Rapid urbanization and human consumption are continuously threatening the balances of natural environmental systems. This study investigated the increasing stress on the natural environment from household consumption at the neighborhood level. We collected and analyzed household-level data of Ward 24 of the Khulna City Corporation (KCC) area to quantify and represent household consumption and entrenching stresses on the natural environment. We followed the component and direct method to determine the ecological footprint (demand). We also derived the biocapacity (supply) from the available bioproductive lands of the study area. Thus, the gap between demand and supply was identified and represented as a stress area through a Geographic Information System (GIS) mapping technique. We found that the per capita ecological footprint accounts for Ward 24 were about 0.7161 gha/capita for the year 2015. Moreover, the biocapacity for the same year was determined as 0.0144 gha/capita for Ward 24. The ecological demand for the household-based consumption of Ward 24 exceeded its ecological capacity by 49.73 times. We found that Ward 24 would require an area that was 162 times larger in order to support the present level of resource demand and waste sequestration. These study findings can play an essential role in policy formulation, ensuring the practices of environmental justice at the local scale.

We developed a machine learning based surrogate model to identify sustainability pathways through rapid scenario generation and defined the safe operating space for achieving them via scenario discovery. We trained a surrogate model to replicate the Land‐Use Trade‐Offs integrated model of the Australian land system. Latin hypercube sampling was used to create many scenarios exploring the impact of uncertainties in key drivers including future socio‐economic development, climate change mitigation, and agricultural productivity at a granular level. Economic and environmental impacts were evaluated against nationally downscaled SDG targets. Scenario discovery revealed new pathways to achieving five SDG targets for 2050 which required crop yield increases above 1.78 times, a carbon price above 100 AU$ tCO₂⁻¹, a >9% biodiversity levy on carbon plantings, and carefully regulated land‐use policy. Machine learning based surrogate modeling teamed with scenario discovery revealed the policy and scenario settings required for a more sustainable future for the Australian land sector.

The study was conducted to present the development stress on surrounding natural environment associated with rapid urban growth. At the beginning of the study, physical situation of urban expansion of Khulna city is revealed. The urban growth scenario is derived through satellite image analysis of different years on land use land cover (LULC) changes around the city. Image analysis is comprised of supervised classification methods to derive the final results of LULC change. Hereafter, the climatic structure is assessed (changes in Built-up footprint) in relation with changing nature of LULC as well as urban microclimatic conditions. A popular method to detect changing urban microclimatic condition is analysing the changes in the urban microclimatic temperature generally known as Urban Heat Island (UHI) effect. The changing nature of LULC and UHI expresses the emergence of increasing stress of urban areas on the surrounding natural environment. As a result in the study LULC and UHI is assessed to examine the ongoing increasing stress. The result shows the overall increase in the built-up area from the year 1989 to 2014 is about 75.74% (1802.382 acres). The water body cover is reduced up to 153.209 acres and a variation in the fallow land cover is 48.43% in the temporal variation of 25 years. The zonal variation in surface temperature varies 29.64° – 40.71° in the city area and 28.77° – 40.16° outer skirts of the city area with in the time span of 1989 to 2014. Finally, it is seen that the ecological footprint due to built-up footprint is also increasing from the year 1989 to 2014. Journal of Bangladesh Institute of Planners, Vol. 8, Dec 2015, pp. 187-194

We developed a machine learning based meta-model to identify sustainability pathways through rapid scenario generation and defined the safe operating space for achieving them via scenario discovery. We trained a meta-model to replicate the Land-Use Trade-Offs integrated model of the Australian land system. Latin hypercube sampling was used to create many scenarios exploring the impact of uncertainties in key drivers including future socio-economic development, climate change mitigation, and agricultural productivity at a granular level. Economic and environmental impacts were evaluated against nationally downscaled SDG targets. Scenario discovery revealed new pathways to achieving five SDG targets for 2050 which required crop yield increases above 1.78 times, a carbon price above 100 AU$ tCO2-1, a > 9% biodiversity levy on carbon plantings, and carefully regulated land-use policy. Machine learning based meta-modelling teamed with scenario discovery revealed the policy and scenario settings required for a sustainable future for the Australian land sector.