Banks and other financial institutions like to make a big deal about their environmental efforts through their ESG teams, but that doesn't mean they're climatically benign. Their engineering teams are generating big emissions merely through the code they write.
A 2017 paper published alongside the ACM SIGPLAN Engineering Language Conference tested a number of the most prevalent programming languages in use today to determine, among other things, how much energy they used. The results showed that Python, despite its popularity was a massive energy hog using 45 times as much energy (4390 joules) as C++ (77 joules) to execute its programs.
Python is an interpreted language. While compiled languages like Ada, C++ and Rust directly translate to instructions for the machine, Python must be read by a separate program first before being translated into machine instructions. Interpreted languages are generally far easier to learn and simpler to use, but this comes at the cost of energy in a big way.
Python is very well-loved in finance. Investment firm Man Group for example have called it the «second language» of the firm. On eFinancialCareers, of the 5,072 jobs currently available for quants and technologists, 1217 mention Python. If not Python, most banks like using alternative interpreted languages; Goldman Sachs' love affair with Slang, is well documented.
The same phenomenon can also be seen in fintech. Stripe, for example, relies heavily on Ruby, which is only narrowly faster than Python. If this evident energy gap exists in small, optimized benchmarking programs, one can only imagine how much additional energy is produced in Stripe's 50 million lines of code.
C++ may be kind on the environment, but it is far from kind on
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