Table of Contents
1. Climate Change and Intangibles
In a book entitled Capitalism without capital: the rise of the intangible economy, Jonathan Haskel and Stian Westlake wrote about the economy of the intangibles. National economies have much like their expectations on investments and labour to be prepared for more globalised markets, transnational and international relations. The authors point out how significant is the decision-making process when it takes into account the elements not physically visible or easily measured. They display some examples in their argumentation related to the technological transformation of our world: “Railways replaced canals, the automobile replaced the horse and cart, computers replaced typewriters, and, at a more granular level, businesses retool and change their mix of investments all the time”. Nevertheless, there is a sort of common backdrop in the scene painted by them. All of these changes have taken place in urban context.
Haskel and Westlake indicate the two intrinsic characteristics of the intangible investments that have to do with the urban lifestyles, i.e., the sunk costs and the spillovers since economies are pretty much oriented for those consumers living in city context. If we read attentively the authors’ ideas from that point, we can also extract some insights that will be certainly ameliorated with a myriad of urban studies and intelligent contributions. We selected an excerpt from the book Capitalism without Capital and inserted the phrase “in urban context”. The idea is to test Haskel and Westlake’s reflection about the intangibles in a possible city description. So, we have the text re-phrased as follows: “[…] we’ll look at how the shift to intangible investment helps us understand four issues of great concern to anyone who cares about the economy: secular stagnation [in urban context], the long-run rise in inequality [in urban context], the role of the financial system in supporting the nonfinancial economy [in urban context], and the question of what sort of infrastructure [in urban context] the economy needs to thrive” […]
2. Statistical Analyses
The literal definition used by the Wold Bank in terms of carbon emissions and its methodology applied for each country including regional and world averages is as follows: “Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide and particulate emissions damage”. The following five graphs plotted show five different correlations for Adjusted Net Savings (ANS) from one year to another. For example, do the values for the 2007 ANS have a strong attraction to the values of the 2008 year? What kind of picture we have for the subsequent binary combinations covering all the time series 2007-2016? The last plotted image is about the normal distributions for each year. We overlapped them in order to check the behaviour of the mean, the distribution of densities and the area of the bell curves. We remember the reader that we are dealing with 269 observations divided into 14 variables for a ten-year period. Rstudio is a very powerful tool in that sense since the programme can process and plot all these pieces of information.
Table 1 brings the figures that were extracted from the World Bank webpage. They helped us elaborate the normal distributions for the adjusted net savings related to carbon emissions calculated before.
It is quite difficult to check the information or at least understand what is in fact happening with all these numbers yelling at us. The following boxplots present each value for each year and country/region seen in the Table 1. With them the figures seem to be more “edible” and easily understood. For example, what do the negative outputs mean in the following quantiles? They reveal two dimensions. One of them is that those countries with negative returns have probably produced more energy, mineral and/or net forest depletion combined with carbon dioxide and particulate emissions damage than their national net savings. Another one reveals that investments in education may affect the national net savings in two dimensions, that is to say, knowledge or training that could augment the economic production and neutralise partially carbon debts in international organizations. We should remember that the World Bank methodology for carbon emissions defines that all these kinds of “carbon outputs” have to be subtracted from the net national savings and education expenditure. This equation produces a number which is represented in the Table 1 for each year and we made explicit with the boxplots. That’s the reason one can find China, United States, Russia, India and Japan, for instance, the top five \({CO_2}\) producers with positive balances. On the other hand, a country with low levels of fundings for education will end up diminishing its possibilities of reaching better positions in the World Bank scale for carbon emissions. Moreover, if not combined with other processes and lifestyles reflecting urban context, such as the need developing countries have for infrastructure construction and industrialization, the methodology can brutally distort the reality of the carbon emissions calculation.
Source: World Bank staff estimates based on sources and methods described in “The Changing Wealth of Nations 2018: Building a Sustainable Future” (Lange et al 2018).