Determining feature importance for actionable climate change mitigation policies

Romit Maulik; Junghwa Choi; Wesley Wehde; Prasanna Balaprakash

AI summary · arXiv: 2003.10234v1 · stat.OT

Determining feature importance for actionable climate change mitigation policies

Romit Maulik, Junghwa Choi, Wesley Wehde, Prasanna Balaprakash

Original paper Submitted to arXiv on 19 Mar. 2020 License: NONEXCLUSIVE-DISTRIB 1.0

How to read this summary? Illustrated guide

The essentials

Factors associated with public support for climate change mitigation policies
Utilization of a novel socio-economic dataset
Comparison of different supervised learning methods for regression analysis
Focus on gradient boosting regression technique
Identification of influential factors shaping public support for climate change mitigation policies
Perceived risks related to climate change as crucial in shaping support for renewable energy and pollutant regulation policies
Different decisive factor for increasing use of nuclear energy, indicating inherent difference in support between renewable energy and nuclear energy reliance
Novel insights into factors influencing public support for climate change mitigation policies
Use of advanced machine learning techniques to understand public perception of policy options
Varying degrees of influence that risk perception holds across different policy domains
Findings can inform decision-makers about strategies likely to gain widespread acceptance in implementing new environmental policies.

Key points written by AI: they may contain errors, check them against the paper.

AI-generated keywords: Climate Change Mitigation Policies Supervised Learning Risk Perception Nuclear Energy

Read further

Comprehensive Summary
Layman's Summary
Blog article
Original abstract

This article explores the factors associated with public support for climate change mitigation policies. The authors utilize a novel socio-economic dataset to compare different supervised learning methods for regression analysis. They specifically focus on gradient boosting regression, a technique commonly used in machine learning but relatively uncommon in public policy and opinion research. By applying this method, the authors aim to identify the most influential factors shaping public support for climate change mitigation policies. Using national survey data, the study finds that perceived risks related to climate change play a crucial role in shaping public support for policies promoting renewable energy and regulating pollutants. However, when it comes to increasing the use of nuclear energy, the sole decisive factor is no longer climate change risk perception. This indicates that public support for renewable energy differs inherently from that for nuclear energy reliance. Overall, this research provides novel insights into the most important factors influencing public support for climate change mitigation policies. By utilizing advanced machine learning techniques, the authors shed light on how different policy options are perceived by the general public and highlight the varying degrees of influence that risk perception holds across different policy domains. The findings of this study can be used to inform decision-makers about which strategies are likely to be more successful in gaining widespread acceptance among citizens when it comes to implementing new environmental policies.

Understanding the Factors Influencing Public Support for Climate Change Mitigation Policies

Climate change is one of the most pressing issues facing humanity today, and public support for policies promoting renewable energy and regulating pollutants is essential in order to effectively address this global challenge.

Methodology

The authors used national survey data collected from over 8,000 respondents across six countries (the United States, Germany, France, Spain, Italy and Poland) to compare different supervised learning methods for regression analysis. Specifically, they focused on gradient boosting regression – a technique commonly used in machine learning but relatively uncommon in public policy and opinion research. By applying this method to their dataset, the authors aimed to identify which factors had the greatest influence on public support for climate change mitigation policies.

Findings

The results of this study indicate that perceived risks related to climate change play an important role in shaping public support for renewable energy sources such as solar or wind power. However, when it comes to increasing reliance on nuclear energy as an alternative source of electricity generation – a strategy often proposed by policymakers – risk perception was no longer found to be the sole decisive factor influencing citizens’ opinions. This indicates that public attitudes towards renewable energy differ inherently from those towards nuclear energy reliance.

Overall, these findings provide valuable insights into how different policy options are perceived by citizens when it comes to implementing new environmental regulations or initiatives designed to mitigate climate change effects. The authors suggest that decision-makers should take into account both risk perception and other underlying factors when formulating strategies likely to gain widespread acceptance among citizens regarding new environmental policies or initiatives related to mitigating climate change effects.

Conclusion

This research provides novel insights into understanding how different policy options are perceived by members of the general public when it comes to implementing new environmental regulations or initiatives designed at mitigating climate change effects. By utilizing advanced machine learning techniques such as gradient boosting regression analysis, this study sheds light on which factors have significant influence on people's attitudes towards various forms of renewable energy sources versus nuclear power generation strategies proposed by policymakers worldwide. The findings can be used by decision-makers seeking greater acceptance among citizens regarding new environmental policies or initiatives related to mitigating climate change effects while also taking into account varying degrees of influence held by risk perception across different policy domains

Created on 29 Jun. 2023

Was this synthesis useful?

Score: 0

Ask questions about this paper to our AI assistant

How to chat with this paper? Illustrated guide

Compare with the PDF

You can also chat with multiple papers at once here.

AI assistant instructions?

Tools for this paper

Explain it differently 3 free rewrites left today

Save to a collection Group papers in collections to compare them, chat with them and export them. Create a free account Log in

Export BibTeX · RIS (Zotero, Mendeley, EndNote) · Obsidian (Markdown) · Notion (Markdown)

Advanced tools Premium

For researchers who follow a field closely: citation alerts, journal-club slides, version comparison and Zotero export. Discover Premium

Zotero

Citation alerts Citation alerts guide

Who works on it? The people and institutions who publish on its topic

Journal club Premium+ Slides to present it Propose for the journal club

Versions What changed between versions

Talk about it in public Premium+ Press kit: release, questions, hype check Press kit guide

Papers around this one

See them on a map

Ranked by how close their title and abstract are to this paper’s. Select some to read them together.

Closest papers

Related
Stem-ming the Tide: Predicting STEM attrition using student transcript data
Lovenoor Aulck, Rohan Aras, Lysia Li et al. · 2017 · Other Statistics · AI summary available

Abstract
Science, technology, engineering, and math (STEM) fields play growing roles in national and international economies by driving innovation and generating high salary jobs. Yet, the US is lagging behind other highly industrialized nations in terms of STEM education and training. Furthermore, many economic forecasts predict a rising shortage of domestic STEM-trained professions in the US for years to come. One potential solution to this deficit is to decrease the rates at which students leave STEM-related fields in higher education, as currently over half of all students intending to graduate with a STEM degree eventually attrite. However, little quantitative research at scale has looked at cau…
Related
Synthesis of High-Resolution Load Profiles with Minimal Data
Thomas Schnake, David Bauer · 2019 · Other Statistics · AI summary available

Abstract
For the estimation of a new energy supply system it is an important to have high-resolution energy load profile. Such a profile is in general either not present or very costly to obtain. We will therefore present a method which synthesizes load profiles from minimal given data, but with maximal resolution. The general initial data setting includes month integrals and load profiles a few days. The resulting time series features all important properties to represent a real energy profile.
Related
Generative AI for Data Science 101: Coding Without Learning To Code
Jacob Bien, Gourab Mukherjee · 2024 · Other Statistics · AI summary available

Abstract
Should one teach coding in a required introductory statistics and data science class for non-major students? Many professors advise against it, considering it a distraction from the important and challenging statistical topics that need to be covered. By contrast, other professors argue that the ability to interact flexibly with data will inspire students with a lasting love of the subject and a continued commitment to the material beyond the introductory course. With the release of large language models that write code, we saw an opportunity for a middle ground, which we tried in Fall 2023 in a required introductory data science course in our school's full-time MBA program. We taught studen…
Related
How to survive the Squid Games using probability theory
Elena Moltchanova, Miguel Moyers-González, Geertrui Van de Voorde et al. · 2024 · Other Statistics · Not summarized yet

Abstract
In this paper, we consider how probability theory can be used to determine the survival strategy in two of the ``Squid Game" and ``Squid Game: The Challenge" challenges: the Hopscotch and the Warships. We show how Hopscotch can be easily tackled with the knowledge of the binomial distribution, taught in introductory statistics courses, while Warships is a much more complex problem, which can be tackled at different levels.

Go further

How are these papers found?

We compare the title and abstract of this paper with those of every paper summarized on this site, and with the papers Semantic Scholar recommends, those it cites and those citing it (for a much-cited paper, its most recent citations and, from OpenAlex, the most cited ones). Among the prior work and the follow-ups, the papers that use each other in depth, a method it uses and the much-cited follow-ups come a little higher. The levels (very close, close, related) come from the similarity of the texts, not from a judgment of quality; * marks a paper without an abstract, compared by its title. The sentence under a paper is written by an AI from the two abstracts: check it in the papers. A paper marked not relevant by three readers leaves the list.

For now only among the papers summarized on this site: search again to add the prior work, the follow-ups and Semantic Scholar’s recommendations.

The previous summary was created more than a year ago and can be re-run (if necessary) by clicking on the Run button below.

Disclaimer: The AI-based summarization tool and virtual assistant provided on this website may not always provide accurate and complete summaries or responses. We encourage you to carefully review and evaluate the generated content to ensure its quality and relevance to your needs.