The sharing economy appears repeatedly in academic literature as a solution to tackle some of the sustainability challenges society is facing. However, few studies quantify impacts of the sharing economy on cities due to the lack of a methodological approaches that allow a systematic and standardized measurement. Therefore, there is need to develop a method to understand how the sharing economy is influencing the sustainability of cities and their development.
This is a complex task that can be approached in different ways. In order to explore the different methodologies we can use for this purpose, I reviewed literature where they have measured the impacts of sharing economy and study cases that present interesting methodological approaches. Among the methods that are suitable to model impacts are Life-Cycle Assessment (LCA), Multi-Regional Input and Output (MRIO) and hybrid models. Each of them presents strengths and weaknesses that need to be consider when selecting the method to model.
In order to decide which method is the most adequate to use, the researcher has to consider the level at which impacts are going to be assessed, what aspects of sustainability are going to be measured and how comprehensive the study should be. Each of these key aspects can be interpreted as followed:
Impacts can be assessed at a macro, meso and micro level. When the objective of the study is quantifying the impacts of a specific Urban Sharing Organization (USO), LCA provides with a suitable approach. For example, with LCA one could calculate the emissions that have been saved due to the service provided by a bike sharing company. On the other hand, if the aim is to assess impacts at a city level the most suitable approaches are MRIO or hybrid models. In this case, emissions changes in all the sectors of the economy can be analyzed.
Depending on the sustainability impacts to be measured the method requires to have certain attributes in order to produce more accurate results. When measuring the impacts of material efficiency policies, such as car sharing, a model capable of capturing stocks might generate more accurate results than one that neglects them. For this, a hybrid model (Material Flow Analysis (MFA) and an MRIO) is a good option.
The methods have different system boundaries or levels of aggregation. In the end, these will determine how comprehensive the results are and which type of analysis can be performed. This is the case when the objective of the study is to identify hotspots in a supply chain of a USO; in this instance, an LCA is a suitable method. If the aim is to identify hotspots at an international level, MRIO can be adequate for this purpose.
Once the method is selected, the next step is to get ahold of the data to model. This can pose limitations because the data might not be available to measure certain sustainability impacts or sectors. In addition, the data can be aggregated at a higher level than needed. All these aspects need to be considered.
Regarding our particular objective to measure the impacts of sharing economy at a city level, the best option seems to be to develop a hybrid model that combines LCA or MFA with MRIO. With MRIO analysis, the impacts at a city level can be captured while keeping a system perspective. The method that is going to be mixed with depends on the sector of the sharing economy that is under analysis and the impact to be measured. What is clear is that there are different possibilities to address this challenge and, throughout the process, assumptions need to be made. However, the results of this type of research can shed light over how sharing economy influences cities, giving us the knowledge to determines in which cases the sharing economy may contribute to more sustainable consumption in cities.