Research Question:
Given these mixed findings and gaps, out study tries to answer:
Is there a correlation between country ESG scores and cryptocurrency adoption rates?
Which specific dimensions out of the three (Environment, Social and Governance) drive the most impact towards cryptocurrency adoption? – The existing literature (See Anderson and Robinson 2021) states the importance of the energy intensity (environmental metric) and regulatory clarity (governance dimension). This research paper dissects the ESG scores and looks them at a granular level by incorporating the country wise score on each metric into the study.
What role do social factors such as public sentiment, existence of digital infrastructure and attitude of the government and financial institutions play in the adoption of cryptocurrencies?
Can a high ESG and a high crypto future coexist? –This paper tries to answer the question of whether growing importance of ESG concerns does or does not cause a slowdown in the adoption of cryptocurrency as a mainstream asset class.
3. Methodology
In order to test the relationship between the ESG metrics and crypto adoption rate, we propose to run a panel data regression test, which is laid out below.
𝐶𝑟𝑦𝑝𝑡𝑜𝑑𝑜𝑝𝑡𝑖𝑜𝑛 = 𝛽0 + 𝛽1𝐸𝑛𝑣𝑖𝑡 + 𝛽2𝑆𝑜𝑐𝑖𝑡 + 𝛽3𝐺𝑜𝑣𝑡𝑖𝑡 + 𝛽4𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝐶𝑟𝑦𝑝𝑡𝑜𝐴𝑑𝑜𝑝𝑡𝑖𝑜𝑛𝑖𝑡 + 𝛽5𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛𝑟𝑎𝑡𝑒𝑖𝑡 + 𝛼 + 𝛾𝑡
+ 𝜀𝑖𝑡
𝐶𝑟𝑦𝑝𝑡𝑜𝑑𝑜𝑝𝑡𝑖𝑜𝑛 denotes a dependent variable. Cryptocurrency adoption rate in country i at time t
𝐸𝑛𝑣𝑖𝑡 denotes an independent variable to capture the country level environment score of country i at time t
𝐺𝑜𝑣𝑡𝑖𝑡 denotes an independent variable to capture the country level governance score of country i at time t
𝑆𝑜𝑐𝑖𝑡 denotes an independent variable which captures country level social score of country i at time 𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝐶𝑟𝑦𝑝𝑡𝑜𝐴𝑑𝑜𝑝𝑡𝑖𝑜𝑛𝑖𝑡 an independent variable which captures country level crypto adoption rate in country i at time t
InflationRate denotes an independent variable which captures the country level inflation rate in country I at time t
α denotes a control variable to control for unobserved heterogeneity across countries
γt also denotes a control variable to control for the global shocks or trends that affect all countries similarly
εit denotes the error term
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Assumptions of the model
No Autocorrelation : Error terms should not be correlated across time
Homoscedasity : The variance of error terms is constant across observations.
Linearity : The dependent and independent variables share a linear relationship.
No Multicollinearity : The independent variables should not be highly correlated with each other. Test in order to check multicollinearity between the independent variables (specially E, S and G) is a must.
Exogenity : Independent variables are assumed to be not correlated to the error term.
Limitations of the Model
Measurement Error : Capturing, scoring and ranking each ESG metric (E, S, G) is a subjective task and can differ based on the data provider (Robeco or others) as it an index derived from multiple metrics. The same is applicable for country level crypto adoption scores. Any bias or inaccuracy in the dataset can affect the coefficient estimates.
Omitted Variable Bias : Despite including control variables in the model, there is a possibility of other factors at play like unobserved country level attributes such as cultural attitude which affect the cryptocurrency adoption metric but are hard to capture.
Data Collection and Rationale
We are using the country wise ESG score provided by Robeco. The rationale behind choosing this specific data source is that Robeco captures an overall composite ESG score and also provides granular level data by providing data on each economic, social and governance metric. The ranking covers 150 countries and the methodology that is followed in order to calculate the scores, is the contribution of the country in question towards various sustainable development metrics, from healthcare to democracy.
The country level cryptocurrency adoption rate is sourced from Chainalysis Global Crypto Adoption Index, which calculates adoption scores by aggregating weighted metrics of peer-to-peer exchange trade volume, retail cryptocurrency activity, and transaction volumes relative to the purchasing power parity per capita and internet-using population within countries. It is to be noted that the crypto adoption scores sourced through Chainalysis are already adjusted on a GDP per capita and Purchasing Power Parity (PPP) basis.
Tests such as the Durbin-Wu-Hausman (DWH) are mandatory to check for potential endogeneity between the variables.
Data Availability and Sources
The datasets and data sources used in this dissertation, are from publicly available sources and easily accessible. The sources for the ESG score and cryptocurrency adoption score are of extreme importance in the study and clearly outlined in the references section. The methodology used in calculating these scores is also described in the paper. No confidential information will be required to complete the research paper and test the hypothesis.
Plan for Project Completion:
Date Activity
28th March Submission of Proposal
1st April to 15th April Collection and Analysis of Data
15th April to 30th April Writing Literature Review
1st May to 10th May Submission of literature review and
feedback from supervisor to make any
required amendments.
11th May to 31st May Continuous analysis of data and writing
methodology
1st June to 15th June Running the econometric tests
15th June to 30th June Interpretation of the results from the tests
and submission of the results to the
supervisor
11th July Final Submission
4. References
Alqudah, M., Ferruz, L., Martín, E., Qudah, H. and Hamdan, F. (2023). The Sustainability of
Investing in Cryptocurrencies: A Bibliometric Analysis of Research Trends. International Journal of Financial Studies, [online] 11(3), p.93. doi: https://www.mdpi.com/2227-7072/11/3/93 [Accessed 19 Mar 2025].
Anderson, A. and Robinson, D. T. (2021). Financial literacy in the age of green investment. Review of Finance, 63 (12), 1572–3097.
Chainalysis (2024) 2024 Global Crypto Adoption Index. Available at:
https://www.chainalysis.com/blog/2024-global-crypto-adoption-index (Accessed: 12th March 2025).
CoinDesk (2023) Ethereum tops new crypto ESG ranking, Bitcoin slammed for heavy energy usage. 13 July. Available at: https://www.coindesk.com/markets/2023/07/13/ethereum-tops-newcrypto-esg-ranking-bitcoin-slammed-for-heavy-energy-usage (Accessed: 15 March 2025)
Deloitte (2023). “Deloitte Global Blockchain”. Available at
https://www2.deloitte.com/us/en/insights/topics/understanding-blockchain-potential/globalblockchain-survey.html (Accessed: 18 March 2025).
Dilek, Ş. and Furuncu, Y. (2019) Bitcoin Mining and Its Environmental Effects, İktisadi ve İdari Bilimler Dergisi, 33(1), pp. 91-105.
Howcroft, E. and Lang, H. (2022). Explainer: Ethereum’s energy-saving Merge upgrade. Reuters. [online] 15 Sep. Available at: https://www.reuters.com/technology/ethereums-energy-savingmerge-upgrade-2022-09-15 [Accessed 12 Mar 2025].
Makarov, I. and Schoar, A. (2021) Blockchain Analysis of the Bitcoin Market. NBER Working Paper No. 28940. Available at: https://www.nber.org/system/files/working_papers/w28940/w28940.pdf
(Accessed: 18 March 2025).
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Meloni, L. and Compiègne, V. (2021). Cryptocurrencies and ESG: a contradiction in terms? [online] Candriam. Available at
https://www.candriam.com/siteassets/medias/publications/brochure/researchpapers/cryptocurrencies/cryptocurrencies_2021_en.pdf#:~:text=recorded%20using%20a%20dece
ntralised%20system%2C,niche%20types%20of%20crypto%20such (Accessed: 20th March 2025)
Pastor, L., Stambaugh, R.F., and Taylor, L.A. (2022) ‘Dissecting Green Returns’, Journal of
Financial Economics, 146(2), pp. 403–424. Available at:
https://www.sciencedirect.com/science/article/pii/S0304405X22001672 (Accessed: 18 March 2025).
Pavel C., Andrej C., Pirmin F., Kancs A (2024) ‘Environmental and social attitudes and
investments in crypto-assets.’ Available at
https://www.tandfonline.com/doi/epdf/10.1080/00036846.2023.2297742?needAccess=true
(Accessed: 15 March 2025)
Robeco (n.d.) How do companies and countries score on sustainability? Available at:
https://www.robeco.com/en-int/sustainable-investing/how-do-companies-and-countries-scoreon-sustainability (Accessed: 18 March 2025).
Srivastava, M., Sahni, H. and Dhyani, P. (2022) Understanding Cryptocurrency Adoption: An Overview of Trends, Motivations, and Barriers. Available at: https://arxiv.org/pdf/2206.14548
(Accessed: 20th March 2025).
UNFCCC (2015) Adoption of the Paris Agreement. FCCC/CP/2015/L.9/Rev.1, United Nations. Available at: https://unfccc.int/sites/default/files/resource/docs/2015/cop21/eng/l09r01.pdf
(Accessed: 17 March 2025).