Climate Policy


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Workshop Papers

Venue Title
AAAI FSS 2022 Using Natural Language Processing for Automating the Identification of Climate Action Interlinkages within the Sustainable Development Goals
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Abstract: Climate action, Goal 13 of the UN Sustainable Development Goals (SDG), cuts across almost all SDGs. Achieving climate goals can reinforce the achievements in many other goals, but at the same time climate mitigation and adaptation measures may generate trade-offs, such as levelling the cost of energy and transitioning away from fossil fuels. Leveraging the synergies and minimizing the trade-offs among the climate goals and other SDGs is an imperative task for ensuring policy coherence. Understanding the interlinkages between climate action and other SDGs can help inform about the synergies and trade-offs. This paper presents a novel methodology by using natural language processing (NLP) to automate the process of systematically identifying the key interlinkages between climate action and SDGs from a large amount of climate literature. A qualitative SDG interlinkages model for climate action was automatically generated and visualized in a network graph. This work contributes to the conference thematic topic on using AI for policy alignment for climate change goals, SDGs and associated environmental, social and governance (ESG) frameworks.

Authors: Xin Zhou (Institute for Global Environmental Strategies (IGES)), Kshitij Jain (Google Inc.), Mustafa Moinuddin (Institute for Global Environmental Strategies (IGES)) and Patrick McSharry (Carnegie Mellon University Africa; Oxford Man Institute of Quantitative Finance, Oxford University)

NeurIPS 2021 Emissions-aware electricity network expansion planning via implicit differentiation (Papers Track)
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Abstract: We consider a variant of the classical problem of designing or expanding an electricity network. Instead of minimizing only investment and production costs, however, we seek to minimize some mixture of cost and greenhouse gas emissions, even if the underlying dispatch model does not tax emissions. This enables grid planners to directly minimize consumption-based emissions, when expanding or modifying the grid, regardless of whether or not the carbon market incorporates a carbon tax. We solve this problem using gradient descent with implicit differentiation, a technique recently popularized in machine learning. To demonstrate the method, we optimize transmission and storage resources on the IEEE 14-bus test network and compare our solution to one generated by standard planning with a carbon tax. Our solution significantly reduces emissions for the same levelized cost of electricity.

Authors: Anthony Degleris (Stanford University); Lucas Fuentes (Stanford); Abbas El Gamal (Stanford University); Ram Rajagopal (Stanford University)

NeurIPS 2021 HyperionSolarNet: Solar Panel Detection from Aerial Images (Papers Track)
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Abstract: With the effects of global climate change impacting the world, collective efforts are needed to reduce greenhouse gas emissions. The energy sector is the single largest contributor to climate change and many efforts are focused on reducing dependence on carbon-emitting power plants and moving to renewable energy sources, such as solar power. A comprehensive database of the location of solar panels is important to assist analysts and policymakers in defining strategies for further expansion of solar energy. In this paper we focus on creating a world map of solar panels. We identify locations and total surface area of solar panels within a given geographic area. We use deep learning methods for automated detection of solar panel locations and their surface area using aerial imagery. The framework, which consists of a two-branch model using an image classifier in tandem with a semantic segmentation model, is trained on our created dataset of satellite images. Our work provides an efficient and scalable method for detecting solar panels, achieving an accuracy of 0.96 for classification and an IoU score of 0.82 for segmentation performance.

Authors: Poonam Parhar (UCBerkeley); Ryan Sawasaki (UCBerkeley); Alberto Todeschini (UC Berkeley); Colorado Reed (UC Berkeley); Hossein Vahabi (University California Berkeley); Nathan Nusaputra (UC Berkeley); Felipe Vergara (UC Berkeley)

ICML 2021 Examining the nexus of environmental policy, climate physics, and maritime shipping with deep learning models and space-borne data (Papers Track)
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Abstract: Ship-tracks are produced by ship exhaust interacting with marine low clouds. They provide an ideal lab for constraining a critical climate forcing. However, no global survey of ship ship-tracks has been made since its discovery 55 years ago, which limits research progress. Here we present the first global map of ship-tracks produced by applying deep segmentation models to large satellite data. Our model generalizes well and is validated against independent data. Large-scale ship-track data are at the nexus of environmental policy, climate physics, and maritime shipping industry: they can be used to study aerosol-cloud interactions, the largest uncertainty source in climate forcing; to evaluate compliance and impacts of environmental policies; and to study the impact of significant socioeconomic events on maritime shipping. Based on twenty years of global data, we show cloud physics responses in ship-tracks strongly depend on the cloud regime. Inter-annual fluctuation in ship-track frequency clearly reflects international trade/economic trends. Emission policies strongly affect the pattern of shipping routes and ship-track occurrence. The combination of stricter fuel standard and the COVID-19 pandemic pushed global ship-track frequency to the lowest level in the record. More applications of our technique and data are envisioned such as detecting illicit shipping activity and checking policy compliance of individual ships.

Authors: Tianle Yuan (University of Maryland, NASA); Hua Song (NASA, SSAI); Chenxi Wang (University of Maryland, NASA); Kerry Meyer (NASA); Siobhan Light (University of Maryland); Sophia von Hippel (University of Arizona); Steven Platnick (NASA); Lazaros Oreopoulos (NASA); Robert Wood (University of Washington); Hans Mohrmann (University of Washington)

ICML 2021 Estimation of Corporate Greenhouse Gas Emissions via Machine Learning (Papers Track)
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Abstract: As an important step to fulfill the Paris Agreement and achieve net-zero emissions by 2050, the European Commission adopted the most ambitious package of climate impact measures in April 2021 to improve the flow of capital towards sustainable activities. For these and other international measures to be successful, reliable data is key. The ability to see the carbon footprint of companies around the world will be critical for investors to comply with the measures and hit climate neutrality. However, with only a small portion of companies volunteering to disclose their greenhouse gas (GHG) emissions, it is nearly impossible for investors to align their investment strategies with the measures. By training a machine learning model on disclosed GHG emissions, we are able to estimate the emissions of other companies globally who do not disclose their emissions. In this paper, we show that our model provides accurate estimates of corporate GHG emissions to investors such that they are able to align their investments with the regulatory measures and achieve net-zero goals.

Authors: You Han (Bloomberg L.P.); Achintya Gopal (Bloomberg LP); Liwen Ouyang (Bloomberg L.P.); Aaron Key (Bloomberg LP)

ICML 2021 BERT Classification of Paris Agreement Climate Action Plans (Papers Track)
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Abstract: As the volume of text-based information on climate policy increases, natural language processing (NLP) tools can distill information from text to better inform decision making on climate policy. We investigate how large pretrained transformers based on the BERT architecture classify sentences on a dataset of climate action plans which countries submitted to the United Nations following the 2015 Paris Agreement. We use the document header structure to assign noisy policy-relevant labels such as mitigation, adaptation, energy, and land use to text elements. Our models provide an improvement in out-of-sample classification over simple heuristics though fall short of the consistency observed between human annotators. We hope to extend this framework to a wider class of textual climate change data such as climate legislation and corporate social responsibility filings and build tools to streamline the extraction of information from these documents for climate change researchers.

Authors: Tom Corringham (Scripps Institution of Oceanography); Daniel Spokoyny (Carnegie Mellon University); Eric Xiao (University of California San Diego); Christopher Cha (University of California San Diego); Colin Lemarchand (University of California San Diego); Mandeep Syal (University of California San Diego); Ethan Olson (University of California San Diego); Alexander Gershunov (Scripps Institution of Oceanography)

ICML 2021 From Talk to Action with Accountability: Monitoring the Public Discussion of Policy Makers with Deep Neural Networks and Topic Modelling (Proposals Track)
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Abstract: Decades of research on climate have provided a consensus that human activity has changed the climate and we are currently heading into a climate crisis. While public discussion and research efforts on climate change mitigation have increased, potential solutions need to not only be discussed but also effectively deployed. For preventing mismanagement and holding policy makers accountable, transparency and degree of information about government processes have been shown to be crucial. However, currently the quantity of information about climate change discussions and the range of sources make it increasingly difficult for the public and civil society to maintain an overview to hold politicians accountable. In response, we propose a multi-source topic aggregation system (MuSTAS) which processes policy makers speech and rhetoric from several publicly available sources into an easily digestible topic summary. MuSTAS uses novel multi-source hybrid latent Dirichlet allocation to model topics from a variety of documents. This topic digest will serve the general public and civil society in assessing where, how, and when politicians talk about climate and climate policies, enabling them to hold politicians accountable for their actions to mitigate climate change and lack thereof.

Authors: Vili Hätönen (Emblica); Fiona Melzer (University of Edinburgh)

ICML 2021 NeuralNERE: Neural Named Entity Relationship Extraction for End-to-End Climate Change Knowledge Graph Construction (Proposals Track)
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Abstract: This paper proposes an end-to-end Neural Named Entity Relationship Extraction model (called NeuralNERE) for climate change knowledge graph (KG) construction, directly from the raw text of relevant news articles. The proposed model will not only remove the need for any kind of human supervision for building knowledge bases for climate change KG construction (used in the case of supervised or dictionary-based KG construction methods), but will also prove to be highly valuable for analyzing climate change by summarising relationships between different factors responsible for climate change, extracting useful insights & reasoning on pivotal events, and helping industry leaders in making more informed future decisions. Additionally, we also introduce the Science Daily Climate Change dataset (called SciDCC) that contains over 11k climate change news articles scraped from the Science Daily website, which could be used for extracting prior knowledge for constructing climate change KGs.

Authors: Prakamya Mishra (Independent Researcher); Rohan Mittal (Independent Researcher)

ICML 2021 Forecasting emissions through Kaya identity using Neural ODEs (Proposals Track)
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Abstract: Starting from Kaya identity, we used a Neural ODE model to predict the evolution of several indicators related to carbon emissions, on a country-level : population, GDP per capita, energy intensity of GDP, carbon intensity of energy. We compared the model with a baseline statistical model - VAR - and obtained good performances. We conclude that this machine-learning approach can be used to produce a wide range of results and give relevant insight to policymakers.

Authors: Pierre Browne (Imperial College London)

NeurIPS 2020 Emerging Trends of Sustainability Reporting in the ICT Industry: Insights from Discriminative Topic Mining (Papers Track)
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Abstract: The Information and Communication Technologies (ICT) industry has a considerable climate change impact and accounts for approximately 3 percent of global carbon emissions. Despite the increasing availability of sustainability reports provided by ICT companies, we still lack a systematic understanding of what has been disclosed at an industry level. In this paper, we make the first major effort to use modern unsupervised learning methods to investigate the sustainability reporting themes and trends of the ICT industry over the past two decades. We build a cross-sector dataset containing 22,534 environmental reports from 1999 to 2019, of which 2,187 are ICT specific. We then apply CatE, a text embedding based topic modeling method, to mine specific keywords that ICT companies use to report on climate change and energy. As a result, we identify (1) important shifts in ICT companies' climate change narratives from physical metrics towards climate-related disasters, (2) key organizations with large influence on ICT companies, and (3) ICT companies' increasing focus on data center and server energy efficiency.

Authors: Lin Shi (Stanford University); Nhi Truong Vu (Stanford University)

NeurIPS 2020 Machine Learning Informed Policy for Environmental Justice in Atlanta with Climate Justice Implications (Proposals Track)
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Abstract: Environmental hazards are not evenly distributed between the privileged and the protected classes in the U.S. Neighborhood zoning and planning of hazardous treatment, storage, and disposal facilities (TSDs) play a significant role in this sanctioned environmental racism. TSDs and toxic chemical releases into the air are accounted for by the U.S. Environmental Protection Agency’s (EPA) Toxic Release Inventories (TRIs) [2,4,7, 14]. TSDs and toxic chemical releases not only emit carbon dioxide and methane, which are the top two drivers of climate change, but also emit contaminants, such as arsenic, lead, and mercury into the water, air, and crops [12]. Studies on spatial disparities in TRIs and TSDs based on race/ethnicity and socioeconomic status (SES) in U.S. cities, such as Charleston, SC, San Joaquin Valley, CA, and West Oakland, CA showed that there are more TRIs and TSDs in non-white and low SES areas in those cities [2,4,7]. Environmental justice recognizes that the impacts of environmental burdens, such as socioeconomic and public health outcomes, are not equitably distributed, and in fact bear the heaviest burden on marginalized people, including communities of color and low-income communities [12]. In our case, environmental justice has a strong tie to climate justice since the TRIs release carbon dioxide and methane into the atmosphere.

Authors: Lelia Hampton (Massachusetts Institute of Technology)

NeurIPS 2020 A Multi-source, End-to-End Solution for Tracking Climate Change Adaptation in Agriculture (Proposals Track)
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Abstract: The impact of climate change on tropical agri-food systems will depend on both the direction and magnitude of climate change, and the agricultural sector’s adaptive capacity, the latter being affected by the chosen adaptation strategies. By extending SEIRS, a Satellite Remote Sensing (SRS) based system originally developed by the International Center for Tropical Agriculture - CIAT for monitoring U.S. Government-funded development programs across cropping areas in Africa, this research proposes the development and deployment of a scalable AI-based platform exploiting free-of-charge SRS data that will enable the agri-food sector to monitor a wide range of climate change adaptation (CCA) interventions in a timely, evidence-driven and comparable manner. The main contributions of the platform are i) ingesting and processing variety sources of SRS data with a considerable record (> 5 years) of vegetation greenness and precipitation (input data); ii) operating an end-to-end system by exploiting AI-based models suited to time series analysis such as Seq2Seq and Transformers; iii) providing customised proxies informing the success or failure of a given local CCA intervention(s).

Authors: Alejandro Coca-Castro (Kings College London); Aaron Golden (NUI Galway); Louis Reymondin (The Alliance of Bioversity International and the International Center for Tropical Agriculture)

ICLR 2020 Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals (Papers Track)
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Abstract: The United Nations' ambitions to combat climate change and prosper human development are manifested in the Paris Agreement and the Sustainable Development Goals (SDGs), respectively. These are inherently inter-linked as progress towards some of these objectives may accelerate or hinder progress towards others. We investigate how these two agendas influence each other by defining networks of 18 nodes, consisting of the 17 SDGs and climate change, for various groupings of countries. We compute a non-linear measure of conditional dependence, the partial distance correlation, given any subset of the remaining 16 variables. These correlations are treated as weights on edges, and weighted eigenvector centralities are calculated to determine the most important nodes. We find that SDG 6, clean water and sanitation, and SDG 4, quality education, are most central across nearly all groupings of countries. In developing regions, SDG 17, partnerships for the goals, is strongly connected to the progress of other objectives in the two agendas whilst, somewhat surprisingly, SDG 8, decent work and economic growth, is not as important in terms of eigenvector centrality.

Authors: Felix Laumann (Imperial College London); Julius von Kügelgen (MPI for Intelligent Systems, Tübingen & University of Cambridge); Mauricio Barahona (Imperial College London)

ICLR 2020 Accelerated Data Discovery for Scalable Climate Action (Proposals Track)
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Abstract: According to the Intergovernmental Panel on Climate Change (IPCC), the planet must decarbonize by 50% by 2030 in order to keep global warming below 1.5C. This goal calls for a prompt and massive deployment of solutions in all societal sectors - research, governance, finance, commerce, health care, consumption. One challenge for experts and non-experts is access to the rapidly growing body of relevant information, which is currently scattered across many weakly linked domains of expertise. We propose a large-scale, semi-automatic, AI-based discovery system to collect, tag, and semantically index this information. The ultimate goal is a near real-time, partially curated data catalog of global climate information for rapidly scalable climate action.

Authors: Henning Schwabe (Private); Sumeet Sandhu (Elementary IP LLC); Sergy Grebenschikov (Private)

ICLR 2020 Nutrient demand, Risk and Climate change: Evidence from historical rice yield trials in India (Proposals Track)
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Abstract: We use data from historical agronomic fertilizer trials to identify the effect of climate change on the average rice yield and the yield variability in India. The contribution of this paper is three folds: firstly, it has a methodological contribution by modelling the input conditional yield densities using a stochastic production structure for a developing country like India. In doing so, it predicts and measure the effects of climate change on rice grown in tropical regions; secondly,it estimates the nutrient demand and its link with the climate change; thirdly, by modelling the yield uncertainty, it characterizes the risk and role for insurance as a tool for tackling climate change in the developing countries.

Authors: Sandip K Agarwal (IISER Bhopal)