Addressing climate change requires coordinated policy efforts of nations worldwide. These efforts are informed by scientific reports, which rely in part on Integrated Assessment Models (IAMs), prominent tools used to assess the economic impacts of climate policies. However, traditional IAMs optimize policies based on a single objective, limiting their ability to capture the trade-offs among economic growth, temperature goals, and climate justice. As a result, policy recommendations have been criticized for perpetuating inequalities, fueling disagreements during policy negotiations. We introduce Justice, the first framework integrating IAM with Multi-Objective Multi-Agent Reinforcement Learning (MOMARL). By incorporating multiple objectives, Justice generates policy recommendations that shed light on equity while balancing climate and economic goals. Further, using multiple agents can provide a realistic representation of the interactions among the diverse policy actors. We identify equitable Pareto-optimal policies using our framework, which facilitates deliberative decision-making by presenting policymakers with the inherent trade-offs in climate and economic policy.

The EPA Is Ending Greenhouse Gas Data Collection. Who Will Step Up to Fill the Gap? With the agency no longer collecting emissions data from polluting companies, attention is turning to whether climate NGOs have the tools--and legal right--to fulfill this EPA function. The Environmental Protection Agency announced earlier this month that it would stop making polluting companies report their greenhouse gas emissions to it, eliminating a crucial tool the US uses to track emissions and form climate policy. Climate NGOs say their work could help plug some of the data gap, but they and other experts fear the EPA's work can't be fully matched. "I don't think this system can be fully replaced," says Joseph Goffman, the former assistant administrator at the EPA's Office of Air and Radiation.

This thesis explores the impact of the Climate Emergency movement on local government climate policy, using computational methods. The Climate Emergency movement sought to accelerate climate action at local government level through the mechanism of Climate Emergency Declarations (CEDs), resulting in a series of commitments from councils to treat climate change as an emergency. With the aim of assessing the potential of current large language models to answer complex policy questions, I first built and configured a system named PALLM (Policy Analysis with a Large Language Model), using the OpenAI model GPT-4. This system is designed to apply a conceptual framework for climate emergency response plans to a dataset of climate policy documents. I validated the performance of this system with the help of local government policymakers, by generating analyses of the climate policies of 11 local governments in Victoria and assessing the policymakers' level of agreement with PALLM's responses. Having established that PALLM's performance is satisfactory, I used it to conduct a large-scale analysis of current policy documents from local governments in the state of Victoria, Australia. This thesis presents the methodology and results of this analysis, comparing the results for councils which have passed a CED to those which did not. This study finds that GPT-4 is capable of high-level policy analysis, with limitations including a lack of reliable attribution, and can also enable more nuanced analysis by researchers. Its use in this research shows that councils which have passed a CED are more likely to have a recent and climate-specific policy, and show more attention to urgency, prioritisation, and equity and social justice, than councils which have not. It concludes that the ability to assess policy documents at scale opens up exciting new opportunities for policy researchers.

Climate-economic modeling under uncertainty presents significant computational challenges that may limit policymakers' ability to address climate change e ff ectively. This paper explores neural network-based approaches for solving high-dimensional optimal control problems arising from models that incorporate ambiguity aversion in climate mitigation decisions. We develop a continuous-time endogenous-growth economic model that accounts for multiple mitigation pathways, including emission-free capital and carbon intensity reductions. Given the inherent complexity and high dimensionality of these models, traditional numerical methods become computationally intractable. We benchmark several neural network architectures against finite-di fference generated solutions, evaluating their ability to capture the dynamic interactions between uncertainty, technology transitions, and optimal climate policy. Our findings demonstrate that appropriate neural architecture selection significantly impacts both solution accuracy and computational e fficiency when modeling climate-economic systems under uncertainty. These methodological advances enable more sophisticated modeling of climate policy decisions, allowing for better representation of technology transitions and uncertainty--critical elements for developing effective mitigation strategies in the face of climate change.

In little more than 1,600 words voicing his scepticism over net zero policies, Tony Blair this week propelled himself and his increasingly powerful institute back into the national debate. In the past eight years, the former prime minister has built a global empire employing more than 900 people across more than 40 countries, providing policy advice to monarchs, presidents and prime ministers. But while Blair's thinktank has brought him influence in his post-Downing Street career, it has also renewed scrutiny on his political views and how they are shaped by his commercial relationships. The Labour MP James Frith said on Wednesday: "I give congratulations to the marketing department at the Tony Blair Institute (TBI), who have managed to time it brilliantly to get maximum coverage." Patrick Galey, the head of fossil fuel investigations at the nongovernmental organisation Global Witness, said: "Blair's well-documented links to petrostates and oil and gas companies ought to alone be enough to disqualify this man as an independent and reliable arbiter of what's possible or commonsense in the energy transition."

In its report The Climate Paradox: Why We Need to Reset Action on Climate Change, the Tony Blair Institute argues that global institutions such as COP and the UN have failed to make sufficient progress in halting climate change. At the same time, it argues, the public have lost faith in climate policies because the promised green jobs and economic growth have failed to materialise, thanks in part to global instability and the Covid pandemic. Writing in the foreword, Sir Tony says: "Though most people will accept that climate change is a reality caused by human activity, they're turning away from the politics of the issue because they believe the proposed solutions are not founded on good policy." He says "any strategy based on either'phasing out' fossil fuels in the short term or limiting consumption is a strategy doomed to fail". He also warns against the "alarmist" tone of the debate on climate change, which he says is "riven with irrationality".

The vast majority of climate policies fail to significantly reduce emissions and so make little difference to stopping climate change, suggesting that governments must work much harder to identify ways to actually shift the needle. Nicolas Koch at the Mercator Research Institute on Global Commons and Climate Change in Berlin and his colleagues discovered this by assessing the impact of 1500 climate policies put into force between 1998 and 2022, covering 41 countries across six continents. They began by using machine learning to identify moments in which a country's emissions dropped significantly, relative to a control group of other nations not included in the analysis. The researchers found 69 of these emissions "breaks" and compared them with a database compiled by the Organisation for Economic Co-operation and Development (OECD) that tracks what types of climate policies were enacted when. While matching policy shifts to emission changes isn't an exact science, the team was able to attribute 63 of these breaks to one or more policy interventions within a two-year interval around the break, in order to allow for lagged or anticipated effects.

The number of standardized policy documents regarding climate policy and their publication frequency is significantly increasing. The documents are long and tedious for manual analysis, especially for policy experts, lawmakers, and citizens who lack access or domain expertise to utilize data analytics tools. Potential consequences of such a situation include reduced citizen governance and involvement in climate policies and an overall surge in analytics costs, rendering less accessibility for the public. In this work, we use a Latent Dirichlet Allocation-based pipeline for the automatic summarization and analysis of 10-years of national energy and climate plans (NECPs) for the period from 2021 to 2030, established by 27 Member States of the European Union. We focus on analyzing policy framing, the language used to describe specific issues, to detect essential nuances in the way governments frame their climate policies and achieve climate goals. The methods leverage topic modeling and clustering for the comparative analysis of policy documents across different countries. It allows for easier integration in potential user-friendly applications for the development of theories and processes of climate policy. This would further lead to better citizen governance and engagement over climate policies and public policy research.

We employ machine learning techniques to estimate household carbon footprints (HCFs) for the average household in each Census tract-geographic areas that represent roughly 4,000 people. We find that there is significant variation in carbon footprints across income and geography; income effects are driven by higher footprints related to transportation and consumer products and services, while geographic effects are primarily a result of the variable carbon intensity of the electricity grid. Using these footprints, we assess the net effects of various climate policies on households in the United States paying particular attention to the distribution across geography, urbanity, and income groups. Our objective is to improve the understanding of the potential for regressivity, geographic transfers, and rural-urban transfers among climate policy options and test for ways to control for transfers-preserving transfers from high-income households to low-income households, but mitigating transfers from rural areas to urban areas and from the Midwest and South to the Coasts. Our focus is on the net increase or decrease of annual household expenses under 12 different policy scenarios, which included both carbon pricing schemes and regulatory standards.

While artificial intelligence (AI) is often associated with the spawning of robots that will take our jobs, Terminator's Skynet, or the unblinking red eyes of Hal 9000 in 2001: A Space Odyssey, its true and immediate effects are best seen by simply observing the innovations -- ones that prove that software can do a variety of tasks better than humans can. If one thing is clear, it's that artificial intelligence has the potential to disrupt every industry, which leads to a big question that should matter to all of us: To what extent can a powerful technology like artificial intelligence be used to help us tackle climate change? To learn more about how we can leverage artificial intelligence to tackle climate change, I had to chat with Priya Donti, who's completing a Ph.D. in Computer Science and Public Policy at Carnegie Mellon University, focused on the role machine learning can play in climate change mitigation solutions. Donti is also a co-chair of Climate Change AI, an organization that unites "volunteers from academia and industry who believe in using machine learning, where it is relevant, to help tackle the climate crisis." Our conversation, which has been edited for length and clarity, discusses the risks, the rewards, and the limitations of using artificial intelligence to combat climate change.