Big Data and Energy Poverty Alleviation

Framework for inferring empirical causal graphs from binary data to support multidimensional poverty analysis

Poverty is one of the fundamental issues that mankind faces. To solve poverty issues, one needs to know how severe the issue is. The Multidimensional Poverty Index (MPI) is a well-known approach that is used to measure a degree of poverty issues in a given area. To compute MPI, it requires information of MPI indicators, which are binary variables collecting by surveys, that represent different aspects of poverty such as lacking of education, health, living conditions, etc. Inferring impacts of MPI indicators on MPI index can be solved by using traditional regression methods. However, it is not obvious that whether solving one MPI indicator might resolve or cause more issues in other MPI indicators and there is no framework dedicating to infer empirical causal relations among MPI indicators. In this work, we propose a framework to infer causal relations on binary variables in poverty surveys. Our approach performed better than baseline methods in simulated datasets that we know ground truth as well as correctly found a causal relation in the Twin births dataset. In Thailand poverty survey dataset, the framework found a causal relation between smoking and alcohol drinking issues. We provide R CRAN package'BiCausality' that can be used in any binary variables beyond the poverty analysis context.

Climate Change Impact on Energy Poverty and Energy Efficiency in the Public Housing Building Stock of Bari, Italy

The public housing stock across the European Union is generally constituted of old buildings (built prior to 1980) with high energy demand and indoor thermal comfort issues, which could be exacerbated by climate change. The aim of this paper was to quantify the impact of climate change on the energy demand of the public housing building stock. A neighbourhood located in Bari (south Italy) is considered as representative of a common construction typology of late 1970s in Italy. Energy models were created and calibrated with real-time data collected from utilities’ bills. The results showed a medium to strong correlation between age and energy consumption (r = 0.358), but no evident correlation between the number of tenants and energy consumption, although a significantly low energy consumption was found in apartments occupied by more than five tenants. An energy penalty of about 7 kWh/m2 of heating energy consumption for every 10 years of increase in the average age of tenants was calculated. Moreover, the impact of future weather scenarios on energy consumptions was analysed and an average annual energy penalty of 0.3 kWh/m2 was found.

Spatial Analysis of Inequality in Thailand: Applications of Satellite Data and Spatial Statistics/Econometrics

To formulate and monitor the progress of development policies, acquiring data with sufficient spatiotemporal details is inevitable. With the increasing availability of open remote-sensing data and open-source software packages, this research suggested the novelty integration of satellite data and spatial analytical methods, enabling a timely and costless framework for assessing the nationwide socioeconomic condition. Specifically, the spatial statistical and spatial econometrical methods were applied to geospatial data to identify the clustering patterns and the localized associations of inequality in Thailand. The spatial statistical results showed that Bangkok and its vicinity had been a cluster of high socioeconomic conditions, representing the spatial inequality of development. In addition, results of the spatial econometrical models showed that the satellite-based indicators could identify the socioeconomic condition (with p-value < 0.010 and R-squared ranging between 0.345 and 0.657). Inequality indicators (i.e., Gini, Thiel and Atkinson) were then constructed by using survey-based and satellite-based data, informing that spatial inequality has been slowly declining. These findings recommended the new establishment of polycentric growth poles that offer economic opportunities and reduce spatial inequality. In addition, in accordance with Sustainable Development Goal 10 (reduced inequalities), this analytical framework can be applied to country-specific implications along with the global scale extensions.

The Use of Big Data via 5G to Alleviate Symptoms of Acute Stress Disorder Caused by Quarantine Measures

This article investigates the role of Big Data in situations of psychological stress such as during the recent pandemic caused by the COVID-19 health crisis. Quarantine measures, which are necessary to mitigate pandemic risk, are causing severe stress symptoms to the human body including mental health. We highlight the most common impact factors and the uncertainty connected with COVID-19, quarantine measures, and the role of Big Data, namely, how Big Data can help alleviate or mitigate these effects by comparing the status quo of current technology capabilities with the potential effects of an increase of digitalization on mental health. We find that, while Big Data helps in the pre-assessment of potentially endangered persons, it also proves to be an efficient tool in alleviating the negative psychological effects of quarantine. We find evidence of the positive effects of Big Data on human health conditions by assessing the effect of internet use on mental health in 173 countries. We found positive effects in 110 countries with 90 significant results. However, increased use of digital media and exclusive exposure to digital connectivity causes negative long-term effects such as a decline in social empathy, which creates a form of psychological isolation, causing symptoms of acute stress disorder.

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

The United Nations’ Sustainable Development Goals (SDGs) set out to improve the quality of life of people in developed, emerging, and developing countries by covering social and economic aspects, with a focus on environmental sustainability. At the same time, data-driven technologies influence our lives in all areas and have caused fundamental economical and societal changes. This study presents a comprehensive literature review on how data-driven approaches have enabled or inhibited the successful achievement of the 17 SDGs to date. Our findings show that data-driven analytics and tools contribute to achieving the 17 SDGs, e.g., by making information more reliable, supporting better-informed decision-making, implementing data-based policies, prioritizing actions, and optimizing the allocation of resources. Based on a qualitative content analysis, results were aggregated into a conceptual framework, including the following categories: (1) uses of data-driven methods (e.g., monitoring, measurement, mapping or modeling, forecasting, risk assessment, and planning purposes), (2) resulting positive effects, (3) arising challenges, and (4) recommendations for action to overcome these challenges. Despite positive effects and versatile applications, problems such as data gaps, data biases, high energy consumption of computational resources, ethical concerns, privacy, ownership, and security issues stand in the way of achieving the 17 SDGs.

Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

The launch of the United Nations (UN) 17 Sustainable Development Goals (SDGs) in 2015 was a historic event, uniting countries around the world around the shared agenda of sustainable development with a more balanced relationship between human beings and the planet. The SDGs affect or impact almost all aspects of life, as indeed does the technological revolution, empowered by Big Data and their related technologies. It is inevitable that these two significant domains and their integration will play central roles in achieving the 2030 Agenda. This research aims to provide a comprehensive overview of how these domains are currently interacting, by illustrating the impact of Big Data on sustainable development in the context of each of the 17 UN SDGs.