Controversies Advantages Research Methodologies

Additionality evaluation approach has been one of the contentious processes as well as the most fundamental in the issues of research, for example, the carbon offset market. The process answers simple questions such as, if the activity did not take place, would the reduction of emission have happened holding everything else constant? Or would a particular project have occurred anyway? (Ferris, 2015). If the evaluator comes up with a yes answer then such a project is not additional. Additionality is the property of a given activity is additional. That is a proposed activity becomes additional if the established policy interventions are believed to cause the activity to take place. Thus the process highlights that in order to demonstrate it, one has to provide counterfactual evidence of what had occurred if a particular activity had not happened.

On the other hand traditional approaches such as success stories and descriptive claim otherwise. Success stories describe the evaluation tools that professionals in diverse disciplines have used for some time, thus, providing useful accomplishments in each field (Celhar & Fairhurst, 2017). Descriptive statistics get used to define the basic features of the data under study (Vetter, 2017). They provide simple summarise regarding the sample together with the measures, as well as graphic analysis. Hence, they help in forming the foundation of quantitative analysis of data.

From the evidence based on researchers using the methodology, among the advantages of the process includes that it makes the evidence-based approaches possible especially when the study concerns climate investments (Marino, Lhuillery, Parrotta & Sala, 2016). Secondly, the process can get used by researchers, funders as well as evaluation practitioners when it includes both ex-ante and ex-post basis. It helps them increase investments effectiveness together with target areas where they lack commercial investment. Additionally, they help in harnessing markets in which to search for new low-cost mitigating opportunities so as to promote innovation in a given area.

Additionality approach has its disadvantages. Among which being that it is easy to understand, however, difficult to apply as compared to the traditional approaches of evaluation. No matter how the approach appears objective as well as quantitative, any test carried out will tend to create some type of false positives (Howard, 2015). That is, the project may appear as if it is additional in spite of it not being so. Also, it can create some false negatives meaning the projects might appear non-additional when in real sense they are in fact. More so, the additionality test can turn out to be more cumbersome than the traditional approaches like success stories and descriptive approaches.

Additionally, it can be time-consuming as well as an expensive process of evaluation. Additionality operates in lack of knowledge concerning the counterfactual outcome because the scenario has not been treated before (Howard, 2015). Thus, the process lacks comparability in it, meaning the researcher will not have enough data. More so the process relies on a single data source (VCPE index). Hence, when it is delayed or not published it renders the metric unviable. In conclusion, the traditional approaches tramp additionality in effectiveness.

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References

• Celhar, T., & Fairhurst, A.-M. (2017). Modelling Clinical Systemic Lupus Erythematosus: Similarities, Differences and Success Stories. Rheumatology, 56, i88–i99.
• Ferris, J. (2015). Additionality and Forest Conservation Regulation for Residential Development. SSRN Electronic Journal. doi: 10.2139/ssrn.2761935.
• Howard, G. (2015). Additionality Violations in Payment for Ecosystem Service Programs: Experimental Evidence. SSRN Electronic Journal. doi: 10.2139/ssrn.2697569.
• Marino, M., Lhuillery, S., Parrotta, P., & Sala, D. (2016). Additionality or crowding-out? An overall evaluation of public R&D subsidy on private R&D expenditure. Research Policy, 45(9), 1715-1730. doi: 10.1016/j.respol.2016.04.009.
• Vetter, T. (2017). Descriptive Statistics. Anesthesia & Analgesia, 125(5), 1797-1802. doi: 10.1213/ane.0000000000002471.