Global open database of coal, oil and gas supply chains
Tom Brown
I'm not sure I saw this go over any lists yet, but there was an open
dataset of coal, oil and gas supply chains published on GitHub in Feb 2021:
https://github.com/Lkruitwagen/global-fossil-fuel-supply-chain
with an accompanying preprint:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3783412
I think many of you will know the WRI's power plant database they use,
but they also have datasets on production sites, shipping routes, etc,
that could be interesting. It seems like they have to use heuristics to
fill in many data points.
Here's the abstract:
"Climate change risks manifest in the real economy, with grave
consequences for welfare of human populations and the biosphere; the
economic returns of industrial sectors; and geopolitical stability.
Understanding the diffusion of risks in real infrastructure networks is
an urgent priority for delivering climate change mitigation, adaptation,
and resiliency. The oil, gas, and coal supply chains are the most
salient progenitors and inheritors of these environmental risks. We
prepare a geospatial arrangement of the global oil, gas, and coal supply
chains using open-source asset-level data. The resulting complex network
has 6.09mn nodes and 15.70mn edges and is implemented in a graph
database. With estimates of annual coal, gas, and oil demand in 13,229
global population centres and 8,165 global power stations, we use a
minimum-cost flow method to estimate global asset-level energy flows. We
develop a method for cross-validating and tuning our network flow
simulation using aggregate country-level import and export statistics.
We demonstrate two analyses of asset-level transition risk: a
counter-factual demand shock scenario consistent with the IEA
Sustainable Development Scenario; and supply shock scenarios developed
by interdicting regionally-aggregated coal, oil, and gas supplies. Our
contribution lies in the global scope of our asset-level supply chain
and the novelty of our minimum-cost flow method. We conclude with a
discussion of further research directions and make the graph database
and supporting code publicly available."
Best wishes,
Tom
--
Tom Brown (he/him)
Professor of Digital Transformation in Energy Systems
Institute of Energy Technology
Technische Universität Berlin
Group website: https://tub-ensys.github.io/
Personal website: https://nworbmot.org/
Visitor Address:
Einsteinufer 25 (TA 8)
10587 Berlin
Robbie Morrison
Hi all
I would like to put in a plug for the World Resources Institute (WRI) regarding their activities in the energy domain. WRI clearly understand what open data is and the benefits of socially‑agreed coherent datasets. See also:
- WRI (19 April 2022). Open-source geospatial solutions in energy access. World Resources Institute (WRI), World Bank, Energy Sector Management Assistance Program (ESMAP), and EnAccess Foundation. Webcast 01:00:39. Broken link. Worked on 30 April 2022 when I watched the video.
In contrast perhaps, the Linux Foundation sponsored LF Energy (LFE) project seeks to promote "open standards and reference architectures". But my attempts to elucidate the underpinning principles for "open" in this context and what legal attributes might apply have yielded nothing thus far. Somewhat ironic given the huge efforts that the free software world has made in defining variously: foundation principles (the four freedoms), license stewards (FSF and OSI), a set of approved licenses, legal interoperability, license notice good practice (such as the FSFE REUSE guidelines), and enforcement methods (litigation the last resort) — and more recently developing the tooling to parse software stacks (also known as a software bill of materials under US law) and ensure licensing compliance. Open energy system analysts have no alternative but to go down much the same track approximately for energy sector data and metadata. I would be very happy if someone associated with LFE could provide their touchstone definitions for "open" in the context of protocols covering data collection and information exchange between and within practitioners and systems. Paying say USD $2000 for a copy of an industry standard reference architecture under full copyright would seem to lie outside the usual take on open information?
On the topic of heuristic data cleansing (that Tom mentioned), I think WRI have been experimenting with AI methods (from memory, also covered in link‑rotted video cited above). For completeness, these other WRI resources might be of interest too:
-
WRI (6 April 2018). Global power plant database — Data — Version 1.0.0. World Resources Institute. Washington DC, USA. Download page.
-
Byers, Logan, Johannes Friedrich, Roman Hennig, Aaron Kressig, Xinyue Li, Laura Malaguzzi Valeri, and Colin McCormick (April 2018). A global database of power plants — Technical note. Washington DC, USA: World Resources Institute (WRI).
with best wishes, Robbie
Dear open modellers,
I'm not sure I saw this go over any lists yet, but there was an open dataset of coal, oil and gas supply chains published on GitHub in Feb 2021:
https://github.com/Lkruitwagen/global-fossil-fuel-supply-chain
with an accompanying preprint:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3783412
I think many of you will know the WRI's power plant database they use, but they also have datasets on production sites, shipping routes, etc, that could be interesting. It seems like they have to use heuristics to fill in many data points.
Here's the abstract:
"Climate change risks manifest in the real economy, with grave consequences for welfare of human populations and the biosphere; the economic returns of industrial sectors; and geopolitical stability. Understanding the diffusion of risks in real infrastructure networks is an urgent priority for delivering climate change mitigation, adaptation, and resiliency. The oil, gas, and coal supply chains are the most salient progenitors and inheritors of these environmental risks. We prepare a geospatial arrangement of the global oil, gas, and coal supply chains using open-source asset-level data. The resulting complex network has 6.09mn nodes and 15.70mn edges and is implemented in a graph database. With estimates of annual coal, gas, and oil demand in 13,229 global population centres and 8,165 global power stations, we use a minimum-cost flow method to estimate global asset-level energy flows. We develop a method for cross-validating and tuning our network flow simulation using aggregate country-level import and export statistics. We demonstrate two analyses of asset-level transition risk: a counter-factual demand shock scenario consistent with the IEA Sustainable Development Scenario; and supply shock scenarios developed by interdicting regionally-aggregated coal, oil, and gas supplies. Our contribution lies in the global scope of our asset-level supply chain and the novelty of our minimum-cost flow method. We conclude with a discussion of further research directions and make the graph database and supporting code publicly available."
Best wishes,
Tom
-- Robbie Morrison Address: Schillerstrasse 85, 10627 Berlin, Germany Phone: +49.30.612-87617