Carbon pricing is a crucial tool in the efforts to address and mitigate climate change. In 2014, only 12% of carbon emissions fell under carbon pricing at USD7 per tonne; now, about 23% of greenhouse gas emissions are priced at USD32 per tonne. However, the regressive nature of carbon pricing can disproportionately affect lower-income populations and potentially reduce political support and public awareness. This raises questions about the dynamics of increasing carbon pricing rates and the optimal balance between inequality, emissions, and economic growth. We find that a critical level of carbon pricing can induce tipping points; incentivizing technological adoption and fuel switching behaviour of energy producers. By combining carbon pricing with redistributive measures at these tipping points, we demonstrate that emissions can be rapidly reduced while maintaining economic growth and decreasing inequality. We also introduce real-time metrics for detecting sector-specific tipping points, without requiring counterfactual analysis. Our research has important implications for the ongoing debate around the relationship between economic growth, inequality, and environmental sustainability.

In 2010, an estimated 860 million people were living in slums worldwide, with around 60 million added to the slum population between 2000 and 2010. In 2011, 200 million people in urban Indian households were considered to live in slums. In order to address and create slum development programmes and poverty alleviation methods, it is necessary to understand the needs of these communities. Therefore, we require data with high granularity in the Indian context. Unfortunately, there is a paucity of highly granular data at the level of individual slums. We collected the data presented in this paper in partnership with the slum dwellers in order to overcome the challenges such as validity and efficacy of self reported data. Our survey of Bangalore covered 36 slums across the city. The slums were chosen based on stratification criteria, which included geographical location of the slum, whether the slum was resettled or rehabilitated, notification status of the slum, the size of the slum and the religious profile. This paper describes the relational model of the slum dataset, the variables in the dataset, the variables constructed for analysis and the issues identified with the dataset. The data collected includes around 267,894 data points spread over 242 questions for 1,107 households. The dataset can facilitate interdisciplinary research on spatial and temporal dynamics of urban poverty and well-being in the context of rapid urbanization of cities in developing countries.

Today, over half of the world’s population lives in urban areas and it is projected that, by 2050, two out of three people will live in a city. This increased rural–urban migration, coupled with housing poverty, has led to the growth and formation of informal settlements, commonly known as slums. In Mexico, 25% of the urban population now live in informal settlements with varying degrees of deprivation. Although some informal neighbourhoods have contributed to the upward mobility of the inhabitants, the majority still lack basic services. Mexico City and the conurbation around it form a mega city of 21million people that has been growing in a manner qualified as ‘highly unproductive, (that) deepens inequality, raises pollution levels’ (available at: https://www.smartcitiesdive.com/ex/sustainablecitiescollective/making-way-urban-reform-mexico/176466/ ) and contains the largest slum in the world: Neza-Chalco-Izta. Urban reforms are now aiming to improve the conditions in these slums and therefore it is very important to have reliable tools to measure the changes that are underway. In this paper, we use exploratory factor analysis to define an index of shelter deprivation in Mexico City, namely the Slum Severity Index (SSI), based on the UN-HABITAT’s definition of slum. We apply this novel approach to the Census survey of Mexico and measure the shelter deprivation levels of households from 1990 to 2010. The analysis highlights high variability in housing conditions within Mexico City. We find that the SSI decreased significantly between 1990 and 2000 as a result of several policy reforms but increased between 2000 and 2010. We also show correlations of the SSI with other social factors such as education, health and fertility. We present a validation of the SSI using Grey Level Co-occurrence Matrix (GLCM) features extracted from Very-High Resolution (VHR) remote-sensed satellite images. Finally, we show that the SSI can present a cardinally meaningful assessment of the extent of deprivation compared with a similar index defined by Connolly (Connolly P (2009) Observing the evolution of irregular settlements: Mexico city’s colonias populares, 1990 to 2005. International Development Planning Review 31: 1–35) that studies shelter deprivation in Mexico.

Slums, characterized by sub-standard housing conditions, are a common in fast growing Asian cities. However, reliable and up-to-date information on their locations and development dynamics is scarce. Despite numerous studies, the task of delineating slum areas remains a challenge and no general agreement exists about the most suitable method for detecting or assessing detection performance. In this paper, standard computer vision methods – Bag of Visual Words framework and Speeded-Up Robust Features have been applied for image-based classification of slum and non-slum areas in Kalyan and Bangalore, India, using very high resolution RGB images. To delineate slum areas, image segmentation is performed as pixel-level classification for three classes: Slums, Built-up and Non-Built-up. For each of the three classes, image tiles were randomly selected using ground truth observations. A multi-class support vector machine classifier has been trained on 80% of the tiles and the remaining 20% were used for testing. The final image segmentation has been obtained by classification of every 10th pixel followed by a majority filtering assigning classes to all remaining pixels. The results demonstrate the ability of the method to map slums with very different visual characteristics in two very different Indian cities.