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Deploying negative emission technologies has become crucial for limiting the global temperature rise to approximately 1.5 °C above preindustrial levels. DAC technologies are being explored as one of the prospective options. These technologies have been thoroughly investigated as a potential project to capture CO 2 emissions and provide purified air, natural gas, or fuel oil. An applied approach, on the other hand, was not taken into consideration while evaluating the influence that this technology has on emissions. For this reason, British Columbia provides a substantial chance to examine emissions that were produced after the DAC actions were put into place in 2015. In this study, the difference-in-differences methodology is employed for the very first time to compare the emissions that are produced by the transport sectors in British Columbia with those emitted by other provinces in Canada. The role that GDP and population play in the release of emissions is also taken into consideration in this paper. Based on the research results, it can be observed that the implementation of DAC initiatives has yielded notable effects. Evidence shows that the DAC effort has led to an average reduction of 0.08 in logarithmic CO 2 emissions in the transport sector. By accounting for GDP and population, the empirical results indicate that DAC technology reduced CO 2 emissions in British Columbia compared to provinces without DAC facilities. DAC initiatives are expected to become increasingly prevalent between the mid-2030s and 2040s. Overall policy implications suggest that there is a need for DAC technologies to collaborate with alternative mitigation technologies, or alternative technologies should collaborate with DAC technologies that are more efficient to achieve the targeted goals in a short time. • The study offers empirical evidence of DAC technology's impact, providing insights beyond theoretical conjectures. • Based on the research results, it can be seen that the implementation of DAC facilities has yielded notable effects. • DAC technology in British Columbia has led to a significant and negative impact on CO 2 emissions compared to other provinces without DAC facilities. • The study highlights the differential effectiveness of DAC across transport sectors, pointing toward a need for tailored implementation strategies. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2) emissions from community-based consumption are a major contributor to global greenhouse gas emissions. However, little is understood about how demographic, socioeconomic, and household factors may contribute to community CO 2 emissions variation. Using traditional regression methods to model community emissions does not discriminate which of these factors are responsible for CO 2 emissions in high carbon emitting communities versus low carbon emitting communities, leading to policy development that does not consider emission variation across communities. To address this issue, we used quantile regression to model these effects on different quantiles of community emissions for 1451 communities across Canada and each province in 2015, respectively. The results showed that population, followed by affluence, were the most important variables affecting total community emissions, while affluence was the most important factor affecting per capita community emissions. However, the effect sizes were not consistent across quantiles, decreasing for population and increasing for affluence from low to high emission communities. Population density was significant across all communities except the lowest quantile communities, with the effect size increasing from smaller to larger communities. Additionally, our measure of poverty was significantly associated with increases in total and per capita emissions for all quantiles at the national level. Our finding that factors responsible for CO 2 emissions varied across communities of different quantiles suggests that successful emission reduction policies must account for the diversity of community characteristics, particularly considering variation in population and affluence. Our study also shows poverty alleviation is an effective means for CO 2 emission reduction and should be considered when adopting emission reduction policies. • We used quantile regression to model the variation in community CO 2 emissions. • Factors responsible for CO 2 varied greatly from low to high CO 2 emission quantiles. • Population and affluence affected CO 2 most but were inconsistent across quantiles. • Emission reduction policies must account for these community emission variations. • Poverty alleviation could have the co-benefit of limiting community CO 2 emissions. [ABSTRACT FROM AUTHOR]

Life cycle inventory (LCI) data gaps present a crucial challenge in research and development using life cycle assessment (LCA). The present study addresses this challenge by using optimized Artificial Neural Networks (ANNs). ANN's adjustable parameters (i.e. topology and hyperparameters) significantly affect prediction capability. Thus, the optimality of those parameters is of paramount importance. However, performing an optimization on the entire ANN's adjustable parameters is computationally demanding, which may result in an intractable problem. To tackle this challenge, the present study proposes a hybrid approach using heuristics and Genetic Algorithm to systematically design optimal ANNs in a tractable fashion. The proposed approach is then assessed through the prediction of data gaps in the Canadian fuel LCI database, GHGenius (an open-access tool for modeling Canadian fuel pathways). This is achieved using an automation workflow that extracts LCI data from GHGenius at the fuel/province/unit process level. It is found that the resulting optimal ANNs not only are accurate in predicting data to fill CO 2 -eq emissions data gaps for unit processes present in the Canadian fuel life cycles but also possess shallower hidden topologies. These salient results are ascribed to the impacts of the input layer on the optimal network. In particular, input layers comprised of categorical and numerical features lead to enhancement of network prediction and/or shallower hidden topology for optimal ANNs. Taken altogether, the present study proposes, implements, and validates a systematic framework to tractably design optimal ANNs for predicting data to fill data gaps in LCI databases. In addition, this study quantifies average contributions of each unit process present in the Canadian fuel life cycles, including fossil-based and renewable pathways, to total CO 2 -eq emissions, which is of particular interest in cut-off analysis. [Display omitted] • Contributions of unit processes of fuel pathways to net emissions are determined. • A tractable approach is proposed to optimally design ANNs to fill data gaps. • The combination of categorical and numerical attributes improves ANNs' optimality. • Emission data gaps are estimated using optimal Artificial Neural Networks (ANNs). [ABSTRACT FROM AUTHOR]