Using machine learning to identify incentives in forestry policy: Towards a new paradigm in policy analysis

Firebanks-Quevedo, Daniel; Planas, Jordi; Buckingham, Kathleen; Cristina, Taylor,; Schwartz, David A.; Naydenova, Galina; Zamora‐Cristales, René

doi:10.1016/j.forpol.2021.102624

Cited by 16 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some study proves that combining rigorous manual labeling methods with machine learning approaches could offer a good performance to reduce the time cost ( 31 ). In addition, some study also demonstrates that there is a good performance in less classification and short sentences ( 32 ). In policy analysis, due to the different data sources, sentences and paragraphs are often used as analysis units in most policy texts, but some studies are not ( 33 ).…”

Section: Discussionmentioning

confidence: 98%

Research on the identification and evolution of health industry policy instruments in China

Jin,

2024

Front. Public Health

View full text Add to dashboard Cite

The application of health industry policies could be discovered more quickly and comprehensively through the automated identification of policy tools, which could provide references for the formulation, implementation, and optimization of subsequent policies in each province. This study applies the Bidirectional Encoder Representation from Transformer (BERT) model to identify policy tools automatically, utilizes Focal Loss to reduce the unbalance of a dataset, and analyzes the evolution of policy tools in each province, which contains time, space, and topic. The research demonstrates that the BERT model can improve the accuracy of classification, that supply and environment policy tools are more prevalent than demand tools, and that policy instruments are organized similarly in four major economic regions. Moreover, the policy’s attention to topics related to healthcare, medicine, and pollution has gradually shifted to other topics, and the extent of policy attention continues to be concentrated on the health service industry, with less attention paid to the manufacturing industry from the keywords of the various topics.

show abstract

Section: Discussionmentioning

confidence: 98%

Research on the identification and evolution of health industry policy instruments in China

Jin,

2024

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…adaptation to climate change 40 ). In related fields like forest and environmental policy, researchers have used text-as-data approaches for identifying the tools (i.e., instrument types) used and objectives formulated 41 , 42 . Further approaches for assessing general and specific policy design elements at scale have, however, not yet emerged.…”

Section: Background and Summarymentioning

confidence: 99%

Towards understanding policy design through text-as-data approaches: The policy design annotations (POLIANNA) dataset

Sewerin,

Kaack,

Küttel

et al. 2023

Sci Data

View full text Add to dashboard Cite

Despite the importance of ambitious policy action for addressing climate change, large and systematic assessments of public policies and their design are lacking as analysing text manually is labour-intensive and costly. POLIANNA is a dataset of policy texts from the European Union (EU) that are annotated based on theoretical concepts of policy design, which can be used to develop supervised machine learning approaches for scaling policy analysis. The dataset consists of 20,577 annotated spans, drawn from 18 EU climate change mitigation and renewable energy policies. We developed a novel coding scheme translating existing taxonomies of policy design elements to a method for annotating text spans that consist of one or several words. Here, we provide the coding scheme, a description of the annotated corpus, and an analysis of inter-annotator agreement, and discuss potential applications. As understanding policy texts is still difficult for current text-processing algorithms, we envision this database to be used for building tools that help with manual coding of policy texts by automatically proposing paragraphs containing relevant information.

show abstract

“…8,9 Wang et al 10 and Sharma et al 11 showcased the use of deep learning methods, such as YOLOv4 and YOLOv5m, in forest resource investigation, vegetation coverage statistics, and plant growth monitoring. Similarly, Firebanks-Quevedo et al 12 employed ML-based methods to formulate forestry policies and identify economic incentives for reforestation. However, a limited number of studies have been conducted to predict the spread of wildfires, a crucial aspect given the multifaceted challenges posed by wildfires, including ecological damage, deteriorating air quality, biodiversity loss, erosion, and soil degradation.…”

Section: Introductionmentioning

confidence: 99%

Predicting large wildfires in the Contiguous United States using deep neural networks

Dhal,

Jain,

Gadepally

et al. 2024

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

Over the last several decades, large wildfires have become increasingly common across the United States causing a disproportionate impact on forest health and function, human well-being, and the economy. Here, we examine the severity of large wildfires across the Contiguous United States over the past decade (2011 to 2020) using a wide array of meteorological, land cover, and topographical features in a deep neural network model. A total of 4538 wildfire incidents were used in the analysis covering 87,305 square miles of burned area. We observed the highest number of large wildfires in California, Texas, and Idaho, with lightning causing 43% of these incidents. Importantly, results indicate that the severity of wildfire occurrences is highly correlated with the weather, land cover, and elevation of the study area as indicated from their SHapley Additive exPlanations values. Overall, different variants of data-driven models and their results could provide useful guidance in managing landscapes for large wildfires under changing climate and disturbance regimes.

show abstract

Using machine learning to identify incentives in forestry policy: Towards a new paradigm in policy analysis

Cited by 16 publications

References 13 publications

Research on the identification and evolution of health industry policy instruments in China

Research on the identification and evolution of health industry policy instruments in China

Towards understanding policy design through text-as-data approaches: The policy design annotations (POLIANNA) dataset

Predicting large wildfires in the Contiguous United States using deep neural networks

Contact Info

Product

Resources

About