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Speaker 1: This is Dana Perkins, and you're listening switched on the

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Speaker 1: BNEF podcast. With around ninety percent of global emissions covered

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Speaker 1: by some sort of net zero target, the transition to

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Speaker 1: a less polluting, greener economy is somewhat inevitable. What's more

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Speaker 1: hotly debated is the magnitude and speed. So as the

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Speaker 1: transition takes place, asset values will invariably shift, and some

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Speaker 1: industries and companies will be more exposed to this risk

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Speaker 1: than others. On today's show, we discuss some of the

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Speaker 1: ways BNF is approaching transition risk across various sectors of

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Speaker 1: the economy. The show gets into two pieces of data

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Speaker 1: analysis at BNF. One is the Clean Energy Exposure Ratings,

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Speaker 1: which help us to look at clean energy specifically and

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Speaker 1: assess the percentage of a company's revenues that come from

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Speaker 1: clean energy. We also discuss the Transition Risk Assessment Company Tool,

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Speaker 1: which helps us understand company specific transition risk and leverages

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Speaker 1: climate scenarios, company level financial data, and key transition assets

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Speaker 1: across nine sectors to help us navigate what these tools

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Speaker 1: are telling investors and the industries undergoing transition. I speak

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Speaker 1: with two members of b and EF's sustainable finance team

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Speaker 1: specifically focused on transition risk, Tiffin Brandily and Mike Daily.

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Speaker 1: We discuss methodology and how to go about ranking a

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Speaker 1: company's clean energy revenue. We also discuss the transparency of

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Speaker 1: revenues generated by different industries, including oil and gas, and

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Speaker 1: how to assess just how clean their energy portfolios really are.

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Speaker 1: We also discuss which sectors are most exposed to transition

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Speaker 1: risk and why. If you want to receive alerts on

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Speaker 1: your device when future episodes of this podcast are released,

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Speaker 1: make sure to subscribe. And if you give us a

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Speaker 1: review on Apple Podcasts or Spotify, that is going to

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Speaker 1: make us more discoverable by others. But right now, let's

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Speaker 1: jump into our conversation with Tiffin and Mike about clean

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Speaker 1: energy exposure ratings and transition risk. Tiffin, thank you for

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Speaker 1: joining today, thanks for having us. And Mike, thank you

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Speaker 1: for joining.

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Speaker 2: Yeah, thank you very much for having me.

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Speaker 1: So we've got two analysts here today to talk. Well,

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Speaker 1: we're going to talk about data, but it's the story

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Speaker 1: that the data tells, and I think it makes the

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Speaker 1: most sense for us to start in a bit of

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Speaker 1: a chronological order in terms of what's happening now versus

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Speaker 1: how is this impacting the future and how companies think

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Speaker 1: about the future. So, in the spirit of what's happening now,

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Speaker 1: let's start with our clean energy exposure ratings and definitionally, Mike,

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Speaker 1: what are these?

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Speaker 2: So, the Keen Energy Exposure ratings are a classification on

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Speaker 2: a company's revenue where we use a combination of BNF

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Speaker 2: data sets like EV sales or country level generation to

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Speaker 2: determine whether or not those revenue streams are in fact

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Speaker 2: clean full you in on what our model can do

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Speaker 2: is that we've ever saying like over fifty thousand companies

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Speaker 2: and from those we've identified over eight thousand companies with

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Speaker 2: some level of keleen energy exposure. So we think about

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Speaker 2: what that kind of means. In twenty twenty two, those

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Speaker 2: eight thousand plus companies tracked more than two point five

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Speaker 2: trillion dollars in clean energy revenues and that's about two

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Speaker 2: point six percent of global GDP. With the exposure ratings,

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Speaker 2: there's two parts what we're offering. We're offering the clean

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Speaker 2: energy exposure percentage as well as the exposure ratings, So

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Speaker 2: the percentage of themselves take those company revenues and it

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Speaker 2: tells you what percentage of a company's revenues are from

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Speaker 2: clean energy sources, whereas the exposure rating is a less

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Speaker 2: granular view. So this is where we group companies into

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Speaker 2: a one to A four buckets, where an A one

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Speaker 2: type rating means that what more than fifty percent of

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Speaker 2: a company's revenue is derived from clean energy, and an

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Speaker 2: A four is where less than ten percent of that

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Speaker 2: revenue comes from clean energy.

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Speaker 1: This is going to lean very heavily towards some industries

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Speaker 1: over others. So what are thees that we tend to

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Speaker 1: find in that A one categorization?

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Speaker 2: So our A one companies on the peer player side,

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Speaker 2: we've mostly seen our renewable manufacturers and developers, I guess

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Speaker 2: fit into these sort of categories, So typical companies that

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Speaker 2: we've seen a lot of. It's been in the automotive side.

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Speaker 2: Companies like Tesla feature quite strongly.

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Speaker 1: That's because it's on a percentage term, right, So a

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Speaker 1: Tesla is inherently itself an electric vehicle. But how about

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Speaker 1: companies like BW where they're definitely looking at that space,

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Speaker 1: definitely involved in selling quite a few electric vehicles, but

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Speaker 1: also have their internal combustion vehicles. That's where you end

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Speaker 1: up finding them further down the track on a two,

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Speaker 1: a three, or a four.

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Speaker 2: Right, yeah, exactly. I think the automotive industry is going

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Speaker 2: to be something super exciting to watch over the next

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Speaker 2: ten years because it's very much a divided market. On

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Speaker 2: the one side, you have your pure player electric vehicle

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Speaker 2: companies like Buid and Tesla. But then on the other

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Speaker 2: side you have your more traditional auto makers, which are

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Speaker 2: you know, they're playing catch up, right, and they've also

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Speaker 2: committed to some pretty bold pledges. I think BMW Over

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Speaker 2: the past year, I guess we've seen their EV sales

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Speaker 2: increase from about thirteen percent up to about twenty five percent.

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Speaker 2: Companies like Volvo have gone from about twenty five percent

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Speaker 2: up to thirty eight percent, and these companies are looking

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Speaker 2: to either partially or fully electrify their EV sales by

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Speaker 2: by twenty thirty. So that's going to be something really

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Speaker 2: interesting to watch.

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Speaker 1: So from one year to the next, you're going to

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Speaker 1: see companies, well you're mentioning a bunch of them moving

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Speaker 1: kind of up the rankings into more exposure. But can

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Speaker 1: this also be used as a way to identify from

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Speaker 1: one year to the next if companies are moving in

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Speaker 1: the opposite direction and deemphasizing. Is that a use Is

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Speaker 1: that a proper use case?

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Speaker 2: You think it'll definitely point out or expose I guess

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Speaker 2: companies that are not committing to their pledges. One company

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Speaker 2: that I've kind of noticed has a pretty strong pledge

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Speaker 2: as well, that was to fully electrify their sales by

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Speaker 2: twenty thirty. But what I've seen in the past year

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Speaker 2: is that the EV sales have not increased at all,

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Speaker 2: or their percentage of EV sales of about five percent. Yeah,

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Speaker 2: the company was in ascent for lack of you know,

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Speaker 2: exposing them.

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Speaker 1: Okay, we'll see maybe over the next couple of years

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Speaker 1: how they move from one category to the next. Let's

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Speaker 1: talk a little bit more about these industries, and thank

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Speaker 1: you for going into some detail on the automakers. But

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Speaker 1: how what sort of trends do you end up seeing

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Speaker 1: from one industry to annex to the next in terms

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Speaker 1: of which ones seem to have more versus less exposure.

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Speaker 2: Definitely noticed a few trends. When we look at the

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Speaker 2: top twenty largest revenue generators from last year, electric utilities

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Speaker 2: has come out on top. They've definitely had more of

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Speaker 2: like a mix to their portfolio. Right, their mix will

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Speaker 2: consist of, you know, some of the long established clean

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Speaker 2: technologies like nuclear or large hydro, as well as some

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Speaker 2: of the more fast growing ones like wind and solar.

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Speaker 2: So electric utilities i'd say have topped with the automotive

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Speaker 2: industry coming in second. I think it was Tesla have

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Speaker 2: raised about eighty one billion dollars last year in clean

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Speaker 2: energy revenues. You know, ninety five percent came from the

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Speaker 2: ev stuff and five percent from their their solar type industry.

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Speaker 2: So definitely trends in terms of industries clean energy exposure,

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Speaker 2: and for the actual model that we built, we've factored

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Speaker 2: that in. Right, We've got different methodologies for how we're

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Speaker 2: approaching the automotive industries versus the electric utilities, versus the

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Speaker 2: renewable energy developers, et cetera.

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Speaker 1: Are there any geographical trends that you end up seeing

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Speaker 1: across these industries and do you see it being emphasized

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Speaker 1: more on certain continents or perhaps even more granular level

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Speaker 1: in certain countries.

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Speaker 3: Yeah.

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Speaker 2: I think one of the things that we identified pretty

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Speaker 2: early on is we tracked the most clean energy revenues

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Speaker 2: from APAC, and that was mostly from China and their

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Speaker 2: dominance of unclean energy supply chains. I think it was

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Speaker 2: particularly in the solar as well as the energy storage

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Speaker 2: type industries. I think it was Emia came in second,

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Speaker 2: but there was a huge gap between the revenues in

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Speaker 2: APAC and EMEA, and then the US came in in last.

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Speaker 2: So there are some u notable trends I think geographically

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Speaker 2: we've seen. I'd say, like when we talk about the

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Speaker 2: type of sectors, different regions had different strengths in the

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Speaker 2: sectors that they were covering. So like on the APAC

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Speaker 2: side that was more as I mentioned, like solar and

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Speaker 2: energy storage, whereas the US there was more stuff around

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Speaker 2: biofuels and electrified transport, and in Amea we saw many

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Speaker 2: legric utilities from the nuclear side adding to that level

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Speaker 2: of clean energy exposure.

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Speaker 1: We've seen well, and we're going to go to transition

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Speaker 1: risk in a second, but before we get there, let's

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Speaker 1: talk a little bit about one sector in particular that

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Speaker 1: is very much in transition. So you've already established the

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Speaker 1: electric vehicles and automotive that moves you hire up the ranking,

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Speaker 1: and then in utilities you're seeing a lot of this electrification.

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Speaker 1: But let's talk specifically about oil and gas, who in

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Speaker 1: many respects, a lot of these companies are referring to

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Speaker 1: themselves as energy companies in a much more holistic way

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Speaker 1: because that's their plan is to be a much more

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Speaker 1: diversified business. Where do you find the oil sector on

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Speaker 1: this list and are they moving around? I guess which

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Speaker 1: categorization do they tend to fall into?

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Speaker 2: The short onswer is oil and gas companies have dominated

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Speaker 2: the A four type rating, so that's less than ten

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Speaker 2: percent clean energy exposure. But one of the huge difficulties

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Speaker 2: that we've seen with these majors is through transparency in

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Speaker 2: the company revenue reporting. What they tend to do is

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Speaker 2: that they tend to group these revenue segments into phrases

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Speaker 2: which make them sound a lot cleaner than they really are.

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Speaker 2: So two examples that kind of jump to mind. Shell

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Speaker 2: has reported on their Renewables and Energy Solutions type division,

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Speaker 2: which mostly includes electricity generation, marketing and trading of power

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Speaker 2: and pipeline gas. And another example is Repsol reported on

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Speaker 2: their commercial and renewables activities, but these mostly include the

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Speaker 2: sale of electricity and gas and the sale of oil

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Speaker 2: products and liquified petroleum gases. So for this whole piece, Like,

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Speaker 2: our main goal for the exposure ratings is not to

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Speaker 2: blame these oil majors, but more to point out the

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Speaker 2: nuances that we're seeing across industries and how we're able

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Speaker 2: to cater in our methodology. We've got an awesome team

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Speaker 2: of BNF sector experts who are doing the research on

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Speaker 2: these companies to make sure that exposure ratings are reflecting accurately,

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Speaker 2: not purely based on what's being reported, but also adding

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Speaker 2: that additional layer to make sure that we are accurately

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Speaker 2: I guess, mapping out the energy transition.

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Speaker 1: Yeah, because what I expect to see is movement across

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Speaker 1: these categories and that this particular data set and this

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Speaker 1: analysis will become increasingly useful over time as we think

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Speaker 1: about net zero targets and how these companies in transition

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Speaker 1: really change. I mean, that is the definition of a transition,

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Speaker 1: right Like, that is what we're here to talk about,

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Speaker 1: is change within the oil and gas space and the

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Speaker 1: company specifically exposed here. What I'm hearing from you is

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Speaker 1: you've really got to look at the specific activities and

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Speaker 1: take a look under the hood, if you will, to

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Speaker 1: understand what's happening. Oftentimes, you also end up finding because

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Speaker 1: these companies are so big, so many different activities. Do

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Speaker 1: you look at the company as one company and it's

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Speaker 1: this percentage of activities as you've already outlined, or would

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Speaker 1: you for any sort of let's say publicly listed oil company,

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Speaker 1: would you actually have multiple different subsidiaries in the ranking

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Speaker 1: evaluated differently because the business unit looking at hydrogen is

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Speaker 1: going to be very different than the business unit that

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Speaker 1: is doing oil exploration.

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Speaker 2: Yeah, I'd say when we're evaluate companies, we're looking at

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Speaker 2: that company, right.

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Speaker 1: Like saying a holistic sense, well.

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Speaker 2: Not just in realistic sense, but at every level. So

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Speaker 2: if we're looking at a subsidiary company and we're looking

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Speaker 2: at their exposure, you may have a subsidiary such as

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Speaker 2: like Brookfield Renewables right where we're evaluating them on their

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Speaker 2: cleenage exposure and they would rank very highly, right, But

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Speaker 2: at the parent level they're involved in a bunch of

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Speaker 2: other operations where at the parent level they wouldn't have

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Speaker 2: I guess as much clean energy exposure. So at every level,

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Speaker 2: we're trying to gauge what level of exposure those companies have.

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Speaker 1: And you mentioned disclosure being really important, So the question

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Speaker 1: I have is on this same industry. Do we get

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Speaker 1: much information from national oil companies.

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Speaker 2: I mean, we do, and we don't. One of the

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Speaker 2: things that that we're leveraging, right is an in ours

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Speaker 2: team that looks at industry type taxonomies and classifying those

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Speaker 2: revenue streams to specific industries and sub industries and subjectivities.

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Speaker 2: So we're leveraging a lot of that. But I would

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Speaker 2: say when it comes to the oil industry, we are

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Speaker 2: doing a lot of research at a company by companies

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Speaker 2: level to make sure that we are capturing the best

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Speaker 2: sort of view on that company. And this is also

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Speaker 2: one of the reasons why we do have both exposure

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Speaker 2: rating and the percentage, because often we don't have that

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Speaker 2: level of granularity, in which case we're happy to say

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Speaker 2: it's in a four type company, but we may not

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Speaker 2: necessarily be able to distinguish the split between the gas

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Speaker 2: portion versus the electricity type generation portion.

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Speaker 1: It doesn't tell the whole story. So that's why you

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Speaker 1: look at it in a couple of different ways.

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Speaker 2: Yeah, yeah, exactly, exactly.

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Speaker 1: Also, then let's pivot a little bit to the transition

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Speaker 1: risk part of it, which is really around well, how

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Speaker 1: things are going to play out for some of these

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Speaker 1: companies in the future if they don't move themselves necessarily

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Speaker 1: up this ranking. So Tivin, can you explain what the

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Speaker 1: transition risk analysis is that we do on our side?

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Speaker 3: Sure, So, first of all, transition risk is the risk

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Speaker 3: arising from climate policies, technology disruption, are shifting consumer patterns

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Speaker 3: and if you think about the discipline of transition risk,

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Speaker 3: we have to go back to twenty fifteen. That time

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Speaker 3: Mark Corney was Governor of the Bank of England and

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Speaker 3: he gave a speech at Lloyd's in London and the

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Speaker 3: speech was called The Tragedy of Horizon, essentially explaining that

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Speaker 3: the financial markets have a very short term view on

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Speaker 3: returns and risk while climate change is essentially a problem

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Speaker 3: for the next generations.

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Speaker 1: Which is a very famous speech in the takeoff of

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Speaker 1: the Tragedy of the Commons right exactly.

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Speaker 3: And in this speech he provided two recommendations. Number one

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Speaker 3: was around disclosure, making sure that corporates and financial institutions

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Speaker 3: have provided enough transparency on their activities and that would

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Speaker 3: have a financial market to price the risk in the transition.

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Speaker 3: So that's what we discussed about with Mike. And the

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Speaker 3: second recommendation was around stress testing. So you got to

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Speaker 3: remember that twenty fifteen were still in the aftermath of

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Speaker 3: the suprime crisis, and we're just one year away from

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Speaker 3: the European Death Crisis twenty fourteen, and so central bankers

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Speaker 3: are still thinking about how to strengthen the financial system

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Speaker 3: from a micro prudential perspective, so that's financial institution level,

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Speaker 3: but also from a macro perspective, and that is the

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Speaker 3: resilience of the whole financial system. And so at BNF

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Speaker 3: we've worked on a tool so Tracked is BNF's Proprietary

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Speaker 3: Transitioners Risk Tool, and it is looking at the revenue

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Speaker 3: projections for more than eleven thousand companies across ten climate scenarios.

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Speaker 3: And so what we're looking at is really trying to

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Speaker 3: understand the whole sensitivity of revenues to the temperature outcome

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Speaker 3: of the scenario, all the way up from three degrees

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Speaker 3: of warming by twenty one hundred this is for the

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Speaker 3: baseline scenarios, all the way down to one point four,

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Speaker 3: one point five, one point seven degrees for the New

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Speaker 3: Energy Outlook and the NGFs scenarios, and so you have

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Speaker 3: this whole temperature sensitivity on revenues, but also each scenario

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Speaker 3: has its own characteristics in terms of which technologies are

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Speaker 3: deployed to solve the climate equation, and so the tool

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Speaker 3: allows investors to explore these risk and opportunities across NGFs

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Speaker 3: and BNF scenarios. So trying to understand whether they have

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Speaker 3: exposures to China where the transition is going very fast,

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Speaker 3: to the US, where we've seen recently policy package being

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Speaker 3: passed into Congress, or to Europe where first of fil

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Speaker 3: demon is already going down, and so it's very important

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Speaker 3: to understand what exposures these firms have in their balance sheet.

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Speaker 2: Now.

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Speaker 3: The third element in order to build our transition risk

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Speaker 3: research is really to look at the changes in the

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Speaker 3: demand for commodities and products in different climate scenarios. And

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Speaker 3: so if you consider baseline scenario where the world would

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Speaker 3: be headed to two degrees or three degrees of warming

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Speaker 3: by twenty one hundred, what you'd have is essentially the

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Speaker 3: flattening of oil, DeMont, gas, DeMont and other commodities, while

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Speaker 3: in net zero transition scenario you would have demond destruction

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Speaker 3: coming from oil gas and so this would have impact

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Speaker 3: on the holy ecosystem of companies in the oil and

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Speaker 3: gas sectors, but also in mining.

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Speaker 1: What time horizon are you looking at when you're evaluating

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Speaker 1: the risk and how far into the future can somebody

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Speaker 1: look when they're thinking about this analysis.

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Speaker 3: So we're looking at from now to twenty to fifteen.

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Speaker 3: And obviously the issue is that most of the financial

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Speaker 3: products have a short lifespan, so that might be two years,

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Speaker 3: might be five years, maximum ten twenty years. But most

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Speaker 3: of what we call physical risk, which is the risk

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Speaker 3: arising from extreme weather events, these are likely to materialize

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Speaker 3: over the next twenty thirty, fifty sixty years. And so

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Speaker 3: this is coming back to mcconnie's speech here. How the

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Speaker 3: real question is how do we price climate risk as

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Speaker 3: a whole into the decisions that the finance industry is

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Speaker 3: making today.

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Speaker 1: I imagine there's a good deal of overlap with the

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Speaker 1: industries that you're covering Tiffin and the ones that Mike

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Speaker 1: is looking at from the clean energy exposure space, and

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Speaker 1: I'm thinking in particular of oil and gas. But really, well,

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Speaker 1: let's take a step back and which industries have you

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Speaker 1: started with your analysis looking at, because well, presumably you've

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Speaker 1: selected them because you think perhaps there's the most to

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Speaker 1: find out regarding their exposure to this risk.

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Speaker 3: I think oil and gas utilities and automakers are the

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Speaker 3: name of the game in terms of transition risk, and

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Speaker 3: they are very interesting developments that are happening right now.

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Speaker 3: So in September twenty twenty three, you've had the International

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Speaker 3: Energy Agency IE that published a report saying that peak

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Speaker 3: them on for fossil fuel, what's going to happen prior

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Speaker 3: to twenty thirty. So this is a view that we've

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Speaker 3: had a been a for the past three years, and

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Speaker 3: we're calling oil picked them on by twenty twenty eight.

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Speaker 3: And in other words, we were saying transition risk for

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Speaker 3: the oil and gas sector organ to materialize within this

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Speaker 3: business cycle. So this is not a matter of twenty fifty.

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Speaker 3: It's very much a matter of today's board decisions and

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Speaker 3: how fast these companies might diversify away from these revenue sources.

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Speaker 3: Twenty twenty two, we've had very high commodity prices and

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Speaker 3: this is kind of hiding some of the risks inherently

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Speaker 3: that they are. So we see this from the baseline

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Speaker 3: scenarios all the way down to the net zero scenarios,

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Speaker 3: where you would have essentially two percent of the market

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Speaker 3: or three percent of the market removed on a yearly basis,

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Speaker 3: So net zero scenario is very stressful and it would

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Speaker 3: remove an equivalent amount of oid production as BPM share

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Speaker 3: produced combined in a single year. So it's a very

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Speaker 3: fast transition. And obviously the main cost for this is

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Speaker 3: fuel economy standards on the one side, and on the

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Speaker 3: other side, the outtake in electric vehicles. Now, there's a

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Speaker 3: few markets that are very interesting to look at. China

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Speaker 3: obviously is one of them. Sinopec, which is China's biggest

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Speaker 3: fuel distributors. They've announced this year that they think peak

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Speaker 3: demand has happened in terms of gasoline. So gasoline is

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Speaker 3: let's say, the most vulnerable fuel out of the barrel,

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Speaker 3: mainly because it is concentrated in lighter duty segments in

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Speaker 3: the automotive market. And so this is not an obscure

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Speaker 3: research house that is saying this. This is the largest

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Speaker 3: a fuel distributor in China, and so this is very meaningful.

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Speaker 3: From now on, the oil and gas industry in China

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Speaker 3: has to deal with demand destruction. This is something that

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Speaker 3: might be surprising for many people, but actually for electric

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Speaker 3: vehicle analysts' experiencing this for a while in markets that

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Speaker 3: are more heads in terms of their electric vehicle deployment.

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Speaker 3: So if you think about Norway that has subsidized evs

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Speaker 3: for a long time, since twenty fifteen, the gasoline demand

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Speaker 3: in Norway has dropped by twenty five percent, and so

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Speaker 3: this is something that will play out as governments and

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Speaker 3: consumers shift towards and electrified transport.

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Speaker 1: How about the data that we get regarding the company's activities,

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Speaker 1: in particular for private companies, I imagine it's exceptionally hard,

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Speaker 1: But all in all, are you able to get the

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Speaker 1: information you need in order to make a fair assessment

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Speaker 1: of rest to these companies?

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Speaker 3: Yes, sore are different ways to slice this question. But

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Speaker 3: you have a global data team at Bloomberg that looks

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Speaker 3: at any type of disclosure, whether it's from a public

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Speaker 3: company or private company. They would go out there and

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Speaker 3: log whatever financial report they find, and then a team

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Speaker 3: would classify revenues in standardized categories, and we use these

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Speaker 3: categories to project our transition risk analy this forward looking

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Speaker 3: at the revenue at risk across different climate scenarios. And

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Speaker 3: so in terms of of these data sets, private companies

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Speaker 3: might be captured, but the vast majority of the companies

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Speaker 3: we have transparency on are really in the public domain.

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Speaker 1: This is going to be an easier transition for some

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Speaker 1: companies and industries in particular than others. So which industry

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Speaker 1: is fair better than others when it comes to looking

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Speaker 1: at well, what are the outputs and what is it

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Speaker 1: telling us?

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Speaker 3: So I think utility is really an interesting case because

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Speaker 3: you have this shift from cool gas more traditional forms

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Speaker 3: of a power generation towards solar, wind batteries, power grids

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Speaker 3: as well. Is we see a lot of upside on

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Speaker 3: great businesses. This is mainly because the world has to electrify.

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Speaker 3: If we consider the fastest transitions or the net zero scenarios,

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Speaker 3: we see electric heat pump deployment driving more electrictic consumption.

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Speaker 3: We see electricals obviously driving more electrictic consumption, and also

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Speaker 3: low temperature heat in industry, and so this means there's

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Speaker 3: a lot of demond created for electricity and utilities, and

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Speaker 3: most of the risks are concentrated around cold gas and

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Speaker 3: the rise of carbon pricing in cet and locations. So

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Speaker 3: we would cover that and build the NAZIS on the

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Speaker 3: back of our new energy outlook, which is our climate

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Speaker 3: scenarios or the scenarios from NGFs to the Network for

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Speaker 3: Greening the Financial System, which is an alliance of central

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Speaker 3: banks that has published open source scenarios.

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Speaker 1: I'm definitely approaching this very much from the perspective of

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Speaker 1: the companies themselves that are exposed to risk. But what

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Speaker 1: I'd like to better understand, and I think oftentimes the

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Speaker 1: questions you get asked have and really revolve around the

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Speaker 1: financial industry and how they're looking at this information. Can

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Speaker 1: you go into some more detail on how the finance

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Speaker 1: universe is actually looking at these risk ratings.

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Speaker 3: So in the finance industry, they are two drivers for

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Speaker 3: transitionerskinazis number one is the regulatory driver, mainly because central

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Speaker 3: banks are rolling out all these climate stress tests. So

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Speaker 3: in the past two years we've seen thirty five different

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Speaker 3: stress tests being conducted globally looking at climate risk. And

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Speaker 3: these stress tests are mainly constructed around the NGFs scenarios,

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Speaker 3: which are these open source scenarios that incorporate both physical

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Speaker 3: risk and transition risk. And so the players that are

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Speaker 3: under the scope of regulations are mostly on the sale side.

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Speaker 3: From the perspective of the byside, you will need also

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Speaker 3: a solution to understand how to adjust portfolios to match

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Speaker 3: these strategic goals of the company.

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Speaker 1: Okay, so Tiffin, I'm going to ask you to pick

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Speaker 1: one industry that you found most interesting in terms of

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Speaker 1: the findings and explain what it's telling us about where

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Speaker 1: this industry is going.

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Speaker 3: So transition risk results for automakers and the automotive industry

440
00:22:42,119 --> 00:22:45,160
Speaker 3: were actually quite different from what we thought we would find,

441
00:22:45,400 --> 00:22:49,040
Speaker 3: and this is because the automotive supply chain is relatively complex.

442
00:22:49,040 --> 00:22:52,160
Speaker 3: So you have these very large international automakers that are

443
00:22:52,320 --> 00:22:56,919
Speaker 3: structuring large ecosystems of autopaths manufacturer around them, and you

444
00:22:57,000 --> 00:23:00,840
Speaker 3: really have to do the analysis bottom up to understand

445
00:23:00,920 --> 00:23:03,600
Speaker 3: the activity of each company. Now, a company that is

446
00:23:03,640 --> 00:23:07,000
Speaker 3: manufacturing gearboxes is very much at risk in the transition

447
00:23:07,080 --> 00:23:09,960
Speaker 3: because electric cars don't have gearboxes. It's the same for

448
00:23:10,040 --> 00:23:14,159
Speaker 3: exhaust systems for example. However, if you consider tile manufacturers,

449
00:23:14,400 --> 00:23:16,199
Speaker 3: now the impact on them will be a bit more

450
00:23:16,280 --> 00:23:19,800
Speaker 3: nuanced and they will not be strongly impacted by the

451
00:23:19,880 --> 00:23:23,200
Speaker 3: shift to evs essentially, and so the idea is really

452
00:23:23,240 --> 00:23:26,720
Speaker 3: building the analysis bottom up, understanding what each businesses do.

453
00:23:27,000 --> 00:23:29,720
Speaker 3: And we have more than eleven thousand companies in the tool,

454
00:23:29,840 --> 00:23:33,000
Speaker 3: but understanding the relationship in terms of the supply chain

455
00:23:33,040 --> 00:23:36,080
Speaker 3: between an auto maker that might be transitioning or might not,

456
00:23:36,440 --> 00:23:39,760
Speaker 3: and which autopats manufacturers they are connected to.

457
00:23:40,359 --> 00:23:44,000
Speaker 1: Sticking with the application for the financial services industry, Mike,

458
00:23:44,080 --> 00:23:47,880
Speaker 1: how is the clean energy exposure reading information really used

459
00:23:48,119 --> 00:23:49,159
Speaker 1: by that community?

460
00:23:49,440 --> 00:23:51,359
Speaker 2: I would say, I mean, there's a few points can

461
00:23:51,760 --> 00:23:54,760
Speaker 2: that jump to mind. One is the cleanage exposure ratings.

462
00:23:54,760 --> 00:23:59,040
Speaker 2: They help investors and lenders uncover their exposure to businesses

463
00:23:59,119 --> 00:24:02,800
Speaker 2: that are driving value creation in the low carbon economy.

464
00:24:02,880 --> 00:24:04,879
Speaker 2: That was a bit of a mouthful, but essentially it

465
00:24:04,960 --> 00:24:07,960
Speaker 2: helps reveal companies that are leading the transition today and

466
00:24:08,000 --> 00:24:11,160
Speaker 2: those that are likely to capture future transition opportunities. So

467
00:24:11,480 --> 00:24:14,280
Speaker 2: another really interesting point is around like how it adds

468
00:24:14,320 --> 00:24:17,720
Speaker 2: value in terms of portfolio construction. Right, So one example

469
00:24:17,800 --> 00:24:20,600
Speaker 2: is we have the Bloomberg Gold and Sacks Clean Energy

470
00:24:20,600 --> 00:24:23,879
Speaker 2: Index that leverages the clean Energy Exposure ratings. So the

471
00:24:23,920 --> 00:24:26,679
Speaker 2: exposure ratings not only our key criteria in terms of

472
00:24:26,720 --> 00:24:29,040
Speaker 2: which companies make it into the index, but they also

473
00:24:29,080 --> 00:24:32,920
Speaker 2: define the portfolio weights of those companies within the index.

474
00:24:33,200 --> 00:24:36,399
Speaker 2: And something that I'm really excited about is the portfolio

475
00:24:36,400 --> 00:24:39,040
Speaker 2: tool that we've launched with the exposure ratings piece, and

476
00:24:39,280 --> 00:24:42,520
Speaker 2: what that portfolio tool does is that it rolls up

477
00:24:42,640 --> 00:24:44,760
Speaker 2: the clean energy revenues of the company up to the

478
00:24:44,760 --> 00:24:47,800
Speaker 2: index or the ETF. And one trend that popped out

479
00:24:47,800 --> 00:24:50,879
Speaker 2: almost immediately is that top equity indices like the smp

480
00:24:51,720 --> 00:24:54,640
Speaker 2: S and MSCI World had very low exposure to clean

481
00:24:54,760 --> 00:24:57,399
Speaker 2: energies of roughly only three to three and a half percent,

482
00:24:57,960 --> 00:25:00,000
Speaker 2: and we saw very similar trends when we look at

483
00:25:00,080 --> 00:25:04,159
Speaker 2: at top or major esg ETFs. Another element of the

484
00:25:04,160 --> 00:25:06,640
Speaker 2: portfolio tool is that it's pretty customs, So if you're

485
00:25:06,680 --> 00:25:09,320
Speaker 2: looking to build out your own custom index, you know

486
00:25:09,359 --> 00:25:11,320
Speaker 2: that's something you can do where you can evaluate the

487
00:25:11,320 --> 00:25:13,840
Speaker 2: clean energy exposure that you would have on the companies

488
00:25:13,880 --> 00:25:14,760
Speaker 2: within that index.

489
00:25:15,000 --> 00:25:17,280
Speaker 1: Because the work that both of you are doing really

490
00:25:17,400 --> 00:25:20,199
Speaker 1: is geared towards not necessarily. I mean, while one of

491
00:25:20,200 --> 00:25:22,880
Speaker 1: the use cases is for the companies themselves to see

492
00:25:22,920 --> 00:25:25,480
Speaker 1: where they fall, really it has to do with helping

493
00:25:25,560 --> 00:25:29,040
Speaker 1: the financial community look at everything in one place and

494
00:25:29,080 --> 00:25:32,919
Speaker 1: take into consideration so many different variables at one time,

495
00:25:33,240 --> 00:25:37,119
Speaker 1: and then I guess which in definition is a ranking?

496
00:25:37,480 --> 00:25:41,360
Speaker 1: How about other ways of ranking companies? We are recording

497
00:25:41,400 --> 00:25:43,800
Speaker 1: here from Europe and one of the things that was

498
00:25:44,320 --> 00:25:47,320
Speaker 1: very hotly talked about last year was the EU Green

499
00:25:47,480 --> 00:25:52,080
Speaker 1: Taxonomy for sustainable activities. Is that something that I guess

500
00:25:52,119 --> 00:25:54,399
Speaker 1: has any interaction with your work? And where are the

501
00:25:54,440 --> 00:25:57,480
Speaker 1: commonalities and differences in terms of how they might complement

502
00:25:57,520 --> 00:25:58,000
Speaker 1: one another.

503
00:25:58,400 --> 00:26:00,800
Speaker 2: Yeah, yeah, I think that's a question You're asked a lot.

504
00:26:00,920 --> 00:26:03,120
Speaker 2: Is you know, what are the differences between the EU

505
00:26:03,160 --> 00:26:07,320
Speaker 2: taxonomy work versus dead creenerage exposures. They're both based on revenues.

506
00:26:07,320 --> 00:26:09,200
Speaker 2: I would say the EU taxonomy is a far more

507
00:26:09,359 --> 00:26:14,160
Speaker 2: complex type classification and it defines which economic activities are

508
00:26:14,240 --> 00:26:17,560
Speaker 2: lined with net zero trajectories by twenty fifty. And what

509
00:26:17,600 --> 00:26:21,679
Speaker 2: the EU Taxonomy does is that it requires organizations like

510
00:26:21,920 --> 00:26:24,760
Speaker 2: large companies or investment firms to report the share of

511
00:26:24,840 --> 00:26:28,880
Speaker 2: their operations that are environmentally sustainable. Right. And there's two

512
00:26:28,920 --> 00:26:31,640
Speaker 2: elements to this. There's the eligibility share as well as

513
00:26:31,720 --> 00:26:34,639
Speaker 2: the alignment share. The eligibility share tries to answer the

514
00:26:34,720 --> 00:26:38,439
Speaker 2: question of is the company's economic activity eligible to the

515
00:26:38,440 --> 00:26:42,360
Speaker 2: Green taxonomy, But being eligible does not necessarily mean being

516
00:26:42,440 --> 00:26:46,120
Speaker 2: green under the EU taxonomy. There's three other elements. Right,

517
00:26:46,160 --> 00:26:50,119
Speaker 2: The economic activity has to substantially contribute to an environmental

518
00:26:50,160 --> 00:26:54,760
Speaker 2: objective such as climate mitigation or circular economy or biodiversity,

519
00:26:54,960 --> 00:26:58,080
Speaker 2: as well as conformed to that do no such harm

520
00:26:58,240 --> 00:27:01,240
Speaker 2: under any other environmental object active and last year, it

521
00:27:01,280 --> 00:27:04,640
Speaker 2: also needs to respect minimum social safeguards. In contrast our

522
00:27:04,640 --> 00:27:08,720
Speaker 2: exposure rate. Things are looking at company reported revenues and

523
00:27:08,840 --> 00:27:11,320
Speaker 2: enhancing up with various benef data sets to figure out

524
00:27:11,359 --> 00:27:14,199
Speaker 2: what percentage of those revenues are are clean. And I

525
00:27:14,200 --> 00:27:16,680
Speaker 2: guess the main point in doing so is to get

526
00:27:16,680 --> 00:27:20,199
Speaker 2: an accurate or a fair sense of how these companies

527
00:27:20,440 --> 00:27:22,800
Speaker 2: are performing ahead of the energy transition.

528
00:27:23,440 --> 00:27:26,280
Speaker 1: So we've spent a lot of time talking about holistically

529
00:27:26,480 --> 00:27:29,840
Speaker 1: how we approach this, all of these different things that

530
00:27:29,920 --> 00:27:33,159
Speaker 1: one has to consider when making assessments of companies and

531
00:27:33,359 --> 00:27:36,359
Speaker 1: entire industries. In fact, we've gone into some specific industries

532
00:27:36,359 --> 00:27:38,639
Speaker 1: as well, and we spend a lot of time here

533
00:27:38,680 --> 00:27:41,639
Speaker 1: at BENF thinking about this. You gentlemen, have lots of

534
00:27:41,640 --> 00:27:44,119
Speaker 1: people to collaborate with. But I want to know is

535
00:27:44,440 --> 00:27:49,240
Speaker 1: how seriously do you think this sort of information, both

536
00:27:49,400 --> 00:27:52,879
Speaker 1: current exposure and future risk is really being taken in

537
00:27:52,920 --> 00:27:55,480
Speaker 1: the outside world. And I'm going to give you I'm

538
00:27:55,480 --> 00:27:57,000
Speaker 1: going to hold your feet to the fire. And I'm

539
00:27:57,040 --> 00:27:58,560
Speaker 1: going to say on a scale of one to ten,

540
00:27:58,760 --> 00:28:01,359
Speaker 1: with ten being people all are looking at this in

541
00:28:01,400 --> 00:28:05,359
Speaker 1: the financial services community very seriously, or in one being

542
00:28:05,600 --> 00:28:08,879
Speaker 1: they're aware it exists but haven't incorporated it yet.

543
00:28:09,000 --> 00:28:11,439
Speaker 3: I'm going to give it a four actually, and the

544
00:28:11,560 --> 00:28:15,000
Speaker 3: reason is because the finance industry is looking at transition

545
00:28:15,160 --> 00:28:19,200
Speaker 3: risk currently from a carbon pricing perspective. So the analysis

546
00:28:19,280 --> 00:28:22,080
Speaker 3: is essentially saying, let me know what is the carbon

547
00:28:22,160 --> 00:28:24,920
Speaker 3: footprint of a company, and I'll multiply this by a

548
00:28:25,000 --> 00:28:28,400
Speaker 3: fictive carbon price. Now, actually carbon prices are only covering

549
00:28:28,440 --> 00:28:30,600
Speaker 3: a quarter of global emissions, and there are a lot

550
00:28:30,640 --> 00:28:33,240
Speaker 3: of free allowances in Europe and in China, and so

551
00:28:33,320 --> 00:28:36,240
Speaker 3: you end up with a meaningful carbon price with maybe

552
00:28:36,280 --> 00:28:39,560
Speaker 3: about you know, ten to fifteen percent of global emissions.

553
00:28:39,640 --> 00:28:42,400
Speaker 3: And so what we do is very different. We're building

554
00:28:42,440 --> 00:28:45,080
Speaker 3: everything from the bottom up, looking at the exposure of

555
00:28:45,120 --> 00:28:48,720
Speaker 3: each company regionally, sectors low carbon data sets, and then

556
00:28:48,760 --> 00:28:51,760
Speaker 3: projecting the changes in the MOND to understand how companies

557
00:28:51,800 --> 00:28:54,920
Speaker 3: will be impacted. So I think there's room for improvement.

558
00:28:55,600 --> 00:28:59,400
Speaker 2: I would probably give the exposure to company cleaner revenue

559
00:29:00,240 --> 00:29:03,160
Speaker 2: of about a three. It is a newer type space

560
00:29:03,160 --> 00:29:06,640
Speaker 2: that we're starting to analyze right and from my perspective,

561
00:29:07,000 --> 00:29:09,960
Speaker 2: I haven't really seen financial institutions, you know, leverage the

562
00:29:10,000 --> 00:29:12,000
Speaker 2: exposure ratings in the way that I think it can

563
00:29:12,040 --> 00:29:14,600
Speaker 2: add a lot of value, particularly like the index creation.

564
00:29:14,880 --> 00:29:17,520
Speaker 2: Since we launched this model, this is something that we're

565
00:29:17,520 --> 00:29:19,520
Speaker 2: sharing a lot more with clients now. We're starting to

566
00:29:19,520 --> 00:29:21,600
Speaker 2: get a lot of feedback. I think clients are starting

567
00:29:21,600 --> 00:29:25,680
Speaker 2: to understand the value of identifying clean revenues within a company,

568
00:29:26,000 --> 00:29:28,160
Speaker 2: and we're starting to see indices in ets being built

569
00:29:28,160 --> 00:29:29,680
Speaker 2: off on the back of them. So I would say

570
00:29:29,720 --> 00:29:32,040
Speaker 2: a three now with the view that by the end

571
00:29:32,040 --> 00:29:33,480
Speaker 2: of the year getting it up to about a five

572
00:29:33,600 --> 00:29:34,040
Speaker 2: or a six.

573
00:29:34,360 --> 00:29:37,760
Speaker 1: It's only fitting that we ended a show about ratings

574
00:29:37,760 --> 00:29:40,720
Speaker 1: with a rating from each of you. So there we are.

575
00:29:42,760 --> 00:29:44,520
Speaker 1: Thank you very much for joining today.

576
00:29:44,520 --> 00:29:53,720
Speaker 2: Brilliant, Thank you so much for having us.

577
00:29:55,040 --> 00:29:58,080
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578
00:29:58,200 --> 00:30:01,960
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579
00:30:01,960 --> 00:30:06,000
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580
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