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Background: The last two decades have witnessed a considerable growth in the literature focusing on LGBTQ2S+ employment, labour market inequality, and income. During the same period, Canada has emerged as a trailblazer in employment protections for both sexual and gender minorities. Unfortunately, the Canadian literature on LGBTQ2S+ employment outcomes and experiences is disperse and underdeveloped. Objective: This paper brings together this disperse research and provides the first systematic review of Canada’s LGBTQ2S+ employment and earnings literature. Methods: We start with a systematic review and thematic synthesis of the broadly defined literature on LGBTQ2S+ poverty in Canada. We use a thematic synthesis to isolate the LGBTQ2S+ literature on employment, labour market inequality, and earnings. Our search of electronic databases took place in April 2018 and was updated in January 2019. Results: A total of 532 abstracts and full texts were screened by reviewers, which resulted in 84 articles included in our final sample. These articles were then sorted by keywords and those pertaining to employment, labour market inequality, and income (n = 31) were included in this analysis. While estimates of sexual minority wage gaps vary depending on the data and methods used, most studies have found wage penalties for gay men and wage premiums for lesbians, relative to their heterosexual counterparts. The literature on bisexual employment is particularly scant but finds that bisexual men and women also earn less than their heterosexual counterparts. Research on the subjective workplace experiences of LGBTQ2S+ individuals find unique challenges, barriers and, at times, exclusion from the Canadian labour market. Conclusions and implications: While the literature on LGBTQ2S+ employment outcomes and experiences in Canada is growing, much is left unknown. The principal limitation for researchers continues to be the dearth of population-based surveys that include questions on sexual orientation, gender identity, and relevant employment characteristics. To date, few studies have explored employment outcomes or the subjective workplace experiences of bisexuals, transgender, two-spirit or other gender minority peoples. [ABSTRACT FROM AUTHOR]

Floating Catchment Area (FCA) methods are a popular tool to investigate accessibility to public facilities, in particular health care services. FCA approaches are attractive because, unlike other accessibility measures, they take into account the potential for congestion of facilities. This is done by 1) considering the population within the catchment area of a facility to calculate a variable that measures level of service, and then 2) aggregating the level of service by population centers subject to catchment area constraints. In this paper we discuss an effect of FCA approaches, an artifact that we term demand and level of service inflation. These artifacts are present in previous implementations of FCA methods. We argue that inflation makes interpretation of estimates of accessibility difficult, which has possible deleterious consequences for decision making. Next, we propose a simple and intuitive approach to proportionally allocate demandand and level of service in FCA calculations. The approach is based on a standardization of the impedance matrix, similar to approaches popular in the spatial statistics and econometrics literature. The result is a more intiuitive measure of accessibility that 1) provides a local version of the provider-to-population ratio; and 2) preserves the level of demand and the level of supply in a system. We illustrate the relevant issues with some examples, and then empirically by means of a case study of accessibility to family physicians in the Hamilton Census Metropolitan Area (CMA), in Ontario, Canada. Results indicate that demand and supply inflation/deflation affect the interpretation of accessibility analysis using existing FCA methods, and that the proposed adjustment can lead to more intuitive results. [ABSTRACT FROM AUTHOR]

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]

The objective of this paper is to fill a gap in the literature on internationalization, in relation to the absence of objective and measurable performance indicators for the process of how firms sequentially enter external markets. To that end, this research develops a quantitative tool for use as a performance indicator of gradualness for firms entering external markets at a sectoral level. The performance indicator is based on firms’ export volumes, number of years operating in the export market, geographic areas targeted for export and when exports began to each area. The indicator is tested empirically in the wine sector. The main contribution of this study is the creation of a reliable international priority index, which can serve more widely as a valuable tool because of its potential use in other industry sectors and geographic areas, and which would allow the analysis of how geographically differentiated internationalization strategies develop. [ABSTRACT FROM AUTHOR]

While climate scientists have developed high resolution data sets on the distribution of climate risks, we still lack comparable data on the local distribution of public climate change opinions. This paper provides the first effort to estimate local climate and energy opinion variability outside the United States. Using a multi-level regression and post-stratification (MRP) approach, we estimate opinion in federal electoral districts and provinces. We demonstrate that a majority of the Canadian public consistently believes that climate change is happening. Belief in climate change’s causes varies geographically, with more people attributing it to human activity in urban as opposed to rural areas. Most prominently, we find majority support for carbon cap and trade policy in every province and district. By contrast, support for carbon taxation is more heterogeneous. Compared to the distribution of US climate opinions, Canadians believe climate change is happening at higher levels. This new opinion data set will support climate policy analysis and climate policy decision making at national, provincial and local levels. [ABSTRACT FROM AUTHOR]