Artificial Intelligence’s application in Carbon Capture and Storage technology presents significant benefits but also poses new challenges in legal regulation. Given the potential for transboundary effects and the need for globally coordinated action, the existing legal principles need to be re-evaluated and expanded upon to ensure the responsible use of AI, especially in Carbon Capture and Storage technology.

Thus, firstly, this Blog Post gives a thematic introduction to Carbon Capture and Storage technology in India. Secondly, this Blog Post examines the use of Artificial Intelligence in Carbon Capture and Storage technology. Thirdly, this Blog Post critically analyzes the need for regulating the use of Artificial Intelligence in Carbon Capture and Storage technology. Lastly, this Blog Post gives recommendations on how to solve the conundrum of the absence of appropriate laws and regulations governing the usage of Artificial Intelligence in Carbon Capture and Storage technology in India.

1. Introduction

Climate change and its effects are evident through various natural disasters affecting all nations collectively. Inaction is not an option, and with evident urgency, specific measures have become essential in fighting climate change. Carbon capture, utilization, and storage will be critical to meet global targets in future decarbonization. By its placement at a crucial point in history, India is an essential cog in the wheel for fighting global climate change. Presently, India is fourth (4th) in terms of the total installed capacity of renewable energy, fifth (5th) in solar energy, and fourth (4th) in wind energy. The Indian government has ambitiously set a target of installing renewable energy capacity to 175 Gigawatts (“GW”) by 2022 and 500 GW by 2030. While the Indian government is taking many steps to reduce carbon footprint, very little attention has been given to carbon capturing.

Carbon Capture and Storage (“CCS”) constitutes a critical piece of the solution matrix that the world is progressively turning towards to combat the omnipresent threat of climate change. With anthropogenic carbon emissions reaching unprecedented levels, a significant global temperature increase is almost inevitable. Given this reality, the quest for effective mitigation measures is at its zenith, precisely where CCS technology comes to the fore.

CCS is a technological intervention that captures the carbon dioxide (“CO2”) emissions produced from using fossil fuels in electricity generation and industrial processes, aiming to avert their discharge into the atmosphere. It is essentially a three-part process encompassing capture, transportation, and secure storage. The technology’s capability to capture up to 90% of CO2 emissions makes it a promising prospect in the arsenal against climate change.

In a world progressively powered by fossil fuels, CCS can considerably mitigate the adverse impact of carbon emissions. Power plants and industries, the primary contributors to global CO2 emissions, can leverage CCS technology to reduce their carbon footprint significantly. Energy consumption is a significant contributor to the generation of carbon footprint. Traditionally, India has been greatly dependent on fossil fuels. In fact, India’s oil imports are substantial and significantly affect the economy. The Electricity Act, 2003 (“2003 Act”) is an essential piece of legislation, and the regulatory commissions perform an important function in promoting renewable energy. In 2010, the Central Electricity Regulatory Commission (“CERC”) introduced the concept of “Renewable Energy Certificates” (“RECs”), which are market instruments used to promote renewable energy in electricity. Further, the CERC has been responsible for other landmark steps, such as overhauling the Indian Electricity Grid Code, reducing regulatory and compliance burdens on businesses for ease of doing business in the renewable energy sector, and introducing various important renewable energy legislations like the “CERC (Terms and Conditions for Tariff determination from Renewable Energy Sources) Regulations, 2020” and the “CERC (Terms and Conditions for recognition and issuance of Renewable Energy Certificate for Renewable Energy Generation) Regulations, 2022.” Due to such efforts by the ERCs in India, its renewable energy capacity has reached 168.96 GW as of 2023, showing the Indian government’s aim to develop a substantial renewable energy capacity.

In Hindustan Zinc Ltd. v. Rajasthan Electricity Regulatory Commission, (2015) 12 SCC 611, the Supreme Court of India (“SC”) held that the State ERC (“SERC”) was correct in promoting renewable energy and mandating generating companies to follow its Regulations in this regard under Section 86(1)(e) of the 2003 Act, read with the National Electricity Policy of 2005, the National Tariff Policy of 2006 and the international obligations under the Kyoto Protocol to which India is a signatory. Further, the SC also held that the State ERC was correct in doing the same “…to discharge the constitutional obligations as mandated under Article 21 — Fundamental Right of the citizens and Article 48-A – the Directive Principles of State Policy and to discharge the Fundamental Duties by the respondents as envisaged under Article 51-A(g) of the Constitution of India.” In the absence of a coherent policy regarding CCS in India, the same principle can be followed as a stop-gap arrangement. However, presently, there is no express law or policy in India to promote CCS. Although a Policy Report by NITI Aayog for policy framework and deployment of CCS exists in India, it does not recommend any concrete laws or regulations that can be implemented to govern and regulate CCS. Similarly, the 2003 Act has no provisions related to data protection, privacy, and security which, in turn, makes the use of AI in CCS increasingly susceptible to various risks and challenges such as cyber-attacks, data leaks, unclear guidelines for the requirement of consent for data sharing, and many other similar risks and challenges. Due to such a lacuna, this Blog Post has subsequently explained that a robust regulatory mechanism is needed for the efficient and pervasive implementation of CCS.

Regulatory bodies in India, notably the CERC and the various SERCs, wield significant influence in shaping India’s energy sector through their power to set tariffs. These tariffs, by extension, dictate the financial viability of energy projects. While the promise of CCS technology in mitigating the devastating impacts of climate change is undeniable, it is essential to critically analyze the economic considerations surrounding the adoption of CCS technology. As it stands, CCS is an expensive technology in terms of initial capital investment and ongoing operational costs. Consequently, without financial incentives, its implementation at a significant scale may remain economically unfeasible. In this scenario, CERC and SERCs could play a transformational role by providing tariff concessions for power projects that incorporate CCS. This can be done in addition to the existing CCS policy framework in India. However, it is crucial to explore the financial implications of these concessions critically as follows:

    ● The Impact on Non-CCS Energy Tariffs:

     A critical factor to consider is the potential repercussions of tariffs for non-CCS energy sources.       There is a risk that these concessions could inadvertently lead to higher tariffs for non-CCS energy,       which could impose an undue burden on consumers.

    ● The Sustainability of Concessions:

      The long-term sustainability of these CCS concessions is another important factor to examine. It is       crucial to assess whether these CCS concessions would maintain their financial feasibility over the        long haul or strain the overall regulatory budget.

    ● Balance with Other Renewable Energy Incentives:

      Lastly, an equally crucial factor is the balance of these concessions against other renewable energy      incentives. The goal should be to ensure a fair and robust clean energy market where different     technologies can compete on an even playing field. We must be cautious of not creating an     environment that disproportionately favors CCS technology at the expense of other viable        renewable energy sources.

When combined with sustainable biomass, the potential of CCS allows for “negative emissions,” effectively drawing CO2 from the atmosphere. This solution is attractive due to its compatibility with existing infrastructure, enabling the power and industrial sectors to operate with reduced emissions. This provides an effective transitional path towards renewable energy without disrupting today's energy demands or tomorrow's ecological balance. However, its deployment is hindered by technological complexity, substantial financial investment for infrastructure, and lack of a definite market for captured carbon. Additionally, safe and secure storage of captured carbon, primarily through geologic sequestration, is a fundamental requirement, given potential risks like induced seismicity, groundwater contamination, and the possible release of stored carbon back into the atmosphere. Thus, despite not being a panacea for climate change, CCS is crucial in the global strategy against it, especially as nations aim to fulfill their Paris Agreement obligations. However, to harness its full potential, it is vital to address associated challenges through robust regulatory oversight, emphasizing the need for a well-crafted legal framework for its regulation.

What also becomes evident is the promise that Artificial Intelligence (“AI”) holds in navigating these challenges and optimizing the technology. AI and technology have become an intertwined part of our lives. Some people like Richard Yonck believe that with the advancement and emergence of AI, our world is becoming increasingly intelligent and is heading towards a future promising evolved higher intelligence. In fact, as per the latest technology and research, scientists are working to create virtual copies of human beings to enable future treatments in medicine. 1 All fields will be increasingly dependent upon AI. In fact, several experts have highlighted the role of AI in understanding climate change and predicting various scenarios. However, all this will need high-quality data collection, which is where regulation of AI and CCS will become necessary. Therefore, the intersection of these factors underscores the need for a comprehensive legal framework that accommodates the role of AI in CCS technology. While the journey towards a more sustainable future is undoubtedly complex, we can hope to steer our world towards a path of resilience and recovery through such intersections of technology, policy, and law.

2. AI and CCS Technology

A. Need for AI in CCS Technology

In the backdrop of the escalating global environmental crisis, AI can be seen as an inevitable ally for the effective deployment of CCS technologies. The compelling need for AI in CCS primarily stems from the complex, data-intensive, and dynamic nature of carbon capture, storage, and utilization processes. The traditional means of managing these processes, typically characterized by rigid and manual interventions, are proving insufficiently agile and responsive to the nuanced challenges posed by a rapidly changing environmental landscape.

To begin with, the complex process of capturing carbon emissions at their source demands a level of accuracy and efficiency that can only be realized through sophisticated machine-learning algorithms. These algorithms, adept at processing large volumes of data, can predict optimal conditions for carbon capture, dynamically adjust these conditions in response to changes, and identify potential risks and inefficiencies in the capture process. The consequence of such a proactive and nuanced approach is not only a reduction in carbon emissions but also an improvement in the overall performance and efficiency of emission-intensive industries.

Similarly, AI proves instrumental in the storage and utilization components of CCS technology. The selection of suitable geological storage sites, the monitoring of stored carbon to prevent leakages, and the conversion of captured carbon into usable products – these are all areas replete with uncertainties and variabilities that necessitate a data-driven, predictive approach. Leveraging AI’s predictive analytics capabilities, researchers can model different storage scenarios, predict and mitigate potential risks, and optimize carbon utilization processes, thereby maximizing the environmental and economic benefits of CCS.

B. Benefits of AI in CCS Technology

At its core, AI is about harnessing computational power to imitate human intelligence and decision-making. This computational strength can be effectively employed to navigate the intricate challenges posed by CCS technology. A case in point would be the optimization of CCS systems. AI algorithms, when employed, can analyze vast amounts of data relating to the performance of CCS systems under different conditions. By parsing through this data, these algorithms can ascertain the most efficient configurations, thereby enhancing the system’s CCS capacity and reducing the associated costs concurrently.

AI can further assist in predicting the behavior of underground storage reservoirs, a critical aspect of the CCS process. AI’s predictive modeling can inform operators about the possible future behavior of these reservoirs, allowing for pre-emptive measures to avoid issues like leakage or seismic events. With safety and environmental concerns being paramount, AI’s predictive capabilities play a pivotal role in risk mitigation and safety assurance, underlining AI’s indispensability in CCS technology.

AI-driven data analysis can also contribute significantly to climate action by highlighting the unseen aspects of carbon emissions. AI can detect and quantify emissions from individual power plants, creating a comprehensive emissions map. Such insights can equip policymakers and businesses with the necessary data to make informed decisions, adding another layer of accountability to the climate action discourse.

The case of the FarmBeats project funded by Microsoft’s “AI for Earth” programme exemplifies the potential of AI in climate action. This project assists farmers in optimizing their use of resources, thereby reducing their environmental impact. Through the strategic use of sensors and drones, data on various environmental factors is collected. AI algorithms analyze this data to make resource optimization recommendations. This illustrates how AI can enhance environmental sustainability across different sectors by optimizing resource use and reducing carbon emissions.

AI can also be influential in managing energy systems such as smart grids, reducing energy waste, and increasing efficiency. Predictive algorithms can anticipate energy demand and supply, allowing for adjustments in production and distribution to prevent wastage and overproduction. As we transition to more sustainable energy systems, AI’s role in managing these transitions and reducing greenhouse gas emissions becomes increasingly critical.

AI’s role in enhancing the potential of CCS technology does not stop at the doors of technology optimization and predictive analytics; it extends further, aiding in the domain of novel technology development. Here, nascent technologies such as Direct Air Capture (“DAC”) can use AI to optimize and scale. DAC is a promising CCS methodology that, at a large scale, could play a pivotal role in climate change mitigation. However, presently, the technology is in its infancy and needs substantial optimization and efficiency improvements. This is where AI comes into play, given its proven track record in enhancing the efficiency and effectiveness of complex systems. Machine learning algorithms can be employed to study the complex chemistry involved in DAC processes. By iteratively learning from these processes, AI can potentially help enhance the capture efficiency and suggest novel methods or materials to further the DAC technology. The impact of such advancements is two-fold. Primarily, it aids in mitigating climate change by developing more efficient CCS technologies. Additionally, it paves the way for economically viable CCS technologies, promoting their widespread adoption and furthering the cause of climate action.

C. Challenges of AI in CCS Technology

However, deploying AI in CCS technology is not devoid of challenges. High-quality data is necessary for AI models to make accurate predictions and recommendations. Since climate data can be sparse, incomplete, or of poor quality, AI’s effectiveness can be limited. This necessitates an investment in data collection and validation to enhance the accuracy and reliability of AI models. This is where the regulation of AI and the collection of data becomes of paramount importance. Trust and transparency are also essential considerations when deploying AI. AI algorithms can be opaque and difficult to interpret, creating challenges in policy decisions based on this technology. Therefore, efforts should be directed toward developing transparent AI systems that can be audited and explained, fostering public trust and acceptance. It is imperative that all data collection is undertaken transparently and with informed consent.

3. The Imperative for Legal Regulation of AI in CCS Technology

A. Transparency and Accountability Concerns

As we critically analyze the need for the legal regulation of AI in CCS technology, the first point to address is the multifaceted nature of AI applications in this field and the diversity of legal questions they engender. From the design of machine learning algorithms to the handling of massive amounts of environmental data, from decision-making processes involving strategic planning of CCS initiatives to optimizing the operations of CCS systems, AI applications span a wide range of activities, each raising unique legal and ethical issues.

Transparency is crucial in engendering trust and acceptance in AI systems, particularly when these systems are employed in areas as significant and impactful as CCS. However, the often “black box” nature of AI algorithms, where their internal workings remain opaque even to their designers, presents a considerable challenge to ensuring transparency. Due to these challenges, the following questions arise:

    ● Can AI decisions be trusted when their bases remain unclear?

The answer is likely to be complex and dependent on multiple factors. The trustworthiness of AI decisions will heavily depend on the robustness of the AI system’s design and implementation, the quality and relevance of the data it is trained on, and the level of human oversight in its deployment and use. Nevertheless, without a clear understanding of the basis of AI decisions, a significant trust deficit remains, complicating its widespread acceptance, particularly in critical areas like CCS. Therefore, it is imperative that AI laws consider the necessary mechanisms to mitigate this trust deficit. Such mechanisms might include mandating explanations in AI design or advocating for hybrid decision-making models where human judgment works in tandem with AI.

    ● Can we fully understand and predict the implications of these AI decisions?

The answer is less straightforward. The “black box” nature of AI makes it challenging to fully comprehend and foresee the potential ramifications of AI-based decisions, particularly in complex and dynamically evolving contexts like CCS. This ambiguity might lead to unintended and possibly harmful outcomes, posing risks to the credibility and reliability of AI-integrated systems. Thus, the framing of AI laws should incorporate guidelines for rigorous testing, monitoring, and validation of AI systems, particularly when applied in critical sectors. Moreover, legal and regulatory mechanisms should be implemented for accountability and redress in cases where AI systems lead to unintended negative consequences.

Therefore, these questions need appropriate consideration for transparency while framing AI laws and regulations.

Then comes the issue of accountability. When an AI system integrated with CCS technology makes a decision that leads to unintended consequences, the following questions arise:

    ● Who is to be held accountable? Is it the designers of the AI system, the operators of      the CCS technology, or the policymakers who allowed its use?

It is imperative to note that the answer is not straightforward. It is significantly influenced by the specifics of the situation, including the context of the decision-making, the nature of the unintended consequences, and the role of different stakeholders in the AI system’s design, operation, and oversight. Generally, one could argue that the responsibility should fall on the entity that exercises the most control over the AI system’s design, implementation, and operation. However, this becomes complicated in practice due to the highly integrated and interdependent nature of AI systems. Designers of the AI system may argue that they only provided the tools and that it was the operators who misused or misapplied them. Conversely, the operators might argue that they operated the system within its intended purpose, and it is the designer’s responsibility if the system did not perform as expected. On the other hand, policymakers might argue that they merely allowed the AI technology’s use based on the information available at the time. This blurring of lines of responsibility necessitates the need for comprehensive regulations that clearly outline obligations and accountability in different scenarios. This would involve establishing legal frameworks that specifically address accountability in AI systems, possibly drawing from existing legal concepts such as “product liability” and “professional negligence.”

    ● What if the AI system was trained on biased data, leading to biased decisions,        exacerbating environmental injustices or inefficiencies in CCS efforts – who bears the         responsibility then?

Arguably, the responsibility should fall on the designers or trainers of the AI system. They are typically responsible for data selection and should ensure that the data used is representative and unbiased. This underscores the need for robust data governance practices in AI system design, which should be reinforced by legal and ethical guidelines. However, in practice, the scenario could be more complex. For instance, the designers might have used the best available data at the time, and the biases might have been unknown or unavoidable. Furthermore, it could be argued that operators and policymakers also share responsibility for not adequately scrutinizing the AI system and its potential biases before its deployment.

Therefore, the aforementioned are just a few examples illustrating the critical need for legal regulation to clearly define accountability in the application of AI in CCS technology.

As we shift towards a more AI-integrated future in CCS, the legal framework needs to address the issue of bias in AI decisions. Unconscious biases in training data can result in AI systems perpetuating or exacerbating these biases in their decisions, as earlier elaborated for the CCS technology. This has significant implications for CCS efforts and their impacts on society, potentially leading to unequal distribution of resources or burdens. Clear regulations are needed to ensure AI systems are designed and trained to minimize bias and promote equitable outcomes in CCS technology.

B. Data Privacy Concerns in Light of GDPR Provisions

The European Union (“EU”) General Data Protection Regulation (“GDPR”), as an archetypal legal framework, has set a global standard for data privacy and protection. In the context of AI integration within CCS technology, understanding GDPR is paramount due to the volume and nature of data involved in these CCS technologies.

However, it is important to note that, while the GDPR serves as a beacon of rigorous data protection standards, its applicability to all countries, particularly India, is limited due to several factors. Firstly, different socio-cultural norms and understandings of privacy may make direct transposition problematic. Secondly, the GDPR's infrastructure and enforcement mechanisms may be beyond the capacity of developing countries like India. Lastly, regulatory discrepancies between the EU and other jurisdictions may lead to legal challenges and complexities. Due to this, there arises an imperative need for countries like India to adopt their own data privacy and protection legislations wherein GDPR principles may be adopted to a limited extent, but not in its entirety.

On a national level, the collection of personal data and information and Sensitive Personal Data and Information (“SPDI”) in India is currently overseen by the “Information Technology (Reasonable Security Practices and Procedures and Sensitive Personal Data or Information) Rules, 2011” (“2011 Rules”). However, acknowledging the rapid development of AI and internet industries, this existing framework is undergoing a considerable transformation. The forthcoming “Digital Personal Data Protection Bill, 2022” (“2022 Bill”) and the draft “Digital India Act, 2023” (“2023 Act”) promise significant changes in the collection and processing of personal data and information and SPDI.

Despite the developments mentioned above, a glaring lacuna within the Indian legal and regulatory landscape, both old and new, is the failure to contemplate the challenges and implications of AI. This shortcoming holds substantial consequences for data collection and processing in innovative sectors like CCS technology, where AI plays a critical role. Taking guidance from GDPR and harmonizing it with India's unique context could be a step towards filling this gap in India's data protection and privacy laws, especially for deploying AI in CCS technology in India.

    i. Collection and Processing of Data: Article 6 of the GDPR

The primary concern of any data-intensive system, such as AI in CCS technology, is the collection and processing of data. Article 6 of the GDPR stipulates the lawfulness of processing personal data. According to Article 6, personal data can be processed if the data subject has given explicit consent, or if the processing is necessary for the performance of a contract, compliance with a legal obligation, protection of vital interests, the performance of a task carried out in the public interest or in the exercise of official authority, or for the purposes of legitimate interests pursued by the data controller or a third party.

Applying this provision to AI in CCS technology, data processing must either be necessary for the technology’s function or occur with the explicit consent of individuals whose data is being processed. Here, the challenge lies in the scope and diversity of data needed, which may inadvertently include personal data. This necessitates robust mechanisms for obtaining informed consent and for ascertaining the necessity of data processing for CCS operations.

    ii. Data Minimization and Purpose Limitation: Article 5 of the GDPR

Article 5 of the GDPR introduces the principles of data minimization and purpose limitation. Data minimization means that personal data must be “…adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed.” Purpose limitation, on the other hand, means that personal data must be “…collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes…”.

These principles pose significant challenges in the context of AI applications within CCS technology. AI systems typically rely on vast data sets for effective functioning. Therefore, the principle of data minimization needs judicious handling of environmental data, ensuring that any personal data utilized is strictly necessary for the functioning of the AI system in CCS technology. Similarly, purpose limitation necessitates a clear delineation of why and how environmental data for CCS technology is processed, and any subsequent use of the data must align with these stated purposes.

    iii. Rights of Data Subjects: Articles 15-22 of the GDPR

GDPR, in Articles 15 to 22, delineates the rights of data subjects, which include the right to access, rectification, erasure (also known as the “right to be forgotten”), restriction of processing, notification obligation, data portability, objection to processing, and rights related to automated decision making, including profiling.

These rights entail numerous obligations for entities utilizing AI in the CCS technology. Ensuring individuals’ rights to access their data and demand rectification or erasure necessitates robust data management systems. Moreover, given that AI in CCS technology involves automated decision-making, mechanisms must be in place to allow individuals to challenge such decisions, particularly when they significantly affect the individual.

    iv. Data Protection by Design and by Default: Article 25 of the GDPR

Article 25 of the GDPR mandates “Data Protection by Design and by Default.” This means that data protection considerations must be embedded in the design stage of any system or process and applied by default throughout its operation. For AI applications within CCS technology, this principle is highly pertinent. AI systems within CCS technology must be designed to prioritize data protection. This might involve integrating features to ensure data minimization, consent management, and secure data storage and processing.

Therefore, when considering the integration of AI into CCS technology, GDPR’s provisions provide vital guidelines for addressing data privacy concerns. Strict adherence to these provisions will not only ensure compliance with data privacy laws but will also foster greater trust and confidence in the use of AI for CCS, thereby potentially enhancing the effectiveness of these technologies in combating climate change. However, translating these provisions into practice in the context of AI in CCS technology will need a concerted effort from policymakers, AI developers, and all other stakeholders, underscoring the need for a collaborative approach to data protection in this emerging and critical field.


The views and opinions expressed by the Authors are personal.

About the Authors

Mr. Varun Pathak is a Partner (Dispute Resolution) at Shardul Amarchand Mangaldas & Co., New Delhi. He is an Advocate-on-Record at the Supreme Court of India. He has completed his LL.M. in Corporate and Commercial Laws from the London School of Economics (LSE).

Mr. Pushpit Singh is a 5 th -Year B.B.A. LL.B. Student at Symbiosis Law School, Hyderabad. He is a freelancing Corporate and Disputes Paralegal. He is also an Indian Institute of Arbitration and Mediation (IIAM) Panel Arbitrator.

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