After a hiatus from posting, I am now ready and able to start to unfold this new series.
Unlike my previous series, which I could post on as I worked through my analysis, and for reasons that will become apparent at the end of this post, I had to substantially complete the entire analysis before I could start posting parts of this series.
That is what I have been doing over the past month...in the fragments of my available time....more and more my life feels like
in wonderland, running harder and harder to just stay in one place. Alice
Recap of Estimating the End
This series is related to another series I posted about a year-and-half ago on this blog, Estimating the End of Global Petroleum Exports (“Estimating the End”). You might want to read (or re-read) that series for background, as I probably will not repeat all of the nuances of its analysis and assumptions.
In “Estimating the End,” I did a form of global Export Land Model (ELM) analysis to predict how petroleum exports from various net exporting regions to various net importing regions would change over the coming decades, and, likely would end in the early to mid 2030s.
To perform my analysis in “Estimating the End,” using the then most recent (2009) data from the BP Statistical Review (“BP Review”), I divided the world into seven regions, and did a non-linear least squares analysis to obtain the best fit of the logistic equation (aka the Hubbert Equation) to each of these region’s petroleum production and consumption data.
To predict the future distribution of petroleum among the seven regions, I made two major simplifying assumptions.
I made a “regionalism” assumption, which presumed that, for any net exporting region, the region’s “future internal consumption needs” would be entirely met by internal production within the region before there were any net exports outside of the region. Those future internal consumption needs, in turn, were defined by the logistic equation best-fit to the reported past petroleum consumption data for that region. The remaining petroleum, not consumed intra-regionally, was presumed to go into a net export pool of petroleum that was available to the net importing regions.
I also made a “fungibility” assumption, which presumed that, for any region having net positive exports, those export were distributed to the other net importing regions with “complete fungibility.” Practically speaking, distributing with complete fungibility entailed assuming that the recent average percentage of the net export pool of petroleum to each of the net importing regions stays the same going forward. For example, if a net importing region, like North America or Europe, over past five years had imported 28 percent of the net export pool, then I fixed this percentage, and assumed that the region would continue to receive this same percentage of the net export pool, until the net export pool went to zero in the early to mid 2030s.
For net exporting regions whose exports dropped to zero (i.e., ex-exporters) while there was still a non-zero net export pool, I considered two boundary scenarios. In a “no-sharing” scenario, I assumed that these ex-exporting regions did not draw into the remaining net export pool—they just went away and lived within their own means of production. In a “sharing” scenario, I assumed that the ex-exporting regions got first drawing rights from the remaining net export pool. For instance, once a exporting region, like South America or Africa, becomes an ex-exporter, they were able to totally make up their predicted internal consumption needs by importing from the remaining exporter regions, i.e., the Middle East and Former Soviet Union, before the any of original net importing regions, e.g., North America or Europe, were allowed to draw from the remaining net-export pool. Of course, the sharing and no-sharing scenarios were intended to set upper and lower boundaries on what would happen to the net export pool. In fact, this explains the reason why I predicted that total global net exports would hit zero some time between 2030 (assuming a sharing scenario) and the mid-2030’s (assuming a no-sharing scenario).
Reprised, Improved and Combined
There were a some aspects to “Estimating the End” which left me unsatisfied, and, the present study is intended to be a reprisal and improvement of that earlier prediction scenario.
First, one of the seven regions, the Asia-Pacific region, was huge in population, about 3.8 billion in 2010, or over half the world population, relative to the other six regions (all each less than 1 billion). Moreover, the Asia-Pacific region comprised a group of countries with diverse petroleum consumption and population growth trends.
To address this, in the present study, I have divided the Asia-Pacific region into three separate regions:
China, and the remaining Asia-Pacific region (rAP). Japan
So, now the present study looks at petroleum production and consumption trends for nine separate regions, as set forth in the BP review’s definition:
1) North America (NA): US (excluding Puerto Rico),
Canada and Mexico
2) South America (SA): Caribbean (including Puerto Rico), Central and
3) Europe (EU): European OECD members (Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Republic of Ireland, Italy, Luxembourg, Netherlands, Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom) PLUS Albania, Bosnia-Herzegovina, Bulgaria, Croatia, Cyprus, Former Yugoslav Republic of Macedonia, Gibraltar, Malta, Romania, Serbia and Montengro, Slovenia.
4) The former Soviet Union (FS):
Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan
5) Middle East (ME):
Arabian Peninsula, Iran, Iraq, Israel, Jordan, Lebanon, . Syria
6) Africa (AF): Territories on the north coast of Africa from
Egypt to Western Sahara. Territories on the west coast of Africa from Mauritania to Angola, including Cape Verde, . Territories on the east coast of Africa from Chad Sudan to . Also Republic of South Africa Botswana, Madagascar, Malawi, Namibia, Uganda, Zambia, . Zimbabwe
8) Japan (JP)
9) The remaining Asia-Pacific (rAP): Brunei, Cambodia, China, China Hong Kong SAR*, Indonesia, Japan, Laos, Malaysia, Mongolia, North Korea, Philippines, Singapore, South Asia (Afghanistan, Bangladesh, India, Myanmar, Nepal, Pakistan and Sri Lanka), South Korea, Taiwan, Thailand, Vietnam, Australia, New Zealand, Papua New Guinea and Oceania) minus China and Japan.
And, of course, the present analysis is based on the most current data provided in the 2012 publication of the BP review, which provides data up to an including 2011.
Second, at the time I prepared “Estimating the End” I recognized that the “regionalism” and “fungibility” assumptions did not exactly portray how petroleum has been circulated in the real world. Certainly there are arguments that such assumptions are not unreasonable, and, without these assumptions, I would not have been able to predict future consumption trends for the seven regions that I considered in that study.
Still, the assumptions of a region’s internal consumption needs being entirely met by internal production, and freezing export percentages to their past five year averages, bothered me as being unrealistic. And my subsequent studies, summarized below, shows that this is often not the case. That is, net exporting regions are often both importing and exporting petroleum, and, net importing regions are often both importing and exporting petroleum. And, my analysis in “Estimating the End” completely ignored the possibility that these import and exports trends could be changing over time.
Additionally, the assumption that a net-exporting region’s internal consumption would continue unfettered, or only be bounded by the logistic equation's best-fit to past consumption trends, bothered me. For instance, it is probably unrealistic that net exporting regions, like the
Middle East, will just continue unfettered increases their internal rate of petroleum consumption in the face of decreased revenues from decreasing petroleum exports. Many countries in the Middle East, and other net exporting regions, are highly dependent on revenues from their petroleum exports. Therefore, I suspect that the future growth in consumption in these regions would at least slow down in order to better support continuing this revenue stream.
To address these issues, in the present study, I have entirely thrown out both of the “regionalism” and “fungibility” assumptions of my ELM analysis. My ability to throw out these assumptions and still make a prediction of future petroleum consumption rate trends was made possible by combining the present study with the results from two previous long, hard studies that I performed earlier this year.
In Inter-Regional Trade Movements of Petroleum, I used the BP review’s yearly reports of petroleum inter-area movements and trade movement data to derive the inter-regional movements between each of the nine regions for each year from 2000 to 2011. Imagine generating a yearly 9x9 matrix of region-to-region import and export data, and then following how the trends in each cell in the matrix changes over time, and you get the idea of what this study entailed. In fact, if you have about 20 minutes to spare, you view an animated summary of the study’s main results here. It turns, out only a few drozen people do have 20 minutes to spare—probably to get more hits, I needed to have Snookie or some other thought leaders perform a narration of the video while dancing around in the nude, but that would defeat the purpose anyways.
The bottom line from this study was that in many cases, the proportion of petroleum exports going from one region to another region has not been static over the past decade. Rather, the amount of petroleum exports going from one region to another region has been dynamically changing, and, to a first approximation, this dynamic change can be modeled with a linear trend analysis.
In Relationship between Petroleum Exports and Production, I used my results from Inter-Regional Trade Movements of Petroleum to examine the relationship between petroleum exports and production for each region. Based on a linear trend analysis of exports and production, I generated export prediction curves that were a function of the production trends for each of the nine regions. Imagine taking the time-dependent trend in the 9x9 inter-area movements matrix and correlating that with the time-dependent changes petroleum production rates for each of the nine regions, and you get the idea. Sorry, no video.
My trend analysis from Relationship between Petroleum Exports and Production allowed me to predict the exports from any one of the nine regions, to any one of the other nine regions, expressed as a percentage relative to the petroleum production rate of the region of interest as a function of time. This means that if I can predict petroleum production for a region (from the non-linear least square fit of the logistics equation to the result petroleum production data for that region), then I can use the relationship revealed in the trend analysis to predict the exports from that region to the other regions.
With this combination of prediction tools, I can now predict future petroleum consumption trends, without making any assumptions involving the extrapolation of past consumption trends—in fact without considering the petroleum consumption data at all.
Let’s Assume its as Easy as PIE
This brings me to the central assumption made in the present study:
Of course, from the perspective of the target region, “total exports from all other regions into the target region” is total imports. In other words, for any one region:
Consumption = Production + Imports - Exports.
I have coined my analysis based on this assumption as my PIE analysis.
My PIE analysis differs from my previous ELM analysis in several important ways. Most importantly, the PIE analysis does not rely upon extrapolations of petroleum consumption trends what-so-ever. In contrast, for my previous ELM analysis, the extrapolated consumption trends of the net exporting regions defined what the net exports out of that region could be. My previous ELM analysis further assumed that consumption in the net importing regions was governed by the size of the net export pool, plus the importing regions' regional production. Exports from the net importing regions, to other regions, were not considered. Imports to the net exporting regions, from other regions, were not considered. And, changes in such imports and exports over time and between regions were not considered. All that changes here with a PIE analysis.
Of course, the reason why these items were not considered in the ELM analsysis was that I first had to extract the inter-regional import and export data and their time dependent trends from the BP review’s yearly inter-area movements summaries, and relate these to the production data for each region.
Now can you see why I had to do the analysis for all nine regions, before I could predict future consumption for any one region?
Without the predicted future production rates, provided from the nonlinear least squares analysis of each of the nine regions, I can’t predict the future import and exports based on my trend analysis of the relationship between exports and production to and from all nine regions. And, to predict petroleum consumption rates, I need all three: internal petroleum production rate, petroleum export rate into (i.e., imports) and petroleum export rates out of, each region.
Next time, I will illustrate these concepts with a PIE analysis of the world’s biggest petroleum producing and exporting region—the