Green Technology Fitness

Abstract

The present study provides an analysis of empirical regularities in the development of green technology. We use patent data to examine inventions that can be traced to the environment-related catalogue (ENV-Tech) covering technologies in environmental management, water-related adaptation and climate change mitigation. Furthermore, we employ the Economic Fitness-Complexity (EFC) approach to assess their development and geographical distribution across countries between 1970 and 2010. This allows us to identify three typologies of countries: leaders, laggards and catch-up. While, as expected, there is a direct relationship between GDP per capita and invention capacity, we also document the remarkable growth of East Asia countries that started from the periphery and rapidly established themselves as key actors. This geographical pattern coincides with higher integration across domains so that, while the relative development of individual areas may have peaked, there is now demand for greater interoperability across green technologies.

Keywords: capabilities; economic development; fitness; green technology.

Figure A1

Panel $a)$ depicts a binary country-technology matrix M consisting of three countries ($c_1$, $c_2$, and $c_3$) and three technologies ($t_1$, $t_2$, and $t_3$); ordering the rows by fitness and the columns by complexity (right), we see that $t_1$ is more complex than $t_2$ and $c_3$ has higher fitness than $c_2$. Panel $b)$ depicts the same matrix M of panel $a)$, to which an additional column ${\tau }_1$ has been added; ordering the rows by fitness and the columns by complexity (right), we see that now $t_2$ is more complex than $t_1$ and $c_2$ has higher fitness than $c_3$. The figure shows that, in general, the addition (or subtraction) of columns to $\mathbf{M}\left(y\right)$ can potentially affect fitness and complexity rankings.

Figure A2

Yearly green classes, applications, and active countries.

Figure A3

Yearly frequency of green technology classes (left:3-digits, right: 2-digits).

Figure A4

Estimation error of the green fitness colour map in Figure 7. The plot is built with the same data of Figure 7. Two layers of information are represented in this figure. (1) In the grey scale, the green fitness ranking estimation error in the Nadaraya-Watson kernel method. White indicates a standard error of ∼$0.2\%$ or less, and black a standard error of ∼$0.4\%$ or more. (2) The iso-lines of the green fitness ranking levels (lowest in deep purple, highest in clear yellow). The plot is obtained by pooling all countries in our database over the time interval 1980–2010. The different shades of black and white confirm our findings: export fitness and GDP per capita are complementary in determining the green technological capabilities of countries.

Figure 1

Example of data construction. (a) Assume that the there are only three patent families to account for in a given period y: one (top) developed in a single country $c_1$ that innovates in three distinct fields ($t_1$, $t_2$, and $t_3$); another one (centre) developed by inventors residing in two countries that innovates in a single technology; and (bottom) a single-country, single-technology patent family. All patents are attributed equal weights and the attribution to country-technology pairs is fractional. (b) The union of the country-technology combinations of all inventions is combined into the weighted matrix $\mathbf{W}\left(y\right)$. (c) $\mathbf{W}\left(y\right)$ is binary to reflect revealed comparative advantage yielding $\mathbf{M}\left(y\right)$, which is the input of the EFC algorithm.

Figure 2

Time evolution of the green fitness ranking of countries from 1980 to 2010. The country labels on the left and right vertical axes are listed from bottom to top in order of increasing fitness in the first and last period of analysis respectively. The lines trace the changes in ranking of each country across decades. Label and line colours refer to the position of countries in the initial ranking: black, violet and purple are associated respectively to the top-, middle-, and bottom-third of the 1980 green fitness ranking. Colours are mixed in 2010, meaning that positions in the ranking have changed substantially for several countries (see e.g., the constant growth of China and South Korea highlighted by the thicker purple lines). The names of the countries associated to the abbreviations reported on the y-axis of the plot are reported in Table A2 of Appendix A.

Figure 3

Time evolution of green complexity ranking of ENV-Tech technologies from 1980 to 2010. The technology labels on the left and right vertical axes are listed from bottom to top in order of increasing complexity in the first and last period of analysis respectively. The lines trace the changes in ranking of each technology across decades. Label and line colours refer to the position of technologies in the initial ranking: black, violet and purple are associated respectively to the top-, middle-, and bottom-third of the 1980 green complexity ranking. Colours are mixed in 2010, meaning that positions in the ranking have changed substantially for several technologies. For instance, notice the constant growth of the ENV-Tech technology ‘Road Transport’ (6_1), and the steady decline of the ENV-Tech technology ‘Technologies Relating to Chemical Industry’ (9_2), highlighted respectively by a thicker orange and purple line. The definitions of the technological codes associated to the abbreviations reported on the y-axis of the plot are reported in Table A1 of Appendix A.

Figure 4

Correlation between green fitness ranking and per capita GDP over the time interval 1980–2010. Green fitness, as a proxy for the green innovative capacity of countries, is positively correlated with income per capita. The figure is obtained by pooling countries and years in our database. The expected value of green fitness is obtained through a non-parametric kernel estimation (black line), while the $95\%$ confidence interval of the expected value (purple shadow) is computed with bootstrap.

Figure 5

3-digit $\mathbf{M}(2010,10)$ with rows and columns ordered by green fitness and green complexity respectively. Colour represents the share of each technology within the technology basket of each country. The matrix shows a semi-triangular shape, accordingly to the EFC narrative, the highest green fitness countries are competitive in almost all technologies, from the most to the least complex, while the basket of technologies of lower fitness countries is limited to less complex technologies.

Figure 6

Composition of national green technology baskets. Each panel illustrates the share of patents produced by a selection of countries in each 2-digit technological field in 1980 (upper part) and 2010 (bottom). Technologies are ordered by increasing complexity. The colour of the bars indicates the ranking of each technology in 1980, while the background colour stands for the 1-digit technology to which each bar belongs (see list on p. 3). The hatched pattern is for technologies that are observed in both time windows.

Figure 7

The three-dimensional relation between export fitness, GDP per capita, and green fitness. The colour map represents the variation of green fitness obtained with a non-parametric Nadaraya-Watson kernel estimation by pooling all countries in our database over the time interval 1980–2010.