Of the G7 countries, Canada, the United Kingdom, and the United States were forecast to have a constant population ******** until 2050. In Japan, Germany, and Italy, the population is forecast to constantly ******* due to aging populations and falling fertility rates. In France, the population was first expected to decline by 2048.

In 2048, the population in Manitoba is projected to reach about 1.84 million people. This is compared to a population of 1.46 million people in 2024.

Projected population according to various scenarios, age groups and gender, Canada, provinces and territories.

Years of life gained after 30 y.o. in Quebec, Canada, 2012 to 2050 (in thousands), under two CVD reduction scenarios.

Life expectancy at age 65 for Quebec, Canada, 2012 to 2050 (years for the baseline; additional years compared to baseline for other scenarios).

Climate and land use/land cover change are expected to influence the stationary nonbreeding distributions of 4 Nearctic–Neotropical migrant bird species experiencing population declines: Cardellina canadensis (Canada Warbler), Setophaga cerulea (Cerulean Warbler), Vermivora chrysoptera (Golden-winged Warbler), and Hylocichla mustelina (Wood Thrush). Understanding how and where these species’ distributions shift in response to environmental drivers is critical to inform conservation planning in the Neotropics. For each species, we quantified current (2012 to 2021) and projected future (2050) suitable climatic and land use/land cover conditions as components of stationary nonbreeding distributions. Multi-source occurrence data were used in an ensemble modeling approach with covariates from 3 global coupled climate models (CMCC-ESM2, FIO-ESM-2-0, MIROC-ES2L) and 2 shared socioeconomic pathways (SSP2-RCP4.5, SSP5-RCP8.5) to predict distributions in response to varying climatic and land use/land cover conditions. Our findings suggest that distribution contraction, upslope elevational shifts in suitable conditions, and limited shifts in latitude and longitude will occur in 3 of 4 species. Cardellina canadensis and S. cerulea are expected to experience a moderate distribution contraction (7% to 29% and 19% to 43%, respectively), primarily in response to expected temperature changes. The V. chrysoptera distribution was modeled by sex, and females and males were projected to experience a major distribution contraction (56% to 79% loss in suitable conditions for females, 46% to 65% for males), accompanied by shifts in peak densities to higher elevations with minimal changes in the upper elevation limit. Expected changes in precipitation had the greatest effect on V. chrysoptera. Hylocichla mustelina experienced the smallest distribution change, consistent with the species’ flexibility in habitat selection and broader elevational range. We recommend defining priority areas for conservation as those where suitable conditions are expected to remain or arise in the next 25 years. For V. chrysoptera in particular, it is urgent to ensure that mid-elevation forests in Costa Rica and Honduras are adequately managed and protected. Methods Bird Occurrence Data We obtained current (2012 to 2021) bird occurrence data containing only Neotropical presence records from eBird (accessed in January 2023; Sullivan et al. 2009) and supplemented with species-specific georeferenced occurrence datasets to bolster presence record sample sizes and the spatial representation of records. 2012 to 2021 was identified as the “current” timeframe to capitalize on increased user engagement with eBird and align with prior research (Hightower et al. 2023). Date ranges for the stationary nonbreeding period were defined using expert input (N. Bayly, E. Cohen, I. Davidson, A. González, J. Hightower, J. L. Larkin, E. Montenegro, D. Raybuck, A. Roth, C. Rushing, C. Stanley, R. L. M. Stewart, and S. Wilson personal communication) to assess frequency distributions of daily presence records in the current timeframe. Experts emphasized date selection 2 weeks before or after most birds initiated or completed migration through the Neotropical flyway to minimize the signal from areas used during migration (C. canadensis: November 16 to March 17, S. cerulea: October 25 to March 10, V. chrysoptera: October 28 to March 31, H. mustelina: November 5 to March 28). eBird occurrence data were filtered in R with the auk package (Strimas-Mackey et al. 2018, R Core Team 2022) to select presence records collected using “traveling,” “stationary,” and “incidental” protocols with observer effort distances ≤2 km (Medina et al. 2023). Duplicate records, as well as outlier records from areas outside of known stationary nonbreeding locations (Fink et al. 2022), were removed. We added the species-specific datasets to filtered eBird datasets and resampled all presence records to a 1-km2 resolution (Fick and Hijmans 2017). The final dataset included 5,765 unique presence records for the current timeframe (C. canadensis: n = 1,586, S. cerulea: n = 546, V. chrysoptera ♀: n = 192, V. chrysoptera ♂: n = 283, H. mustelina: n = 3,158). We partitioned V. chrysoptera records by sex as it is a sexually dimorphic species allowing for possible identification by plumage. The sexes are known to segregate by habitat and elevation resulting in conservation planning bias in favor of higher elevations for males (Bennett et al. 2019). Thus, we removed records that did not specify sex (n = 1,860). Climate and Land Use/Land Cover Data We downloaded historical (1970 to 2000 averages) monthly climatic and bioclimatic raster datasets at a 30-arc second (~1-km2) spatial resolution from the WorldClim data repository (Fick and Hijmans 2017). Historical climate data aided predictions of current climatic and LULC conditions with documented ecological patterns (Acevedo et al. 2012). Bioclimatic covariates were selected based on literature review, expert input, principal component analysis (PCA) correlation circles, and predictor contribution percentages. PCA correlation circles and predictor contribution percentages were used to identify multicollinearities among bioclimatic covariates (Fick and Hijmans 2017, Guisan et al. 2017). We selected bioclimatic covariates for ensemble modeling (Thuiller et al. 2009, Guisan et al. 2017) that were above the expected average contribution percentage, a product of the covariate eigenvalues (Dray et al. 2023). Further, elevation and slope were derived from a ~1-km2 digital elevation model (Fick and Hijmans 2017) in ArcGIS Pro 3.0.0 (Esri Inc. 2022). Global LULC projections based on simulations of 16 plant functional types (i.e., forest, grassland, and cropland) and urban expansion (see figure 2 in Chen et al. 2022) were included to simulate effects of LULC change. We used the resulting covariates in ensemble modeling to capture species responses from the current timeframe based on historical climate (C. canadensis: n = 23 covariates, S. cerulea: n = 24, ♀ V. chrysoptera: n = 23, ♂ V. chrysoptera: n = 23, H. mustelina: n = 26). For future (2050) climatic and LULC conditions, we obtained climatic datasets (2041 to 2060 averages) identical to the historical dataset from WorldClim for 3 individual GCCMs: the CMCC-ESM2 (Cherchi et al. 2019), the FIO-ESM-2-0 (Bao et al. 2020), and the MIROC-ES2L (Hajima et al. 2020). For each GCCM, we used 2 2041 to 2060 SSP-RCP scenarios which represent independent climatic futures: SSP2-RCP4.5 and SSP5-RCP8.5 (Fick and Hijmans 2017). SSP2-RCP4.5 (hereinafter, best-case) represents a future where climate-smart practices increase and nonrenewable resource use declines (Van Vuuren et al. 2011, Riahi et al. 2017). In contrast, SSP5-RCP8.5 (hereinafter, worst-case) represents a future where technological advances and increased fossil fuel extraction lead to maximum global emissions (Van Vuuren et al. 2011, Riahi et al. 2017). The spatial extent used to extract climate and LULC data was identical among scenarios to project current species responses onto future climates (Guisan et al. 2017, Hightower et al. 2023). To accommodate potential distribution shifts in latitude and longitude by 2050, we initially included areas in the periphery of current stationary nonbreeding locations (Fink et al. 2022) for the spatial extents of each focal species. Preliminary analyses resulted in extralimital projections of species occurrence when suitable climatic and LULC conditions occurred well outside the current distribution of each species. To limit these projections, we defined the northern and southern termini of each spatial extent with a combination of the unique presence records and known current stationary nonbreeding locations (Fink et al. 2022). We applied a spatial constraint that prevented extralimital projections of occurrence that exceeded 200 km from known occurrences, but we filled gaps in presence record coverage where species are known to occupy (Fink et al. 2022). The 200-km distance was selected to accommodate reasonable dispersal distances for each species in the current timeframe (Barbet-Massin et al. 2012, Freeman et al. 2018). Ensemble Modeling and Projected Distributions We used an ensemble modeling framework within the R package biomod2 (Thuiller et al. 2009, Guisan et al. 2017) to model current and future projections of suitable climatic and LULC conditions for the 4 focal bird species (V. chrysoptera ♀ and ♂ separately). To address multicollinearity and biases in ecological studies (Fotheringham and Oshan 2016), we incorporated 4 successful modeling algorithms (Qiao et al. 2015, Guisan et al. 2017): generalized linear model (GLM), generalized boosting model (GBM), generalized additive model (GAM), and random forest (RF). Default settings in biomod2 were kept for GBM and RF, while settings were modified for GLM and GAM: We set the relationship between presence records and covariates to a polynomial function for GLM (Hightower et al. 2023), while the GAM modeling function was set to GAM_mgcv (Wood 2017). Predictive performance of individual models. For each modeling algorithm plus 1 full model (models that are calibrated and validated over an entire pseudo-absence dataset), we used 5K-fold cross-validations with 70% and 30% of the occurrence records allocated for training and validations, respectively (Guisan et al. 2017). We evaluated modeling algorithm performances using TSS and receiver operating characteristic (ROC) metrics, where TSS values > 0.6 are good and ROC values > 0.9 are excellent (Thuiller et al. 2009, Guisan et al. 2017). We randomly generated pseudo-absence points in the modeling framework due to limited true-absence records in the Neotropics during the current timeframe. The number of pseudo-absences and presence records were roughly equal to aid in decision tree dynamics for GBM and RF

This statistic shows the median age of the world population from 1950 to 2100. By 2100, the global median age is projected to be 41.9 years of age.

The statistic shows the gross domestic product (GDP) per capita in Canada from 1987 to 2023, with projections up until 2029. In 2023, the gross domestic product per capita in Canada was around 53,607.4 U.S. dollars. Canada's economy GDP per capita is a measurement often used to determine economic growth and potential increases in productivity and is calculated by taking the GDP and dividing it by the total population in the country. In 2014, Canada had one of the largest GDP per capita values in the world, a value that has grown continuously since 2010 after experiencing a slight downturn due to the financial crisis of 2008. Canada is seen as one of the premier countries in the world, particularly due to its strong economy and healthy international relations, most notably with the United States. Canada and the United States have political, social and economical similarities that further strengthen their relationship. The United States was and continues to be Canada’s primary and most important trade partner and vice versa. Canada’s economy is partly supported by its exports, most notably crude oil, which was the country’s largest export category. Canada was also one of the world’s leading oil exporters in 2013, exporting more than the United States. Additionally, Canada was also a major exporter of goods such as motor vehicles and mechanical appliances, which subsequently ranked the country as one of the world’s top export countries in 2013.

Kanadas Bevölkerungswachstum setzt sich fort und das Land erreicht 2023 eine Gesamtbevölkerung von geschätzt rund 39,3 Millionen Einwohnern. Damit hat sich die Einwohnerzahl Kanadas binnen eines Jahres um rund 480.000 Menschen erhöht. Für das Jahr 2024 wird eine Gesamtbevölkerung Kanadas von rund 39,7 Millionen Menschen prognostiziert. Die Geburtenrate ist dabei nur durchschnittlich hoch - das Wachstum resultiert vor allem aus der Einwanderung. Hohe Lebensqualität in Kanada zieht Zuwanderer an Kanada ist seit jeher ein klassisches Einwanderungsland und bereits seit 1971 ein offiziell multikulturelles Land – der Multikulturalismus ist gesetzlich in der Verfassung verankert. Die Lebenszufriedenheit der Kanadier ist sehr hoch und das Land wird international geachtet, was sich auch im positiven Einfluss Kanadas auf die Weltgemeinschaft ausdrückt. Nur zwei von vielen Gründen warum Kanada für Zuwanderer so attraktiv ist:

Platz 1 im Ranking der beliebtesten Länder Platz 1 im Ranking der höchsten Lebensqualität Platz 5 im Ranking der kinderfreundlichsten Länder Platz 4 im Ranking der beliebtesten Länder für Frauen

Natürliche Bevölkerungsentwicklung oder Migration? Grundsätzlich kann bei der Bevölkerungsentwicklung zwischen dem natürlichen Bevölkerungswachstum und der Zuwachsrate (allgemeines Bevölkerungswachstum) unterschieden werden:natürliches Bevölkerungswachstum

Das natürliche Bevölkerungswachstum ergibt sich aus der Verrechnung von Geburten und Todesfällen.

Zuwachsrate

Bei der Zuwachsrate wird das natürliche Bevölkerungswachstum mit dem Migrationssaldo, also dem Saldo aus Immigration (Einwanderung) und Emigration (Auswanderung) verrechnet.

Zusammenhang

Industrieländer benötigen im Allgemeinen eine Geburtenrate (Fertilitätsrate) von durchschnittlich 2,1 Kindern je Frau, um den Bestand der Population konstant zu halten (Bestandserhaltungsniveau). Für ein positives Bevölkerungswachstum wird dementsprechend eine höhere Geburtenrate oder ein positiver Migrationssaldo benötigt.

Global municipal solid waste generation is projected to grow by over 75 percent by 2050, reaching nearly 3.8 billion metric tons, in a scenario where no urgent action is taken. In 2020, more than two billion metric tons of municipal solid waste were generated worldwide. Waste management disparities In 2022, New Zealand and Canada topped the list of OECD countries by volume of municipal waste sent to landfills per capita, at over 600 kilograms. In stark contrast, countries like Denmark and Germany send less than 10 kilograms per person to landfills annually. These two European countries also have some of the highest municipal waste recycling rates worldwide. Access to waste collection services A significant portion of the world's population lacks access to basic waste collection, with nearly three billion people not having access to these services in 2020. Collection rates were particularly low in Central and South Asia, as well as Sub-Saharan Africa, at less than 40 percent. This gap in service provision presents both a challenge and an opportunity for waste management companies to expand their operations and improve global waste management practices.