challenges of sustainability reporting. As Cathie So points out, the fusion of open-source innovations and crypto-economics in Web3 underscores the importance of integrated strategies for SDG reporting. This approach not only tackles data-related obstacles but also paves the way for detailed, audit-ready disclosures using platforms tailored for SDG reporting.

Practical examples highlight the potential of this integration. Take Akshaya Patra, for instance. Collaborating with Accenture Labs, the organisation harnessed all three technologies: AI for predicting meal demand, IoT for overseeing cooking processes, and blockchain for ensuring supply chain transparency. The result? They managed to serve an additional one million meals annually while saving approximately £400,000.

This combination of technologies effectively addresses data fragmentation and creates stronger frameworks for SDG reporting. IoT captures real-time data, AI processes it to identify patterns and generate insights, and blockchain ensures that data is securely managed and transparently shared among stakeholders.

Modern platforms like neoeco are a testament to how automation, real-time data, and secure record-keeping can enhance compliance with global sustainability standards. Organisations aiming to adopt these technologies can utilise integrated platforms that align with SDG reporting requirements. By combining AI-driven automation with IoT data streams and blockchain verification, these platforms enable comprehensive, audit-ready disclosures that meet international benchmarks.

Each technology complements the others, filling in gaps and amplifying their collective impact. Blockchain ensures tamper-proof records of AI training data and IoT sensor inputs, while AI refines blockchain consensus mechanisms and interprets data from IoT devices. Together, they empower organisations to establish more reliable and thorough sustainability monitoring systems.

To meet the ever-evolving requirements of SDG reporting, organisations must strategically implement and integrate these technologies. The evidence is clear: those who invest in these solutions will not only meet reporting demands but also drive meaningful progress towards sustainability goals.

FAQs

How do technologies like AI, IoT, and blockchain improve SDG data collection and reporting?

Technologies like AI, IoT, and blockchain are reshaping how SDG data is collected and reported, making it more reliable, transparent, and actionable. IoT devices play a key role by gathering real-time data on environmental and social metrics, ensuring a constant stream of up-to-date information. This data is then processed by AI, which identifies patterns, generates insights, and delivers predictive analytics to guide smarter decision-making. At the same time, blockchain ensures the data remains secure, traceable, and tamper-proof - building trust, especially in intricate supply chains.

When these technologies come together, organisations can create ESG reports that are audit-ready and adhere to global standards like ISSB and CSRD. Platforms such as neoeco take advantage of these advancements to automate sustainability reporting, offering detailed insights into environmental, social, and governance impacts. This helps businesses stay on track with their SDG commitments and compliance objectives more effectively.

What challenges do organisations face when adopting emerging technologies like AI, IoT, and blockchain for SDG monitoring?

When organisations aim to integrate cutting-edge technologies for monitoring Sustainable Development Goals (SDGs), they often face a series of challenges. Among the most pressing are high implementation costs, technical complexity, and the critical need to uphold data privacy and security. These factors can create significant barriers, especially for smaller entities or those with limited budgets.

Another major hurdle is ensuring that these technologies are scalable and can maintain data integrity, particularly when dealing with decentralised systems. Without proper measures, inconsistencies in data can undermine the effectiveness of SDG monitoring efforts.

On top of this, organisations frequently grapple with limited resources and a lack of in-house expertise, which can slow down the adoption process. Compliance with regulations and resolving interoperability issues between different systems add further layers of complexity. Overcoming these obstacles is essential to harnessing the potential of AI, IoT, and blockchain technologies in streamlining SDG data collection and reporting.

How can organisations tackle regulatory challenges and high energy use when using blockchain for SDG data?

Organisations can navigate regulatory challenges by working closely with policymakers to create clear frameworks that balance compliance with the need for innovation. This approach ensures blockchain technology not only stays aligned with sustainability objectives but also adapts to changing regulations.

When it comes to energy consumption, switching to energy-efficient blockchain protocols like proof-of-stake (PoS), instead of the more energy-intensive proof-of-work (PoW), is a practical solution. Pairing this with the integration of renewable energy sources into blockchain operations can greatly minimise the carbon footprint. These steps make it possible for blockchain to support the collection of SDG data while meeting sustainability goals and regulatory requirements.

Related posts

• ESG Data Collection Checklist for Enterprises

• How AI Improves ESG Reporting Accuracy

• How AI Enhances Dynamic Emissions Factor Modeling