The Effect of Population Growth on the Environment: Evidence from European Regions

Weber, Hannes; Sciubba, Jennifer Dabbs

doi:10.1007/s10680-018-9486-0

Cited by 133 publications

(64 citation statements)

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“…Europe is the continent with the lowest percentage of land with stable productivity levels in PAs and with the largest share of protected land with increasing land productivity. These results may be related to the relatively high population density and share of agricultural land use in protected areas, which is higher in Europe than in any other continent, and could explain some of the dynamics observed here [39]. Also, rural land abandonment processes have triggered the expansion of forests and woodlands in mountain areas and former agricultural lands in many European countries, often showing increasing land productivity in these areas as measured through the NDVI [40].…”

Section: Plos Onementioning

confidence: 70%

Land productivity dynamics in and around protected areas globally from 1999 to 2013

et al. 2020

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Tracking changes in total biomass production or land productivity is an essential part of monitoring land transformations and long-term alterations of the health and productive capacity of land that are typically associated with land degradation. Persistent declines in land productivity impact many terrestrial ecosystem services that form the basis for sustainable livelihoods of human communities. Protected areas (PAs) are key to globally conserve biodiversity and ecosystem services that are critical for human well-being, and cover about 15% of the land worldwide. Here we globally assess the trends in land productivity in PAs of at least 10 km 2 and in their unprotected surroundings (10 km buffers) from 1999 to 2013. We quantify the percentage of the protected and unprotected land that shows stable, increasing or decreasing trends in land productivity, quantified as long-term (15 year) changes in above-ground biomass derived from satellite-based observations with a spatial resolution of 1 km. We find that 44% of the land in PAs globally has retained the productivity at stable levels from 1999 to 2013, compared to 42% of stable productivity in the unprotected land around PAs. Persistent increases in productivity are more common in the unprotected lands around PAs (32%) than within PAs (18%) globally, while about 14% of the protected land and 12% of the unprotected land around PAs has experienced declines in land productivity. Oceania has the highest percentage of land with stable productivity in PAs (57%), whereas Europe has the lowest percentage (38%) and also the largest share of protected land with increasing land productivity (32%). We discuss the observed differences between PAs and unprotected lands, and between different parts of the world, in relation to different types and levels of human activities and their impact on land productivity. Our assessment of land productivity dynamics helps to characterise the state, pressures and changes in and around protected areas globally. Further research may focus on more detailed analyses to disentangle the relative contribution of specific drivers (from climate change to land use change) and their interaction with land productivity dynamics and potential land degradation in different regions of the world.

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Section: Plos Onementioning

confidence: 70%

Land productivity dynamics in and around protected areas globally from 1999 to 2013

et al. 2020

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“…Globally, the UAE has one of the highest per capita water consumption rates in the world, at approximately 500 L per day [1]. Groundwater and water supply can be influenced by population growth and land use /land cover (LULC) changes [2][3][4][5]. The increase in LULC is expected to extensively increase the rate of evaporation and depletion in groundwater level and quality [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Land Use/Land Cover Changes Impact on Groundwater Level and Quality in the Northern Part of the United Arab Emirates

2020

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This study aims to develop an integrated approach for mapping and monitoring land use/land cover (LULC) changes and to investigate the impacts of LULC changes and population growth on groundwater level and quality using Landsat images and hydrological information in a Geographic information system (GIS) environment. All Landsat images (1990, 2000, 2010, and 2018) were classified using a support vector machine (SVM) and spectral analysis mapper (SAM) classifiers. The result of validation metrics, including precision, recall, and F1, indicated that the SVM classier has a better performance than SAM. The obtained LULC maps have an overall accuracy of more than 90%. Each pair of enhanced LULC maps (1990–2000, 2000–2010, 2010–2018, and 1990–2018) were used as input data for an image difference algorithm to monitor LULC changes. Maps of change detection were then imported into a GIS environment and spatially correlated against the spatiotemporal maps of groundwater level and groundwater quality. The results also show that the approximate built-up area increased from 227.26 km2 (1.39%) to 869.77 km2 (7.41%), while vegetated areas (farmlands, parks and gardens) increased from about 76.70 km2 (0.65%) to 290.70 km2 (2.47%). The observed changes in LULC are highly linked to the depletion in groundwater level and quality across the study area from the Oman Mountains to the coastal areas.

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“…Of course, it is still uncertain whether this trend will continue over the coming years. But the current trend already has considerable consequences in many areas, including a severe shortage of housing and child care, overcrowded public and private transport in large as well as smaller cities, and a considerable backlash against efforts to protect the environment and the fi ght against climate change (Weber/Sciubba 2019). In the past, many cities got rid of public housing projects or cut educa-tion budgets because it was thought they were not needed in a future characterised by population ageing and decline.…”

Section: Introductionmentioning

confidence: 99%

How Well Can the Migration Component of Regional Population Change be Predicted? A Machine Learning Approach Applied to German Municipalities

Weber

2020

CPoS

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For several decades, demographic forecasts had predicted that the majority of Germany’s cities and rural areas would experience population decline in the early 21st century. Instead, recent trends show a growing population size in three out of every four German districts. As a result, there are currently severe shortages of housing and childcare in regions that were projected to decline but have instead grown in recent years. Other regions, by contrast, continue to lose young people in particular. Most of these differences between regions stem from within-country as well as international migration. An important question for both regional demographic research as well as local policy-makers is thus how well net migration rates in cities and rural districts can be predicted several years into the future. In this study, we develop models that predict migration (both within-country as well as international migration) at the level of municipalities for two demographic groups, namely young people aged 18 to 24 years, and families (people aged 30 to 49 years and underage children). We collect data on economic, demographic and other characteristics such as distances to large cities or universities for around 3,000 German municipalities (Gemeinden). The model is trained on a subset of these data from the period 2005-2009 and predicts net migration rates among young people on an unseen test dataset in the future (i.e. for the period 2011-2015). The results show that the model can predict future net migration by young people aged 18 to 24 years reasonably well (R² > 0.5), although there were quite significant changes during the period under study, for example refugee immigration to Germany. Family migration, on the other hand, cannot be predicted equally well (R² = 0.25). Some important lessons emerge concerning the predictability of regional and international migration and the usefulness of demographic forecasts for local policy-makers.

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The Effect of Population Growth on the Environment: Evidence from European Regions

Cited by 133 publications

References 50 publications

Land productivity dynamics in and around protected areas globally from 1999 to 2013

Land productivity dynamics in and around protected areas globally from 1999 to 2013

Land Use/Land Cover Changes Impact on Groundwater Level and Quality in the Northern Part of the United Arab Emirates

How Well Can the Migration Component of Regional Population Change be Predicted? A Machine Learning Approach Applied to German Municipalities

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