NLP for Climate Policy: Creating a Knowledge Platform for Holistic and Effective Climate Action

Abstract

Climate change is a burning issue of our time, with the Sustainable Development Goal (SDG) 13 of the United Nations demanding global climate action. The worldwide scientific evidence exhibits that the current processes of market-led economic development are unsustainable. Realizing the urgency, in 2015 in Paris, world leaders signed an agreement committing to taking voluntary action to reduce carbon emissions. However, the scale, magnitude, and climate action processes vary globally, especially between developed and developing countries. Therefore, from parliament to social media, the debates and discussions on climate change gather data from wide-ranging sources essential to the policy design and implementation. The downside is that we do not currently have the mechanisms to pool the worldwide dispersed knowledge emerging from the structured and unstructured data sources.The paper thematically discusses how NLP techniques could be employed in climate policy research and contribute to society's good at large. In particular, we exemplify symbiosis of NLP and Climate Policy Research via four methodologies. The first one deals with the major topics related to climate policy using automated content analysis. We investigate the opinions (sentiments) of major actors' narratives towards climate policy in the second methodology. The third technique explores the climate actors' beliefs towards pro or anticlimate orientation. Finally, we discuss developing a 'Climate Knowledge Graph.'The present theme paper further argues that creating a knowledge platform would help in the formulation of a holistic climate policy and effective climate action. Such a knowledge platform would integrate the policy actors' varied opinions from different social sectors like government, business, civil society, and the scientific community. The research outcome will add value to effective climate action because policymakers can make informed decisions by looking at the diverse public opinion on a comprehensive platform.

HAMCap: A Weak-Supervised Hybrid Attention-Based Capsule Neural Network for Fine-Grained Climate Change Debate Analysis

“…Loureiro et al [31] assessed the sentiments and emotions of the tweets related to CC in the U.K. and Spain with NLP tools and explored how these relate to different preferences and concerns about energy policies. Swarnakar et al [32] thematically discussed how NLP techniques could be employed in CC research and exemplified four NLP methodologies to explore four different extension tasks.…”

Towards Climate Awareness in NLP Research

“…Positive impact. NLP is relevant in several aspects to the UN sustainable development goals (Vinuesa et al, 2020;Conforti et al, 2020;Swarnakar and Modi, 2021). Jin et al (2021) defined a framework for the social impacts of NLP, of which environmental impacts are a special case.…”