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Sustainable Development in Sub-Saharan African Countries through Utilization of Big Data in Life Cycle Assessment – a Case Study of Mozambique
The concept of Sustainable Development was globally popularized by the publication of the report “Our Common future” by the World Commission on Environment and Development in 1987 (Drexhage & Murphy, 2010; Spangenberg, 2000). Sustainable Development is defined as “development which meets the needs of the present without compromising the ability of future generations to meet their own needs”(World Commission on Environment and Development, 1987) and is often related to 3 primary components also referred as “triple-bottom-line”: economic, social and environmental components (Sarkis, Meade, & Presley, 2008). The definition of Sustainable Development highlights the respect to the needs of current and future generations (Rauschmayer, Omann, & Frühmann, 2012). The Sub-Saharan Africa region is economically growing very fast. In 2013 the economic growth was expected to pick 4.9 % and 5 ½ in 2014. Despite this rapid growth, the Sub-Saharan Africa is still the world’s poorest region (International Monetary Fund, 2014).
In recent past years, some countries in the region have discovered considerable quantities of natural resources (example of Mozambique and Tanzania) which leads to increase in investments (Cho & Tien, 2014) and establishment of new industries. The environmental problems associated with the activities of those new industries and natural resources exploration are critical and not well addressed with the use of advanced ICT based tools.
Mwambazambi (2011) highlights that “Africa’s natural environment and its resources are an irreplaceable part of the African heritage and constitute a capital of vital importance to the continent and mankind as whole as well as the ever-growing importance of natural resources from the economic, social, cultural and environmental points of view”. There is the need for sustainable exploration of the natural resources.
The governments in the Sub-Saharan Africa face challenges related to environmental protection (Mwambazambi, 2011). This is because there are challenges with the implementation of the right strategies for environmental management, lack of compliance to policies and regulations, inadequate environmental, and challenges to build strong environmental institutions.
Melville (2010), states that “organizational adoption of sustainability strategies necessitates new data regarding environmental impacts, new information about what causes and effects, and knowledge sharing about what works, what doesn’t and why ”.
Life Cycle Assessment is crucial in resource management and combined with Big Data can bring valuable benefits, although it is important to consider the challenges of Big Data in Life Cycle Assessment (Cooper, Noon, Jones, Kahn, & Arbuckle, 2013).
Nowadays, environmental data is collected from different sources, containing different structures and presented in different types. This data is growing very fast and is collected from different sources: sensors data, geospatial, retrieved from data repositories or generated from other systems (Günther, 1997; Villars, Olofson, & Eastwood, 2011). The conventional systems used in the Sub-Saharan African region are not able to handle the complexity and volume of the new data sets.
The benefits and success of Big Data are undoubtable, many companies are adopting Big Data, although there remain many technical challenges that must be addressed to fully realize the potential of Big Data (Jagadish et al., 2014). It is crucial to address carefully the technical challenges in order to extract efficiently value from large and complex data sets (Katal, Wazid, & Goudar, 2013).
The technology that has emerged and is most commonly used as the way to tackle the challenges of Big Data is Apache Hadoop (Villars et al., 2011). Besides being Open Source, Hadoop does not require expensive and highly reliable hardware infrastructure. It can be implemented using commodity servers, what reduces the cost of acquisition of new hardware when adopting it (White, 2009).
The adoption of new technologies in the Sub- Saharan African countries is very slow and there are few researches in the technology adoption for the sub-Saharan African context (Musa, Meso, & Mbarika, 2005).
This project seeks to contribute to the knowledge gab in Sub-Saharan African technological adoption and also foster the implementation of Big Data in Life Cycle Assessment.