The study visualizes Spanish fish and meat processing companies using financial, environmental, and social ratios.
problem Mapping financial, environmental, and social performance of Spanish processing companies.
method Used compositional data and principal-component analysis biplot for statistical analysis.
result Identified clusters of companies with similar financial, environmental, and social performance.
A new framework assesses financial and ESG risks for sustainable investing.
problem Measuring risk and reward in sustainable investing considering environmental, social, and governance factors.
method Proposes axiomatic definitions for ESG-coherent risk measures and reward-risk ratios based on bivariate random variables.
result Empirical analysis ranks stocks using the proposed measures.
AI investors signal higher debt in ESG firms, boosting portfolio management.
problem Determining the value of ESG investing amid AI investment trends.
method Cross-sectional regressions of ESG scores and debt ratios of S&P 500 firms.
result ESG scores signal higher debt in firms, supporting ESG investing.
Enhances traditional MV model for socially responsible investors.
problem Traditional MV models ignore ESG scores relevant to socially responsible investors.
method Implemented an amended MV model considering ESG scores.
result SR investors can achieve competitive SR portfolios with a trade-off between Sharpe Ratio and ESG scores.
Although reinforcement learning has made great strides recently, a continuing limitation is that it requires an extremely high number of interactions with the environment. In this paper, we explore the effectiveness of reusing experience from the experience replay buffer in the Deep Q-Learning algorithm. We test the ef…
New eco-systemic prudential policies aim to finance green companies, reducing systemic financial risk.
problem Insufficient financing for green companies despite available savings and monetary management.
method Reorient corporate accounting towards socio-environmental solvency, facilitating access with public guarantees.
result Green financing increases, reducing systemic financial risk and promoting less leveraged investments.
Study finds environmental liability insurance reduces industrial carbon emissions.
problem Reduction of industrial carbon emissions.
method Two-way fixed effect model using provincial (city) level panel data from 2010 to 2020.
result Environmental liability insurance reduces industrial carbon emissions at both direct and indirect levels, with varying effects.
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.
The paper introduces a new financial market for environmental indices to attract investors.
problem Inherent risks and sustainability concerns in environmental investments.
method Quantitative measures, econometric analysis, dynamic asset pricing tools, and financial options.
result Monetization and construction of country-specific environmental indices as dollar-denominated assets.
Study shows environmental spending positively impacts company profitability.
problem Impact of environmental spending on company profitability.
method Panel data regression analysis using E-Views.
result Environmental spending positively impacts profitability metrics.
Proposes a new framework for environmental CVA with robust wrong-way risk.
problem Limited operational implementations of translating environmental scenarios into CVA.
method Three components: hazard rate mapping, tail generators, and KL divergence-based wrong-way risk bound.
result Nature CVAs can vary significantly across different ecosystem generators.
The paper examines how ESG constraints affect portfolio optimization in large datasets.
problem Investment optimization with ESG constraints in large portfolios.
method Asymptotic analysis of out-of-sample Sharpe ratio, regularization matrix estimation, and adaptive portfolio selection.
result The proposed adaptive ESG-constrained portfolio yields a high out-of-sample Sharpe ratio while meeting ESG requirements.
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.
Study assesses environmental management accounting practices in Bangladesh.
problem Low environmental management accounting practices in Bangladeshi manufacturing companies.
method Developed a compliance checklist and evaluated practices using binary scoring.
result Environmental management accounting practices are poor in Bangladeshi manufacturing companies.
Hedonic models predict 84-92% of U.S. real estate prices, highlighting environmental factors' impact.
problem Predicting real estate prices using hedonic models with environmental factors.
method P-spline generalized additive models for real estate prices, contrasting with linear and polynomial models.
result GAM models explain 84-92% of U.S. real estate price variance, with environmental factors contributing minimally.
Study identifies contagion in aggregated defaults despite environmental changes.
problem Identify contagion in aggregated default counts with fluctuating probabilities.
method Compare three contagion mechanisms (Davis-Lo, Torri, Vasicek) under i.i.d. and hierarchical specifications.
result Threshold contagion is largely absorbed into environmental heterogeneity, while cumulative contagion leaves a persistent signature.
MMformer improves forecasting of environmental time series data.
problem Accurately forecasting environmental change trends for policy-making.
method Meta-learning MTS model combining self-attention and adaptive transferable multi-head attention.
result MMformer outperforms other models in air quality and climate datasets, reducing prediction errors by 50% in MSE and 20% in MAE.
RST improves environmental time series classification accuracy using randomized B-spline trees.
problem Improving accuracy in classifying complex environmental time series.
method Randomized Spline Trees (RST) integrates randomized functional representations into ensemble learning.
result RST variants outperform standard Random Forests and Gradient Boosting on most environmental time series datasets.
Bayesian optimization adapted for experiments with changing environmental conditions.
problem Optimizing experiments influenced by uncontrollable environmental factors.
method Extends Bayesian optimization to handle both controllable and uncontrollable parameters, fitting a global surrogate model and optimizing only controllable parameters conditionally on measurements of uncontrollable variables.
result The proposed ENVBO algorithm finds solutions for the full domain of the environmental variable more efficiently and cost-effectively than traditional methods.
Study uses ML and statistical models to analyze climate impacts of industrial growth.
problem Understanding and predicting environmental impacts of industrial activities.
method Comparative analysis of ML and statistical models on time series data.
result ML models outperform statistical models in predicting environmental impacts.
We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light lev…
Novel Orlicz regrets consistently bound environmental variable statistics.
problem Consistent evaluation of stochastic environmental variables like water quality indices.
method Proposed novel Orlicz regrets for upper and lower bounds.
result Explicit linkage between Orlicz regrets and divergence risk measures.
We analyse a multiplex of networks between OECD countries during the decade 2002-2010, which consists of five financial layers, given by foreign direct investment, equity securities, short-term, long-term and total debt securities, and five environmental layers, given by emissions of N O x, P M 10 SO 2, CO 2 equivalent…
Proposes balancing revenue and environmental impact in assortment planning.
problem Maximizing revenue while considering environmental impact in retail assortment planning.
method Multi-objective optimization using Higg Material Sustainability Index.
result Shows it's possible to have lower environmental impact without significant revenue loss.
Optimizes stock portfolios with profit, risk, and sustainability.
problem Balancing profit, risk, and sustainability in stock portfolio management.
method Developed a novel utility function combining Sharpe ratio and ESG scores; used genetic algorithm for optimization.
result System outperforms traditional reinforcement learning methods and improves on risk and sustainability metrics.
One of the primary aspects of sustainable development involves accurate understanding and modeling of environmental phenomena. Many of these phenomena exhibit variations in both space and time and it is imperative to develop a deeper understanding of techniques that can model space-time dynamics accurately. In this pap…
BN^2MF identifies unknown exposure patterns in environmental mixtures.
problem Identifying unknown exposure patterns in environmental mixtures.
method Bayesian non-parametric non-negative matrix factorization (BN^2MF) with non-negative continuous priors and a non-parametric sparse prior.
result Estimates patterns of chemical exposures without specifying the number of patterns.
A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed. To enable persistent sensing and estimation in such a setting, it is beneficial to have a time-varying underlying environmental model. Here we present a planning and learning method that enables an autonomous ma…
Boosting algorithms predict financial vulnerability of farmers in Chile and Tunisia.
problem Predict financial vulnerability of farmers in Chile and Tunisia using environmental data.
method Interpretable boosting algorithms based on ridge-regularized generalized linear models.
result Interaction effects improve predictive power only when included in two-step boosting.
Novel spatio-temporal LSTM model forecasts oceanic variables across sensors and scales.
problem Data sparsity and lack of connected spatial and temporal information in environmental datasets.
method SPATIAL LSTM architecture that learns across spatial and temporal scales.
result Framework accurately forecasts oceanic variables with comparable performance to state-of-the-art models.
Improves robust transfer learning with side information.
problem Addressing environmental shift in MDPs with side information.
method Estimate-centered uncertainty sets with side information integration.
result Improved robust policy with reduced sub-optimality gap.
Unified framework for growth models with environmental risk and pollution-dependent disasters.
problem Analyzing how rare but catastrophic shocks interact with capital accumulation and pollution in stochastic growth models.
method General Poisson point process formulation leading to non-local HJB equations with closed-form solutions.
result Unified framework captures how environmental degradation amplifies macroeconomic vulnerability and strengthens incentives for abatement.
Study improves motor insurance claim prediction using geographic data.
problem Limited location identifiers in public actuarial datasets.
method Zone-level modeling framework with environmental and orthoimagery data.
result Geographic information improves MTPL claim prediction accuracy.
Model predicts phytoplankton subpopulations based on environmental factors.
problem Predicting phytoplankton dynamics under changing environmental conditions.
method Sparse mixture of multivariate regressions model.
result Identifies environmental covariates influencing phytoplankton subpopulations.
Framework improves marine mammal monitoring in noisy underwater environments.
problem Underwater bioacoustic monitoring challenges due to overlapping calls and variable noise.
method Multi-step attention-guided framework with segmentation and mid-level fusion.
result Improved signal discrimination, reduced false positives, reliable representations.
A framework uses deep learning for spatio-temporal data prediction.
problem Interpolation of continuous spatio-temporal fields on irregular points.
method Decomposes spatio-temporal processes into products of basis functions and spatial coefficients.
result Effectiveness in reconstructing coherent spatio-temporal fields.
GeoLifeCLEF 2020 dataset pairs species observations with environmental data.
problem Understanding geographic species distribution.
method Presented a dataset of species observations with environmental features.
result Advances in location-based species recommendation.
Water pollution is a major global environmental problem, and it poses a great environmental risk to public health and biological diversity. This work is motivated by assessing the potential environmental threat of coal mining through increased sulfate concentrations in river networks, which do not belong to any simple …
Spacematch matches office workers with suitable workspaces based on their preferences.
problem Matching workers with suitable workspaces in ABW environments.
method Developed a web-based mobile app to collect occupant preferences and IoT sensors for real-time environmental feedback.
result Occupant preferences can be segmented and matched to build a recommendation platform.
Environmental acoustic sensing involves the retrieval and processing of audio signals to better understand our surroundings. While large-scale acoustic data make manual analysis infeasible, they provide a suitable playground for machine learning approaches. Most existing machine learning techniques developed for enviro…
NLR models often perform worse than LR for outlying input data in environmental sciences.
problem NLR models often give poor predictions for input data outside the training domain.
method Screened input data for outliers, using linear extrapolation for outliers based on NLR within the non-outlier domain.
result NLROR approach reduces poor extrapolation and tends to outperform NLR and LR for outliers. In this paper we propose a novel environmental sound classification approach incorporating unsupervised feature learning from codebook via spherical K-Means++ algorithm and a new architecture for high-level data augmentation. The audio signal is transformed into a 2D representation using a discrete wavelet transform …
Paper integrates ML with physics models for engineering and environmental challenges.
problem Complex science and engineering problems require new methodologies combining physics-based models and ML.
method Structured overview of integrating physics-based models with ML techniques.
result Taxonomy of existing techniques and potential research gaps identified.
Firms should keep capital to offer sufficient protection against the risks they are facing. In the insurance context methods have been developed to determine the minimum capital level required, but less so in the context of firms with multiple business lines including allocation. The individual capital reserve of each …
Study quantifies firm risks from nature decline, showing significant equity losses.
problem Estimating the financial impact of nature deterioration on companies.
method Developed metrics (Country Degradation Index, Nature Risk Score) and assessed five environmental hazards.
result Global equities lose 26.8% in a nature decline scenario, with worst firms losing 75%.
A two-stage decision support system optimizes long-short portfolios under ESG considerations.
problem Optimizing long-short portfolios under environmental, social, and governance (ESG) considerations.
method First stage: Multi-criteria evaluation using TODIMSort and MEREC. Second stage: Non-convex portfolio optimization with Omega ratio.
result ESG-enhanced long-short portfolios outperform non-ESG and market-value-weighted benchmarks.
According to a recent investigation, an estimated 33-50% of the world's coral reefs have undergone degradation, believed to be as a result of climate change. A strong driver of climate change and the subsequent environmental impact are greenhouse gases such as methane. However, the exact relation climate change has to …
New ESGM scores include a 'Missing' pillar to account for unpublished ESG data.
problem Unpublished ESG data affects the reliability of ESG scores.
method Formulated a new 'Missing' pillar and introduced ESGM scores.
result ESGM scores improve risk assessment and avoid exclusion of assets.