Related papers: A Comprehensive Approach to Carbon Dioxide Emissio…
This study employs the Causal Machine Learning (CausalML) statistical method to analyze the influence of electricity pricing policies on carbon dioxide (CO2) levels in the household sector. Investigating the causality between potential…
The method is proposed for estimation of regional CO2 and other greenhouses and pollutants production responcibility. The comparison of CO2 local emissions reduction data with world CO2 atmosphere data will permit easy to judge for overall…
Commuting contributes substantially to urban greenhouse gas emissions and represents a critical focus for climate mitigation efforts. This paper explores the multifaceted nature of commuting-related carbon dioxide emissions by analyzing the…
Given the fact that climate change has become one of the most pressing problems in many countries in recent years, specialized research on how to mitigate climate change has been adopted by many countries. Within this discussion, the…
Linear regression models, especially the extended STIRPAT model, are routinely-applied for analyzing carbon emissions data. However, since the relationship between carbon emissions and the influencing factors is complex, fitting a simple…
Climate change and global warming have been trending topics worldwide since the Eco-92 conference. However, little progress has been made in reducing greenhouse gases (GHGs). The problems and challenges related to emissions are complex and…
Accurately estimating high-resolution carbon emissions is crucial for effective emission governance and mitigation planning. While conventional methods for precise carbon accounting are hindered by substantial data collection efforts, the…
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…
Global ambient air pollution, a transboundary challenge, is typically addressed through interventions relying on data from spatially sparse and heterogeneously placed monitoring stations. These stations often encounter temporal data gaps…
New quality productive forces (NQPF), digital economy advancement, and artificial intelligence (AI) technologies are becoming crucial for promoting sustainable urban development. This study proposes a Multi-head Attention Deep & Cross…
Air pollution is a serious issue in the world. Around 98% of cities with a population of over 100,000 people in low and middle-income countries do not meet air quality standards, while in high-income countries, the number has decreased by…
Carbon Dioxide (CO2) is one of the most important greenhouse gases after water vapor (H2O) which plays significant role in the climate process. Accurate space-based measurement of CO2 is of great significance in inferring the location of…
Accurate tracking of anthropogenic carbon dioxide (CO2) emissions is crucial for shaping climate policies and meeting global decarbonization targets. However, energy consumption and emissions data are released annually and with substantial…
Mitigation of climate change will highly rely on a carbon emission trajectory that achieves carbon neutrality by the 2050s. The ocean plays a critical role in modulating climate change by sequestering CO2 from the atmosphere. Relying on the…
Combining global gridded population and fossil fuel based CO2 emission data at 1km scale, we investigate the spatial origin of CO2 emissions in relation to the population distribution within countries. We depict the correlations between…
This paper studies stochastic convergence of per capita CO$_2$ emissions in 28 OECD countries for the 1901-2009 period. The analysis is carried out at two aggregation levels, first for the whole set of countries (joint analysis) and then…
Probabilistic projections of baseline (with no additional mitigation policies) future carbon emissions are important for sound climate risk assessments. Deep uncertainty surrounds many drivers of projected emissions. Here we use a simple…
A machine learning algorithm is developed to forecast the CO2 emission intensities in electrical power grids in the Danish bidding zone DK2, distinguishing between average and marginal emissions. The analysis was done on data set comprised…
Climate change is profoundly affecting nearly all aspects of life on earth, including human societies, economies and health. Various human activities are responsible for significant greenhouse gas emissions, including data centres and other…
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…