Related papers: Machine Learning Techniques for Multifactor Analys…

This research presents a three-step causal inference framework that integrates correlation analysis, machine learning-based causality discovery, and LLM-driven interpretations to identify socioeconomic factors influencing carbon emissions…

Analyzing air pollution data is challenging as there are various analysis focuses from different aspects: feature (what), space (where), and time (when). As in most geospatial analysis problems, besides high-dimensional features, the…

This paper is a continuation of the statistical modeling of the nonlinear relationship between atmospheric CO2 and attributable variables that can account for emissions, based on data from EU countries, in order to compare the relevant…

Consumption-based carbon emission measures aim to account for emissions associated with power transmission from distant regions, as opposed to measures which only consider local power generation. Outlining key differences between two…

This study proposes a novel analytical framework that integrates DBSCAN clustering with the Elastic Net regression model to address multifactorial problems characterized by structural complexity and multicollinearity, exemplified by carbon…

Dynamic grid emission factors provide a temporally resolved signal about the carbon intensity of electricity generation in the power system. Since actual carbon dioxide emission measurements are usually lacking, such a signal must be…

Carbon neutralization is an urgent task in society because of the global warming threat. And carbon trading is an essential market mechanics to solve carbon reduction targets. Macro carbon price prediction is vital in the useful management…

Terrestrial carbon fluxes provide vital information about our biosphere's health and its capacity to absorb anthropogenic CO$_2$ emissions. The importance of predicting carbon fluxes has led to the emerging field of data-driven carbon flux…

Decarbonizing road transport requires consistent and transparent methods for comparing CO2 emissions across vehicle technologies. This paper proposes a machine learning-based framework for like-for-like operational assessment of internal…

Accurate estimation of greenhouse gas (GHG) is essential to meet carbon neutrality targets, particularly through the calculation of direct CO2 emissions from electricity generation. This work reviews and compares emission factor-based…

Understanding how climate and innovation policies perform during socio-technical transitions remains a central challenge in innovation studies. Empirical analyses of the relationship between economic growth and carbon emissions continue to…

Corporate carbon emissions data is disclosed by approximately 65% of large and mid-sized companies globally, despite being a key indicator of corporate climate performance. With investors increasingly looking to integrate climate risk into…

Sovereign credit ratings summarize the creditworthiness of countries. These ratings have a large influence on the economy and the yields at which governments can issue new debt. This paper investigates the use of a Multilayer Perceptron…

Building on the carbon reduction targets agreed in the Paris Agreements, many nations have renewed their efforts toward achieving carbon neutrality by the year 2050. In line with this ambitious goal, nations are seeking to understand the…

Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research. We introduce a framework that makes this easier by providing a simple interface for tracking realtime…

Through an aviation emissions estimation tool that is both publicly-accessible and comprehensive, researchers, planners, and community advocates can help shape a more sustainable and equitable U.S. air transportation system. To this end, we…

Carbon dioxide (CO2) emissions have emerged as a critical issue with profound impacts on the environment, human health, and the global economy. The steady increase in atmospheric CO2 levels, largely due to human activities such as burning…

Carbon taxes are increasingly popular among policymakers but remain politically contentious. A key challenge relates to their distributional impacts; the extent to which tax burdens differ across population groups. As a response, a growing…

Putting a price on carbon -- with taxes or developing carbon markets -- is a widely used policy measure to achieve the target of net-zero emissions by 2050. This paper tackles the issue of producing point, direction-of-change, and density…

Despite policymakers deploying various tools to mitigate emissions of ozone (O\textsubscript{3}) precursors, such as nitrogen oxides (NO\textsubscript{x}), carbon monoxide (CO), and volatile organic compounds (VOCs), the effectiveness of…