The municipal solid waste (MSW) remains a great challenge in most cities of developing countries, as the majority of the generated waste is either not collected or is dumped in open uncontrolled non-engineered landfill sites, creating significant pollution due to the leakage of landfill leachate in the surrounding environment. In developing countries, a complete transition to a zero-landfill scenario is less likely to happen in the near future due to various socio-economic challenges. Therefore, the existing landfills in developing countries need holistic waste management thinking with more efforts on waste to energy conversions. This study highlights the challenges with existing MSW management practices of Curitiba, Brazil, and suggests some holistic and sustainable landfill management techniques. This is accomplished through the (i) identification of the suitable sites for setting up transfer stations (TSs), (ii) route optimization for MSW transportation, and (iii) analysis of the life expectancy of the existing landfill with waste valorization techniques for enhancing circularity of MSW of the city. The study has identified six potential TSs, making use of various geological criteria and constraints as suggested by the United States Environmental Protection Agency using GIS-based spatial analysis, which could save fuel cost of approximately 1.5 million Brazilian Real (BRL) per year for the solid waste transportation (from the source to the landfill site). This research has also made a value addition in this specific field with the preparation of a digitized road network map of the study region. Further, the sensitivity-based scenario analysis highlights that the lifespan of the existing landfill (until 2030) might be extended to 2058 if the city achieves the targeted recycling rate of 85% compared with the current rate of 23%. The results would be useful for policy-makers to adopt the crucial MSW scenario to achieve a circular economy in the waste management of the city of Curitiba.

The Energy Performance Certificate (EPC) is an important information tool to improve the energy performance (EP) of buildings. However, establishing the EP of building is tedious, time-consuming, and numerous input parameters are required in its estimation. However, the usefulness of EPC for the implementation of customized solutions by the supply-side actors require that EPCs are available for all buildings, easily accessible, credible, and recent. However, this is not the case at present. This could be addressed by employing remote sensing dataset along with GIS based spatial analysis techniques. In the present study, the spatial regression analysis technique is implemented in identifying the spatial relation between the input variables and the EP of selected 4541 buildings within Växjö municipality, Sweden.

The input variables used in the study include the land surface temperature (LST) maps of summer and spring of 2020 derived through the thermal band of Landsat 8 satellite data, built-up and openland neighbourhood maps prepared from the land use/land cover map 2020 of the study region. Building topology including year of construction, type, category, and complexity of buildings are also used to identify the relation between the input variables and the EP of those selected buildings. Results of spatial regression analysis reveal a significant positive relation between the LST and EP of buildings (regression co-efficient are 0.86 and 0.95 in spring and summer respectively).

The stronger correlation in summer could be because of the availability of higher intensity of solar radiation which gets absorbed by the built-up regions. Results suggest that the LST maps derived from satellite imageries could provide information on the EP of buildings. This could be beneficial to local decision makers and policy regulators in identifying the buildings with lower EP with better accuracy with less dependence on EPC data which are sometimes not available or not updated. The results could also be beneficial to investment bankers, real estate companies during the purchase and sale of a building. Policy makers and renovation companies could get benefited with the results in preliminary identification of the potential hotspots for district energy renovation where the EP of buildings is poorer. This could help achieve the goal of sustainable urban planning targeting energy reduction, climate adaptation, through implementation of effective energy management strategies in the building sector.

Urbanization, as a result of anthropogenic activities, reduces the vegetated and green spaces and thereby increases the impervious surfaces in cities. This in turn increases the surface temperature of cities when compared to the rural regions resulting in the formation of urban heat island. Especially, in under-developed countries, it is very crucial to obtain timely and accurate information on the urban trend and its development due to the higher increasing rate of population growth and lack of infrastructural facilities and regulations to mitigate the adverse consequences of urbanization. The current study analyzes the urban development of Greater Accra, Ghana using Landsat 7 dataset acquired in 2002, 2013, and 2020. Further, the influence of urban growth on the land surface temperature (LST) and land cover variability (LCV) indices including NDVI (Normalized Difference Vegetation Index), NDBI(Normalized Difference Built-Up Index), and NDWI (Normalized Difference Water Index) is identified during the study periods. Results suggest that the area of built-up quadrupled between 2002 and 2020 in the study region.Shannon’s entropy-based analysis suggests that the urban development in the region is heterogeneous or dispersive in nature. Further, the regression analysis highlights a strong positive relation between LST and NDBI (0.755) and a negative relation is found associated between LST and NDVI (0.4417) and LST and NDWI (0.76). Results of the study could be useful to design sustainable urban socio-economic and environmental planning policies in the study region.

Human activities are responsible for almost 75% of the land cover changes depleting the natural resources globally. These land cover changes with decreasing vegetation and water bodies increase the heat emissions from the land surface thereby influencing the climate changes regionally and globally. This paper highlights the interaction and interlinkages between the land use changes due to human activities (Sustainable Development Goal (SDG) 15) and its consequences on climate changes (SDG 13) using spatial analysis techniques. In this context, the climate change of Växjö Municipality, Sweden resulting from the urban development is demonstrated through LST (Land Surface Temperature), NDVI (Normalised Difference Vegetation Index), NDBI (Normalised Difference Built-Up Index) and land cover changes as indicators through Landsat 8 data of 2014, 2016, 2018 and 2020. The land cover maps prepared through Support Vector Machine algorithm indicate that the area of built-up had doubled during the study periods with decreasing openlands. The LST maps prepared from the thermal bands of the Landsat 8 data showed an increase in the mean surface temperature from 7.3℃ to 11.1℃ between 2014 and 2020. The study also aims to study the seasonal variations in the relationship between LST, NDVI and NDBI by making use of Landsat 8 dataset acquired during the spring, summer, and autumn seasons of 2019. Results suggest a strong positive relationship between LST and NDBI (0.74) whereas a negative relationship is found associated between LST and NDVI (0.65) and between NBDI and NDVI (0.71). Further the land cover and LST maps of 2014 and 2020 are used in the simulation of urban and LST maps of 2050 through Cellular Automata model to highlight the impact of urban development on the climate changes of Växjö Municipality. The simulation result predicts that the built-up area of 2020 might quadruple in 2050. The simulation analysis also predicted an increase in LST with increasing urbanization in the study region. This study emphasises that the land cover changes in the process of urban development is also a contributing factor for climate change in the study region which is evident from the increase in mean surface temperature (3.8°C) from 2014 to 2020.

Municipal solid waste (MSW) management has emerged as one of the major environmental challenges globally. The consequences of inappropriate waste management are manifold and the trend would continue if immediate interventions are not taken for its reversion amid rapid urbanization and current consumption patterns of individuals. The concept of circular economy (CE) can contribute to a paradigm shift in the transformation of the traditional linear approach that does not favour reuse, recycle, recovery concept. Modern and proven waste management practices with collection systems, recycling facilities, sanitary landfills, and waste-to-energy (WtE) and nutrient recovery offer opportunities to improve urban environment through the valorization of waste and by-products in a CE. This study scrutinizes the existing literature on the assessment of circularity and helps to develop a unified circularity framework in the management of MSW in cities. Key aspects such as tools for measuring circularity, nexus and trade-offs, and conditions in promoting CE are discussed. Finally, this paper elucidates the need for circularity, including enablers and inhibitors for promoting circularity in the management of MSW with a case study in the city of Curitiba, Brazil.

Uncontrolled open dumping and burning of municipality solid waste (MSW) has resulted in soil, water, and air pollution in many urban cities in India. Landfills are the most common cost-effective solution for MSW management in many developing countries like India. However, the identification of suitable landfill sites always remains a challenging task as it involves selection of several environmental criteria set by the local authorities. The objective of this study is to identify the most potential landfill sites proposed by the Government in Bengaluru Metropolitan Region, Karnataka state, India using Geographic Information System enabled Analytical Hierarchy Process based multi-criteria evaluation technique. Several criteria and constraints as recommended by the local authorities along with the proximity to the solid waste processing plants are used to identify the potential landfill sites in the study region. The study identified three highly suitable sites (Neraluru, Gudhatti, Madivala) for landfills which are not only environmentally sustainable but also economically attractive as they are closer to the solid waste processing plants minimizing the transportation cost involved in the disposal of solid waste from the source to the final disposal sites in the study region.

Chennai is one of the most densely populated cities in India facing challenges in shifting the city to metropolitan or mega city in the last two decades with continuing agglomeration. To model the growth of Chennai city, we have used cellular automata-based urban growth models based on the historical datasets. In the present study, urban growth of Chennai Metropolitan Area (CMA) was predicted for the year 2017 based on 2010 and 2013 dataset and Chennai city master plan using neural-network-coupled agent-based cellular automata (NNACA) model. Eight different agents of urbanization including transportation, hotspots, and industries were used in the prediction modeling. On validating the 2017 predicted outputs, NNACA model with hotspots proved to be better (hits: 498.52 km2) than that of without hotspots (hits: 488.31 km2). Out of the total eight agents of urbanization, the most influencing agent of urbanization of 2017 was identified to be the neighborhood of ‘Existing built-up of 2013’ using ‘sensitivity analysis’. Further, the urban sprawl of CMA for 2010, 2013 and 2017 was measured through Shannon’s entropy. The study area was divided into five directional and distance-based zones with the State Secretariat as the center. Entropy values suggest the need for more careful planning for further development in the southern region of CMA which has undergone congested urban growth while urbanization is dispersed in the northern part of the study region which can be thought for future urban developments.

Urban Growth Models (UGMs) are very essential for a sustainable development of a city as they predict the future urbanization based on the presentscenario. Neural Network based Cellular Automata models have proved topredict the urban growth more close to reality. Recently, deep learning basedtechniques are being used for the prediction of urban growth. In this currentstudy, urban growth of Chennai Metropolitan Area (CMA) of 2017 was predicted using Neural Network based Cellular Automata (NN-CA) model andDeep belief based Cellular Automata (DB-CA) model using 2010 and 2013urban maps. Since the study area experienced congested type of urbangrowth, “Existing Built-Up” of 2013 alone was used as the agent of urbanization to predict urban growth in 2017. Upon validating, DB-CA model provedto be the better model, as it predicted 524.14 km2 of the study area as urbanwith higher accuracy (kappa co-efficient: 0.73) when compared to NN-CAmodel which predicted only 502.42 km2 as urban (kappa co-efficient: 0.71),while the observed urban cover of CMA in 2017 was 572.11 km2. This studyalso aimed at analyzing the effects of different types of neighbourhood configurations (Rectangular: 3 × 3, 5 × 5, 7 × 7 and Circular: 3 × 3) on the prediction output based on DB-CA model. To understand the direction and type ofthe urban growth, the study area was divided into five distance based zoneswith the State Secretariat as the center and entropy values were calculated forthe zones. Results reveal that Chennai Corporation and its periphery experience congested urbanization whereas areas away from the Corporationboundary follow dispersed type of urban growth in 2017.

Unplanned urbanization would pose serious threats to both environment and mankind. Hence, urbangrowth model (UGM) becomes mandatory to predict future growth of a city. In the current study, urbangrowth of Sriperumbudur Taluk, Tamil Nadu, India was predicted using three types of Cellular Automata(CA) model namely Traditional CA (TCA) model, Agents based Cellular Automata (ACA) Model and NeuralNetwork coupled Agents- based Cellular Automata (NNACA) model. The urban maps of the study regionfor the years 2009, 2013 and 2016 along with the influencing agents of urbanization namely transporta-tion, industries, elevation and also hotspot locations based on the Government policy were used in themodeling. Analytical Hierarchical Process (AHP) technique was adopted to estimate the weights of theagents for suitability map preparation in ACA model. On validating 2016 predicted outputs, NNACAmodel proved to be the better urban model (kappa coefficient - 0.72) when compared to TCA and ACAmodels (kappa coefficient - 0.6 each). Shannon’s entropy measure revealed that the urbanization is con-centrated in the north-east direction and it is predicted to have an urban sprawl area of 157 km2in 2020using NNACA model while the observed urbanization is 113 km2of the area in 2016.