Global Drivers 2030-2060 Overview

The Global Drivers 2030-2060 identifies and analyzes the major drivers projected to influence change in the 2030-2060 timeframe. The focal question of this project was:

What are the drivers that emerge from publicly available analysis regarding the 2030-2060 horizon?

The project was a meta-study examining openly available reports found on the Internet. Initially, the net was cast very broadly:

1. Studies using a recognized foresight of futures process (Framework Foresight, etc.).
2. Reports or studies using other kinds of economic, sociological, scientific, or academic analysis.
3. Opinion pieces or editorials by subject matter experts.
4. Marketing or business white papers supported with evidence.

For the purposes of identifying Global Drivers in the 2030-2060 timeframe, the focus was on those meeting criteria #1 that used some type of foresight methodology. Items meeting #2, #3, and #4 were included in the index but screened out from this drivers report. This helped manage the volume and got us closer to finding drivers, but as we will see even materials from #1 were haphazard about what constituted a driver.

The works had to be publicly available - accessible to anyone in the general public, without the need for special qualifications, permissions, or privileges. We note upfront that this excludes much foresight work that is done for clients in a proprietary way.

A Google spreadsheet housing the drivers kept the following information about each:

• time horizon
• geographic focus of analysis
• type of organization producing analysis
• sectoral focus of analysis
• methodology of analysis
• and source location of analysis.

The time horizon of 2030-2060 for the drivers was a hard screening criteria, that is, the timeframe had to be in that window in order to be included in this analysis. We did include works that were right at the upper or lower date boundary.

Initially, drivers were grouped according to traditional STEEP (social, technological, economic, environmental, and political) categories. Subsequently, the drivers were regrouped using a new PPEPSIII framework.

This project did not conduct an extensive analysis of geography, type of organization, sector, method, or source location. There are some observations made in the discussion, but a detailed analysis of these categories is reserved for subsequent projects. The focus here is on the identification and framing of the drivers.

A team of futurists went through hundreds of foresight reports pulled together for the Open Foresight Hub. Several rounds of reading and analysis ultimately identified 677 candidate drivers for analysis and clustering. There was a strong guiding ethos to include potential drivers rather than exclude, and as will be discussed later, it might have been possible to reduce this number significantly.

We experimented with the idea of “mega-driver” as a thematic constellation or grouping of related drivers. But we ultimately felt introducing another new term would add more confusion to an already confused landscape of how to characterize units of change. Given the overall goal of improving access to foresight work, we were reluctant to add a new term to the lexicon.

For the clusters of drivers, a high-level theme that generally extended globally was the path taken. It was apparent early on that there is quite a range of ideas as to what constitutes a driver. Our sense was that adding another term was more likely to added to the confusion. Indeed, an objective emerged to consolidate the various interpretations of driver under a common umbrella. A perhaps unexpected side benefit was that by sticking to the initial conception of a driver, we think we might help bring some consistency to how drivers are defined and named, which is captured in the article Defining Driver.

The large number of candidate drivers – 677 – suggested we were going to need an organizing framework. The most popular framework in use by futurists is STEEP (social, technological, economic, environmental, and political) and all its variations: PEST takes out environmental, PESTLE adds legal, and STEEPED adds either energy or education and demographics. These frameworks are a helpful way to ensure a comprehensive and balanced set of drivers and themes are considered in a research project. For this study, the drivers are organized into a proposed new framework, called PPEPSIII (pronounced Pepsi), developed by futurist David Jonker and outlined in the article PPEPSIII framework.

The PPEPSIII framework brings a more holistic and inclusive perspective for thinking about drivers in this case but can also be usefully applied to organizing any signals of change. In addition to ensuring a balanced set of drivers, it can also help to surface key tensions that exist among the major drivers, and stakeholder agendas, that dominate discussions of the 2030-2060 time horizon. For example:

• Boundaries and standards of living: There is a clear tension between our efforts to live within planetary boundaries on the one hand and attempts to improve the human condition for billions on the other. Various forms of innovation are being pursued in order for humanity to exist within this band of sustainably prosperous living.
  
• Innovation and infrastructure constraints: This urgency to innovate on the one hand comes into tension with the constraints and limitations of society’s current infrastructures.
  
• Uneven development: Lastly, many tensions exist between the economic, political, and social systems that must all be modernized together in order to find collective success.
  

Below, we will show how we organized the drivers for this project into the PPEPSII framework.

Our goal was to report what the research said the drivers were and to resist the temptation to put our own spin on them. For example, in one of the clustering activities, a megadriver called “drawing new identity maps” was identified. It pulled together a wide range of drivers in a creative fashion. But since this megadriver was not identified in the literature, we didn’t include it as such. Our objective was to identify the themes that came from the literature.

Start with the topic that is the change and then characterize that change; so instead of increasing climate change > Climate change increasing. This was done with all the driver names. Avoid “the” or putting the topic in the middle. Makes it clear what the change is, and it makes it easier to sort and analyze.

The table below lists drivers by order of mentions (most mentions first; least is last).

Table 1: List of 27 Global Drivers

The drivers are distributed fairly evenly across the PPEPSIII framework.

Figure 1: Global drivers by PPEPSIII category

For starters, there is a clear need for definitional consistency on what a driver is! See the Defining Driver article.

The drivers identified in the study feel like baseline drivers, more than alternative futures drivers. However, on reflection it makes sense that the largest clusters from the many reports would represent baseline. Alternative drivers, by definition, should appear less often in the collection of reports. Indeed, there were many “stragglers” mentioned a few times that did not make the cut in our quest to get as close to a common global consensus as we could. Digital twins, for instance, were mentioned a couple of times, but not enough to be a core driver. However, it does show that some “alternative” drivers were indeed identified. Having said that, the baseline is perhaps disproportionately represented.

It also makes sense that most drivers are already widely present. Given our view that the drivers cause change, it is really by projecting the drivers that we get difference. For instance, we might choose to frame climate change impacts as increasing. That driver can be projected in different ways. For instance, in a collapse future they would speed up and get worse. In a transformation future they might be increasingly mitigated or prevented.

In bringing 677 candidate drivers to 27, we looked for the center or common core. For instance, a candidate driver that indeed suggested that climate change impacts are worsening may have been folded into a more general climate change impact are increasing.

Many drivers came from region-specific projects. Interestingly, the drivers in these studies were very much aligned with studies with a more global scope. There were degrees of difference. For instance, two candidate drivers may have had economic growth, but the regional one would have a different growth rate. There were not appreciable differences in the drivers themselves. For instance, health concerns focused on obesity in an affluent nation and AIDS in one report on Africa.

The nature of the selection process may bias the drivers. For instance, many of the free foresight reports are often policy pieces about needs in the developing world. Hence, the reason we see urbanization as the #1 driver. More generally, most of the global drivers drove change in the most affluent nations 10-20 years ago.

By being super inclusive – which makes sense given the open foresight nature of the project - perhaps half the 677 candidate drivers were, upon further analysis, not really drivers, but we carried them through the total process, which absorbed a lot of the team’s time and energy. If we screened them out early, this would have saved a lot of time later on. On the other hand, even though the process might have been inefficient, it did force the team to really dig and clarify just what we collectively thought a driver was.

Categories: global drivers 2030 2060