This research uses reinforcement learning to find optimal emission offsets in greenhouse gas markets.
problem Finding optimal emission offsets in greenhouse gas markets to control excess emissions.
method Utilized reinforcement learning, specifically Nash-DQN, to estimate market Nash equilibria.
result Emitting firms can achieve significant financial savings by abiding by the Nash equilibria found in the market.
Machine learning predicts greenhouse gas emissions for undisclosed companies.
problem Lack of GHG emissions data for most companies.
method Trained machine learning model on disclosed data to estimate emissions.
result Model accurately predicts emissions for undisclosed companies.
Deep learning speeds up real-time emission monitoring.
problem Real-time greenhouse gas emission monitoring under transient conditions.
method Bayesian inference with deep learning surrogate of CFD outputs.
result Near-real-time predictions with orders-of-magnitude faster runtimes.
Deep learning predicts road GHG emissions with speed, density, and past ERs.
problem Predicting GHG emissions from road networks to mitigate environmental impact.
method Developed a deep learning framework using LSTM networks with exogenous variables.
result LSTM with speed, density, GHG ER, and in-links speed from previous minutes performs best.
Model predicts methane emissions from oil sands tailing ponds, suggesting significant environmental impact.
problem Estimating methane emissions from inactive oil sands tailing ponds.
method Physics constrained machine learning model using real-time weather data and laboratory experiments.
result Active oil sands tailing ponds emit between 950 to 1500 tonnes of methane per year, equivalent to 6000 gasoline vehicles.
It will be difficult to gain the agreement of all the actors on any proposal for climate change management, if universality and fairness are not considered. In this work, a universal measure of emissions to be applied at the international level is proposed, based on a modification of the Greenhouse Gas Intensity (GHG-I…
This work models GHG offset credit markets to find optimal strategies for market participants.
problem Optimizing GHG offset credit markets to reduce emissions and penalize excess emissions.
method Characterized optimal behavior in single-player and two-player GHG offset credit markets using optimal stopping and control problems, and mixed-Nash equilibria.
result Market participants benefit from optimal OC trading and generation, highlighting the importance of acting optimally.
Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requires accurate measurements readily available to consumers, regulators and investors. In this case study, we propose a new real-time consumption-based accounting approach based on flow tracing. This…
An innovative method optimizes engine calibration to improve efficiency and reduce emissions.
problem Complex engines with many tunable parameters require efficient calibration methods.
method Combines Principal Component Decomposition with constrained Bayesian Optimization to minimize pressure curve deviation.
result Optimal engine calibration found after 64.4s with a 0.017% efficiency gain.
Model estimates non-reported GHG emissions for companies using machine learning.
problem Incomplete GHG emissions reporting by companies.
method Interpretable machine learning model tailored for non-reporting companies.
result Model accurately estimates emissions for diverse company groups.
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, w…
CAI automates extraction and validation of corporate GHG emission metrics.
problem Manual extraction of corporate GHG emission metrics is labor-intensive and error-prone.
method CAI uses LLMs to automate extraction and validation of metrics from corporate disclosures.
result CAI improves data collection efficiency and accuracy by automating the process.
Challenge forecasts EV charging station usage accurately.
problem Better understanding of EV charging behaviors needed.
method Hierarchical forecasting of EV charging station occupancy.
result Hierarchical forecasting improves EV charging station occupancy predictions.
Green stocks show less factor exposure heterogeneity compared to brown stocks.
problem Exploring differences in factor exposure between green and brown stocks.
method Examined S&P 500 firms grouped by greenhouse gas emissions, analyzing factor exposure over 2014-2020.
result Green stocks have less factor exposure heterogeneity than brown stocks, except for the value factor.
As global greenhouse gas emissions continue to rise, the use of stratospheric aerosol injection (SAI), a form of solar geoengineering, is increasingly considered in order to artificially mitigate climate change effects. However, initial research in simulation suggests that naive SAI can have catastrophic regional conse…
Paper uses neural networks to predict NOx emissions from gas turbines.
problem Predicting NOx emissions from degrading gas turbines.
method Applied neural network algorithm to model NOx emissions from nine process variables.
result Neural network model optimizes process variables for minimal NOx emissions.
A new method uses variational autoencoders to speed up greenhouse gas sensitivity calculations.
problem Computational inefficiency in generating LPDM sensitivities from gas mole fraction observations.
method Developed a convolutional variational autoencoder (CVAE) to emulate LPDM sensitivities in a low-dimensional space.
result The CVAE-based emulator outperforms traditional methods and can be applied to various LPDMs.
Investment managers face harder choices in green stocks due to reduced performance variability.
problem Difficulty in deploying talent in green stocks due to reduced performance variability.
method Analysis of S&P 500 firms' greenhouse gas emission levels and peer performance ratios.
result Performance variability has decreased in green stocks, making it harder for managers to choose.
Recent changes to greenhouse gas emission policies are catalyzing the electric vehicle (EV) market making it readily accessible to consumers. While there are challenges that arise with dense deployment of EVs, one of the major future concerns is cyber security threat. In this paper, cyber security threats in the form o…
Scaling relations, such as the IPAT equation and the Kaya identity, are useful for quickly gauging the scale of economic, technological, and demographic changes required to reduce environmental impacts and pressures; in the case of the Kaya identity, the environmental pressure is greenhouse gas emissions. However, when…
Conventional economic analysis of stringent climate change mitigation policy generally concludes various levels of economic slowdown as a result of substantial spending on low carbon technology. Equilibrium economics however could not explain or predict the current economic crisis, which is of financial nature. Meanwhi…
Long-range climate forecasts use integrated assessment models to link the global economy to greenhouse gas emissions. This paper evaluates an alternative economic framework outlined in part 1 of this study (Garrett, 2014) that approaches the global economy using purely physical principles rather than explicitly resolve…
To achieve the ambitious aims of the Paris climate agreement, the majority of fossil-fuel reserves needs to remain underground. As current national government commitments to mitigate greenhouse gas emissions are insufficient by far, actors such as institutional and private investors and the social movement on divestmen…
Paper uses Gaussian processes to solve AC-OPF with renewable uncertainty.
problem Optimizing power grids with fluctuating renewable sources.
method Data-driven approach using Gaussian processes.
result Efficiently solves chance-constrained AC-OPF with uncertainty.
This study analyzes how weather impacts bike sharing usage in Washington D.C.
problem Understanding how weather affects bike sharing usage patterns.
method Gathered bike usage and weather data, used k-means clustering algorithm to identify clusters.
result Weather significantly impacts bike usage, with temperature and precipitation being the most influential factors.
Underwater gas reservoirs are used in many situations. In particular, Carbon Capture and Storage (CCS) facilities that are currently being developed intend to store greenhouse gases inside geological formations in the deep sea. In these formations, however, the gas might percolate, leaking back to the water and eventua…
Successful implementation of California's Renewable Portfolio Standard (RPS) mandating 33 percent renewable energy generation by 2020 requires inclusion of a robust strategy to mitigate increased risk of energy deficits (blackouts) due to short time-scale (sub 1 hour) intermittencies in renewable energy sources. Of the…
Higher environmental performance linked to more tax avoidance, especially for financially constrained firms.
problem Tax avoidance practices in relation to environmental performance.
method Entropy balancing, propensity score matching, instrumental variable method, Heckman test.
result Higher environmental performance correlates with increased tax avoidance, particularly for financially constrained firms.
Study predicts room occupancy using machine learning, achieving high accuracy.
problem Efficient room occupancy management for energy conservation.
method Leveraged diverse machine learning models, focusing on Random Forest.
result Random Forest model achieved highest predictive accuracy.
Develops adaptive algorithms for sustainable fertilizer use in agriculture.
problem Sustaining high yields while reducing environmental impacts of fertilizer use.
method Nonlinear model-based bandit algorithms linking biological processes to decision-making.
result Faster learning and higher profits with interpretable recommendations.
Model predicts volatility and dependencies in EUA and energy prices.
problem Analyzing uncertainty and dependencies in European carbon and energy prices.
method Probabilistic multivariate conditional time series model with VECM-Copula-GARCH structure.
result Forecasting performance evaluated in an extensive rolling-window study.
Intelligent control for greenhouses using deep reinforcement learning.
problem Uncertain nonlinear system of greenhouse environment control.
method Model Embedded Deep Reinforcement Learning (MEDRL) with computer vision and crop growth models.
result Precision and convenience in precise control of greenhouse environment.
Proposes a Carbon Equivalence Principle for financial products to align incentives and drive sustainability.
problem Align financial market incentives with carbon emissions to limit global warming.
method Introduces a Carbon Equivalence Principle requiring financial products to describe equivalent carbon flows alongside cash flows.
result Transparency of carbon flows in financial products can align incentives and reduce future costs, necessitating project re-structuring and financial net-zero designs.
Paper presents LSTMMDN for hourly bike flow estimation in Copenhagen.
problem Sparse or unavailable hourly bike flow data for safety analysis.
method Hybrid LSTM MDN model for hourly bike flow estimation.
result 66-77% more accurate bike flow estimates compared to calibration factors.
The study models and forecasts natural gas prices using skewed, heavy-tailed distributions.
problem Modeling and forecasting natural gas prices with heavy tails and conditional heteroscedasticity.
method State-space time series models under skewed, heavy-tailed distributions.
result The proposed model reduces out-of-sample CRPS by 13% for Day-Ahead and 9% for Month-Ahead forecasts.
MethaneMapper detects methane emissions with high accuracy and reduced model size.
problem Challenges in detecting and quantifying methane emissions from AVIRIS-NG data.
method Spectral absorption wavelength aware transformer network, introducing two novel modules.
result Achieves 0.63 mAP in detection and reduces model size by 5x.
This study analyzes how carbon pricing affects credit risk measures in a portfolio.
problem Impact of carbon pricing on credit risk measures in a portfolio.
method Adapted stochastic multisectoral model to account for GHG emissions costs and carbon prices.
result Carbon pricing distorts firm value distributions, increases banking fees, and reduces profitability.
AI helps Vancouver identify where off-street parking saves time and space.
problem On-street parking inefficiencies and associated costs in Vancouver.
method Developed AI models for on-street and off-street parking, comparing time costs.
result Many areas off-street parking saves time and space, aligning with city goals.
AI methods are energy-intensive, but efficiency alone isn't enough for sustainability.
problem AI methods are energy-intensive and contribute to climate change.
method Critically examines the limitations of efficiency in improving environmental sustainability of AI.
result Efficiency alone is insufficient to address the environmental impacts of AI.
Effective plant growth and yield prediction is an essential task for greenhouse growers and for agriculture in general. Developing models which can effectively model growth and yield can help growers improve the environmental control for better production, match supply and market demand and lower costs. Recent developm…
Study reduces emissions in portfolios with error-prone emissions data.
problem Portfolio optimization with firm-level emissions intensities measured inaccurately.
method Introduced a scope-specific penalty operator to rescale asset payoffs based on revenue-normalized emissions intensity.
result Reduces average Scope~1 emissions intensity by roughly 92% while maintaining similar Sharpe ratios.
Study examines how market dynamics affect emissions trading prices and abatement efforts.
problem Effectiveness of emissions markets depends on regulatory standards, costs, and abatement levels.
method Radner equilibrium framework that considers intertemporal decision-making and uncertainty.
result Variations in regulatory standards, costs, and abatement levels influence allowance prices and abatement efforts.
Optimizes gradual reduction of excess carbon emissions to net-zero.
problem Achieving net-zero carbon emissions through gradual reduction of excess emissions.
method Stochastic control approach to identify optimal emission strategy under constraints.
result Identifies the emission strategy that maximizes future profit from excess emissions.
Optimal dynamic allocation of carbon allowances reduces emissions efficiently.
problem Reducing carbon emissions from firms over time with dynamic allocation and trading.
method Variational approach to solve the Stackelberg game between regulator and firms.
result Optimal policies lead to constant abatement effort and allowance price, outperforming static allocations.
In emissions trading, the initial allocation of permits is an intractable issue because it needs to be essentially fair to the participating countries. There are many ways to distribute a given total amount of emissions permits among countries, but the existing distribution methods, such as auctioning and grandfatherin…
Tackling climate change is at the top of many agendas. In this context, emission trading schemes are considered as promising tools. The regulatory framework for an emission trading scheme introduces a market for emission allowances and creates a need for risk management by appropriate financial contracts. In this work,…
Study examines how industrial emissions evolve over time in response to various factors.
problem Understanding how firm-level emissions change over time in response to environmental regulation, economic conditions, and organizational constraints.
method Used a time-varying mean-group estimator to link emissions data with firm characteristics and macroeconomic indicators over 1992-2023.
result Firm-level characteristics and aggregate conditions have different impacts on emissions growth at different times.
Study finds carbon emissions affect stock value, but not bought emissions.
problem Determining if carbon emissions impact stock value and whether this is due to direct or indirect emissions.
method Fixed-effects analysis with propensity score weighting to control for selection bias.
result Firms with higher Scope 1 emissions have a statistically significant positive carbon premium, but Scope 2 emissions do not.