A financing ecosystem supported by data flows that capture the sustainability performances of companies and supply chains is needed to drive investments towards green growth and achieving the SDGs.
Improving the environment, mitigating climate change and encouraging efficient resource use are key to achieving the United Nations Sustainable Development Goals (SDGs) and the Paris Agreement climate targets. According to the OECD, $2.5 trillion is required annually to finance these efforts.
The challenge to mobilize this magnitude of financing does not necessarily arise from the lack of financial resources. For investors, the most cited concerns are the ambiguity of what constitutes “green” investing and their inability to properly assess risks and returns of related investments, particularly climate risks. For governments, despite the efforts to develop green taxonomies, the greatest difficulty lies in the availability and quality of information and tailored applications for the evaluation of financial activities. Both investors and governments, therefore, need enabling tools to unleash the financing required for meeting the SDGs.
A digital green finance future
Market players are already exploring innovative tools that direct financial resources towards sustainability. This includes, for example, the integration of digital technologies into the delivery of financial services, which led to the emergence of the fintech industry.
Thanks to its process automation, fintech can enhance the competitiveness of green assets and projects by reducing information asymmetries as well as search and transaction costs. Niche technology players deploy big data, machine learning and artificial intelligence to gather and analyse large amounts of complex data. Investors can leverage digitalization to generate insights on environmental trends and projects’ or companies’ performances. This could be accompanied by decision automation – smart algorithms and machines could provide investors with quick, simple and tailored analyses of unstructured data and automatically generate customized green investment portfolios that optimize returns.
Throughout the investment life cycle, investors would be able to track the green investments made in order to monitor and verify the immutability and delivery of the green claims of assets and projects. If the underlying assets shared real-time information, technological applications could update investors’ portfolio regularly, allowing them to adjust investment decisions in real time.
Two approaches to green data pooling
Fintech tools, however, do not tell which projects and assets are “green”, how to fully capture environmental risks associated with an asset, and what the return on investment is after deducting social-environmental costs.
What is needed is a financing ecosystem backboned by data flows that allow investors to understand and quantify risks and returns and drives investment towards “green”. Within this ecosystem – on the basis of accurate data capturing the sustainability performances of companies and supply chains – regulatory mechanisms such as standards, regulations and institutional monitoring would send better signals to level the playing field for green finance.
Against this background, two data collecting and pooling strategies have been explored:
- The top-down approach combines data from satellite-based sensors, power smart meters, web scraping, and traditional environmental, social and governance (ESG) reporting, and generates spatial ESG insights about assets, companies, supply chains, or countries. This data collection approach gives civil society unprecedented monitoring capacity as evident in the incident of methane release in US state of Florida, where a technology company spotted the release based on data from the European Space Agency’s sentinel-5P satellite, followed by news coverage and investigation by US authorities.
- The bottom-up approach is based on data generation and collection throughout the life cycle of economic activities. This approach has given rise to the emergence of financial products, risk analyses, rules and decision-making processes that are fundamentally different from those of the traditional financing institutions. The fintech platform of Alibaba, for example, follows this approach. Using data recorded on its e-commerce platform, Alibaba builds a credit scoring system iteratively, which helps determine the creditworthiness of loan applicants, enables it to automate the loan process in a real-time manner without any need for guarantee or collateral, with considerably low bad-debt ratios. Collaborating with a data ecosystem of this nature, financial institutions and ESG investors would be enabled to innovate their decision mechanisms and put green objectives at their heart.
Incentives for data sharing
What underlies these approaches is the need for sharing sufficient, accurate and well-connected data. In the case of the top-down approach, the challenge lies in accessing the data that can define the ESG features of assets. Such data – including location, ownership, production, etc. – can be sensitive and their relevance to ESG is often decided on a case-by-case basis. Usually, asset owners are holders of full records of data. Since data sharing entails shifting of power and possibly risk of privacy breach, it necessarily triggers resistance to full disclosure.
In the case of the bottom-up approach, data is not collected selectively. Rather, all steps in the life cycle of businesses are recorded. In this sense, all data could be used measuring ESG performance. For instance, transaction data such as shipping orders, mode of transport and to/from addresses are at the same time data used to calculate the carbon footprint of supply chains. Driven by algorithms, various datasets could be extracted in different settings of green investment to evaluate and predict the social environmental impact of a project or an asset. It is expected that platforms would expand their reach beyond online shopping and trade with the development of industrial internet. However, the challenge persists – large-scale platforms entail concentration of power and have limited incentive to share the data, while small platforms lack sufficient data coverage to even sustain their own growth and would have to turn to proper data sharing schemes.
To create comprehensive data flows that support green finance, an incentive mechanism for data sharing is needed. Three factors could help establish one:
- Data pricing models. Application programming interface (API) management is a compelling example of a data pricing model. APIs are a set of functions and procedures that allows access to data or a service in order to provide greater functionality to the user of an app or website. Companies have developed pricing strategies, such as free public versions with limited functions and then a tailored usage plan for business/enterprise customers. While being offered as software as a service (SaaS) solutions, the essence of the business model monetizes digital resources through extended networks on the basis of the number of API calls or data used. Through means and strategies of data monetization, asset owners could be equipped with new channels of revenue and may have more incentive to share data, while complying with data privacy and security regulatory requirements.
- Expanded stakeholders. Consumers’ increasing demand for more information combined with the trend of “using instead of owning” generates a sense of ownership for producers and users of a product or a property. In the context of data, this implies that producers and users would rely on data generated on both sides along the life cycle of a product. For example, some investors are exploring models of pooling technology resources, project and services capacity, and financial options on a platform for distributed sustainable infrastructure, such as micro grids. This is combined with “turnkey offerings and energy as a service” to consumers. Sharing the data within the ecosystem is not only a condition to become a stakeholder in developing, building and operating the infrastructure, but also the basis of business growth and revenue streams. As a consequence, these business models decentralize the power associated with data and instead incentivize more data sharing.
- Privacy enhancing computation. A group of technologies have emerged to establish a new form of data sharing while protecting privacy at the highest level. Take the secure multiparty computation as an example. This cryptographic protocol enables the computation across multiple parties while the data of each party remains private. In other words, participants of the system would get the value of the data without the necessity of accessing it. Those technologies can bypass the conflicting nature of data as private and public goods and avoid the concentration of power associated with large-scale platforms.
The way forward
Despite the technical readiness, policy intervention and guidance are what is still needed in order for green finance to grow. The role of policy-makers entails facilitating effective green data sharing, growing business models for the deployment of privacy enhancing computing, as well as simplifying the regulatory environment and reducing compliance burdens. It should be noted that data sharing also concerns the protection against unauthorized access and data breach. It is thus crucial to raise data literacy, facilitate dialogues among stakeholders on the implications of data exchanges and enhance the capacity to make decisions on data sharing.
