A Simple Climate Policy Proposal: Spending Climate Scrip by Scope

Introduction

In the city of Monterey, California, water is scarce, as it is elsewhere in California. In Monterey, the local regulatory environment is innovative and unique. Homes and businesses are required to “find” water whenever they develop an additional need for it. If I want to build a new house in Monterey where none exists, I must find the water. This might mean reaching out to other home owners and actually paying for the purchase and installation of low flow toilets in their homes, until the community collectively “finds” enough water to replace one household’s useage.

Let’s do the same for climate and add incentives. If Corporate America must go it alone because Congress can’t pass Cap and Trade without 60 Democrats, this could be a valid strategy, but to work, this must be a calibrated compulsory regulatory regime with environmentally assertive pricing. A voluntary or self-regulated system likely won’t be environmentally effective at all, but we may get desperate enough to try. Here’s the proposal.

Level Emissions from New Development. If I build or create a new home or business entity, I must “find” the Greenhouse Gases from other businesses and homes in the community, by investing in their low-carbon practices and technologies. To make this cap more effective, encourage local investments. Allow businesses and households to pay it forward by banking these community investments if they can for future quarters of development. Put a significant interest rate on temporary failures to keep up and punish long term non-compliance with the cap as heavily as possible, and refuse to permit such development. Note that the transient emissions impact of the building process itself is accounted for in the following paragraphs.

Make Profit the Engine of Emissions Reductions. For every dollar in profit that I earn as a corporation, make me spend a dollar in carbon scrip and set a tiered exchange rate by Scope. If I earn a quarterly profit of $1 million dollars, I am on the hook for that monetary value in physical Greenhouse Gas emissions reductions as scrip, and I can achieve them almost however I want, as long as they can be physically or financially verified according to rigorous regulatory standards. To use a pricing example that leads to big reductions, if Greenhouse Gas emissions reductions are worth $10 in profit scrip per ton, then I must reduce 100,000 tons of Greenhouse Gas in that quarter to make up my $1 million in actual quarterly profit, or face a punitive tax or fine on my actual emissions that is expensive enough to hurt me significantly, making me want to avoid it. I can “find” those emissions by investing in my local community if needed. Let the profit incentive itself be the engine of Greenhouse Gas emissions reductions.

Create a Tiered System. Create a tiered pricing structure that can be controlled by an objective economic authority such as the Federal Reserve and assign regulators to make sure that no more than one economic entity is claiming credit for a given reduction in emissions. Don’t allow companies to cheat by using outsourcing or other financial or structural sleights of hand to move emissions from tier to tier. The following allowances are conceptual illustrations, not actual policy suggestions, and they represent fictional money, not real trades or actual financial prices on carbon. That’s reserved for markets.

Reduce Scope 1, the operational emissions for which a corporation is directly responsible: my fleet fuel usage; the VOCs that my paints emit in the factory; my backup diesel generator emissions; the coal that my power plant burns; and my use of biomass combustion are all examples. Put the highest price on any reductions that I can achieve in Scope 1 to make it well worth my trouble, both to design new behaviors and engineering processes, and to document them to auditable standards using an environmental record. Sniff my smokestack if necessary or allow me to do it myself if I am combusting fuels or releasing fumes from a factory. For Scope 1 emissions, allow $1,000 profit per ton of emissions reduced. If can reduce my Scope 1 emissions by 100 tons of Greenhouse Gas in a quarter at $1,000 profit per ton, then I’m 10% there.

Reduce Scope 2, which is largely my electricity, heat, and steam usage by allowing $75 profit per ton equivalent. If I need to spend another $300,000 in Scope 2 scrip to meet my obligations, then I’ve reduced another 4,000 tons in emissions that quarter over the previous quarter of operations and investment.

Reduce Scope 3 at $50 profit per ton if I turn to my Value Chain to meet more of my obligations. Employee commute emissions reductions may also be priced as Scope 3 emissions reductions. The actual metabolism of my sector or industry or even my business is not as important as the incentive, and regulators can dial up a more assertive pricing structure as companies get leaner.

Reduce Scope 4, my corporation’s downstream product use emissions, by designing for efficiency, conservation, and lifecycle benefits at a price that regulators choose for policy effectiveness. Since these emissions reductions in Scope 4 are highly desirable, they need not necessarily be cheap.

Beyond scopes, if I invest in the surrounding community in order to achieve the reductions I need instead of taking ownership myself, let that price be much cheaper at say $5 profit per ton. And lastly, if I resort to commodity trading on the open market to get the reductions I need, let me account for that carbon at $1 profit per ton, so that I must reduce 1,000,000 tons per quarter at the real market price if this is my only strategy. Keep in mind that if the U.S. commodity markets are actually moving at $4 real per ton, I’m paying $4,000,000 in real money for the reductions that I didn’t invest in using carbon scrip, but I only made $1,000,000 in profits. Perhaps this should be my punitive fine for a failure to reduce my real emissions at the exchange rates that regulators have set between scrip and real money, by Scope. For this business, if the quarterly $1,000,000 is representative of long-term performance, then $4,000,000 alone represents a year of earnings. That’s a significant return-period loss event that threatens the viability of my business, just because I traded rather than reducing or investing in community or ecosystem emissions reductions. That shell game gets expensive.

Segregate the Commodity Markets. If the commodity markets absolutely must be the primary mechanism of reduction, then create tiered or pooled commodity markets, with separate pools for each tier or Scope. Don’t allow entities to trade emissions they don’t own for a premium, but rather ensure that it be done cheaply. Emissions that trade at the highest premium must be transactions between Scope 1 owners and Scope 1 owners, to ensure that such traders maintain adequate skin in the climate trading game rather than a dangerous climate trading bubble that would fuel a sustainability melt-down. Make these electronic transactions rather than OTC transactions to ensure transparency, but insist on ownership as well.

As I see it, the proposal is effective and free of negative incentives or moral hazard. The complexity and the cost of measuring, proving, documenting, or auditing emissions reductions are incented by the high premium placed on those emissions reductions as Scope 1 reductions. The easier and cheaper the proof, the more worthless the climate scrip that I must use, the more sheer tonnage of emissions reductions I must create in order to to make up for that.

Prediction and control are clearly going to be perceived as an issue here. Policy makers want reliability, but in my opinion they’re unlikely to find it under any regime. Cap and Trade alone might get us 5% over time or 10% at best in my opinion. To achieve 20% in the current political climate in Washington is just impossible, and Congress’ efforts are likely to be incompetent to the extent that they are watered down in the Senate. So while Cap and Trade seems to give us a reliable emissions target so that we know what we’re achieving, it’s not going to work that way. A Carbon Tax will also be difficult to calibrate to a specific emissions target, at least at first, before we know how the economy will respond. In contract, this proposal guarantees carbon neutral development and guarantees a bend in the emissions curve that is constructively encouraged by the profit motive. No, the policy doesn’t tell us how steep the bend would be or what target would be achieved and when, but it reliably and aggressively reduces emissions in a way that aligns with economic performance. It’s a worthy proposal.

Beginning the Solar Car Supply Chain GHG Inventory

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The Team Ledger

Now that my “guerilla internship” in state-of-the-science GHG accounting methods is underway, I’m going to chronicle my learning curve and share my new skills with readers. First let’s briefly introduce some veteran concepts for readers who are new to sustainability:

Scope 1 Emissions: These are the emissions that an entity or project should have in nearly complete control. These are direct emissions. The classic example is the tailpipe emissions from your car. Natural gas consumption is another classic. It’s not the stuff you buy; it’s what you burn.

Scope 2 Emissions: These are the indirect emissions that an entity or project has less control over. The classic example is electric power consumption. Flip a light switch or power a lathe in the shop, and you’ll see Scope 2 emissions.

Scope 3 Emissions: These are the least direct emissions of all, and they usually have to do with purchased goods and services, or contracts. If you are a municipality and you contract your recycling and solid waste disposal, those are Scope 3 emissions. Scope 3 emissions are fraught with measurement difficulty and controversy, yet they also offer some of the greatest opportunities and incentives for voluntary emissions reduction. The methodological difficulties posed by Scope 3 emissions include “double counting,” which is the fact that my Scope 3 emissions are your Scope 1 or 2 emissions.

In this blog, over multiple postings, we’re going to take care of much of the difficulty and controversy regarding Scope 3 emissions measurement. Specifically, we’re going to remove the double counting using methods that I hope to eventually make available online, in an open source and transparent fashion, for free.

I said that this blog was about solar cars, so lets get started. Here is a hypothetical solar car project budget taken conceptually from multiple generations of Yale’s Team Lux. The budget will not necessarily resemble any particular historical car, but it is entirely plausible and realistic. The budget is organized by industry sector, because our Scope 3 GHG inventory will be based on Sectors.

Here we go. To protect the team’s competitive interests, certain budget line items are anonymous. For example, the car’s acrylic canopy worth $2,000 is listed under its Sector heading only, and not identified as an acrylic canopy. Contact this author for more data.

Team Subgroup	Economic Sector	Dollars Spent In-Kind
Body	Aircraft Manufacturing	$20,000
Body	Plastics Material and Resin Manufacturing	$2,000
Body	Aircraft Manufacturing	$800
Frame & Mechanics	Automotive Parts Manufacturing	$3,420
Frame & Mechanics	Nonferrous Metal, Except Copper and Aluminum, Shaping	$900
Frame & Mechanics	Aluminum Extruded Product Manufacturing	$150
Frame & Mechanics	Tire Manufacturing	$600
Frame & Mechanics	Iron, Steel Pipe & Tubes from Purchased Steel	$75
Frame & Mechanics	Welding & Soldering Equipment Manufacturing	$450
Frame & Mechanics	Other Household & Institutional Furniture Manufacturing	$6,000
Electronics & Array	Semiconductors and Related Device Manufacturing	$9,068
Electronics & Array	Other Basic Inorganic Chemical Manufacturing	$990
Electronics & Array	Storage Battery Manufacturing	$20,000
Electronics & Array	Motor & Generator Manufacturing	$10,300
Electronics & Array	Relay & Industrial Control Manufacturing	$10,000
Electronics & Array	Wiring Device Manufacturing	$247
Electronics & Array	All Other Electronic Device Manufacturing	$1800
Electronics & Array	Electronic Computer Manufacturing	$5,000
Logistics	Air Transportation	$700
Logistics	Retail Trade	$2648
Logistics	Hotels and Motels, Including Casino Hotels	$1203
Logistics	Automotive Repair and Maintenance, Except Car Washes	$3750
Logistics	Insurance Carriers	$8550
Logistics	Other Amusement, Gambling, and Recreation Industries	$900
Business	Information Services	$5472
Business	Postal Service	$1622
Business	Business Support Services	$600
Business	Software Publishers	$1500
Business	Courier and Messenger Services (Factory Gates to Yale Campus, PPI Analysis)	$3000
Business	Legal Services	$300
Business	Electronic Computer Manufacturing	$2500
TOTAL	ALL SECTORS	$124,545

It might seem reasonable to simply scale known Sector emissions by the amount spent. So if the Air Transportation Sector purchases $1.56 million from other Sectors, adds $915,000 in value, and emits 1810 metric tonnes of “equivalent” CO2, then we can scale that all down to $700.

Unfortunately, that’s double counting. It also tacitly holds the solar car project accountable for essentially the entire economy, or at least a good sample of it. And it’s not just a factor of two too large, but generally two to some power, to put it crudely. Consider that in a free market, it takes two to tango; and remember my early comments about Scopes 1, 2, and 3. My Scope 3 is your Scope 1, and in a regulated carbon market with a cap or carbon tax, you don’t want to double count. So in a free market, we assume that the buyer is on the hook for 50% of the emissions created by the transaction, and the seller is on the hook for the other 50%. This turns out to be a kind of approximate power law as we walk out onto the many computational nodes of these Sector branches, but it’s also a messy power law thanks to the presence of Scopes 1 and 2, which make the data less clean, and it’s a different power law for every real value chain in the economy.

So, we will model these cascading transactions explicitly, using an explicit cascading model in which Scopes 1 and 2 are set aside and only Scope 3 transactions are multiplied by half. There are two ways to go about this: inverse modeling, and brute force iteration.

I’ve already mapped out the former in pseudo-code, which I am also providing to readers as well. The code will not be very clear at first, but as I tap it out and shape it in future blogs, it should become more clear.

Psuedo Code for Scope 3 GHG Value Chain Emissions

Initialize GHG Data Matrix to Zero
Set GHG Data Matrix for Round 1 to Budget Values * Given Sector Scaled GHG Values
Calculate First-Order Initial Estimate of GHG Emissions:
Estimate = Sum of Given Sector Emissions from National Accounts Scaled to Ledger Values
‘ As discussed, this is an overestimate
‘Ignore Scopes 1 and 2 and set aside those values in each Round
Round = 1
Do While (delta/Estimate) < ____%
‘delta is a revision of the Estimate downwards. The initial estimate above is double counting.
‘ so delta/Estimate is a proposed convergence criteria and is always a subtraction
Round += 1
Do i = 1, NumberOfSectors (‘this is the Budget itself when Round = 2)
Do j = 1, NumberOfSectors (‘breakout the previous Round into next Round of Sectors)
GHGMatrix(Round,i) +=
NationalAccountsDollars(i,j) * _
GHGMatrix(Round-1,j) * _
SectorGivenGHGEmissions(i)
Enddo
Enddo
delta = .5 * Sum(GHGMatrix(Round, : ) )
Estimate = Estimate – delta
Enddo

It will be interesting to work with a trial data set and see if this works in practice.

So to review. I covered the three Scopes and gave a brief explanation of Scope 3 accounting before diving into a hypothetical team budget. I then explained why the Accounts themselves would provide an overestimate of solar car project GHG emissions. And lastly, I followed up on that comment with a proposed algorithm for Scope 3 accounting that traverses the value chain and takes 50% of each Scope 3 transaction in the value chain. Stay tuned!

A Primer on GHG Economic Accounting

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In my view, a new state-of-the-art is emerging in the economic accounting of GHG emissions. Emissions from operations and value chains will increasingly be quantified using top-down methods that are less process- and more results-oriented, fuzzier but more accurate, bigger, less mathematically complex, and also less transparent to laypeople. I’m a big fan of the new models, because they commit fewer errors of omission in their estimations of “Scope 3” GHG emissions, and are therefore less biased than the life cycle method. I will attempt to interpret the new methods so as to make them more transparent and understandable, and possibly even fun.

The methods are known to economists as input-output models, and they’ve been around in other forms for quite some time. Historically, input-output models have been applied to the industrial production of goods and to flows of capital between industries. The guy who invented the input-output method, Wassily Leontief, won the Nobel prize in economics for his contribution, which made possible the derivation of a general equilibrium economic theory. Now, start-up companies in Silicon Valley, regional economics think tanks like the Association of Bay Area Governments, and other entities interested in GHG emissions are increasingly making use of this method to express the embodied energy consumption and GHG emissions in the flows between different parts of the economy. These players are scaling their models from single company operations to national economies and back again. Examples of input-output systems include corporate operations, corporate value chains, customer ecosystems, entire industries, geographic regions such as the Bay Area, and national accounts. This blog entry will stick with industrial Sectors as economic black boxes with financial and environmental inputs and outputs, and no transparent process inside.

Through some mathematical wizardry, the economist “inverts” a matrix table of Sector inputs and outputs. The result of the economist’s mathemagic looks like this:

Example Input-Output Results*

Sector	Example of Sector	Total Purchase	Indirect Purchase	Value Added	CO2e Emitted
Energy Generation	Power Utilities	1,730,000	350,000	1,000,000	10500 Metric Tonnes
Wholesale Trade	Wholesale Gas Stations	1,550,000	230,000	1,000,000	279 Metric Tonnes
Retail Trade	Grocery Stores	1,390,000	260,000	1,000,000	381 Metric Tonnes
Air Travel	Airlines	2,090,000	530,000	1,000,000	1810 Metric Tonnes

*Carnegie Mellon University Green Design Institute. (2009) Economic Input-Output Life Cycle Assessment (EIO-LCA) US Dept of Commerce 1997 Industry Benchmark (491) model [Internet], Available from: [Accessed 5 Jul, 2009]

These numbers are real. They are available from Carnegie Mellon University’s Green Design Institute’s input-output model, which uses United States National Accounts Data for 1997. Other data options are available, and custom projects can be modeled by selecting a basket or portfolio of purchases.

How did the economist calculate CO2 emissions? By treating the environment like a “Sector” of its own – conceptually and mathematically – a Sector in which there are inputs and outputs in the form of greenhouse gases rather than money or jobs. The same can be done for other air pollutants, for energy, and for toxic releases. The result is a total accounting that spans a range of influence and accountability, from what a company can directly influence and is responsible for, to the farthest reaches of its value chain. That’s a lot of responsibility, and it can be very revealing to consumers and producers who were previously unaware of the full scope and complexity of their impact. These consequences are literally ramifications, like the branches of a complex value chain tree that extends deep into the entire economy. This approach does not draw conclusions about ownership. Nor does it say that these emissions would not otherwise have taken place. Ethically, most people want to know what emissions were committed by their actions that would not otherwise have been committed.

The alternative is to explicitly model each and every implied transaction in the value chain, cascading downwards through the branches of the tree and applying a coefficient to each transaction. Each transaction is then considered a free market handshake in the sense that producer and consumer each bear say half the blame. These fractions, applied to each node of the resulting tree, can then be integrated upwards into scopes of responsibility across the value chain. That’s a lot of computing power. And it’s one reason that private companies are moving into this space, because building those models is a value add for companies who know that they’ll benefit from an environmental inventory that takes ownership into account.

The good news is that I intend to do this and make it open source, or at least free online. I’ll begin with the ledger from the undergraduate solar car project that I contributed to as an undergraduate student at Yale. I’ll simulate what Team Lux’s budget must have been, and then cascade the impacts of each purchase decision upstream through the value chain, taking half the CO2e value of each transaction, until the answer converges. And, I intend to create a nice online GUI and tutorial. The results will be combined with a life cycle analysis of sustainable solar car design and implementation, and will include some Value Scenarios that assess the value of Team Lux’s stakeholder relationships as it was positively and negatively impacted by the effects of time, pervasiveness, and systemic on the uses of solar technology. I don’t yet have a firm time frame for providing this, but I’ll stake my job search on it.

So, to review. There’s a new state-of-the-art that quantifies the GHG emissions of entire value chains. It’s called input-output modeling. At first glance, it seems to allow corporations to estimate their emissions using nothing but their ledger and some simple scaled numbers by Sector. But, the method double counts, by holding companies accountable for the entire economy, including the half of each value chain transaction that is the responsibility of the seller. The solution is to explicitly model each value chain transaction explicitly, in cascading fashion, lopping off half the emissions associated with each transaction. The good news is that you don’t have to know your value chain inside and out in order to do this, because the National Accounts dataset will map those transactions for you. For more information on input-output modeling including the dataset itself, go to the mother lode: eiolca.net.

Thanks for reading!

p.s. A few key points here for atmospheric modelers. First, economic input-output is a form of linear programming, but the atmospheric Bayesian inversions that you know and love are definitely not linear programming! Don’t get the two confused. Atmospheric inverse models are nonlinear. The problem that I describe above is indeed a matrix inverse problem, because it assumes that each Sector’s output is a linear combination of inputs from other Sectors. But, it is an analytical matrix inversion as far as I know, rather than a statistical Bayesian problem. Hope that helps! Happy skill building.

Sustainable Humanism as a Business Ethos

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A prominent executive for a large Silicon Valley software firm recently noted that the “Land of Green” is littered with the remnants of a soon-to-be-dead language. He described the bleak image of a beach littered with myriad words and phrases, like “phthalate” and “bio-diesel,” that reflect a drilled-down attention to detail on the part of the environmental community. He expressed a deep yearning to step back, from the beach to the horizon. He called this “Going to the Land Beyond Green.”

“How do I implement abstract phrases like ‘climate protection’ in my corporate operations, in my value chain, and among my customers?” he seemed to be asking. And in a separate post, he asked, “How do I measure it? What metrics tell me whether or not I’ve succeeded? What measurables will give me intrinsic environmental performance across my enterprise? And isn’t that ultimately value? Aren’t we just saying that sustainability is value?”

In order to get at an answer, let’s move away from the beach analogy for a moment and consider the analogy of a house in which we live. It’s the House of Green. Perhaps the person who lives there has collected a few too many boxes and other middens, so that the house has become cluttered with seashells and beach stones. The house is also crowded with fragmented and redundant efforts on climate in particular, as our executive points out. It has too many tiny rooms that do the same thing.

And yet this house is the future House of Sustainability. It has a foundation and a design and some complex systems, too. I like the house image, because it’s a familiar, concrete metaphor for software executives who are accustomed to thinking in terms of foundations, frameworks, modularity, and architecture.

You are the new architect who has been assigned to transform the House of Green into the House of Sustainability. You’re here to do a redesign because the original architect missed the big picture. You’re here to jack up the house and redo the foundation, and make the rooms more modular. After cleaning up the cluttered collection of words and concepts brought in from the nearby beach, you realize something.

The house is not alive without its human inhabitants. The cluttered belongings mean nothing without them. As the architect, you know well the thrill of putting your feet on the floor every morning. You want to create buildings that live thanks to their human inhabitants. Because the quality of present and future human life is the measure of your success in sustainability. You realize that “green” lacked this meaning because it was not about human life.

These are the things that I see when I step back from the beach: I see the morality play at the top of the human pyramid of needs. It’s a kind of altitude that sees what it takes to move from green to Sustainable. It’s like walking into Whole Foods and seeing morality play itself out in the sugary philosophical gloss of waxed organic vegetables competing with organic chocolate bars and gluten free, rice-based zuchini bread. You move to a higher perceptive focal plane. You look down the pyramid of human needs, to the base, and care about whether children have clothes that fit them, and nutritious food, and clean water to drink.

When I read company sustainability reports, I often hear this same concern in the voice of the company as a community, that as an enlightened and broadly defined fiduciary for our future wealth, you care about the human side of things. You want to do well by your employees, your communities, and your customers. This is why companies like yours chose to engage their stakeholders. It reflects your deepest core values.

The ultimate measure of sustainability is therefore the future human cost and benefit of today’s operations. Ultimately, in my opinion, this is what we need to model, measure, and forecast directly. It is also a material ROI over time, expressed in social and environmental terms, like how many people you will employ at a living wage, or whether you will improve our environmental standard of living tomorrow. Your employees and your customers are your communities, and your ROI on sustainability lives at the scale that your communities do, which in most cases is the global scale.

We are fiduciaries to our future selves. That’s why we invest. Let’s find metrics and feedbacks that accurately reflect the future human costs and benefits of our business practices, and report them as measures of intrinsic enterprise sustainability. Let’s do it until the House of Sustainability rings like a bell, alive with the voices and footsteps of children.

What trees taught me about Operations Research

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There’s a Bill Morrissey folk tune about a young  bride who chooses to burn birch one night for warmth, while her stern, unromantic husband argues for oak. Oak, says the husband, “will burn as long and bright as a July afternoon; birch will burn itself out by the rising of the moon.” Morrissey tells us that the young bride then secretly burns the passionate wedding birch after her husband has gone off to bed: “she thought of heat; she thought of time; she called it an even trade.” Mathematically, Morrissey is commenting on the integral of a function over time, a function in which there is a tradeoff between duration and intensity. The existence of that tradeoff suggests a constraint on the area under a curve called an integral constraint. It’s a quantity that is conserved.

In a counter-intuitive twist to Morrissey’s notions of the steady but boring oak tree, my research affirmed that oaks live fast and die young with respect to water. They are huge risk-takers. In a Mediterranean oak savanna, this is how the coast live oak out-compete the black oak, in a competitive game determined largely by physical factors such as the darkness and moisture of tree leaves. It was this competitive game that I modeled with Dennis Baldocchi and Monique LeClerc in a Tellus paper.

And yet, despite their love of risk, oak trees do sustainability every day. They’ve been here longer than humans have, and they’re very resilient and conservative in the sense that they survive for the long term. The mathematics of  those trees – the constraints they respect, the optimums they solve for, and the physiological metrics with which they signal important quantities – have much to teach us about solving carbon-constrained operational problems for business. Specifically, oak trees have something to tell us about optimization as it applies to complex operational systems and the attempt to maximize economic value in a carbon-constrained world.

In the language of Operations Research, my U.C. Berkeley environmental research involved a least cost path analysis of integral-constrained photosynthesis over time. It means that photosynthesis varies seasonally in some kind of optimum way. The rest is just a mathematical construct to describe that optimum and how it relates to other things, but it’s a construct that transfers well to business.

Here’s the graph of a photosynthetic “path” through time. The data in the graph express an oak tree’s carbon constraint over time, called photosynthetic capacity. It’s a direct reflection of leaf nitrogen, which is needed for an important photosynthetic enzyme. This is a graph from a paper by Dennis Baldocchi and some of his other colleagues at UC Berkeley.

There are ecologists who think of this graph through time as a carbon-constrained optimum with respect to water. Water is the ecological cost of photosynthesis, and that cost varies daily and seasonally, over a path through time, depending on heat and humidity. When a leaf opens a stomate so that carbon can diffuse in and be fixed by photosynthesis, moist inner membranes are exposed to the sun’s energy. Water is lost through evaporation. That water cost varies over the “path” of photosynthesis through time, since some days are hotter or more humid than others. If spring is wet and summer is dry, then the trees are tuned to party hard in the spring.

The scientist’s goal is to predict precisely which tuning and which calibration will allow the oaks to out-compete their neighbors in the quest for more water. What do we optimize for? What is the right integral constraint? Is water the only cost of photosynthesis, or do other nutrients such as nitrogen come into play? Scientists like Dennis define supply and demand metrics for water and brainstorm concrete ecological switches and lags that would trigger different photosynthetic responses on the part of the tree.

The existence of ecological triggers reflects the pragmatism of evolution – and the fact that trees do not think or predict, only respond in the moment, in a genetically programmed fashion. There’s no tree computer that will let Mother Nature type in an order for future photosynthesis. Trees don’t solve algorithms in that sense, but they do solve problems in the sense that individual trees that fail die, and only the successful trees survive. But ecosystems do not necessarily record or communicate their evolutionary failures so that we can learn from them.

If “water” is money and “photosynthetic capacity” is a carbon constraint, then we have an analogy to the business operations of a company, building software as a service, to measure and model operational costs in a carbon constrained, cap-and-trade world. The oak trees are modeling this for us, and they are solving the problem in a particular way that has led to their longevity. Perhaps the right question to ask then, is what makes this particular solution so sustainable. Why does it work? Is there something about the oak’s solution to a least cost path analysis that succeeds? Has the tree accounted for the uncertainties associated with a varying carbon constraint in a way that business can learn from? How do we optimize our operations under an uncertain carbon regime in which the price of carbon might be volatile? Should we look to nature?