Urbanization as Socioenvironmental Succession: The Case of Hazardous Industrial Site Accumulation

Modeling spatio-temporal assessment of land use land cover of Lahore and its impact on land surface temperature using multi-spectral remote sensing data

Urban sprawl, also widely known as urbanization, is one of the significant problems in the world. This research aims to assess and predict the urban growth and impact on land surface temperature (LST) of Lahore as well as land use and land cover (LULC) with a cellular automata Markov chain (CA-Markov chain). LULC and LST distributions were mapped using Landsat (5, 7, and 8) data from 1990, 2004, and 2018. Long-term changes to the landscape were simulated using a CA-Markov model at 14-year intervals from 2018 to 2046. Results indicate that the built-up area was increased from 342.54 (18.41%) to 720.31 (38.71%) km². Meanwhile, barren land, water, and vegetation area was decreased from 728.63 (39.16%) to 544.83 (29.28%) km², from 64.85 (3.49%) to 34.78 (1.87%) km², and from 724.53 (38.94%) to 560.63 (30.13%) km², respectively. In addition, urban index, a non-vegetation index, accurately predicted LST, showing the maximum correlation R² = 0.87 with respect to retrieved LST. According to CA-Markov chain analysis, we can predict the growth of built-up area from 830.22 to 955.53 km² between 2032 and 2046, based on the development from 1990 to 2018. As urban index as the predictor anticipated that the LST 20-23 °C and 24-27 °C, regions would all decline in coverage from 5.30 to 4.79% and 15.79 to 13.77% in 2032 and 2046, while the temperature 36-39 °C regions would all grow in coverage from 15.60 to 17.21% of the city. Our results indicate severe conditions, and the authorities should consider some strategies to mitigate this problem. These findings are significant for the planning and development division to ensure the long-term usage of land resources for urbanization expansion projects in the future.

AnthroShift in a warming world

Thirty years after the UN Conference on Environment and Development created the UN Framework Convention on Climate Change, efforts to respond to the issue continue to be insufficient to meet the challenges of the climate crisis. This perspective builds on the experience of society’s responses to the COVID-19 pandemic to understand what is needed to get to meaningful climate action. It applies the framework of the AnthroShift to assess how transformational social change is likely to emerge. The paper concludes by determining that the most plausible pathway to an effective social response to the climate crisis would be driven by civil society. However, the level of mass mobilization needed is only possible if society is experiencing large-scale and sustained levels of risk that have tangible long-term consequences in terms of social cost to people and property.

Environmental Inequality and Residential Sorting in Germany: A Spatial Time-Series Analysis of the Demographic Consequences of Industrial Sites

Previous research has shown that low-income households bear a higher exposure to environmental pollution than high-income households. Some scholars have argued that selective siting of industrial facilities accounts for such environmental inequality, while others have argued that those citizens who can afford to move out of polluted regions do so, and the socioeconomically disadvantaged are sorted into polluted areas. Yet empirical evidence regarding the processes of environmental inequality is not conclusive. We build on an original data set that includes annual georeferenced data of 6,570 highly polluting industrial facilities in Germany from 2008 to 2017 and validate the fluctuation in facilities with geographical land-use data. We then connect the facilities to income and demographic data for 4,455 municipalities and investigate sociodemographic changes before and after the appearance of new facilities. Spatial models are employed to measure local relative changes, and fixed-effects individual slopes estimators are used to account for selection on economic trajectories. Results provide only limited support for the selective siting thesis but show that an area's average income decreases after the appearance of new industrial facilities, thereby resonating with the selective migration hypothesis. In contrast, facility closure does not attract, or reattract, more affluent households.