Dr. Jane Thomason is a globally recognised thought leader on Metaverse and Web3. She is at the forefront of understanding and navigating the evolving landscape of emerging technologies. She is on the editorial board of “The Journal of Metaverse” and was featured by CNN in their series ” Decoding the Secrets of the Metaverse”. She is also AIBC Eurasia’s “Web 3 Leader of the Year 2023.” As an Industry Associate at University College London’s Centre for Blockchain, she actively contributes to the academic and research community.
The increasing impact of climate change on our planet is a significant issue for society. Environmental, Social, and Governance (ESG) criteria are used to measure investments’ performance in terms of their impact on the environment and climate change. Technology will be crucial in achieving the SDGs by 2030, with 70 per cent of the 169 targets defining the world’s sustainability goals able to be positively influenced through digital technologies.
Technology and Artificial Intelligence, in particular, can be used to enable investors to assess the sustainability of investments and prioritise investments likely to affect the environment positively. Thus, investors can improve their targeting and increase the impact of their investments.
The market demands a move towards sustainable investing, and funds dedicated to sustainable and responsible investing are growing. This is prompting companies to improve their ESG practices to attract capital. Regulatory pressure is also increasing, with governments worldwide developing and implementing regulations on AI ethics, data privacy, and responsible AI deployment. Compliance with these regulations is becoming necessary, driving companies to adapt their strategies accordingly. Importantly, consumers are more conscious of the ethical implications of AI technologies. They demand products and services that align with their values, pressuring companies to prioritise ethical considerations in AI development and deployment.
With these market pressures, companies that excel in ESG performance can gain a competitive edge by attracting top talent, fostering innovation, and enhancing brand reputation, ultimately leading to increased market share and resilience in the long term. More investors are incorporating ESG into their investment decision-making process, making ESG increasingly crucial for securing debt and equity capital. AI is becoming a critical adjunct to measuring and monitoring sustainability.
AI and ESG Monitoring and Impact
Data analytics monitor emissions, air quality, and pollution levels. They include data collection, monitoring, cleaning, integration, analysis, visualisation, and prediction. Machine learning algorithms can help forecast the supply of low-carbon power technologies, such as wind and solar, which are less harmful to the environment than high-carbon power technologies that rely upon fossil fuels, which makes it possible to have cheaper and cleaner fuels to power the base load and react to unforeseen events requiring a demand spike. Renewable energy requires a more accurate forecast of renewable power and demand. Some machine learning algorithms are being used to improve the scheduling and forecasting of low-carbon power supply. Predicting supply and demand makes it possible to have cheaper and cleaner fuels to power the base load and react to unforeseen events requiring a demand spike.
Optimising Forecasting to reduce energy usage.
AI can be used for forecasting by absorbing historical data, analysing these data, and producing more accurate forecasts than available forecasting tools. Renewable energy in grids will require vastly more precise estimates for renewable power, such as solar, that comes down to the grid and demand. This has been seen in solar and wind power and optimising forecasts for agricultural yields. AI forecasting can help increase the efficiency and optimisation of climate models. Structured data collected via the Internet of Things (IoT) and secured on a blockchain, combined with AI Measurement Reporting and Verification methodologies, increases the trust and utility of the data to support more efficient and effective decision-making and solutions for climate and sustainability.
Digital Twins
Digital Twin technologies are being deployed for weather and environment monitoring and prediction. This allows examining climate projections and rainfall with hydrological models to see what effect that would have on flooding over the coming decades and enables forecasting of where a flood would occur. Connecting and leveraging different models to evaluate impact on a larger scale is possible, considering more than one variable at a time—for example, AI and data from various sources (wind, rain, etc.). Digital twins also allow the simulation of the impact of natural disasters on networks through real-time interaction and accurate 3D registration of virtual and real objects using Augmented Reality.
GIS AI and Satellite Imaging
Geospatial data collection of habitat information and accurate measurements of forest borders can support preservation efforts, and Geospatial sensing is used for monitoring emissions and air quality. IoT and sensors are used for remote monitoring of emissions, air quality, and climate indicators, as well as early warning networks to detect signs of critical climate phenomena or unwanted human presence in protected areas.
Precision Investing for Impact
ESG investing is becoming increasingly important for investors, and automation and efficiency are two key benefits of using technology in this area. By automating data collection, analysis, and reporting, investors can save time and resources while identifying ESG investment opportunities more efficiently. In addition, technology can enhance objectivity by removing human biases from decision-making, enabling investors to make data-driven, consistent investment choices. Impact measurement is also crucial for sustainable investments, and technology can help by tracking KPIs related to ESG goals, allowing investors to monitor the progress of their investments towards sustainability outcomes and make adjustments. Predictive modelling is another valuable tool, helping companies estimate the future impact of different scenarios by analysing historical data. Natural language processing can help analyse textual data, such as company reports or news, to extract relevant information about environmental practices and identify potential areas for improvement. Image recognition can also be used to analyse satellite images or photos to detect environmental changes, such as deforestation or pollution, monitoring a company’s impact on ecosystems. Finally, supply chain analysis can help investors identify areas where a company’s operations may have a significant environmental impact, allowing them to make more informed investment decisions.
In summary, technology presents multiple opportunities for ESG investing. First, human-AI collaboration can lead to more robust and informed ESG investment strategies. By combining human analysts’ expertise with AI’s power, investors can leverage both strengths to make more effective decisions. Second, technology can enhance ESG data analysis by speeding and precision, uncovering patterns, and identifying relevant insights on company ESG performance. Third, technology can help mitigate ESG risks by identifying and assessing ESG risks more effectively. For example, it can uncover dangers in climate change, supply chain practices, and labour standards. Fourth, technology can drive ESG integration by incorporating ESG criteria into predictive models, helping generate sustainable financial returns. Fifth, technology can innovate ESG ratings and benchmarks, creating robust and standardised frameworks to compare and evaluate ESG investment strategies. This fosters transparency, trust, and informed decision-making in ESG investment strategies. Sixth, analysing environmental, social, and governance (ESG) data can predict the long-term financial performance of sustainable investments. It can also identify potential risks and opportunities that may take time to be apparent to human analysts. Seventh, technology can help assess climate risk, identify climate-related risks, and help companies develop strategies to mitigate them. Eighth, technology can help investors identify investment opportunities aligning with sustainable goals, make informed decisions, and allocate capital to companies prioritising environmental and social responsibility. Ninth, technology can track and analyse supply chain data, ensuring transparency and identifying potential risks related to environmental and social issues.
Technology can analyse financial and non-financial data by identifying irregular patterns or suspicious behaviour to detect fraudulent activities. Eleventh, technology can measure companies’ and organisations’ social impact by analysing data from various sources, such as social media or customer feedback. AI algorithms can provide insights into a company’s actions’ positive or negative effects on society. Finally, automated green financial planning and investment services can help investors create portfolios based on environmental criteria. These solutions include automated green investment advice, automated green portfolio allocation, or risk assessment based on ecological criteria. In conclusion, technology presents numerous opportunities for ESG investing, ranging from human-AI collaboration to automated green financial planning and investment services.
What are the Risks?
While technology can be a valuable tool for ESG investing, it has risks. One of the biggest challenges is the quality and availability of ESG data, which can be subjective, inconsistent, and difficult to obtain. Another risk is the potential for bias in AI algorithms, which can perpetuate or amplify social, racial, or gender biases. Interpretability and transparency are also essential concerns since AI models can be complex and challenging to interpret, and there can be a need for more transparency regarding how AI systems arrive at their conclusions. The dynamic nature of ESG factors presents another risk, making it challenging to keep AI models up-to-date with evolving ESG factors.
Additionally, ESG considerations often involve qualitative assessments and subjective judgments that may be difficult to translate into quantitative inputs for AI algorithms. Sustainable investing involves subjective evaluations and value judgments that require human interpretation and contextual understanding. Relying solely on AI may overlook essential nuances and qualitative aspects of ESG analysis. Lack of standardisation is another risk. Developing consistent standards and methodologies is necessary to enhance the reliability and effectiveness of AI-driven ESG analysis. Finally, ethical considerations like privacy, data security, and responsible use of technology are also crucial. Ensuring ethical practices and aligning AI-driven solutions with societal and environmental goals is essential.
Conclusion
As the market demands a move towards sustainable investing, companies need to improve their ESG practices to attract capital. Companies that excel in ESG performance can gain a competitive edge by attracting top talent, fostering innovation, and enhancing brand reputation, ultimately leading to increased market share and resilience in the long term. AI is becoming a critical adjunct to investment decisions. Technological opportunities can help investors make more effective decisions, mitigate risks, and drive sustainable financial returns while prioritising environmental and social responsibility.