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In Canada, greenhouse gas (GHG) emissions continue to remain high despite the federal government's commitment to reduce GHG emissions. Environmental policy across Canadian provinces has largely lacked consistency with some provinces implementing targeted policy such as carbon taxes while others have strongly resisted the federal government's attempts to encourage such policy. This study examines the impact of environmental policy on GHG gas emissions across Canadian provinces with policy uniquely measured by public and business sector spending on environmental protection as well as notable province-specific policies. An extended STIRPAT model is specified with the inclusion of proxies for environmental policy using panel data from Canada's ten provinces from 1995 to 2019. The cointegration and the error correction modelling approaches are employed to estimate the non-stationary panel data. A pooled mean group estimation is applied to provide short run and long run coefficients. The findings indicate that although per capita GDP is the main driver of GHG emissions in Canada, environmental policy has a significant mitigating effect, as shown by the negative association between emissions and private and public sector spending on environmental protection in the long run. Furthermore, provinces with targeted policies, such as Ontario's coal phase-out and British Columbia's carbon tax, experienced significant emissions reductions. The theoretical implication is the successful addition of policy variables to the STIRPAT model, providing the model a new application for future research. [Display omitted] • Assess the impact of environmental protection policies on per capita greenhouse gas emissions in Canada. • Utilize public and private expenditures on environmental protection, along with provincial policies. • Extend the STIRPAT model to incorporate environmental protection policies in an empirical model. • Demonstrate a significant negative relationship between environmental protection policies and GHG emissions. • Offer insights with implications for global environmental sustainability efforts. [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]