A new book asks if AI can be used for better governance and sharper climate policies

Designing climate policies is one of the most difficult challenges in governance today. It demands that governments strike a delicate balance between environmental ambition, development, economic growth, political realities and social equity. But even when the right policies exist on paper, their true impact depends on how and where they are implemented. The challenge lies in the fact that climate change touches every sector, and every sector is deeply interconnected. A policy targeting one issue in isolation, say clean energy, can fall short if it does not account for dependencies across transport, housing, industry or land use. To be truly effective, climate policies must take an ecosystem-wide view. And yet, this is far easier said than done.

Anyone who has worked in government will recognise how difficult such coordination is in practice. Sectoral silos, fragmented data, legacy systems, competing mandates and sometimes even political turf battles can stand in the way of alignment. Budget allocations may differ sharply between departments, and timelines often conflict. In the developing world, this complexity is further amplified by the weight of competing development priorities. Policymakers are not just working on climate. They are managing flood recovery, fixing roads, building water systems, responding to public grievances and delivering on promises of economic growth, all at the same time. It is easy to say that climate action should be integrated into every department’s work. But that overlooks just how stretched many public officials already are. If an official in a climate-vulnerable district is spending most of their energy fighting floods or wildfires, when will they have the time to plan how to prevent the next one? Where will they find the funds? And if every season brings more disasters, when do they break out of this vicious cycle?

AI has the potential to help here, not by replacing human decisionmaking, but by clarifying and complementing it. By analysing vast amounts of cross-sectoral data, forecasting risks, identifying overlaps and mapping co-benefits, AI can support governments in designing policies that are holistic, better targeted and ultimately more impactful. But for that to happen, governments need the capacity, tools, political will and financing to use such intelligence well. In an ideal world, every city would have the foresight of Climaville, with governments led by visionary leaders like Mayor Ashman. But that is not always the case. Most governments are navigating difficult trade-offs, with limited time, data and resources. If we want climate policy to succeed, then we must support governments in making smarter choices faster, and that includes putting the best tools of intelligence in their hands.

We would suggest that AI for better climate policies and governance should serve to address three broad ambitions:

Freeing up policymakers’ time for what really matters

Many policymakers, especially in vulnerable regions, spend a significant portion of their time firefighting and responding to citizen grievances, chasing compliance paperwork, or manually appraising and reviewing project proposals, including those that have climate implications. These are essential tasks, but they are also time-consuming and often repetitive. AI can take over many of these routine functions. For instance, AI-powered chatbots can handle a large share of citizen service requests, freeing up officials to focus on core policy work. Similarly, project appraisal, which usually means reading through long reports and risk assessments, can be made much faster and sharper using AI. These models can not only evaluate the proposal itself but also scan thousands of other data points that may not be mentioned in the report. They can draw connections with climate predictions, regional hazard models, infrastructure plans, previous project outcomes, and even the latest scientific and financial analyses. This allows for a more complete picture of whether a project is viable, what risks it might face in the future, and how it compares to other similar interventions across geographies. Imagine a state planning a 2 gigawatt (GW) solar park in a drought-prone district. On paper, the site looks ideal with abundant sunlight, available land and strong investor interest. But when an AI system evaluates the plan, it uncovers deeper risks that are invisible to the human eye. It finds that the district’s water table has been declining for 12 consecutive years and solar panel cleaning alone could require 6 to 8 million litres of scarce water every month. Climate models indicate that dust storms will become longer and more frequent, reducing panel efficiency and doubling cleaning cycles. At the same time, grid simulations reveal that the nearest substation will face instability by 2031 unless at least 200 megawatt (MW) of storage is added. Based on these insights, the AI recommends shifting part of the project to a neighbouring block, adopting a hybrid battery and pumped hydro storage strategy, and using precision cleaning systems to save water. In seconds, AI turns a seemingly straightforward solar plan into a resilient and climate-proof energy asset. These are not things that a single official can check in a day, but AI can. In doing so, it allows limited human resources to be spent on strategic decision-making rather than administrative heavy-lifting.

Sharpening decision-making through smarter insights

AI can give policymakers the power to simulate outcomes before they act. With hybrid models that combine climate, economic and machine-learning data, planners can test how emissions, public health or jobs might respond to a certain policy, whether it is a fuel tax, a subsidy for electric vehicles or a change in land use. This makes policy not just reactive but predictive. It also helps make trade-offs transparent. It could also show that for a policy to succeed, support may be needed from other stakeholders, including different ministries or departments within the government. It can highlight how one policy might affect others or ripple across sectors, and how those stakeholders are likely to react. This allows policymakers to anticipate challenges early and design interventions that are more holistic, coordinated and inclusive.

For example, a city considering a congestion charge might use AI to forecast its impact not just on traffic, but on pollution hotspots, local business activity and public transport usage. Tools that use Natural Language Processing (NLP) to scan thousands of policy documents globally, can support this effort by letting policymakers compare what has worked in other countries. AI can also be used to simulate financing scenarios, such as testing whether a policy could be sustained under different climate stress conditions or commodity price changes. This improves both the design and durability of climate action.

Making climate action trackable and responsive

Passing a climate law or a policy is only the beginning. Real progress depends on whether the policy is implemented as intended, and whether it continues to deliver benefits over time. AI enables real-time monitoring of progress and offers alerts when things veer off track. For example, AI systems can track energy usage patterns, air quality data and construction timelines to verify if an energy efficiency regulation is being followed. They can also help identify blind spots, such as where a policy is leading to unintended emissions shifts elsewhere, or where compliance costs are disproportionately hurting smaller businesses. More importantly, AI can guide proactive interventions. If a region is showing early signs of crop failure or power outages due to climate impacts, AI can help trigger early support or funding reallocations before the problem escalates. This is particularly important for long-term sustainability. A solution that looks great today may become irrelevant or even counterproductive in five years unless there is a system to track and adapt it. AI offers that system, not as a replacement for governance but as its support structure.

In summary, the role of AI in policymaking is to automate and to augment. It enables better use of limited resources, sharper insight into trade-offs, and quicker responses to change. It helps ensure that finance and governance systems are designed not just to launch climate solutions but to sustain and evolve them over time. If our cities and nations are to scale climate action at the pace and depth required, such intelligence is not optional but essential.

Excerpted with permission from Smarter Than the Storm: Championing the AI-Climate Nexus for a Truly Sustainable Future, Amitabh Kant and Siddharth Sinha, HarperCollins India.