Given the absence of literature investigating the validity of such estimations, this dissertation applies a baseline-and-credit methodology to recalculate the avoided emissions from Vestas’ and Siemens Gamesa’s respective wind turbines. The results are compared to prior claims by Vestas and Siemens, which finds that these companies had reported underestimated avoided emissions.
The disparity is principally attributed to the application of average electricity generation emissions factors by Vestas and Siemens. This supports existing literature that marginal emissions factors should be applied to calculate more accurate emissions estimates, as seen in this work. Recommendations for how Vestas and Siemens can improve future avoided emissions calculations are provided, as well as insight on the role of policy in expediting the development of best methodological practice.
Climate change has been proven as an existential global threat to society, and the international scientific community has agreed that to avoid the most catastrophic impacts, meaningful economy-wide decarbonisation efforts must occur. The resulting global impetus towards achieving deep-emission reductions has catalysed climate-orientated investment into companies contributing to sustainable, economic growth. This is commonly tracked with carbon metrics that measure a company’s emissions along its value chain or from year-over-year emission reductions. To supplement the release of accurate and transparent information, governing institutions have increased sustainability disclosure requirements to ensure companies report the full climate impact of their operations. This is evident in the recent creation of the EU CSRD that encompasses the double materiality concept and by the recently proposed amendments to the SEC climate regulations. However, this dissertation questions if current frameworks and disclosure requirements appropriately capture everything that stakeholders need to make informed decisions about a company’s climate performance.
Interest in this topic was largely motivated by the recent removal of Tesla from the S&P 500 ESG Index. The S&P Dow Jones Indices commented on this exclusion, noting it was largely attributable to Tesla’s poor carbon strategy. Meanwhile, the oil and gas multinational, Exxon Mobil, remained in the top ten largest constituents of the ESG Index post-rebalance. This raises an important question: what is missing from current sustainability criteria that allows an innately emissive oil and gas giant to outscore an electric vehicle company that offers legitimate climate solutions and enables economy-wide emission reductions? This dissertation presents avoided emissions (hereinafter AE) as one answer and argues it provides an essential piece of additional information that can quantifiably demonstrate a company’s positive contributions towards mitigating climate change. However, current reporting standards are not emphasising its inclusion, partly due to concerns about the accuracy of calculating AE. This dissertation, therefore, explores limitations in current practice and the feasibility of enhancing AE calculations based on best methodological practice.
This dissertation contributes to and supports existing literature in several ways, but most notably addresses a key knowledge gap regarding a lack of research assessing the accuracy of AE claims by companies. This dissertation, therefore, set out to investigate the potential inaccuracy of AE estimates by applying a shadow-accounting technique to recalculate the AE of two companies, namely Vestas and Siemens Gamesa, based on their respective installation of wind turbines in 2021. A baseline-and-credit methodology is applied in this paper that follows the GHG Protocol’s Policy and Action Standard. Under the guidance of the Standard, this paper utilises a consequential approach to GHG accounting that includes estimating baseline and intervention emissions over a time series to determine lifetime AE.
The AE claims by Vestas and Siemens are compared to the results of this dissertation and are both found to be underestimated, which is largely due to the application of AEFs instead of MEFs. The AE in the best and worst-case scenario of this paper were respectively larger than Vestas’ estimate by a factor of 1.7 and 1.6., while the AE in the best and worst-case scenario were only 1.4 and 1.05 times greater compared to Siemens’ estimate. Critically, however, this dissertation notes this difference would be greater if Vestas and Siemens had included other GHG effects in their assessments, such as an emissions penalty. Moreover, this paper notes that the smaller variation between AE in this work and Siemen’s estimation is attributable to the electricity generation emission factor applied by Siemens, which represents global average fossil fuel emissions and closely resembles the MEFs applied in this work.
Further, based on observed variations in calculated AE, this paper draws principles from best methodological practice and asserts that Vestas and Siemens, as well as all companies calculating AE, can increase the accuracy of future estimates by incorporating the following four techniques:
- Applying region-specific MEFs for each country with installations
- Utilising a time-series analysis
- Stating the methodology, assumptions and all key activity parameters applied in the assessment
- Accounting for all major GHG effects from the installation of wind turbines
Additionally, the results also demonstrate the need to enhance current AE practice. The dissertation asserts that if policy and frameworks are updated to include AE, it would require such emissions to be calculated in a credible, transparent, and accurate manner, thus catalysing the development of better AE methodological practice. Potential avenues for such enhancement include amending the double materiality concept, EU CSRD, EU Taxonomy, SBTi, International Financial Institutions Technical Working Group on GHG Accounting, and the GHG Protocol.
 Blood, D. and Levina, I. (2020). Measuring Portfolio Alignment: Assessing the position of companies and portfolios on the path to net zero.
 Boffo, R., Marshall, C. and Patalano, R. (2020). ESG Investing: Environmental Pillar Scoring and Reporting. S&P Dow Jones Indices (2020). Index Carbon Metrics Explained. PwC (2022). How to measure your ESG performance.
 European Commission. (2019). Guidelines on Reporting Climate-Related Information.
 US Securities And Exchange Commission. (2022). SEC proposes rules to enhance and standardise climate-related disclosures for investors.
 WRI (2014). Greenhouse Gas Protocol Policy and Action Standard. An accounting and reporting standard for estimating the greenhouse gas effects of policies and actions.
07 November 2022