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The Climate-Economy Interface for Carbon Price Automation & Investments
Prompt CF1: Smart Contract can provide innovative automated approaches to the mobilization of capital for financing climate action, for example from voluntary or compliant carbon pricing. However, this ideally requires linking the climate state with economic costs and benefits calculations so that mathematical models act as the ‘oracles’ that determine key variable for smart contracts to execute. Consider the role of Integrated Assessment Models and propose the architecture for how these would integrate into a carbon pricing mechanism. Propose how a voluntary carbon price mechanism could be implemented and how its proceeds used to invest in climate action projects.
Prompt Host: Yale openlab (Martin Wainstein) // Join the prompt hosting team >
Discord Channel: https://discord.gg/aKN5hAQ

Reference Architecture

The diagram below traces an ideal flow of linked information to connect the physical properties of the planet's climate with economic variables: it ranges from the planetary state, through Climate Models (physical modeling), and Climate-Economy Integrated Assessment Models (IAMs) which are used to calculate values such as the social cost of carbon, costs and investments scenarios and needed emission pathways. This is represented by linking outputs of one model with input on the other in a linked chain.
Earth level data is first detected by sensors, both by remote sensing (i.e. satellites) and a ground (and sea) network of sensors. Data from these sensors is fed into complex climate models that simulate many of the physical properties of the climate system across the entire globe. This creates projection of climate scenarios which are in turn fed intro Climate-Economy-Society models that consider social aspects (eg. population), economic variables like economic growth and damage and losses from warming and climate impacts, as well as energy projections. These integrated models are particularly use to project what are ideal ('cost effective') emission trajectories at an aggregate and sectoral level. Model as also used to ask answer questions of 'what if?' (what if we dont act on climate change?, or what if we just half our emissions?).
The metadiagram distinguishes between how the information coming out of these models can be used in voluntary mechanisms vs. policy and compliance mechanisms. In fact, Integrated Assessment Models are crucial to inform major climate policies, as shown by the bottom layer of the diagram.
Particularly, it shows how and Integrated Assessment Models can be used to calculate the social cost of carbon, and it proposed how this could feed into a voluntary carbon price contract, connected to a 'climate fund.' This would be a particularly powerful idea to create since it allows every actor that is a steward and understands the value of pricing carbon to engage a carbon $ without waiting for regulations to define it. A voluntary carbon $ could be accrued by a shared fund destined to mobilize capital for climate action projects, eg. mitigation and adaptation work.
The general premise of what is proposed here can be summarized in the below basic flow chart. In this case, smart contracts can have different uses, but they are triggered by information that is calculated be the IAMs, which are fetched by an 'oracle', as well as by social agreement that are defined within the contract. A social agreement for example can include a threshold that helps decisions not be solely dependent on the IAMs algorithm.
How integrated assessment models can integrate into smart contracts

Proposing a voluntary carbon $ and climate fund linkage

Considering how smart contracts could be used to create trusted rules —informed by highly educated and computational calculations (i.e. from IAMs)— we can propose a connection where a carbon $ can automatically be calculated for climate stewards to adopt (eg. individual, companies and subnational actos) and participate by essentially the following steps of 1) calculating their emission footprint (eg. using the ghg protocol), 2) apply the carbon $ and 3) define and execute their corresponding tax. The carbon $ should adjust itself based on the emissions reduction progress and climate state (eg. $ going up with inactions, and down with further mitigation actions, making it cheaper to act early i.e. using ideal system dynamics). Furthermore, social agreements can be used to determined tiers of a tax based on the steward's economic reality, and have governance mechanisms to define these variables. The carbon $ smart contract should also issue an automatic certificate (eg. immutable token) that attests to the payment of the voluntary 'tax.' These certificates could conceivably be accepted by the corresponding fiscal entity overseeing the steward's accounting requirements, and act in lieu of an official roll out of carbon $ later on, or have other benefits (but this would be regulatory specific).
The revenue generated by a voluntary carbon $ can be managed by a separate smart contract, one that could operate like a Decentralized Autonomous Organization that would determine algorithmically, and/or based on distributed governance, how to allocate the funds to climate action projects. The fund would provide capital at a lower market cost to allow high impact climate projects, that in turn are governed by the economics of 'project finance,' which create profits used to repay the source of capital plus interests and thus make the initial fund have revolving capital.
The model below attempts to visualize this integrated idea on how mode data and oracles feeds smart contracts that channel capital flows:
The below diagram is shown as a summary of how the initial climate and IAM models would be connected to this capital mobilization system:
MODEL ACRONYM LEGEND:
GCM: General Circulation Model RCM: Regional Climate Models ESM: Earth System Models CMIP: Coupled Model Intercomparison Project MAGICC: Model for the Assessment of Greenhouse Gas Induced Climate Change DICE: Dynamic Integrated Climate-Economy Model

Background information

See the following links to have a better notion of how models are used to infer current reality and projections of the climate system:
Specific models:
PIK Specific model: REMIND World Climate Research Programme: CMIP Melbourne University hosted model: MAGICC
Magicc is a light model that is used to plot the relationship between CO2 concentration and temperature.

The Social Cost of Carbon—

Different models are used to calculate how much carbon should be costed out in the human world. This is definitely subjective, since part of the cost of carbon relates to existential value (eg. millions of years of photosynthesis leading to coal, oil and gas underground reserves) which is virtually priceless.
See this link for an overview of what is the SCC.
A famous model used to calculate the social cost of carbon is the DICE model, which gave its author Nordhaus the 2019 Nobel Prize in economics:
Summary of DICE model Summary of DICE model with details Nordhaus calculations using DICE. DICE model video.
Carbon Price report:
CarbonPricing_FullReport.pdf
4MB
PDF
Report of the High-Level Commission on Carbon Prices
this Commission concludes that the explicit carbon-price level consistent with achieving the Paris temperature target is at least US$40–80/tCO2 by 2020 and US$50–100/tCO2 by 2030

Articles on Climate Finance under 1.5

Below are a selection of articles that present interesting insights and ideas on what climate finance means to achieve a 1.5oC. Please add more to this section:
Innovative Approaches: Underwriting 1.5°C: competitive approaches to financing accelerated climate change mitigation. Discusses pay for performance and presents alternatives to subsidies such as auctioned price floors
The Economics of 1.5oC Climate Change. Presents different economic variables, including carbon pricing under a 1.5oC
Review Report: Global Climate Finance: An Updated View 2018 Overview of climate finance numbers and state of the world, presented by the Climate Policy Initiative Summary Blog: The role of finance and investment in meeting the 1.5°C goal which cites the IPCC report where is states that “annual average investment needs in the energy system of around 2.4 trillion USD2010 between 2016 and 2035, representing about 2.5% of the world GDP”.

Tasks and contribution opportunities

  • Propose & Create: a numerical model of how the proposed voluntary carbon $ idea could work in practice. What are key factors that you would consider to define the carbon $? Who could voluntarily adopt a carbon $? Consider that eg. companies often report they include a 'shadow' carbon $, but those funds never leave the company.
    • Consider a simple approach to carbon price calculation (eg. take for granted the $40-80 stated by the high-commission starting 2020 and propose hot to determine and allocate the value between this range), OR calculate it based on how much funds need to be deployed to transform the energy system and work backwards from there, OR a different simple approach.
    • Consider a more complex approach that would incorporate Integrated Assessment Models in your working (see for example the DICE task).
  • Research & Develop: whether the DICE model can be loaded into a public GAMS, and hosted in a server with an API, so that it can run different version based on different calibrated values. If so, build a basic input-output user interface for it using basic bootstrap react. This would allow it to work as an oracle.
  • Prototype: a simple proof-of-concept of how a lite version of an Integrated Assessment Model mentioned in this prompt could interact with a smart contract.
  • Propose and prototype: How would the shared investment fund that receives voluntary carbon $ operate in practice? Is it feasible to have it run by a DAO, how would this look like at the smart contract architecture level? Or should it be something the is managed by the existing Climate Funds, such as the GreenClimateFund? How would it determine how to invest and mobilize capital to? Could it use specific algorithms to filter through projects and investment opportunities and decide automatically or should it have a specific board? How about governance?
  • Propose: more tasks for this prompt!
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