HARNESSING AI TO FIGHT CLIMATE CHANGE : A ROADMAP FOR ACTION

INTRODUCTION

One of the most important issues of our day is climate change, which poses grave risks to the environment, society, and economy. It is brought on by the buildup of greenhouse gases (GHGs) in the atmosphere, which are mostly produced by human activities like the burning of fossil fuels, changing land uses, and agriculture. The average global temperature has increased by roughly 1.1°C since the pre-industrial era, and the Intergovernmental Panel on Climate Change (IPCC) predicts that by the end of the century, it will have increased by 1.5°C to 4.5°C, depending on the emission scenarios. Significant and irreversible effects on the natural and human systems will result from this, including food insecurity, water scarcity, extreme weather, melting glaciers, rising sea levels, and health and social unrest.

As per the Paris Agreement, limiting the increase in global temperature to significantly less than 2°C necessitates cutting greenhouse gas emissions, strengthening carbon sinks, and preparing for the unavoidable consequences of climate change. But in order to accomplish these objectives, never-before-seen levels of collaboration, creativity, and action at all scales and sectors are needed. New and revolutionary solutions are desperately needed to speed up the shift to a low-carbon and climate-resilient future because the current policies and technologies are insufficient to meet the climate targets.

By offering data-driven insights, innovations, and solutions, artificial intelligence (AI) has the potential to assist in reducing the effects of climate change and assisting in adaptation. Artificial intelligence (AI) is a general term that covers a wide range of methods and applications that allow machines to carry out tasks like perception, reasoning, learning, decision-making, and communication that would typically require human intelligence. Large and complicated datasets can be analysed, systems and procedures can be optimized, hypotheses can be generated and tested, and new goods and services can be developed with AI. AI can also improve human abilities, promote teamwork, and give stakeholders more power.

With a focus on four main areas—emissions measurement and monitoring, energy efficiency and emissions reduction, climate modelling and forecasting, and climate resilience and adaptation—we examine the state of the art and future directions of AI for climate change in this article. We give a summary of the key obstacles and possibilities, the most recent and upcoming AI methods and applications, best practices, and suggestions for each field. We also suggest a course of action that outlines the key initiatives and measures that scholars, practitioners, decision-makers, and interested parties should take to leverage AI’s potential to combat climate change.

We contend that if AI is applied responsibly, inclusively, and cooperatively, it can be an effective tool for combating climate change. We contend that if AI is applied responsibly, inclusively, and cooperatively, it can be an effective tool for combating climate change. We also talk about the possible drawbacks and hazards of AI, including data quality concerns, moral dilemmas, and societal ramifications, and we offer solutions. In order to progress the field of AI for climate change, we emphasize the necessity of interdisciplinary and transdisciplinary research and innovation, as well as of cultivating a culture of learning and experimentation.

THE STATE-OF-THE-ART IN CLIMATE CHANGE AI TODAY AND ITS POTENTIAL FUTURE DIRECTIONS, WITH AN EMPHASIS ON FOUR MAJOR AREAS ALONG WITH THE CHALLENGES AND OPPORTUNITIES :

1) Emissions Measurement and Monitoring: AI being a search engine could help to Measure and Monitor GHGs from different sources, such as industries, power plants, vehicles, and buildings. Artificial Intelligence (AI) can be used to track and estimate emissions at various locations and scales by analysing sensor data, satellite imagery, and other information sources. AI can also assist in reporting and verifying emissions data, as well as offering incentives and feedback on actions taken to reduce emissions. Blockchain, smart contracts, computer vision, natural language processing, machine learning, remote sensing, and computer vision are a few of the current and developing artificial intelligence methods and applications for measuring and tracking emissions.

THE MAIN CHALLENGES AND OPPORTUNITIES ARE: –

-> Quality Data: The quality of the data sources, such as satellite photos, sensors, and reports, determines how accurate and trustworthy the emissions data are. By identifying and fixing mistakes, anomalies, and outliers as well as by enhancing and augmenting data with new information, AI can assist in enhancing the quality of data. But data accessibility, availability, and standardization also affect data quality, and these factors can differ between nations and industries. As a result, greater data harmonization and sharing are required, in addition to data security and privacy protection.

-> Ethical concerns: Data ownership, accountability, and transparency are a few of the ethical concerns that may arise from using AI for emissions measurement and monitoring. How is the emissions data used and shared, for instance, and who owns and controls it? Who bears accountability and liability for the emissions data and the decisions made using it? How can the public and stakeholders be given access to transparent and understandable versions of the emissions data and AI algorithms? Stakeholder participation and engagement are necessary, as well as careful regulation and consideration of these issues.

2) Emission Reduction and energy efficiency: Artificial Intelligence mitigate emissions and enhance energy efficiency in various different sectors such as, electricity, transportation, and agriculture etc., AI can be used to integrate more renewable energy sources, like solar and wind, and to optimize energy systems, like smart grids, smart buildings, smart appliances, and smart cars. AI can also assist in the development and application of low-carbon solutions, including biofuels, electric cars, and carbon capture and storage. Artificial intelligence (AI) methods and applications that are currently being used to reduce emissions and improve energy efficiency include digital twins, generative adversarial networks, reinforcement learning, deep learning, and evolutionary algorithms.

THE MAIN CHALLENGES AND OPPORTUNITIES ARE: –

-> System complexity: There are many players, variables, and uncertainties in the energy systems, which make them dynamic and complex. Artificial Intelligence (AI) can assist in modeling and simulating energy systems as well as in providing adaptable and ideal solutions for various goals and scenarios. AI must take into account the trade-offs and interdependencies between the energy systems as well as the effects that the solutions will have on society and the environment. As such, human supervision and intervention, along with more integrated and holistic approaches, are required.

-> Social ramifications: AI’s application to energy efficiency and emissions reduction may have an impact on behavior, equity, and employment. What impact, for instance, will AI have on the employment and skill sets of those employed in the energy industry? How will the application of AI affect consumers’ and producers’ energy-saving and consumption habits? Stakeholder empowerment and involvement are necessary, as is careful assessment and mitigation of these implications.

3) Climate Modelling and forecasting: Artificial intelligence (AI) can help increase the precision and dependability of these critical tools for planning and executing adaptation and mitigation measures as well as for comprehending the causes and effects of climate change. AI may be utilized to create and test scenarios, hypotheses, and counterfactuals as well as process and analyze large and complex datasets like climate observations, simulations, and projections. In addition to offering useful insights and suggestions, artificial intelligence (AI) can assist in identifying and quantifying opportunities, risks, and uncertainties. Long short-term memory, attention mechanisms, transformers, neural networks, convolutional neural networks, recurrent neural networks, and long short-term memory are some of the current and developing AI methods and applications for climate modelling and forecasting.

THE MAIN CHALLENGES AND OPPORTUNITIES ARE:

-> Data accessibility: The quality and accessibility of climate data, including information on temperature, precipitation, wind, and sea level, is essential to the functioning of climate models and forecasts. By improving data resolution, completing data gaps, and supplementing data with information from other sources like crowdsourcing and social media, artificial intelligence (AI) can help increase the availability of data. However, the infrastructure for data collection and storage—which may be inadequate in some areas and industries—also affects the availability of data. As a result, increasing data investment and innovation is required, along with maintaining data compatibility and interoperability.

->Model uncertainty: Because of the complexity and inherent variability of the climate system, as well as the assumptions and limitations of the models, there is uncertainty in the climate models and forecasts. By enhancing model performance, calibration, and validation as well as by offering uncertainty quantification and propagation, artificial intelligence (AI) can assist in lowering model uncertainty. Nevertheless, the choice and interpretation of the model also affects model uncertainty, and these decisions can be impacted by the prejudices and inclinations of the modelers. As a result, greater model diversity and comparison are required, as well as greater model explain-ability and transparency.

4) Climate resilience and adaptation: AI can improve human and natural systems’ ability to withstand and adapt to the effects of climate change, including extreme weather events, rising sea levels, droughts, floods, heat waves, wildfires, and diseases. AI can also assist in the development and application of adaptation strategies, including climate-smart agriculture, disaster risk reduction, ecosystem restoration, and climate finance. Anomaly detection, image segmentation, object detection, semantic segmentation, sentiment analysis, and natural language generation are a few of the current and developing AI techniques and applications for climate resilience and adaptation.

THE MAIN CHALLENGES AND OPPORTUNITIES ARE:

-> Data integration: Various forms of data, including biophysical, socioeconomic, and institutional data, must be integrated in order to assess and manage climate risks and impacts. AI can assist in integrating data from many formats, scales, and sources to present a thorough and coherent picture of the state of the climate and available adaptation strategies. Data definitions, standards, and indicators must also be harmonized and aligned; these requirements can differ among nations and industries. As a result, greater cooperation and coordination with regard to data is required, along with the maintenance of data consistency and quality.

-> Ethical concerns: Issues of justice, fairness, and accountability may arise from the application of AI to climate resilience and adaptation. How, for instance, will the application of AI impact how various groups and geographical areas be exposed to different climate risks and impacts? What impact will AI have on communities’ and stakeholders’ ability to participate in and be empowered during the adaptation process? What impact will AI use have on the accountability and liability of the institutions and actors involved in the adaptation outcomes? Stakeholder participation and engagement are necessary, as well as careful regulation and consideration of these issues.

MAIN ARGUMENTS IN SUPPORT OF AI AS A VALUABLE TOOL

• AI can help measure and monitor greenhouse gas emissions from various sources, such as industries, power plants, vehicles, and buildings. This can provide accurate and reliable data for tracking and reporting the progress and performance of the climate policies and actions, and for providing feedback and incentives for emission reduction. AI can also help verify and report emissions data, and to ensure transparency and accountability among the stakeholders.

• AI has the potential to improve energy efficiency and lower emissions in a number of industries, including manufacturing, transportation, agriculture, and electricity. AI can be used to integrate more renewable energy sources, like solar and wind, and to optimize energy systems, like smart grids, smart buildings, smart appliances, and smart cars. AI can also assist in the development and application of low-carbon solutions, including biofuels, electric cars, and carbon capture and storage. This can reduce expenses, save energy and resources, and boost competitiveness and productivity.

• AI has the potential to increase the precision and dependability of climate models and forecasts, which are crucial for comprehending the origins and effects of climate change as well as for developing and putting into practice adaptation and mitigation plans. AI can be used to create and test scenarios, hypotheses, and counterfactuals as well as process and analyze large and complex datasets like climate observations, simulations, and projections. AI can also be used to provide actionable insights and recommendations, as well as to identify and quantify uncertainties, risks, and opportunities.

• Artificial Intelligence has the potential to improve human and natural systems’ ability to withstand and adjust to the negative effects of climate change, including extreme weather, rising sea levels, droughts, floods, heat waves, wildfires, and diseases. Artificial Intelligence (AI) has the potential to offer early warning and emergency response systems, as well as monitor and evaluate the exposure and vulnerability of various sectors and regions. AI can also assist in the development and application of adaptation strategies, including climate-smart agriculture, disaster risk reduction, ecosystem restoration, and climate finance. This can lessen losses and damages, save lives and livelihoods, and improve coping and recuperation skills.

• However, there are a number of risks and difficulties associated with using AI for climate action, including concerns about data quality, moral dilemmas, and societal ramifications. As a result, it’s critical to apply AI responsibly, inclusively, and cooperatively by abiding by the values and standards of reliable AI, which include:

a) Human agency and supervision: AI should uphold and strengthen human autonomy, self-determination, and dignity. It should also be supervised and influenced by humans.

b) Technical robustness and safety: AI should be safe, dependable, and resilient. It should also try to prevent or reduce mistakes and harm.

c) Data governance and privacy: AI should guarantee the accuracy and integrity of the data while also respecting and defending the rights of individuals and organizations to privacy and data.

POTENTIAL RISKS AND LIMITATION AND SUGGESTIONS TO ADDRESS THEM:

-> Data quality: AI’s ability to learn, analyze, and optimize relies on the availability and quality of the data it uses. However, due to a number of variables, including data collection techniques, data sources, data standards, and data processing, the data on climate change may be erroneous, inconsistent, biased, or incomplete. This could result in mistakes, ambiguities, or misinterpretations and could have an impact on the validity and dependability of the AI outputs and outcomes. In order to solve this problem, it’s critical to guarantee the accuracy and consistency of the data through techniques like data augmentation, cleaning, validation, and verification. Additionally, it’s critical to guarantee the data’s accessibility and availability through the use of techniques like data sharing, harmonization, and protection.

-> Ethical concerns: When it comes to the problem of climate change and its solutions, artificial intelligence may bring up ethical concerns like justice, fairness, accountability, and transparency. AI, for instance, may have an impact on how climate risks and impacts are distributed among various populations and geographical areas, as well as produce winners and losers in the low-carbon transition. AI may also have an impact on how communities and stakeholders engage in and are empowered by the climate action process, as well as on their values and preferences. AI may also create difficulties for oversight and regulation, as well as impact the accountability and liability of the institutions and actors in the results of climate action. The principles and guidelines of trustworthy AI, such as human agency and oversight, privacy and data governance, diversity, non-discrimination and fairness, societal and environmental well-being, and accountability, must be followed in order to address this issue.

DIFFERENT PROJECTS AND PROGRAMS OF CLIMATE CHANGE IN WHICH AI IS USED

• A Project to Create and Implement a Remote Sensing System that makes use of Artificial Intelligence (AI) to Find and Track Plastic Pollution in the Oceans and to Deliver Scientific Information and understanding for Ocean Preservation and Cleanup.

• A project to install and assess a smart grid system that makes use of artificial intelligence (AI) to maximize the integration of renewable energy sources, like solar and wind, and to improve the electricity network’s dependability and energy efficiency.

• An AI-powered generative adversarial network will be developed and tested in order to generate and evaluate low-carbon options like carbon capture and storage, electric cars, and biofuels, as well as to offer suggestions and feedback for their application.

• An application and enhancement program for artificial intelligence (AI) neural networks to process and analyze climate observations, simulations, and projections; to produce accurate and dependable climate models and forecasts for various scenarios and goals.

• A project to develop and implement an artificial intelligence (AI) based natural language generation system for communication and dissemination of climate models and forecasts, as well as to offer practical insights and suggestions for mitigation and adaptation tactics.

• A program to monitor and evaluate how vulnerable various industries and areas are to the effects of climate change, including rising sea levels, droughts, floods, heat waves, wildfires, and diseases, by utilizing and improving an AI-powered image segmentation system.

• An initiative to create and put into practice a sentiment analysis system that leverages AI to gauge public awareness of the potential and constraints of AI in relation to climate change, as well as to promote the uptake and expansion of AI solutions.

STATUTORY PROVISIONS

• The UK is proposing legal instruments to implement its commitments under the Climate Change Act of 2008 and the Paris Agreement, known as the UK Draft Statutory Instruments on Climate Change. These include energy efficiency laws, carbon budgets, carbon pricing, and greenhouse gas emission regulations.

• The National Laws and Policies on Climate Change Adaptation are an international examination of the legal and policy structures that oversee climate change adaptation in various nations. These include laws and regulations pertaining to the creation and exchange of information, institutional arrangements, planning for adaptation, and adaptation measures.

• Access UK legislation on climate change and related topics, including air and water pollution, environment protection, public liability insurance, and animal welfare, can be found on the Legislation.gov.uk website. Acts and regulations that establish the requirements and duties for different industries and players to address climate change are among them.

• The Ministry of Environment and Forests provides the Act and Rules governing environmental protection in India. These include laws and regulations pertaining to environmental protection, animal welfare, public liability insurance, air and water pollution, and forest preservation. These include national action plans on climate change and the National Green Tribunal Act, in addition to other laws and policies that address adaptation and mitigation of climate change.

• The UNFCCC provides Zimbabwe’s national legislation on climate change. Natural resources, forests, dangerous materials, air pollution, water, and shared land are all covered by these laws and regulations. These also include the National Climate Policy and the National Climate Change Response Strategy, two laws and policies that deal with mitigating and adapting to climate change.

• The request for an advisory opinion on climate change from the International Court of Justice (ICJ) was made by the United Nations General Assembly. The opinion concerns the responsibilities of states regarding climate change. In addition to addressing the obligation and liability of States for damages caused by climate change, the request aims to make clear the legal norms and principles that govern the prevention, mitigation, and adaptation of climate change.

RESOURCES AND CAPACITIES FOR THE IMPLEMENTATION AND MANAGEMENT OF THE PROJECTS AND PROGRAMS

• In order to guarantee the accuracy and consistency of the data and AI algorithms, the transparency and comprehensibility of the AI processes and results, and the ethical and social ramifications of the AI solutions, the project management cycle and the agile methodology are used.

• Stakeholder collaboration and engagement is necessary to guarantee the involvement and empowerment of pertinent parties and stakeholders, including communities, policymakers, researchers, and practitioners, throughout the course of a project or program.

• Using the input and lessons gained, identify and manage risks and challenges that may arise during the implementation and management of the project or program, such as data quality, model uncertainty, and ethical concerns, and use them to improve the performance and quality of the project or program.

CONCLUSION

In conclusion, I have reviewed the current state of the art and the future directions of AI for climate change, focusing on four key areas: emissions measurement and monitoring, emissions reduction and energy efficiency, climate modeling and forecasting, and climate resilience and adaptation. I have also proposed a roadmap for action, outlining the main steps and actions for researchers, practitioners, policymakers, and stakeholders to harness the power of AI for climate change. Have also argued that AI can be a valuable tool for climate action, if it is used in a responsible, inclusive, and collaborative manner. Discussed the potential risks and limitations of AI, such as data quality, ethical issues, and social implications, and suggested ways to address them.

However, I have acknowledged that AI is not a silver bullet, and that it cannot solve the climate change problem alone. AI is only one of the many tools and approaches that can be used to tackle the complex and multifaceted challenges of climate change. Therefore, highlighted the need for interdisciplinary and transdisciplinary research and innovation, as well as for fostering a culture of learning and experimentation, to advance the field of AI for climate change. Believing that by combining the strengths and perspectives of different disciplines and sectors, such as natural sciences, social sciences, engineering, humanities, arts, and business, we can create more effective and sustainable solutions for climate change. Also, believed that by embracing a culture of learning and experimentation, we can foster creativity and innovation, and learn from our successes and failures, to improve our understanding and practice of AI for climate change.

Author: Abhinav Jindal, 4th year law student at Symbiosis law school, Nagpur 

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