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Infections with antimicrobial resistant pathogens are a major threat to human and animal health worldwide. Further, reduction of livestock-associated antimicrobial use (AMU) is often identified as an area of focus. Selective dry cow therapy (DCT) warrants consideration as an important way to decrease AMU on Canadian dairy farms. In addition, teat sealants (TS) are a nonantimicrobial alternative for prevention of intramammary infection during the dry period. Therefore, objectives of this study were to determine how antimicrobials and TS are used at dry-off on Canadian dairy farms to determine selective DCT uptake and enacted selection protocols. It was expected that these data will provide a baseline understanding of DCT practices and highlight areas for future intervention to further reduce AMU. An observational study was conducted utilizing 2 in-person questionnaires conducted between July 2019 and September 2021 on 144 participating dairy farms in 5 Canadian provinces (British Columbia = 30, Alberta = 30, Ontario = 31, Québec = 29, and Nova Scotia = 24). Overall, 45 farms (31%) reported adopting selective DCT, 95 (66%) enacted blanket DCT, and 4 (3%) did not provide antimicrobial DCT. Farms enacting selective DCT had approximately 50% less intramammary antimicrobials used at dry-off compared with blanket DCT farms. Cow somatic cell count history was the most common criterion for selective DCT decision-making, followed by previous clinical mastitis history, bacteriological culture, and milk production. A slight majority of farms (56%) applied TS to all cows at dry-off, whereas 17 farms (12%) used TS selectively, and 46 farms (32%) did not use TS. Larger herds more often used TS, and farms with an automatic milking system more often used TS selectively than applied to all cows. Results highlighted the variability in antimicrobial treatment and TS use protocols at dry-off on Canadian dairy farms, and the potential for further antimicrobial reduction with increased adoption of selective DCT. [ABSTRACT FROM AUTHOR]

Unlike other high-income countries, Canada has no national policy for drugs treating rare diseases (orphan drugs). Nevertheless, in 2022, the Canadian government committed to creating a national strategy to make access to these drugs more consistent. Our aim was to study whether recommendations made by the Canadian Agency for Drugs and Technology in Health (CADTH) translated into coverage decisions for orphan drugs in Ontario, the largest Canadian province. This study is the first to look at this question for orphan drugs, which are at the center of policy attention. We included 155 orphan drug-indication pairs approved and marketed in Canada between October 2002 and April 2022. Cohen's kappa was used to test the agreement across health technology assessment (HTA) recommendations and coverage decisions in Ontario. Logistic regression was used to test which factors, relevant to decision-makers, might be associated with funding in Ontario. We found only fair agreement between CADTH's recommendations and coverage decisions in Ontario. Although a positive and statistically significant association between favorable HTA recommendations and coverage was found, more than half of the drugs with a negative HTA recommendation were available in Ontario, predominately through specialized funds. Successful pan-Canadian pricing negotiations were a strong predictor of coverage in Ontario. Despite efforts to harmonize access to drugs across Canada, considerable room for improvement remains. Introducing a national strategy for orphan drugs could help increase transparency, consistency, promote collaborations, and make access to orphan drugs a national priority. • Recommendations by the Canadian Agency for Drugs and Technology in Health do not always translate to provincial coverage decisions for new drugs. • We explored whether Canadian Agency for Drugs and Technology in Health's recommendations are aligned with coverage decisions for orphan drugs in Ontario. • Negative health technology assessment recommendations did not necessarily translate to no pan-Canadian pricing negotiations. • More than half the drugs with negative health technology assessment recommendations were available in Ontario through specialized funds. • A national strategy for orphan drugs could prioritize access to these treatments at a national level and harmonize access, at least to some extent, across Canada. [ABSTRACT FROM AUTHOR]

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]

Calf rearing practices differ among farms, including feeding and weaning methods. These differences may relate to how dairy producers view these practices and evaluate their own success. The aim of this study was to investigate perspectives of dairy producers on calf rearing, focusing on calf weaning and how they characterized weaning success. We interviewed dairy producers from 16 farms in Western Canada in the following provinces: British Columbia (n = 12), Manitoba (n = 2), and Alberta (n = 2). Participants were asked to describe their heifer calf weaning and rearing practices, and what they viewed as successes and challenges in weaning and rearing calves. Interviews were recorded, transcribed, and subjected to qualitative analysis from which we identified the following 4 major themes: (1) reliance on calf-based measures (e.g., health, growth, and behavior), (2) management factors and personal experiences (e.g., ease, consistency, and habit), (3) environmental factors (e.g., facilities and equipment), and (4) external support (e.g., advice and educational opportunities). These results provided insight into how dairy producers view calf weaning and rearing, and may help inform the design of future research and knowledge transfer projects aimed at improving management practices on dairy farms. [ABSTRACT FROM AUTHOR]

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]

Through survey and focus groups in two provinces in Canada misunderstanding and confusion surrounding Low-Dose Ionizing Radiation (LDIR) is explored specifically surrounding medical procedures, risk, and benefits. Generally people associated the word radiation with harm, but when asked participants were not concerned about LDIR. Approximately equal numbers (40%) thought LDIR was 'difficult' as those that thought it was 'easy' but research results reveal confusion about the definition of and sources of LDIR. Most people believed the benefits of LDIR outweighed the risks. Further, many had inaccurate views of 'high' dose radiation. Scientists and the Canadian regulator were determined to be the most trusted sources of information while elected officials and industry representatives the least trusted. Participants wanted more information on whether LDIR was a problem in Canada, what the risks were associated with it, as well as the applicable protections, rules and regulations. Focus group participants expressed a preference for face-to-face exchange of information, but mass media remains an important source of information as the first-place people check for answers. Future research surrounding behavioural science and LDIR communications, and deep LDIR science communication will be important in addressing this issue. • People do not have high levels of knowledge of low dose ionizing radiation (LDIR). • Most people respond well to the term LDIR and believe benefits outweigh negatives. • Scientists are most trusted communicators followed by the regulator. • Some participants wanted more information surrounding radiation. [ABSTRACT FROM AUTHOR]

The present study empirically investigates whether targeted government spending and environmental policies promote green growth, as measured by greenhouse gas productivity. Government spending for pollution abatement (GEPA) represents non-market environmental policies encompassing both enforcement regulations and stakeholder incentives, while policy dummy represents province specific targeted environmental policies. Using panel data from 1995 to 2020 across ten provinces of Canada, an empirical model based on STIRPAT has been developed and estimated, addressing various econometric issues to ensure the robustness and consistency of the results. The findings confirm that both GEPA and targeted environmental policies are crucial determinants in achieving green growth. Additionally, variables such as business sector expenditures for R&D, the share of renewable energy, per capita GDP, and population also contribute to green growth. These results highlight the importance of the stick-and-carrot approach as a key strategy and policy tool for sustainable development. The implications of these findings extend beyond Canada and hold valuable lessons for emerging and developing countries that predominantly rely on command-and-control regulations without adequate support. To effectively address the challenge of achieving green growth, a coordinated approach that combines targeted government spending and environmental policies, supported by innovation and renewable energy exploration, is essential. Overall, this study's findings have significant implications for environmental sustainability on a global scale. [Display omitted] • Examines the effect of targeted government spending and environmental policies on green growth in Canada. • Uses greenhouse gas productivity as an index of green growth. • Estimates the empirical model developed addressing advanced econometric issues related to panel data for 1995–2020. • Controls renewable energy, innovation, per capita income, population. • Have significant implications for environmental sustainability on a global scale. [ABSTRACT FROM AUTHOR]

• A novel bridge weigh-in-motion method is presented using parametric dynamic bridge-vehicle interaction formulation. • Modal parameters extracted from ambient vibration tests are used to accurately simulate the bridge structures. • The formulation offers superior computational efficiency since it eliminates the need for calibrated finite element models. • The proposed method resulted in accurate estimations even when the vehicle travel path differs from calibrations. • The Proposed method was validated using a full-scale case-study arterial highway bridge in Canada. Dynamic bridge weigh in motion systems use models that can simulate the dynamic behaviour of a bridge subjected to moving traffic. Currently available models use complex and computationally expensive Moving Force Identification (MFI) methods that utilize finite element models to estimate the bridge torsional and transverse dynamic behaviour. This paper presents a novel dynamic parametric BWIM method that utilizes the experimentally estimated modal parameters for simulating the response of a bridge structure to moving loads. The estimated modes of an in-situ structure inherently capture the true bridge behaviour for any generalized geometry, boundary conditions and load position. Therefore, it enables the complex torsional and transverse bending behaviour of the bridge to be captured. In this paper, the mathematical derivation of the analytical model is presented in detail, then model calibration and weighing procedures are outlined, followed by the validation of the proposed method using a full-scale case study arterial highway bridge in the Canadian Province of New Brunswick. The proposed dynamic BWIM method offers a novel solution for the development of real-time BWIM systems that is efficient to calibrate and accurate in vehicle identification. [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]

This study addresses a gap that exists in studying waste business characteristics and the performance of a waste management system (WMS). Temporal and spatial analyses of the WMS in four Western Canadian provinces are systematically assessed over a 16-year period. The average waste business size in Canada ranged from 13.4 to 22.1 employees per business. Jurisdictions with lower disposal rates generally have larger waste companies. An average employee handled 1098 to 1426 t of waste annually. The diversion rates tended to be higher when less waste is managed per employee, suggesting total employee count may be important to improve WMS sustainability in Canada. Waste transfer costs are substantial during the study period, totaled $1276 million annually, or about 40.7% of the total government waste spending. BC only spent an average of 2.5% of the budget on operation of recycling facilities, but has the highest waste diversion rate. Expenditure distribution alone is unable to explain BC's superior waste diversion performance. Capital expenditures have been increasing from $15.7/t to $26.2/t. In this study, a business with more full-time employees correlates well with higher operating revenues. Part time workers were more predominant in SK, and may be responsible for the subpar diversion rate and operating revenue. More research is needed to determine waste business characteristics given increasing interest in circular economy and WMS efficiency. • Waste management systems (WMS) in Canada are examined over a 16-year period • Capital expenditures increased gradually, with an average of $22.7/t • Total employee count may be vital to improve WMS sustainability in Canada • A business with more full-time employees correlates with higher operating revenues • Studies on business characteristics and their effect on WMS performance are limited [ABSTRACT FROM AUTHOR]

The heavy reliance on information and communication technology (ICT) has prompted the importance of cyber security more than ever in history. Although there are various studies that explored the importance of cybersecurity solutions, the impact of production and usage of these solutions on the natural environment and their economic impact has been rarely researched. The objective of this research is to investigate the environmental and economic impact of cybersecurity solutions. An extensive Life Cycle Assessment (LCA) has been performed on three cybersecurity solutions to assess and compare their environmental impact. Based on global warming potential, the embodied carbon footprint was determined. Carbon footprint and operational costs were used to calculate Life Cycle Cost (LCC) analysis and these data were then used to compare the eco-efficiency of the three cybersecurity solutions. This research compares three cybersecurity solutions: Next Generation DataGuard (NGD), Current State of Art Solution (CSAS), and a Generic Cybersecurity Solution (GCS), to identify an economically viable and environmentally friendly cybersecurity solution. The result of the analysis indicated that NGD was approximately 58% and 88% more energy-efficient than CSAS and GCS, respectively. Also, the LCC of NGD was approximately 6%- 12% lower than CSAS and 22%- 40% lower than GCS in selected provinces in Canada. The eco-efficiency of CSAS was approximately 60% less than NGD. Next Generation DataGuard is found to be a successful solution in this research among the three solutions. This study will be useful to researchers, cybersecurity industries, and policymakers to develop informed decisions on cybersecurity solutions. [Display omitted] • The life cycle environmental and economic performance of three cybersecurity solutions were compared. • Next Generation DataGuard (NGD) was approximately 58% and 88% more energy-efficient than current state of art solution and generic cybersecurity solution, respectively. • The NGD displayed superior environmental and economic performance for all Canadian cities. [ABSTRACT FROM AUTHOR]

There is strong evidence for climate change leading to a rise in temperatures and a change in precipitation trends. These environmental changes pose a threat to pavement infrastructure worldwide. Therefore, it is necessary to modify pavement design procedures to consider climate change. In addition, it is necessary to consider suitable pavement materials for future climate. The objective of this study is to develop a framework for selecting an appropriate adaptation strategy to mitigate climate change impact. To fulfill this, the influence of climate change on long-term pavement performance in Canada has been quantified over sixteen Canadian pavement sections located over various provinces in Canada. In addition, the fundamental causes of pavement deterioration due to climate change were determined using ten different climate change models. Various adaptation strategies such as upgrading asphalt binder grade, increasing the thickness of asphaltic concrete layer, increasing the base layer thickness, and using stabilized base layers were analyzed to reduce pavement deterioration and to extend the service life of the pavement. Unlike the other studies, pavement temperatures were determined using Enhanced Integrated Climate Model (EICM) to determine the change in binder grade for the future climate in the adaptation process. The study found that by 2070 all examined locations will require an upgrade in binder grade and the majority will require an upgrade in mixture gradation. Furthermore, the east and west coast will be more vulnerable to climate change and require additional measures in comparison to Central Canada. Newfoundland and Prince Edward Island are the only locations that will need to consider a change in asphalt thickness while British Columbia is the only location eventually requiring a stabilized base. This study emphasizes the necessity of climate change adaptation strategies for Canadian asphaltic concrete pavements. [Display omitted] • Different combinations of adaptation strategies are necessary at each location. • The Canadian coasts generally require more adaptation measures than Central Canada. • By 2070, all Canadian provinces will require an upgrade in asphalt binder. [ABSTRACT FROM AUTHOR]

[Display omitted] • An innovative method for prion recovery from swabs was developed. • Recovery of prions decreased as swab-drying time was increased. • Recovery of CWD prions from stainless steel and glass was approximately 30%. • RT-QuIC enhanced CWD prion detection by 4 orders of magnitude. • Surface-recovered CWD prion was sufficient for efficient RT-QuIC detection. Chronic wasting disease (CWD) has been identified in 30 states in the United States, four provinces in Canada, and recently emerged in Scandinavia. The association of CWD prions with environmental materials such as soil, plants, and surfaces may enhance the persistence of CWD prion infectivity in the environment exacerbating disease transmission. Identifying and quantifying CWD prions in the environment is significant for prion monitoring and disease transmission control. A systematic method for CWD prion quantification from associated environmental materials, however, does not exist. In this study, we developed an innovative method for extracting prions from swabs and recovering CWD prions swabbed from different types of surfaces including glass, stainless steel, and wood. We found that samples dried on swabs were unfavorable for prion extraction, with the greatest prion recovery from wet swabs. Using this swabbing technique, the recovery of CWD prions dried to glass or stainless steel was approximately 30% in most cases, whereas that from wood was undetectable by conventional prion immunodetection techniques. Real-time quake-induced conversion (RT-QuIC) analysis of these same samples resulted in an increase of the detection limit of CWD prions from stainless steel by 4 orders of magnitude. More importantly, the RT-QuIC detection of CWD prions recovered from stainless steel surfaces using this method was similar to the original CWD prion load applied to the surface. This combined surface swabbing and RT-QuIC detection method provides an ultrasensitive means for prion detection across many settings and applications. [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]

• Hydro-Québec can provide annual storage ranging from 3 to 6 TW h for high VRE markets. • Price arbitraging with high VRE markets leads to sustainable changes for hydropower. • Hydropower maintenance flexibility will gain more value with VRE growth. • Revenues from price arbitraging with high VRE markets remain low. Northeastern North America is transitioning rapidly from fossil fuel generation to variable renewable energy (VRE) sources such as wind and solar. Their integration into electricity grids poses challenges in terms of matching supply with demand. Once VRE represent a large share of installed capacity, intermittency and variability of their production will become a major issue. Hydro-Québec, the public electric utility in the Canadian province of Québec, has a large hydropower generation fleet. Its storage capacity enables adjusting hydro production, when the system needs it, to support VRE integration. In this paper, potential impacts of VRE growth within neighbouring grids are simulated through operational production management models. The results show that Hydro-Québec has the potential to facilitate this energy transition but that the current electricity market structure does not provide incentives for an optimal contribution. [ABSTRACT FROM AUTHOR]