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In the first representation, the trajectories of some MAs from 1990 to 2010 are shown. The results suggest that metropolitan areas with high Fitness are generally more likely to have a more significant increase in GDPpc. The second representation shows the average vector field of the trajectories from 1995 to 2005, which indicates that metropolitan areas with a high Fitness generally show an increase in GDPpc, except for those that already have a very high GDPpc. In the third representation, the overall trend of all MAs whose trajectories are coloured according to the community of belonging is shown. The results suggest that the Fitness trends of all clusters are decreasing, except for the Emerging and Chinese clusters.
The author notes that their approach is a "phenotypic" one, which means that they only consider information about metropolitan areas and patents, and do not perform a "genotypic" analysis, which would require knowledge of the capabilities to make the technologies and the processes by which MAs can use their abilities to produce them. The author acknowledges this limitation but suggests that their measure needs little data to be computed.

The author presents a graph (Figure 4) showing the Fitness rankings of metropolitan areas from 1990 to 2020. Fitness refers to the number of patents filed by inventors in a metropolitan area per year, normalised by the population of that area. The graph shows a significant increase in the number of patents filed by Chinese metropolitan areas from 1990 to 2020, with many Chinese cities dominating the top 30 rankings by 2010. In 2020, Suzhou in China was at the top of the rankings, followed by other Chinese cities like Nantong. Only two non-Chinese metropolitan areas, both from Korea, were in the top 30 rankings in 2020. The author suggests that this surge in patent filings by Chinese metropolitan areas was part of a coordinated strategy to rapidly develop and modernise the country. The author also notes that there have been some studies that have criticised the increase in China's patenting activities.
The authors discuss the results of their analysis of coherent diversification in technological production. They present the results in Fig. 5, which shows the correlation between coherence, fitness, and the change in GDPpc (Gross Domestic Product per capita) for metropolitan areas. They observe that coherence correlates with a positive change in GDPpc better than fitness. This means that the coherence of technological products is a better predictor of economic growth than a metropolitan area's complexity.

Furthermore, the authors note that out of the top 100 MAs ranked by coherence, 79 are Chinese. This result supports the idea that China has a coherent diversification strategy in technological production. To test the robustness of this result, the authors perform a test without Chinese MAs and find similar results. They also show that high coherence is not related to low diversification.
Overall, the authors suggest that their findings can help policymakers and business leaders make informed decisions about technological diversification strategies. By understanding the importance of coherence in technological production for economic growth, decision-makers can design more effective policies and investment strategies.
The author discusses a study on technological innovation in metropolitan areas based on data on patent production. They focus on the signals of specialisation and diversification by applying the Fitness and Complexity framework and novel methods for bipartite networks to the technological production of metropolitan areas. The findings indicate that metropolitan areas tend to specialise in technology sectors, particularly for some technological categories, such as cars or electronics, but the biggest ones are able to diversify, and some manage to be more generalists, although their focus shifts to complex technologies.
Chinese metropolitan areas give the best example of similar metropolitan areas in a single country, and they are organised into three coherent clusters specialised in similar technological baskets. The coherent diversification strategies of China are in line with previous results analysing technology spillovers in Chinese regions. The author also highlights similarities between emerging metropolitan areas and highly technological metropolitan areas. Interestingly, the network of similarities among metropolitan areas shows a clear geographical boundary between highly developed Asian and Western (European/American) metropolitan areas.
The author applies the Fitness and Complexity framework to understand the evolution of the quality in technological innovation of metropolitan areas and their clusters. In line with previous results, a high Fitness can be correlated with a high GDP per capita. Metropolitan areas with a complex technological basket show higher increases in GDP per capita in the following years than metropolitan areas developing more basic technologies. The complexity of innovation in Chinese metropolitan areas is very high, and their GDP per capita displays rapid growth. Chinese metropolitan areas are not only able to diversify their innovation patterns by aiming for a more complex technological basket but also do this in a coherent and coordinated way.
The authors suggest in conclusion several potential future applications of their theoretical framework. They propose that the methods could be used to assess the best diversification strategy for metropolitan areas at different scales and capabilities and to forecast future technology production. The authors also highlight the importance of coherent diversification and suggest that their findings on the strategy of Chinese metropolitan areas could be used to inform policy in other countries. Additionally, the authors note the unique signal from metropolitan areas focused on car production and suggest that future studies could focus on optimal diversification strategies and forecasting technology production in these areas, especially in light of the forthcoming changes in the automotive industry due to the shift towards electric cars. Overall, the authors' work provides insights into the patterns of technological production and diversification across metropolitan areas, and their findings could have practical implications for policymakers and businesses.