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Big Data and AI Monitoring - a Climate Vulnerability Intervention

Big data can help expose the cracks in climate responses from the government, AI can keep an eye on inefficiency.

Artwork by Tanishk Katalkar

India is the 7th most vulnerable country to climate change. Climate vulnerability is a multifaceted process affected by social, political, and economic forces at the local and international levels. Some people are more vulnerable to climate-related disasters as compared to others, due to human systems such as governance. It can be theorized that human systems thus lead to climate vulnerability in a majority of the developing world. Governance seeks to regulate climate issues through policy responses and disaster relief management; it is an important aspect that influences vulnerability; Within governance, many processes are dominated by specific influential stakeholders, and the policies and responses enacted through administration may benefit some and disenfranchise the rest. A large body of science looks at the technical aspects of vulnerability, but the social factors influencing vulnerability are equally important.


Climate vulnerability is created and worsened by human systems such as governance. Governance can be visualised using computational models to identify the human factors influencing climate vulnerability in India and other at-risk countries.

Computational social sciences aim to construct intricate models of human behavior, breaking them down into a sequence of relationships and interactions. These models further help identify opportunities for intervention by delving into various aspects of human phenomena.Today computational systems can map out the most complicated human systems such as the functioning of governments, which can strengthen governmental processes for climate change. This field of study can improve governmental responses to climate change, by identifying the delays in implementing policies, improving the quality of monitoring systems, better relief programs for climate-related disasters, and figuring out how responsibility for climate action can be distributed among different levels of government.

Computational methods are prevalent across disciplines, such as physics, biology, computer science, and more; social sciences are no different. In computational social sciences, scientists use this intersection to map out human systems using computational methods. This means that processes such as the government, a large company, or cities, can be mapped scientifically and accurately. Models such as network analysis, stochastic modeling, and content analysis are used to create such system maps. One interesting application of computational social sciences is modeling government systems, and how they respond to climate change and climate policies. Such a model would employ the use of big data and artificial intelligence to monitor the observations over a long period of time. Figure 1 depicts a network analysis of keywords used across articles about Big Data and sustainability - and can be considered an example of computational social science models.


FIGURE 1. The network of keywords co-occurrence in climate-related Big Data articles.


Governments around the world, including all levels of Indian government, respond to climate change in 2 ways: relief responses (short term) and policy responses (long term). Climate disasters can sometimes require immediate attention from the government, such as flash flooding, severe drought, and cyclones; in these cases, governments provide relief responses such as evacuation, rebuilding, medical care, and geoengineering techniques to mitigate the effects of the natural disaster at hand. Policy responses like regulating the relief efforts and the contribution of year-round activities to natural disasters and global temperature rise can be considered a long-term response to these problems. In both cases of climate responses - policy and relief - governments are bound by their architecture in the quickness, effectiveness, and justice of their responses.


Some scientists argue that the ‘architecture’ of governments is based upon ‘institutions’ which are just “humanly devised constraints that shape human interaction”. These institutions are conceptualized as clusters of norms, principles, regimes, and other institutions. This conceptualization is used to study global governance architecture, and the same principles can also be applied to studying national governments such as the Union Government of India, which operates at very large scales compared to other national governments. The study of institutions involves the analysis of formal rules, such as laws, and informal rules, such as norms of behavior, conventions, and established practices. This analysis can point out the operation of regulated as well as unregulated systems, and thus paints a broad picture of governance architecture.


Exploring the governance of India can provide factual evidence regarding several issues previously investigated through subjective research. Firstly, such a study will highlight the government's capacity to handle climate responses in emergencies (climate disasters) and long-term policies. Second, it can highlight the extent of delays in the institutional responses to the above situations. Third, it can shed light on the (de) centralization of government response to climate change, by making the division of responsibility, sources of funding, and bureaucratic delays evident. Lastly, it could shed light on judicial processes that provide or stray away from justice for those most affected by climate responses. All the above points are interlinked and it is hard to distinguish capacity for response, institutional delays, (de) centralization, and access to justice. While the issues highlighted above are insights into previously published literature, they can in no way compare to the inferences drawn from a detailed computational study. However, conducting research into the current modes of governance can help to identify the government departments that are laggards, processes that can be improved, and how effective communication pathways can be created.


A potential solution to the outlined issues is the effective digitization of processes such as data entry and communication, over the traditional mediums of paper and telephone, which can help improve the speed of communication and lower institutional delays. Capacity building will, thus, take a more deliberate approach to governance and long-term interventions and the national and local levels. Access to justice can be improved through increased transparency of data and expedition of judicial processes, which can also be ensured through higher digitization. Furthermore, artificial intelligence can be used to monitor an established system of governance providing real-time insights into steps taken for improvement. Artificial intelligence needs to be combined with human monitoring to ensure that the insights are at the highest level of accuracy and reliability.


References

Biermann, F. (n.d.). The Fragmentation of Global Governance Architectures: A Framework for analysis.


Bohle, H., Downing, T. E., & Watts, M. (1994). Climate change and social vulnerability. Global Environmental Change-human and Policy Dimensions, 4(1), 37–48.


North, D. C. (1990). Institutions, institutional change and economic performance. https://doi.org/10.1017/cbo9780511808678


Ribot, J. (1995). The causal structure of vulnerability: Its application to climate impact analysis. GeoJournal, 35(2), 119–122.


Sebestyén, V., Czvetkó, T., & Abonyi, J. (2021a). The Applicability of Big Data in Climate Change Research: The importance of System of Systems Thinking. Frontiers in Environmental Science, 9. https://doi.org/10.3389/fenvs.2021.619092

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