Climate Justice


Workshop Papers

Venue Title
NeurIPS 2023 Integrating Building Survey Data with Geospatial Data: A Cluster-Based Ethical Approach (Papers Track)
Abstract and authors: (click to expand)

Abstract: This research paper delves into the unique energy challenges faced by Alaska, arising from its remote geographical location, severe climatic conditions, and heavy reliance on fossil fuels while emphasizing the shortage of comprehensive building energy data. The study introduces an ethical framework that leverages machine learning and geospatial techniques to enable the large-scale integration of data, facilitating the mapping of energy consumption data at the individual building level. Utilizing the Alaska Retrofit Information System (ARIS) and the USA Structures datasets, this framework not only identifies and acknowledges limitations inherent in existing datasets but also establishes a robust ethical foundation for data integration. This framework innovation sets a noteworthy precedent for the responsible utilization of data in the domain of climate justice research, ultimately informing the development of sustainable energy policies through an enhanced understanding of building data and advancing ongoing research agendas. Future research directions involve the incorporation of recently released datasets, which provide precise building location data, thereby further validating the proposed ethical framework and advancing efforts in addressing Alaska's intricate energy challenges.

Authors: Vidisha Chowdhury (University of Pennsylvania); Gabriela Gongora-Svartzman (Carnegie Mellon University); Erin D Trochim (University of Alaska Fairbanks); Philippe Schicker (Carnegie Mellon University)

ICLR 2023 Topology Estimation from Voltage Edge Sensing for Resource-Constrained Grids (Papers Track)
Abstract and authors: (click to expand)

Abstract: Electric grids are the conduit for renewable energy delivery and will play a crucial role in mitigating climate change. To do so successfully in resource-constrained low- and middle-income countries (LMICs), increasing operational efficiency is key. Such efficiency demands evolving knowledge of the grid’s state, of which topology---how points on the network are physically connected---is fundamental. In LMICs, knowledge of distribution topology is limited and established methods for topology estimation rely on expensive sensing infrastructure, such as smart meters or PMUs, that are inaccessible at scale. This paper lays the foundation for topology estimation from more accessible data: outlet-level voltage magnitude measurements. It presents a graph-based algorithm and explanatory visualization using the Fielder vector for estimating and communicating topological proximity from this data. We demonstrate the method on a real dataset collected in Accra, Ghana, thus opening the possibility of globally accessible, cutting-edge grid monitoring through non-traditional sensing strategies coupled with ML.

Authors: Mohini S Bariya (nLine); Genevieve Flaspohler (nLine)

ICLR 2023 Projecting the climate penalty on pm2.5 pollution with spatial deep learning (Proposals Track)
Abstract and authors: (click to expand)

Abstract: The climate penalty measures the effects of a changing climate on air quality due to the interaction of pollution with climate factors, independently of future changes in emissions. This work introduces a statistical framework for estimating the climate penalty on soot pollution (PM 2.5), which has been linked to respiratory and cardiovascular diseases and premature mortality. The framework evaluates the disparities in future PM 2.5 exposure across racial/ethnic and income groups---an important step towards informing mitigation public health policy and promoting environmental equity in addressing the effects of climate change. The proposed methodology aims to improve existing statistical-based methods for estimating the climate penalty using an expressive and scalable predictive model based on spatial deep learning with spatiotemporal trend estimation. The proposed approach will (1) use higher-resolution climate inputs, which current statistical methods to estimate the climate penalty approaches cannot accommodate; (2) integrate additional predictive data sources such as demographics, geology, and land use; (3) consider regional dependencies and synoptic weather patterns influencing PM 2.5, deconvolving the effects of climate change from increasing air quality regulations and other sources of unmeasured spatial heterogeneity.

Authors: Mauricio Tec (Harvard University); Riccardo Cadei (Harvard University); Francesca Dominici (Harvard University); Corwin Zigler (University of Texas at Austin)

NeurIPS 2022 Deep Hydrology: Hourly, Gap-Free Flood Maps Through Joint Satellite and Hydrologic Modelling (Papers Track)
Abstract and authors: (click to expand)

Abstract: Climate change-driven weather disasters are rapidly increasing in both frequency and magnitude. Floods are the most damaging of these disasters, with approximately 1.46 billion people exposed to inundation depths of over 0.15m, a significant life and livelihood risk. Accurate knowledge of flood-extent for ongoing and historical events facilitates climate adaptation in flood-prone communities by enabling near real-time disaster monitoring to support planning, response, and relief during these extreme events. Satellite observations can be used to derive flood-extent maps directly; however, these observations are impeded by cloud and canopy cover, and can be very infrequent and hence miss the flood completely. In contrast, physically-based inundation models can produce spatially complete event maps but suffer from high uncertainty if not frequently calibrated with expensive land and infrastructure surveys. In this study, we propose a deep learning approach to reproduce satellite-observed fractional flood-extent maps given dynamic state variables from hydrologic models, fusing information contained within the states with direct observations from satellites. Our model has an hourly temporal resolution, contains no cloud-gaps, and generalizes to watersheds across the continental United States with a 6% error on held-out areas that never flooded before. We further demonstrate through a case study in Houston, Texas that our model can distinguish tropical cyclones that caused flooding from those that did not within two days of landfall, thereby providing a reliable source for flood-extent maps that can be used by disaster monitoring services.

Authors: Tanya Nair (Cloud To Street); Veda Sunkara (Cloud to Street); Jonathan Frame (Cloud to Street); Philip Popien (Cloud to Street); Subit Chakrabarti (Cloud To Street)

NeurIPS 2022 Urban Heat Island Detection and Causal Inference Using Convolutional Neural Networks (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Compared to rural areas, urban areas experience higher temperatures for longer periods of time because of the urban heat island (UHI) effect. This increased heat stress leads to greater mortality, increased energy demand, regional changes to precipitation patterns, and increased air pollution. Urban developers can minimize the UHI effect by incorporating features that promote air flow and heat dispersion (e.g., increasing green space). However, understanding which urban features to implement is complex, as local meteorology strongly dictates how the environment responds to changes in urban form. In this proposal we describe a methodology for estimating the causal relationship between changes in urban form and changes in the UHI effect. Changes in urban form and temperature changes are measured using convolutional neural networks, and a causal inference matching approach is proposed to estimate causal relationships. The success of this methodology will enable urban developers to implement city-specific interventions to mitigate the warming planet's impact on cities.

Authors: Zachary D Calhoun (Duke University); Ziyang Jiang (Duke University); Mike Bergin (Duke University); David Carlson (Duke University)

NeurIPS 2022 ForestBench: Equitable Benchmarks for Monitoring, Reporting, and Verification of Nature-Based Solutions with Machine Learning (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Restoring ecosystems and reducing deforestation are necessary tools to mitigate the anthropogenic climate crisis. Current measurements of forest carbon stock can be inaccurate, in particular for underrepresented and small-scale forests in the Global South, hindering transparency and accountability in the Monitoring, Reporting, and Verification (MRV) of these ecosystems. There is thus need for high quality datasets to properly validate ML-based solutions. To this end, we present ForestBench, which aims to collect and curate geographically-balanced gold-standard datasets of small-scale forest plots in the Global South, by collecting ground-level measurements and visual drone imagery of individual trees. These equitable validation datasets for ML-based MRV of nature-based solutions shall enable assessing the progress of ML models for estimating above-ground biomass, ground cover, and tree species diversity.

Authors: Lucas Czech (Carnegie Institution for Science); Björn Lütjens (MIT); David Dao (ETH Zurich)

NeurIPS 2021 Memory to Map: Improving Radar Flood Maps With Temporal Context and Semantic Segmentation (Papers Track)
Abstract and authors: (click to expand)

Abstract: Global flood risk has increased due to worsening extreme weather events and human migration into growing flood-prone areas. Accurate, high-resolution, and near-real time flood maps can address flood risk by reducing financial loss and damage. We propose Model to Map, a novel machine learning approach that utilizes bi-temporal context to improve flood water segmentation performance for Sentinel-1 imagery. We show that the inclusion of unflooded context for the area, or "memory," allows the model to tap into a "prior state" of pre-flood conditions, increasing performance in geographic regions in which single-image radar-based flood mapping methods typically underperform (e.g. deserts). We focus on accuracy across different biomes to ensure global performance. Our experiments and novel data processing technique show that the confluence of pre-flood and permanent water context provides a 21% increase in mIoU over the baseline overall, and over 87% increase in deserts.

Authors: Veda Sunkara (Cloud to Street); Nicholas Leach (Cloud to Street); Siddha Ganju (Nvidia)

ICML 2021 Leveraging Machine Learning for Equitable Transition of Energy Systems (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Our planet is facing overlapping crises of climate change, global pandemic, and systemic inequality. To respond climate change, the energy system is in the midst of its most foundational transition since its inception, from traditional fuel-based energy sources to clean renewable sources. While the transition to a low-carbon energy system is in progress, there is an opportunity to make the new system more just and equitable than the current one that is inequitable in many forms. Measuring inequity in the energy system is a formidable task since it is large scale and the data is coming from abundant data sources. In this work, we lay out a plan to leverage and develop scalable machine learning (ML) tools to measure the equity of the current energy system and to facilitate a just transition to a clean energy system. We focus on two concrete examples. First, we explore how ML can help to measure the inequity in the energy inefficiency of residential houses in the scale of a town or a country. Second, we explore how deep learning techniques can help to estimate the solar potential of residential buildings to facilitate a just installation and incentive allocation of solar panels. The application of ML for energy equity is much broader than the above two examples and we highlight some others as well. The result of this research could be used by policymakers to efficiently allocate energy assistance subsidies in the current energy systems and to ensure justice in their energy transition plans.

Authors: Enea Dodi (UMass Amherst); Anupama A Sitaraman (University of Massachusetts Amherst); Mohammad Hajiesmaili (UMass Amherst); Prashant Shenoy (University of Massachusetts Amherst)

NeurIPS 2020 Machine Learning Informed Policy for Environmental Justice in Atlanta with Climate Justice Implications (Proposals Track)
Abstract and authors: (click to expand)

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)