Machine Learning Reveals Gaps in Climate Policy Research

## Machine Learning Reveals Gaps in Climate Policy Research: An Expanded Analysis

The urgency of addressing climate change demands a comprehensive and evidence-based approach to policy development and implementation. Current climate mitigation efforts are demonstrably insufficient to meet the goals outlined in the Paris Agreement, highlighting the critical need for accelerated learning from the scientific literature on climate policies. However, this literature is vast, dispersed, and challenging to categorize, posing significant hurdles to systematic analysis and informed decision-making. The article “Machine learning map of climate policy literature reveals disparities between scientific attention, policy density, and emissions,” published in npj Climate Action, addresses these challenges by leveraging machine learning techniques to map and analyze the climate policy research landscape. This allows for the identification of critical gaps and disparities in scientific attention, policy implementation, and emissions reduction efforts. This expanded analysis will delve into the article’s methodology, findings, and implications in detail, providing crucial context, real-world examples, and critical insights to enhance understanding and inform future research and policy directions.

### The Imperative of Rapid Learning in Climate Policy

The Paris Agreement, a landmark achievement in international climate cooperation, set the ambitious goal of limiting global warming to well below 2°C, and preferably to 1.5°C, compared to pre-industrial levels. However, despite these commitments, global carbon emissions continue to rise, jeopardizing the achievement of these targets. The Intergovernmental Panel on Climate Change (IPCC), the leading international body for assessing climate change, underscores the urgency of achieving net-zero emissions within the next few decades to avoid the most catastrophic impacts of climate change.

This necessitates a dramatic acceleration of emissions reduction efforts, demanding rapid and effective learning from the existing body of knowledge on climate policy. This learning process involves understanding which policy instruments are most effective in different contexts, identifying potential barriers to implementation, and adapting strategies to maximize impact. This is where the challenge lies: the sheer volume and complexity of the climate policy literature make systematic analysis and knowledge synthesis extremely difficult, hindering the translation of research findings into practical policy solutions.

### Challenges in Systematizing Climate Policy Literature

Several factors contribute to the difficulty in effectively utilizing the climate policy literature.

• Vast and Dispersed Literature: The sheer number of publications related to climate policy, spanning diverse academic disciplines, geographical regions, and policy sectors, makes it challenging for researchers and policymakers to stay abreast of the latest findings. This dispersion hinders the identification of relevant studies and the synthesis of evidence across different contexts.
• Defining System Boundaries and Policy Typologies: Establishing clear boundaries for what constitutes a climate-relevant policy is a complex task. Many policies, such as those related to energy, transportation, or agriculture, can have significant implications for greenhouse gas emissions, even if climate change is not their primary focus. Furthermore, the lack of consistent and comprehensive climate policy typologies makes it difficult to compare and contrast different policy instruments and assess their effectiveness.
• Scale and Diversity of Research: The climate policy literature encompasses a wide range of research methodologies, analytical frameworks, and geographical contexts. This heterogeneity makes it challenging to synthesize evidence and draw generalizable conclusions about the effectiveness of different policy instruments.
• Limitations of Traditional Review Methods: Traditional, hand-based approaches to literature review are time-consuming, resource-intensive, and prone to bias, making them inadequate for systematically analyzing the vast and rapidly growing climate policy literature. The Intergovernmental Panel on Climate Change (IPCC), tasked with providing comprehensive assessments of climate change science, faces difficulties stemming from the scale and diversity of relevant research, highlighting the need for innovative methods to make evidence synthesis more efficient.

### Machine Learning: A Transformative Tool for Climate Policy Research

Recognizing the limitations of traditional approaches, the authors of the article “Machine learning map of climate policy literature reveals disparities between scientific attention, policy density, and emissions” turned to machine learning and natural language processing (NLP) techniques to systematically map and analyze the climate policy literature. This innovative approach offers several advantages:

• Scalability: Machine learning algorithms can process large volumes of text data quickly and efficiently, enabling the analysis of thousands of research publications.
• Objectivity: Machine learning models can reduce bias in literature selection and categorization, providing a more objective and comprehensive overview of the research landscape.
• Automation: Machine learning pipelines can automate many of the tasks involved in systematic mapping, such as identifying relevant studies, extracting key information, and categorizing policy instruments.
• Real-time Analysis: Machine learning can facilitate the creation of “living systematic maps” that are continuously updated with new research findings, providing a dynamic and up-to-date overview of the climate policy landscape.

### Methodology: A Detailed Examination

The study employed a rigorous methodology to ensure the accuracy and reliability of its findings. The key steps in the methodology included:

1. Literature Retrieval: The researchers used a comprehensive search query to retrieve relevant publications from the OpenAlex database, a large open-access bibliographic resource. The query was designed to identify studies related to climate change mitigation policies, including specific policy instruments such as carbon pricing. They retrieved 1,026,371 studies with their query.
2. Defining Climate Policy Instruments: The authors developed a clear and unambiguous definition of climate policy instruments, focusing on policies that explicitly target greenhouse gas emissions reductions or are unambiguously motivated by the desire to reduce emissions. This definition helped to establish clear system boundaries and ensure consistency in literature selection. As such, an article on subsidies for renewable energy technologies would not be included unless emission reductions or climate targets were mentioned, while an article on a carbon tax or GHG emissions trading system would be included.
3. Development of a New Policy Typology: The researchers created a new typology of climate policies, bridging the existing typologies used in the Climate Change Laws of the World (CCLW) and the Climate Policy Database by the NewClimate Institute (CPDB). This typology provided a structured framework for categorizing policy instruments based on their means of implementation, encompassing agreements, economic instruments, regulatory instruments, information, education and training, and governance, strategies, and targets. The typology is defined by the means by which governments or municipalities pursue policy goals. At the top level, means can be either making agreements with other actors, either state or non-state (agreements), providing funding or altering economic incentives (economic instruments), mandating what other actors must or must not do (regulatory instruments), providing information or building capacity in the expectation that it changes the behaviour of other actors (information, education, and training), or setting targets or strategies, or altering institutional arrangements (governance, strategies and targets).
4. Hand-Coding and Training Data: The researchers manually screened and coded a subset of 2580 documents, labeling them with relevant information such as policy instrument types, sectors, geographical locations, and research methodologies. This hand-coded data served as the training data for the machine learning models.
5. Machine Learning Model Training: The researchers trained machine learning classifiers, specifically transformer-based language models, to classify documents based on their content. They evaluated several pre-trained language models, including ClimateBERT, which had been specifically trained on climate-related texts. Based on F1 scores (which are bounded between 0 and 1 and describe the harmonic mean of precision, the proportion of predicted positive cases that are truly positive, and recall, the proportion of truly positive cases which are predicted positive), ClimateBERT predicted the categories inclusion, sector, and the top level of the instrument typology best.
6. Model Evaluation and Validation: The researchers used cross-validation techniques to evaluate the performance of the machine learning models and ensure their generalizability to unseen data. This rigorous evaluation process helped to identify the most accurate and reliable models for classifying climate policy literature.
7. Geoparsing: They used a pre-trained geoparser to extract the locations mentioned in each study.
8. Data Analysis and Visualization: The researchers analyzed the classified literature to identify trends and patterns in climate policy research, comparing the distribution of research publications with the distribution of enacted policies and greenhouse gas emissions. They created visualizations to illustrate these relationships and highlight key disparities.

### Key Findings: Unveiling Disparities in Climate Policy Research

The machine learning-based mapping of the climate policy literature revealed several important findings:

• Growing Climate Policy Literature: The study estimated that there are approximately 84,990 papers relevant to climate policy instruments, with a significant increase in publications since 2020. This highlights the growing interest and activity in climate policy research.
• Dominance of Economic Instruments and Governance Strategies: Economic instruments, such as carbon pricing and subsidies, and governance strategies and targets were the most commonly studied policy types. However, a closer examination revealed that many of the governance, strategies, and targets papers focused primarily on targets, rather than on the specific mechanisms for achieving those targets. 80% of studies on governance, strategies and targets published in 2022 did not refer to any other instruments apart from agreements.
• Sectoral Variations in Policy Instruments: The type of policy instrument studied varied substantially across sectors. For example, economic instruments were more prevalent in the industry sector, while regulatory instruments were more common in the buildings sector.
• Disparities in Scientific Attention, Policy Density, and GHG Emissions: A comparison of the geographical distribution of climate policy literature with the distribution of enacted policies and greenhouse gas emissions revealed significant disparities. The study found that some countries, such as the UK and Sweden, received a disproportionately high level of scientific attention relative to their emissions, while others, such as the USA and China, were understudied relative to their emissions.
• Under-Representation of Industry Sector Policies: The study identified a stark under-representation of industry sector policies in both the scientific literature and policy implementation, despite the fact that the industry sector accounts for a significant portion of global greenhouse gas emissions. Industry accounted for 23% of global GHG emissions, but only 8% of scientific attention, and 13% of implemented policies.
• Diverging Attention between Science and Policy on Economic and Regulatory Instruments: The share of studies focusing on economic instruments was higher than the share of policies using economic instruments, while regulatory instruments were comparatively understudied across all regions. Scientific attention to policies of different types may match the share of policies of different instrument types to a greater or lesser extent in each country, keeping in mind the limitations in the scope of both literature and policy databases.

### Implications and Recommendations: Guiding Future Research and Policy

The findings of this study have several important implications for future research and policy:

• Need for More Research on Industry Sector Policies: The under-representation of industry sector policies highlights the need for more research on effective strategies for reducing emissions in this critical sector. This research should focus on identifying technological innovations, policy instruments, and behavioral changes that can drive decarbonization in industry.
• Greater Focus on Regulatory Instruments: The comparative understudy of regulatory instruments suggests that researchers should pay more attention to the design and implementation of these policies, exploring their effectiveness, cost-effectiveness, and potential for scaling up. The IPCC describes regulatory instruments as enjoying “greater political support”, but being “more economically costly”. The disproportionally low attention to regulatory instruments could be a sign that science focuses on those policies seen to be more efficient policies at the expense of policies which command political support.
• Addressing Geographical Disparities in Research: The disparities in scientific attention across different countries and regions highlight the need for more research on climate policy in understudied areas. This research should focus on understanding the specific challenges and opportunities for emissions reductions in these regions, and developing tailored policy solutions.
• Bridging the Gap between Science and Policy: The diverging attention between science and policy on economic and regulatory instruments suggests the need for greater dialogue and collaboration between researchers and policymakers. Scientists should strive to conduct research that is relevant to the needs of policymakers, while policymakers should be more open to evidence-based decision-making.
• Promoting Evidence Synthesis: The study highlights the need for more systematic reviews and meta-analyses of the climate policy literature to synthesize evidence and provide a more comprehensive understanding of policy effectiveness. Filling this evidence synthesis gap will support the IPCC and other climate assessments, and enhance policy learning. A search for evidence synthesis keywords in the database (“systematic map” OR “systematic review” OR “meta-analysis”) provides some suggestive evidence that there may be a substantial evidence synthesis gap in the climate policy instruments literature.

### Conclusion: Leveraging Machine Learning for Effective Climate Action

The article “Machine learning map of climate policy literature reveals disparities between scientific attention, policy density, and emissions” demonstrates the transformative potential of machine learning for climate policy research. By systematically mapping and analyzing the vast climate policy literature, this study has identified critical gaps and disparities in scientific attention, policy implementation, and emissions reduction efforts. These findings provide valuable insights for guiding future research and policy, helping to ensure that climate mitigation efforts are evidence-based, effective, and equitable.

The authors have made their map available as a community resource in interactive and searchable form at https://climateliterature.org/#/project/policymap. They aim to enhance the value of this resource by automating the process of updates, making it a living systematic map.

As climate change continues to pose an existential threat to humanity, it is imperative that we leverage all available tools and technologies to accelerate the transition to a low-carbon future. Machine learning, as demonstrated in this study, can play a crucial role in this effort by helping us to better understand the climate policy landscape, identify effective solutions, and bridge the gap between science and policy. Embracing these innovative approaches is essential for achieving the ambitious goals of the Paris Agreement and securing a sustainable future for all.