# Economic complexity and the sustainability transition: A review of data,
methods, and literature

Bernardo Caldarola†, Dario Mazzilli, Lorenzo Napolitano‡, Aurelio Patelli,
Angelica Sbardella  Enrico Fermi Research Center, via Panisperna 89a, 00184,
Rome (Italy)  †UNU-MERIT and University of Maastricht (Netherlands)  ‡European
Commission Joint Research Centre (JRC-Seville<EMAIL_ADDRESS>
###### Abstract

Economic Complexity (EC) methods have gained increasing popularity across
fields and disciplines. In particular, the EC toolbox has proved particularly
promising in the study of complex and interrelated phenomena, such as the
transition towards a greener economy. Using the EC approach, scholars have
been investigating the relationship between EC and sustainability, proposing
to identify the distinguishing characteristics of green products and to assess
the readiness of productive and technological structures for the
sustainability transition. This article proposes to review and summarize the
data, methods, and empirical literature that are relevant to the study of the
sustainability transition from an EC perspective. We review three distinct but
connected blocks of literature on EC and environmental sustainability. First,
we survey the evidence linking measures of EC to indicators related to
environmental sustainability. Second, we review articles that strive to assess
the green competitiveness of productive systems. Third, we examine evidence on
green technological development and its connection to non-green knowledge
bases. Finally, we summarize the findings for each block and identify avenues
for further research in this recent and growing body of empirical literature.

##  1 Introduction

The notion of Economic Complexity (EC) has been widely used to encompass a set
of methods that characterize the productive and technological composition of
economies (countries, regions, cities) relying on complex systems science
(Hidalgo and Hausmann, 2009, Tacchella et al., 2012). EC methods have proved
to be particularly effective in predicting future patterns of economic growth
using information on the export basket of countries (Tacchella et al., 2018).
The key intuition behind the EC approach is that economic development and
growth are the result of the specialization and diversification patterns of
economies, which emerge from underlying hidden interactions between elements
in the society (Balland et al., 2022, Pugliese et al., 2019a ). More
specifically, EC focuses on the role played by the accumulation of
(unobserved) productive and technological capabilities in driving economic
diversification and growth (Hausmann et al., 2007, 2011, Pugliese et al.,
2017, Sbardella et al., 2018b ). By preserving information on what economies
produce, rather than merely how much, the EC literature is able to “describe
and compare economies in a manner that eschews aggregation” (Hausmann and
Hidalgo, 2011). The EC approach yields a complementary perspective on several
domains of economically relevant human activity (e.g. trade, technical
innovation, scientific research) with respect to conventional (i.e. aggregate)
indicators on productive inputs or performance. This emphasis on the content
of the activity baskets of countries or regions resonates with other
approaches in economics that understand economic growth through the lens of
sectoral allocation of productive factors, such as the structuralist
literature (Prebisch, 1950, Lewis, 1954, Hirschman, 1958, Lin et al., 2011),
and that identify in the role of capabilities the main factor driving
innovation, embodied in the evolutionary economics literature (Cimoli and
Dosi, 1995, Dosi and Nelson, 1994, Nelson and Winter, 1982, Teece and Pisano,
1994).

The quality and diversity of an economy’s productive and technological
portfolio have broader implications than simply economic growth. For instance,
the composition of economic and technological specialization has strong
implications for the environment, as the footprint of different products and
technologies can differ substantially. By the same token, the accumulation of
technological capabilities may put countries on trajectories that can mitigate
or exacerbate climate change. In this respect, EC methods can prove
particularly useful to understand and guide the sustainability transition.

The transition towards a more sustainable and low-carbon socioeconomic system
is a top policy priority, with the EU Green Deal aiming at climate neutrality
by 2050111https://eur-lex.europa.eu/legal-
content/EN/TXT/?qid=1596443911913&uri=CELEX%3A52019DC0640#document2, the
Inflation Reduction Act in the United States (Bistline et al., 2023) and the
ambitious renewable energy targets of China’s 14th Five-Year Plan For
Renewable Energy With Quantitative Targets For 2025 (Li et al., 2019, Yang and
Wang, 2023). However, decarbonizing the economy by phasing out high-emission
and energy-intensive industries will require radical transformations and
profound structural change at the core of socioeconomic systems. Moreover,
this will have to account for the heterogeneous capacity of geographical areas
and industries to achieve climate neutrality along with the possible long-
lasting effects on income, spatial, and environmental inequalities.

To inform policy on how to address such a complex transformation, wherein
geographical, structural and institutional elements interact, in the last few
years an approach has emerged in the literature (see among others Barbieri et
al., 2020, 2022, Montresor and Quatraro, 2020, Santoalha and Boschma, 2021)
that draws from sustainability studies, evolutionary economic geography
(Boschma and Frenken, 2006, 2018) and EC. Embracing a complexity perspective
may in fact be more effective than traditional approaches in accounting for
the interconnected nature of this process of change (Common and Stagl, 2005)
providing policy-relevant, data-driven and granular evidence to embrace
socioeconomic complexity at different geographical scales.

While environmental goods and technologies have been mainly studied as
homogeneous, aggregate quantities in the extant literature, they actually
display high degrees of heterogeneity in terms of functions, underlying know-
how, life cycle stages, and links to pre-existing specialisation patterns.
Therefore, the capacity of EC methodologies to keep away from aggregation and
to provide feasibility diagnostics at the level of single products or
technologies may prove extremely relevant in analyzing the potential
directions of green development for each country or region,

Coupling the geographical distribution of productive and technological
capabilities with environmental and socioeconomic variables, various scholars
have applied the EC toolbox to try to answer three main broad questions: (i)
What is the relationship between complexity and sustainability? (ii) What are
the properties of green products and technologies, and are these inherently
different from non-green ones? (iii) How can we assess the readiness of
national/regional knowledge bases and productive structures for the green
transition?

Contributions addressing the first question investigate the empirical
relationship between countries’ environmental outputs, such as Greenhouse
Gases (GHG) emissions, and the complexity of their productive structures,
assessed by focusing on the export dimension through various EC metrics. These
studies are far from unanimous, as they vary in their samples or estimating
methods. This yields contrasting results on the effect of EC on the
environment, ranging from linear positive to non-linear relationships à la
Kuznets curve (1955).

The second and third questions have roots in the evolutionary economic
geography literature and are more varied in terms of techniques and scales of
observation. They mainly examine the geography of green competitiveness of
national/regional knowledge bases or productive structures by analyzing
specialization profiles in green products or technologies, different
dimensions of relatedness between green and non-green technological or
productive activities, and their relationship with a series of socioeconomic
indicators – e.g., inequality or digital literacy. As is common in the EC
literature, these works especially stress the potential complementarity
between existing capabilities and future specialization in low-carbon
technologies or products.

The EC approach emphasises the role played by productive and technological
capabilities in promoting advancement across domains of human activity. From
the point of view of green goods and innovative activities, this implies that
as countries and regions strive to move towards sustainable practices, EC
offers a framework to analyze the composition and diversification of their
economies, and to identify the dynamic set of capabilities that are most
conducive to green productive or technological specialization, allowing
countries to progress along the sustainability ladder. As we show in this
review, EC provides a valuable lens through which policymakers and researchers
can understand and navigate the intricacies of the sustainability transition.

This literature review aims to summarize the empirical evidence produced so
far to answer the three aforementioned questions from an EC perspective. It
should be noted that other recent contributions have tackled the link
connecting EC and socioeconomic sustainability from several angles. Notably,
the recent review by Montiel-Hernández et al., (2024) focuses on the ways in
which EC has proven to be useful to relevant aspects of the economic
literature such as economic growth, innovation, employment, education levels,
poverty reduction, life expectancy, environmental degradation, public
policies, etcetera. The paper identifies five areas of interest for EC, namely
investigating the causal pathways linking EC to sustainability dimensions;
developing new indicators of EC; exploring how EC can be used to promote
sustainable development policies as well as international research cooperation
on this topic; and integrating EC into tertiary education programs. At the
same time, several contributions have ventured into a detailed analysis of how
EC indicators correlate with empirical measures of socioeconomic
sustainability (some of which are reviewed by Hidalgo, (2021), and more
extensively in Section 3.1 of this paper). What makes this review
complementary to the aforementioned contributions is that it focuses
explicitly on the methodologies and data that have been used to link EC to
environmental sustainability only, in an attempt to answer the macro-questions
listed above. Our effort is particularly timely given the rapid expansion of
this literature, and it provides scholars with a clear roadmap on how to
produce results that are comparable to the ones produced in the recent past.

The remainder of this paper is organized as follows. We first review the data
on products and technologies used to apply EC methods to the study of the
sustainability transition, highlighting drawbacks and advantages entailed by
different types of data (Section 2.1). Second, we offer a methodological
contribution by attempting to unify the methods used to estimate and analyze
measures of EC and relatedness (Section 2.2). Third, we canvas the literature
that links complexity measures to the concept of environmental sustainability,
in order to summarize the debate on the role of complexity in explaining
environmental issues and in identifying viable avenues into the sustainability
transition (Section 3). Finally, we attempt to identify limitations of the
existing literature and methods, proposing further research avenues in the
field (Section 4).

##  2 Data and Methods

The data employed in EC-based analysis of the green economy are drawn from two
main sources: patents and trade flows. This section presents in detail the
most commonly used datasets and classifications of green activities, firstly
focusing on patent data and secondly on trade data. This data is used in EC
following a common empirical framework: a bipartite matrix is constructed by
cross-tabulating products (technologies) and geographical areas (countries,
regions, cities…). The bipartite matrix is then filtered, projected, ordered,
and processed in different ways, depending on the specific purposes of the
analysis (see Section 2.2).

###  2.1 Data

####  2.1.1 Patent data

The main data sources for green or non-green patenting activity at the
national or local level are the European Patent Office’s (EPO) PATSTAT and the
OECD’s REGPAT. PATSTAT is a comprehensive database covering patent
applications filed in more than 70 national and international patent offices,
including the most important ones – e.g. the EU, US, and Japan patent offices.
REGPAT is instead published annually by the OECD and covers the subset of
PATSTAT patent applications filed at the EPO since its inception in 1978.
While PATSTAT covers a wider set of patent documents and a longer time-span,
REGPAT covers EPO’s applications, that are high-quality and extremely reliable
– we refer to the paper’s Appendix Appendix for a more detailed comparison
between these two data sources.

The technological fields associated to the patents recorded in PATSTAT and
REGPAT follow two classifications: the International Patent Classification
(IPC) and the Cooperative Patent Classification (CPC). Both classifications
follow a similar hierarchical structure, spanning from codes with a very
detailed description (e.g., G02B 1/02: optical elements […] made of crystals,
e.g. rock-salt) to codes aggregating many detailed technologies under a
broader common technological area (e.g., G02: optics; G: physics). For
example, at 4-digits, the classifications have around 600 unique codes, while
at 8-digits there are approximately 7000 codes in the IPC and 10000 in the
CPC. Despite the strong similarities between the two classifications, only the
CPC has a section dedicated to Climate Change Mitigation and Adaptation
Technologies (CCMTs), the ’Y02/Y04S’ tagging scheme, nested under section Y.
Table 1 reports the 1-digit codes and the titles of the sections comprising
the IPC and CPC classifications.

Table 1: Sections of the IPC and CPC classifications.†

Code | Title | IPC | CPC
---|---|---|---
A | Human necessities | Y | Y
B | Performing operations; Transforming | Y | Y
C | Chemistry; Metallurgy | Y | Y
D | Textiles; Paper | Y | Y
E | Fixed constructions | Y | Y
F | Mechanical engineering and weapons | Y | Y
G | Physics | Y | Y
H | Electricity | Y | Y
Y | New technologies, including CCMT‡ & Smart Grids | N | Y

‡Climate Change Mitigation and Adaptation Technology.

Table 2: CPC Y02/Y04S (2018) tagging scheme (European Patent Office, 2018).

Class or Subclass | Title and description
---|---
Y02 |  | TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION
---
AGAINST CLIMATE CHANGE
Y02A | Technologies for adaptation to climate change
Y02B |  | Climate change mitigation technologies related to buildings, e.g. housing, house appliances or
---
related end-user applications, including the residential sector
Y02C | Capture, storage, sequestration or disposal of greenhouse gases
Y02D |  | Climate change mitigation technologies in information and communication technologies,
---
i.e. information and communication technologies aiming at the reduction of
their own energy use
Y02E |  | Reduction of Greenhouse Gases (GHG) emissions, related to energy generation,
---
transmission or distribution, including renewable energy, efficient
combustion,
biofuels, efficient transmission and distribution, energy storage, and
hydrogen technology
Y02P | Climate change mitigation technologies in the production or processing of goods
Y02T | Climate change mitigation technologies related to transportation, e.g. hybrid vehicles
Y02W | Climate change mitigation technologies related to wastewater treatment or waste management
Y04 |  | INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT
---
ON OTHER TECHNOLOGY AREAS
Y04S |  | Systems integrating technologies related to power network operation, communication or
---
information technologies for improving the electrical power generation,
transmission,
distribution, management or usage, i.e. smart grid technologies including
hybrid vehicles
interoperability.

The contributions to eco-innovation presented in this review rely mainly on
the Y02/Y04S tagging scheme of the CPC and the OECD Env-Tech classification,
based on a mixture of IPC and CPC codes, which cover a wide range of a wide
range technologies related to sustainability objectives. These include energy
efficiency in buildings, energy generation from renewable sources, sustainable
mobility, and smart grids.

In response to the increasing attention and concerns about climate change
mitigation and renewable energy generation, there has been a large increase in
the number and scope of patent applications in environment-related domains in
the recent past (European Patent Office, 2013).

However, searching for environment-related patent documents was not
straightforward at the beginning because a dedicated classification system for
sustainable technologies was not available. In fact, before 2011, no specific
branch of the IPC or of other technology classification covered environment-
related inventions. A permanent solution to this issue became available in
2013 following the publication of the Cooperative Patent Classification, which
was jointly developed by the the EPO and the United States Patent and
Trademark Office (USPTO), in an attempt to harmonize their patent
classification procedures. Since then, the CPC has become increasingly popular
as a classification standard, complementing or substituting the IPC in a
growing number of patent offices worldwide.

As illustrated in Table 1, two types of codes can be found in the CPC
classification: codes starting with the letters A to H, which are similar to
IPC codes and represent the traditional classification of technologies; and
codes starting with Y, which are used to tag cross-sectional technologies
already indexed somewhere else in the classification222USPTO-CPC Section Y.
https://www.uspto.gov/web/patents/classification/cpc/html/cpc-Y.html. In
addition to the Y02 class, the new subclass Y04S dedicated to smart grids was
integrated into the CPC section Y. As shown in Table 2, the Y02 class consists
of more than 1000 tags related to sustainable technologies organized in 9 sub-
classes.

The enduring popularity of the IPC classification and its predominant coverage
of patents filed before the publication of the CPC prompted efforts to
maximize the informative content on eco-innovation present both in the IPC and
CPC classifications over a longer time period. In this context, in 2015 the
OECD (Haščič and Migotto, 2015) developed Env-Tech, an expert-based catalogue
of environment-related technologies based on the IPC classification, last
updated in 2016 (OECD, 2016), which can be used to tag green patent documents
in PATSTAT or other patent datasets. Env-Tech identifies 94 environment-
related technology areas that group 4- to 16-digit IPC and CPC codes, building
on the CPC Y02 class whenever possible. The catalogue relies on a keyword
search strategy identifying patent documents that correspond to each ’target
environmental technology field’ (Haščič and Migotto, 2015, p.19), covering
CCMTs but also environmental management and water-related adaptation
technologies (class 1 and 2). When it is not possible to identify single
IPC/CPC classes that define alone the technological field of interest, it
employs a combination of different IPC/CPC patent classes, also at different
aggregation levels.

Finally, in addition to the CPC and ENV-TECH classifications, the World
Intellectual Property Organization’s IPC Committee of Experts proposed the IPC
Green Inventory (IPC-GI).h 333https://www.wipo.int/classifications/ipc/green-
inventory/home, accessed February 2024. IPC-GI streamlines patent searches for
Environmentally Sound Technologies, as identified by the United Nations
Framework Convention on Climate Change, with codes spread across various IPC
technical fields and the purpose of consolidating them into a single, easily
accessible repository. However, given its limited coverage, the IPC calls for
caution in its use for research.

####  2.1.2 Trade data

Since The environmental goods and services industry manual for data collection
and analysis was published by the OECD, (1999), a wide array of lists and
taxonomies of green products has been proposed. Here we briefly discuss the
lists that are most commonly used in EC analyses, highlighting the main
critical issues in classifying environmental goods. Readers should keep in
mind that the EC literature has focused, especially in earlier works, on
establishing the level of complexity and growth potential of an economy by
extracting information from its trade specialization profile. In order to do
so, a harmonized global classification of products with a uniform, global
interpretation has been crucial to develop the framework. The Harmonized
System (HS) classification satisfies these requirements and has been the most
widely used in the field. HS is a standardized numerical method of classifying
traded products used by customs authorities to identify products; it is
maintained by the World Customs Organization (WCO) and it is updated every
five years. HS comprises more than 5,000 commodity subheadings, which are
identified by a 6-digit code and arranged according to a nested structure
going up to 96 2-digit Chapters and 21 1-digit Sections.

The first issue to take into account when constructing a comprehensive
classification of green products is in the very nature of the HS
classification system, which was not conceptualized to accommodate a
green/non-green dichotomy or information on the goods’ efficiency. The
introduction in the HS of several 6-digit subheadings which group together new
environmental goods was announced (Steenblik, 2020) in 2020; the WCO is slowly
addressing this issue by updating the HS system with more codes for green
products. However, the updated classification is not yet available. Therefore,
a clear-cut identification of environmental goods within existing product
classifications may at times prove to be a difficult task. For instance, up
until recently, it was impossible to distinguish between combustion engines
and electric vehicles. Other classifications or surveys exist, although they
focus on specific environmental aspects or on final use, and do not present
the standardization and granularity properties required by EC techniques. A
second crucial requirement for a comprehensive and effective green goods
classification is a unanimous definition of what green products are. This
clearly depends on specific needs: for instance, different requirements need
to be satisfied to fulfil regulatory and research goals, or to define (tax)
incentives towards more sustainable practices and energy sources.

While stressing a series of open questions, partially still unanswered, a 1999
OECD report (OECD, 1999) defines the environmental goods and services industry
as the set of activities aimed at measuring, preventing and mitigating
environmental damages related to eco-systems, water, air, soil, and noise
pollution, or waste management. This includes cleaner technologies, products
and services that reduce environmental risk and minimize pollution and
resource use. In the report, a list of 121 environmental goods satisfying this
definition was proposed. More recently, according to the International
Monetary Fund (IMF)444The definition of environmental goods of the IMF Climate
Change Indicator Dashboard, ’Trade in Environmental Goods’ data-set can be
found at:
https://climatedata.imf.org/datasets/8636ce866c8a404b8d9baeaffa2c6cb3_0/explore,
where international trade flows in environmental goods – defined according to
the OECD 1999’s list (OECD, 1999) as updated by the IMF – are reported.
environmental goods are connected to environmental protection or goods that
have been adapted to be more environmentally friendly. To achieve an
encompassing classification for policy implications, evaluation, and tariff
regularization, in 2009 the WTO published a broader list of green products
(WTO, 2009) comprising 480 products. While the list is comprehensive, many
inaccuracies and biases have been pointed out by a EUROSTAT report published
in the same year (EUROSTAT, 2009). The WTO also published two shorter friend
and core lists composed respectively of 154 and 26 products. Similar efforts
were made by the OECD for the 2010 Toronto G20 summit which proposed an
updated list of 150 Plurilateral Environmental Goods and Services (PEGS).
Moreover, in 2012 the Asian-Pacific Economic Cooperation (APEC) (APEC, 2012)
put forward a list of 54 environmental goods subject to reduced tariffs.
Finally, by combining and revising the WTO friend, APEC, and PEGS lists, in
2014 the OECD published the most recent list of green products, the Combined
List of Environmental Goods (CLEG) (Sauvage, 2014) comprising 248 products
relevant to tackling climate change.

In addition to the fact that any list of environmental products cannot be
considered final as the HS classification needs constant updates and revision,
regardless of the specific definition, two main shortcomings are shared by all
green product classifications:

  * •

Final use: it is not possible to ascertain the actual final use of a large
number of products. Many commodities labelled as green (e.g., filters, pumps,
and pipes) may be used also for non-environmental purposes. While statistics
can be computed based on surveys, their reliability on a global scale is yet
unclear.

  * •

Greener products: depending on the goal of the study/application, one could
consider products that are less raw material-intensive, more energy efficient,
easier to dispose of, or have longer life spans. However, these properties
crucially rely on the comparison with other products belonging to the same
category, comparisons that are, even in principle, very difficult to carry
out.

Therefore, a clear trade-off between accuracy and comprehensiveness arises,
and, as we will see below, the selected contributions using green products
have proposed different identification strategies to, albeit not fully,
overcome these issues.

###  2.2 Methods

The basic intuition of the EC approach is that specific activities, such as
exported products, industries, patents, or scientific research are important
because they constitute different learning opportunities and development
possibilities. EC explicitly builds on the heterogeneity and interactions
between different economic actors, assuming that knowledge grows not by
accumulating ‘more’ of the same, but by adding new and different elements to
existing capacities. In this framework, economic performance is seen as the
result of the accumulation of different types of non-tradeable inputs and
capabilities, that are not empirically observable but can be inferred by
studying the structure of the specialization profiles of countries. Therefore,
EC can be seen as an indirect measure of the capability endowment of a
country. The notion of capabilities was developed at the firm level in the
evolutionary economics literature to describe the dynamic know-how allowing
firms to develop and introduce new products and services in the market
(Penrose, 1959, Teece et al., 1997, 1994). In the EC literature, productive
capabilities describe location-based attributes, which encompass further
intangible aspects contributing to building and effectively exploiting
productive efficiency, such as the institutional setups, education systems,
policies, and infrastructures needed by a country to learn how to specialise
in more complex activities (Hausmann and Rodrik, 2003, Hausmann et al., 2005,
Sutton and Trefler, 2016). Information on capabilities is gathered from a
binary network that connects geographical areas to the activities in which
they hold a comparative advantage.

In the following, we present the main metrics and tools used in the EC
literature analyzing different dimensions of sustainability.

####  2.2.1 Complexity measures

EC analyses often start from the observation of empirical bipartite networks
connecting geographical areas – be they countries, regions, or cities – to
different types of economically relevant activities – such as patenting
(Balland and Rigby, 2017, Pugliese et al., 2019a , Sbardella et al., 2018a ),
manufacturing of products (Hidalgo and Hausmann, 2009, Tacchella et al.,
2012), or scientific research (Patelli et al., 2023) – in which they
specialize. These bipartite networks are typically assessed by evaluating the
comparative advantage of the geographical area in the selected activity, using
Balassa’s Revealed Comparative Advantage (RCA) index (Balassa, 1965). RCA
measures the share of an activity in a geographical area with respect to a
reference distribution, often the activity’s global share, and is thus
interpreted as a proxy for above-average competitiveness in that activity. The
index (and its refinements – see Bruno et al., 2023) remains the workhorse for
binarizing the adjacency matrices of bipartite networks in the EC literature.
In formula, the RCA of geographic area $g$ in activity $a$ of volume $W$, can
be written as follows:

$[RCA]_{ga}=\frac{W_{ga}(\sum_{g^{\prime}a^{\prime}}W_{g^{\prime}a^{\prime}})}{(\sum_{a^{\prime}}W_{ga^{\prime}})(\sum_{g^{\prime}}W_{g^{\prime}a})}=\frac{W_{ga}}{\sum_{a^{\prime}}W_{ga^{\prime}}}{\Large/}\frac{\sum_{g^{\prime}}W_{g^{\prime}a}}{\sum_{g^{\prime}a^{\prime}}W_{g^{\prime}a^{\prime}}}.$
(1)

RCA assigns a real and positive value to each combination of geographical
areas and activities. $[RCA]_{ga}=1$ if the weight $W$ of $a$ in the basket of
activities of geographical area $g$ is the same as the global weight of
activity $a$ relative to all activities. The resulting distribution of RCA
values is skewed with large tails (Patelli et al., 2022). High-variability
continuous values can be impractical in most applications, especially when the
only information required is on the presence/absence of specialization of
geographical area $g$ into activity $a$. Therefore, the RCA matrix is usually
binarized by setting a threshold value of 1.555Even though the intuitive unit
threshold is standard in the EC literature, different values may be
appropriate depending on the specific application. A matrix entry equal to 1
is inserted in the binarized matrix cell when $[RCA]_{ga}\geq 1$ – i.e., $g$
has a comparative advantage in $a$ –, while an entry equal to zero is inserted
otherwise:

$M_{ga}=\left\\{\begin{array}[]{l}1\qquad if\,\,[RCA]_{ga}\geq 1\\\ 0\qquad
otherwise.\end{array}\right.$ (2)

The binary RCA network displays strong empirical evidence of diversification
and nestedness patterns in all the activities studied in EC: many geographical
areas are competitive in large set of activities, in contrast to Ricardian
specialisation patterns. However, there is more to the binary $M$ matrices
than just the diversification of the geographical areas represented by their
rows. $M$ matrices are in fact usually nested across domains of activity,
irrespective of whether the columns of $M$ represent products, technologies,
or scientific fields (Patelli et al., 2022) and scales of analysis (Laudati et
al., 2023, Pugliese et al., 2019b , Sbardella et al., 2017). The concept of
nestedness originates from ecology (Atmar and Patters, 1993) and refers to the
observation that less diversified geographical areas (species) typically have
comparative advantages in the activities (ecological niches) pursued by more
diversified geographical areas. This pattern can be made explicit when the
presence/absence $M$ matrix is shown with the proper ordering of rows and
columns, resulting in the emergence of a hierarchical model represented by
triangular-like matrices (Cristelli et al., 2015).

Based on the information contained in the binary matrix $M$, it is possible to
define synthetic EC indicators capturing relevant information on the network
structure, and thus on the geographical areas’ specialization profiles. The
rationale of EC algorithms is that the complexity of the economies under
analysis and the complexity of the activities in which they are specialized
can be determined recursively by taking advantage of the information provided
by the composition of their activity portfolio. The first indicator of
Economic Complexity was proposed by Hidalgo and Hausmann, (2009), the ’Method
of Reflections’ (MR) aimed at capturing the diversification of a country’s
export profile. At the $n$-th iteration, for a geographical area $g$ and
activity $a$, MR proposes an iterative procedure to capture the
diversification of geographical area $g$, $k_{g}^{n}$, and of activity $a$,
$k_{a}^{n}$:

$\begin{array}[]{r@{}l@{\qquad}l}k_{g}^{(n)}=\frac{1}{k_{g}^{0}}\sum_{a}M_{ga}k_{a}^{(n-1)}&\\\
\\\ k_{a}^{(n)}=\frac{1}{k_{a}^{0}}\sum_{g}M_{ga}k_{g}^{(n-1)}&\\\
\end{array}$ (3)

with initial conditions $k_{g}^{0}=\sum_{a}M_{ga}$ and
$k_{a}^{0}=\sum_{g}M_{ga}$, the diversification of $g$ and the ubiquity of
$a$. The MR rationale is that the complexity of a geographic area, interpreted
as its generalized diversification, is driven by the average complexity of the
activities it specializes in. By the same token, the average complexity of
each activity is driven by the average complexity of the areas displaying a
comparative advantage in it. Since the iterative model in Equation 3 has a
trivial solution where each component is $1$ (Caldarelli et al., 2012, Kemp-
Benedict, 2014), the same authors later proposed a more practical definition
of the indices based on the eigenvector associated with the second largest
eigenvalue of a matrix derived from the MR (Mealy et al., 2019): the Economic
Complexity Index (ECI) of countries, and the Product Complexity Index (PCI),
in the case of products (Hausmann et al., 2011, 2014). Both indices converge
to those evaluated using MR only after considering the standardized version
(removing the mean and dividing by the standard deviation), although there is
a certain carelessness in the economic complexity literature that refers to
the ECI/PCI only as the second-largest eigenvalue. Their generalization to the
case of other activities, such as patents, is straightforward: the same
mathematical procedure is applied to the binary matrix based on the data on
the activity of interest.

However, the ECI-PCI approach presents a non-negligible conceptual drawback
due to the fact that ECI and PCI are defined by averages (Pietronero et al.,
2017) and not by sums, hence smoothing out the information on the cumulative
nature of capabilities encapsulated in the geographical area-activity
networks, a pillar of the economic reasoning behind the EC approach (Dosi and
Nelson, 1994, Teece and Pisano, 1994). This means that, for example, if we
consider two fictitious countries, with the first specialized in all the
activities present in the network, and the second holding a comparative
advantage only in one activity of average complexity, they will paradoxically
display the same ECI value. Interestingly, Mealy et al., (2018) define a Green
Complexity Index (GCI) simply as the sum of the complexity of green products
(while product complexity is still calculated on the entire set of products
using ECI), with the final sum accounting for country diversification in green
products, partially overcoming this crucial aspect of ECI.

While ECI is based on the idea that countries are diversified, the observation
of nested patterns uncovers complex and interdependent systems that cannot be
easily described through linear models and averages. A more suitable metric
for the characterization of nested patterns in the $M$ matrix is the Economic
Fitness and Complexity (EFC) algorithm (Tacchella et al., 2012): the Fitness
of a geographical area $g$ is defined as the sum of the complexity of all the
activities in which the area is specialized (i.e., if $RCA_{ga}\geq 1$).
Therefore, Fitness increases as the activity basket of $g$ become larger: a
geographical area with a more advanced set of capabilities will have a more
diversified portfolio of activities, spanning from the most to the least
complex ones, and will therefore have a higher Fitness score.666It has been
shown that Fitness and Complexity can be interpreted as potential functions,
related to the bipartite network $M_{ga}$, which defines a forbidden region of
the matrix given by the requirement of efficiency in the allocation of
resources of countries and products (Mazzilli et al., 2024). The extensive
nature of Fitness is complemented by the definition of the Complexity of
activity $a$, which is driven mainly by the lowest Fitness areas holding a
comparative advantage in $a$, since complex activities are rare and appear
almost exclusively in the portfolio of high-Fitness areas. Consequently, an
area with low Fitness has a smaller endowment of capabilities and thus
operates exclusively in less complex domains. Operationally, the EFC iterative
algorithm is defined as the fixed point of the following coupled equations,
that are suitably normalized at each iterative step:777The normalization step
is unnecessary for the evaluation of the fixed point (Mazzilli et al., 2024),
however, it is helpful for the numerical procedures and the stability of the
code.

$\left\\{\begin{array}[]{r@{}l@{\qquad}l}\widetilde{F}_{g}^{(n)}=\sum_{a}M_{ga}Q_{a}^{(n-1)}&&F_{g}^{(n)}=\frac{\widetilde{F}_{g}^{(n)}}{\langle\widetilde{F}_{g}^{(n)}\rangle_{g}}\\\
\\\
\widetilde{Q}_{a}^{(n)}=\frac{1}{\sum_{g}M_{ga}\frac{1}{F_{g}^{(n)}}}&&Q_{a}^{(n)}=\frac{\widetilde{Q}_{a}^{(n)}}{\langle\widetilde{Q}_{a}^{(n)}\rangle_{a}}\end{array}\right.$
(4)

It is noteworthy to mention that both Fitness and ECI are relative measures,
based on the structure of the geographical area-activity matrix $M$ at a given
time of observation of the economic system under analysis. Therefore, their
magnitudes cannot be compared longitudinally across time as, at each given
time point, the underlying bipartite networks change. This issue is not often
discussed in the literature, but it should be a pressing concern when these
metrics are included in longitudinal regression settings (many such examples
are discussed in Section 3.1). It is however possible to overcome this issue
by using ECI/Fitness rankings, albeit losing information on the EC index’s
magnitude, or more soundly by setting a reference scale (Mazzilli et al.,
2024), i.e. by assigning to a reference country a fixed value, or by adding a
dummy country that is always specialized in all activities, therefore imposing
an invariant of scale across time periods.

Another frequent issue that appears in the EC literature is the need to
analyze systems across different geographical scales. Often, at finer
geographical scales (i) local data-set do not have global coverage; (ii) data
quality is lower since volumes are smaller and errors become more relevant.
While issue (ii) is straightforward, issue (i) is often overlooked, although
it may pose problems in evaluating a reference distribution for computing the
RCA (e.g., it is very different to assess the complexity of products when
looking at global trade flows or only at European countries or regions). To
overcome these issues, a measure of exogenous fitness has been proposed
(Operti et al., 2018, Sbardella et al., 2018a ). To compute the Fitness of
subnational areas, this approach computes the Complexity of economic
activities at the country level, and subsequently evaluates the Fitness of
subnational areas by plugging the country-level complexity in the computation
of the Fitness equation (first term of Equation 4). In such a way, (i) the
signal-to-noise ratio in the data is higher; (ii) it is possible to provide a
more realistic assessment of complexity evaluating competition dynamics at the
global level. In principle, the same rationale may be implemented in the ECI
definition (Equation 3). In particular, Green Technology Fitness, introduced
by Sbardella et al., 2018a  and used in other works reviewed in the present
paper, is a measure of Fitness computed on climate change mitigation
technologies (see Section 2.1), which, when computed at the regional level,
employs the exogenous fitness rationale.

####  2.2.2 Relatedness

Another key set of techniques from the EC toolbox aims at measuring
relatedness888These analytical tools can be traced back to Jaffe, (1986) and
to the measure of corporate coherence introduced by Teece and Pisano, (1994).,
a measure of the pairwise similarity between activities – be they products
(Hidalgo, 2009, Zaccaria et al., 2014)999See also Tacchella et al., (2023) for
a multi-product, non-linear approach based on machine learning., technologies
(Breschi et al., 2003, Napolitano et al., 2018), or both technologies and
products (Pugliese et al., 2019a , de Cunzo et al., 2022). The measure of
relatedness between two activities is based on the observation of their
empirical co-occurrences in the specialisation profiles of different
geographical areas, and is connected to the probability that having a
comparative advantage in the first activity will also lead to a comparative
advantage in the second. This is done relying on the assumption that countries
that can successfully specialise in an activity have developed a set of
capabilities that will enable them to diversify into related activities.
Intuitively, activities that share similar inputs will be situated close to
each other in the network, and proximity in the network should indicate a
relatively high probability of jumping from an activity $a$ to a neighboring
one, $a^{\prime}$.

Relatedness is usually represented as a network of connected activities, where
links between activities are based on their degree of similarity. There are
several methods to evaluate relatedness, resulting in different and
heterogeneous networks that cannot always be easily compared. However, most of
these networks present a core-periphery structure, with hubs and leaves
associated with similar types of activities or arranged into meaningful
communities. The first approach to relatedness in the EC literature is the
Product Space, proposed by Hidalgo et al., (2007), initially based on the
country-product international trade network, and later employed also for
different set of activities. The similarity between activity $a$ and activity
$a^{\prime}$ is evaluated using _proximity_(Hidalgo, 2009), a measure of
pairwise normalized geographical co-occurrence, i.e. summing over the
geographical dimension $g$:

$\phi_{aa^{\prime}}=\min\left\\{\frac{\sum_{g}M_{ga}M_{g,a^{\prime}}}{\sum_{c}M_{ga}},\frac{\sum_{c}M_{ga}M_{ga^{\prime}}}{\sum_{c}M_{ga^{\prime}}}\right\\}.$
(5)

Originally, proximity was interpreted as the conditional probability of being
competitive in activity $a$ given competitiveness in activity $a^{\prime}$
(see p. 3 of Hidalgo et al., 2007 SI). 101010Please notice that even though
each value of $\phi_{pp^{\prime}}$ is real, positive, and bounded to 1,
proximity is not a conditional probability. In fact, a conditional probability
$P(A|B)$ requires that $\sum_{A}P(A|B)=1$, while
$\sum_{p^{\prime}}\phi_{pp^{\prime}}$ depends on the dimension of the network
(the linear sizes of $M_{cp}$) and is typically larger than 1.

The EC literature has later proposed a more statistically sound approach to
relatedness that allows to evaluate time-lagged connections between pairs of
activities, also between two layers of different types of activities such as
products and technologies, or technologies and scientific fields (de Cunzo et
al., 2022, Pugliese et al., 2019a , Sbardella et al., 2022, Zaccaria et al.,
2014), allowing to model time-lagged spillovers across different dimensions of
capability. Initially focused on international trade, Zaccaria et al., (2014)
put forward a refined methodology to assess relatedness, the Product
Progression Network. This approach has three main advantages: i) it is
dynamic, as it takes explicitly into account time by comparing the pairwise
activity co-occurrences within the same geographical area at different points
in time, instead of contemporaneous co-occurrences; ii) it filters each link
of the resulting network using a null model so that only the statistically
significant links are stored: a crucial advancement, because the presence of a
link may be simply due to the ubiquity of a product or to the diversification
of a country (Cimini et al., 2019, Saracco et al., 2015, 2017); iii) instead
of focusing on pairwise correlation between two activities of the same type
(e.g. two products), it has been employed to study also the co-occurrence
among different types of activities, such as links between products and
technologies (de Cunzo et al., 2022, Pugliese et al., 2019a ).

The mathematical formulation of this approach relies on the so-called assist
matrix $B$, which estimates the probability of having a comparative advantage
in activity $a$ in geographical area $g$ in year $y_{2}$, conditional on
having a comparative advantage in activity $a^{\prime}$ in the same area in a
previous year $y_{1}=y_{2}-\Delta t,\Delta t\geq 0$ (typically a lag of
$\Delta t=5$ years is considered) and is defined as follows:

$B^{\alpha\beta}_{aa^{\prime}}(y_{1},y_{2})=\sum_{g}\frac{M^{\alpha}_{ga}(y_{1})}{u^{\alpha}_{a}(y_{1})}\frac{M^{\beta}_{ga^{\prime}}(y_{2})}{d^{\beta}_{g}(y_{2})},\quad
u^{\alpha}_{a}=\sum_{g}M^{\alpha}_{ga},\,\,d^{\alpha}_{g}=\sum_{a}M^{\alpha}_{ga}$
(6)

where $\alpha$ and $\beta$ indicate that the matrices may contain information
on different activity types (e.g., $\alpha$ being patents, and $\beta$
products). In this case $B_{aa^{\prime}}$ is defined as a conditional
probability, since $\sum_{a^{\prime}}B_{aa^{\prime}}=1$, $0\leq
B_{aa^{\prime}}\leq 1$.

However, the assist matrix, like any other relatedness measure, is based on
nested networks, where diversified actors are active in most of the
activities, and as a consequence, a co-occurrence may not be informative per
se. Hence, a null-model should be employed to discount the fact that co-
occurrences can be random, and are more likely for more ubiquitous activities
or more diversified geographical areas (Cimini et al., 2019). The most common
null model used in the literature to assess the statistical significance of
the conditional probabilities $B_{aa^{\prime}}(y_{1},y_{2})$, is the Bipartite
Configuration Model (BiCM) (Saracco et al., 2015, 2017), a maximum-entropy
approach for the randomization of bipartite networks – we refer to (Cimini et
al., 2019) for a more detailed discussion on null models for complex networks.

##  3 Review of the literature

In this section, we propose a selection of papers analyzing different
dimensions of the sustainability transition using EC techniques. This exercise
is focused not only on highlighting the main findings but especially on
illustrating the variety of methods employed, which lead often to inconclusive
results due to their low comparability. For this reason, we conduct a
purposive and ad hoc literature review, with the aim of illustrating the
empirical applications of the methods discussed in the previous section.
Systematically reviewing all the evidence on the use of EC metrics and methods
to study the sustainability transition falls beyond the scope of this article;
instead, we strive to offer a representative account of the empirical EC
applications in the field.

The rationale for the subdivision of the literature in three blocks follows
rather naturally from the shared characteristics of the empirical applications
of EC tools to study environmental sustainability. We divide the corpus of
empirical literature based on the unit of analysis of the empirical study,
namely i) geography (national or sub-national), ii) products and iii)
technologies. A graphical representation of this representative corpus is
summarized by Fig. 1, which identifies a number of features shared across the
empirical contributions, namely: unit of observation (group), data, methods,
definition of sustainability, and sample adopted. The following subsections
reflect the organization of the first group of columns (group) in Fig. 1.

The first subsection includes contributions that have explored the association
between complexity metrics at the geographical level (mainly countries and
regions) and variables related to environmental sustainability, such as
aggregate CO2 and GHG emissions. As can be inferred from Fig. 1, large share
of this literature uses the Economic Complexity Index (computed using Eq. 3)
as explanatory variable, with a few exceptions that use instead the Economic
Fitness and Complexity (Eq. 4). The association between EC and environmental
sustainability is analyzed in regression settings, which sometimes resort to
panel dynamic techniques. Occasionally, causal claims are made by the authors.
The sample of countries considered is rather heterogeneous, which hampers the
rationalization of the findings.

Figure 1: Summary of the reviewed articles, classified by comparable
dimensions. ’Group’ refers to the unit of analysis of the articles, the main
feature considered in dividing empirical contributions in three different
blocks, mirrored in the subsections that follow. ’Data’ indicates the data
sources employed; ’Methods’ refers to the specific methodologies adopted to
analyze the data, beyond the EC toolbox; ’Sustainability’ refers to the main
definition of environmental sustainability adopted by each paper; ’Sample’
indicates the specific dataset used.

The second subsection focuses on the identification of green products, aiming
to measure the green potential for green diversification based on export
patterns. As shown in 1, the main tool used in this branch of studies is the
product space. Their goal is to analyse the current status of countries’
productive system from the point of view of green product. Most of these works
do not try to asses relationship with target variables such as CO2 emission or
other measures of sustainability. Mainly following Mealy and Teytelboym,
(2022), they use distances and proximity in the products space to construct a
composite measure of ’greeness’ of productive systems. Relying on export data,
the geographical coverage is usually extended to most of the world, with few
focuses on specific countries or regions.

The third subsection is constituted by empirical articles that use patent data
to assess the readiness of regions for the green transition based on their
existing capabilities examining the geographical patterns of green innovative
capacity. As can be appreciated from Fig. 1, this literature often explores
the link and potential complementarity between green and non-green know-how,
or productive capacity, mainly through proximity-based measures of relatedness
between green and non-green technologies (Eq. 5), with a few exceptions
relying on an asset matrix-based definition of relatedness (Eq. 6), and
exploring geographical patterns through the definition of Green and non-green
technology based measures of Fitness (Eq. 4). These dimensions of relatedness
or green technological complexity are used by different contributions as
explanatory variables for a series of socioeconomic dimensions, such as
inequality, digital literacy, or support for environmental policies.

###  3.1 Economic Complexity and the environment

EC indices have been widely used by academics and policymakers to predict
economic growth (Hidalgo and Hausmann, 2009, Tacchella et al., 2012, Cristelli
et al., 2013, Tacchella et al., 2018). However, the pursuit of economic growth
has been put under scrutiny by academics and society alike, due to its
association to environmental degradation and climate change (Raworth, 2017,
IPCC, 2022). Since the measurement of EC relies heavily on the nature of the
products exported by countries or regions, which in turn impact the
environment with different intensity (as they embody different levels of GHG
emissions, have different energy requirements, and produce more or less
polluting byproducts), there has been growing interest in understanding the
relationship between the complexity of countries’ productive structures and
environmental impacts.

An ever-growing body of literature (Table LABEL:tab:ec_environment) has
investigated this relationship, looking especially at the export dimension of
complexity and at country-level measures of environmental impacts. Contrarily
to what early contributions seemed to suggest (Hidalgo, 2021), the last column
in Table LABEL:tab:ec_environment hints towards aggregate findings that are
far from unanimous, due to differing samples, geographical focus, and
estimation techniques. This despite the fact that most of this evidence uses
the Economic Complexity Index (Fig. 1) as explanatory variable; nevertheless,
estimation techniques adopted to retrieve the effect of EC on the environment
– and the way in which environmental impacts are measured – differ widely.

One strand of this literature assumes a linear relationship between EC and
environmental impacts, producing mixed evidence. In a rather comprehensive
study, Romero and Gramkow, (2021) analyze the relationship between 67
countries’ complexity levels and their CO2 emissions, measured as aggregate,
per capita, and product-specific. The latter is proxied by the Product
Emission Intensity Index (PEI), which averages the emissions of countries
exporting a product with comparative advantage – following a methodology
similar to Hartmann et al., (2017). The study finds that lagged ECI is
associated with a reduction of both emissions intensity and per capita, and
that lower emissions are associated with more interconnected (complex)
products. Looking exclusively at OECD countries, Dogan et al., (2021) also
find a positive relationship between EC and the reduction of CO2 emissions
between 1990-2014. In addition, the authors show that the complexity of
exports interacts positively with the consumption of renewable energy,
contributing to mitigating environmental impacts in high income countries. One
possible underlying mechanism is the emergence of greener occupations – and
therefore greener economic activity – in regions with higher complexity,
although the spatial dynamics of evolution towards the green occupations space
prove to be rather sticky (Dordmond et al., 2021).

Although the studies mentioned above seem to agree on the positive
contribution of EC to the environment, comparing their results is not always
straightforward. For instance, Boleti et al., (2021) rely on a measure of
environmental performance – the Environmental Performance Index111111 The
Environmental Performance Index used in this study is obtained aggregating 40
indicators across 11 issues, such as Climate Change Mitigation; Air Quality;
Sanitation and Drinking Water; Heavy Metals; Waste Management; Biodiversity
and Habitat; Ecosystem Services; Fisheries; Acid Rain; Agriculture; Water
Resources. For more information, see the latest EPI report:
https://epi.yale.edu/downloads/epi2022report06062022.pdf – to show that
increased complexity is associated with better environmental performance
across 88 low- and high-income countries over a (short) period spanning
between 2002 and 2012. However, in the same period, higher complexity is also
associated with worse air quality121212 Air quality is a composite index used
to compute the EPI, and which in turn aggregates different indicators that
quantify the number of age-standardized disability adjusted life-years lost
per 100,000 persons (DALY rate) due to exposure to: PM2.5 exposure, household
solid fuels, ozone exposure. Moreover, the air quality indicator includes
measures of population-weighted annual average concentration at the ground
level for: NOx, sulfur dioxide, carbon monoxide, and volatile organic
compound., higher emissions of CO2 (kg per 2011 PPP USD of GDP), methane (kg
of CO2 equivalent), and nitrous oxide (thousand metric tons of CO2
equivalent).

Some studies also find that EC is linked to an increase in CO2 emissions. For
instance, Neagu and Teodoru, (2019) examine the heterogeneous effects of EC on
GHG emissions in European countries. Their work shows that higher EC is
positively linked to the growth of GHG emissions by countries, although this
happens faster for countries with relatively lower levels of EC. These results
are consistent with other studies focusing exclusively on the most complex
countries: using time-series estimation techniques, Martins et al., (2021)
find that higher EC is positively associated, in an unidirectional way, the
levels of CO2 emissions in the top 7 countries in the EC ranking.
Nevertheless, integration in international trade has mitigated such negative
effects, allowing early industrializers to shift towards knowledge-intensive,
less polluting tasks. Similarly, Rafique et al., (2022) resort to dynamic
panel data estimation techniques to find that ECI – along with urbanization
and export growth – is positively linked to the ecological footprint131313The
authors rely on an index – the Ecological Footprint Index – which includes
factors such as area occupied by forests, cropland, grazing, built-up land,
fishing, and CO2 emissions. in the top 10 complex countries.

The mixed evidence on the relationship between EC and environmental
sustainability could also be explained by the fact that such linkage may be
non-linear. It has been argued that countries increase their environmental
impact – in terms of GHG and pollutant emissions – as they industrialize,
reaching a peak in their emissions per capita. However, as they move towards
more sophisticated activities and services, emissions per capita start to
decrease, while GDP keeps growing. The reversed U-shape of the relationship
between GDP and emissions has been named Environmental Kuznets Curve (EKC)
(Grossman and Krueger, 1991, Selden and Song, 1994, Grossman and Krueger,
1995), building on the work by Kuznets, (1955), who observed the same reversed
U-shaped relationship between inequality and economic growth unfolding along
with the process of structural transformation141414 The EKC hypothesis
suggests that the link between economic growth and its environmental impact
depends on two channels: the change in composition of economic activities, and
in the techniques of production (Grossman and Krueger, 1991). As countries
shift from agricultural to industrial activities, there is a rise in carbon-
intensive economic activities, leading to a positive correlation between GDP
per capita and emissions. Thereafter, as modern techniques of production are
introduced – leading to more efficient energy use and a shift to less energy-
intensive services – the relationship between GDP per capita and emissions
becomes negative. This results in a reversed U-shaped relationship between GDP
per capita and emissions.. A growing body of literature has attempted to
identify a complexity-based EKC; the intuition behind a reversed U-shaped
relationship between EC and CO2 emissions lies in the fact that, as countries
become ’fitter’ and accumulate productive capabilities, they shift their
specialization towards more knowledge-intensive goods, with the latter being
greener.

An ECI-based EKC has been identified especially for high-income countries,
which find themselves at a mature post-industrial stage – that is, beyond the
peak of emissions that coincided with higher intensity of employment
manufacturing industries. In particular, ECI-based EKCs have been empirically
tested for France (Can and Gozgor, 2017) and the US (Pata, 2021), as well as
for a sample of 25 European countries (Neagu, 2019) and for leading exporters
(Zheng et al., 2021). However, the same cannot be said for emerging economies,
as they are still intensive in manufacturing or extractive industries, which
are associated with higher GHG emissions. For instance, empirical tests of the
EKC in China – both at the aggregate (Yilanci and Pata, 2020) and regional
level (Akadiri et al., 2022) – find no evidence of a reversed U-shaped
relationship between complexity and CO2 emissions. On the contrary, higher
complexity appears to be associated with higher CO2 emissions in both cases,
despite the negative association between regional complexity and coal
consumption in China (Dong et al., 2020). In the case of Brazilian regions
(Swart and Brinkmann, 2020), the EKC hypothesis is validated using a quadratic
fit between ECI and several indicators of environmental quality. The
hypothesis holds for waste generation, but not for forest fires,
deforestation, and air pollution. It is however important to distinguish that,
in this case, the EKC tested does not refer to carbon emissions – which have
global consequences on climate change – but to factors related to local
environmental degradation.

It is important to mention that both the theoretical and empirical grounds of
the EKC hypothesis – as well as its normative implications (Savona and Ciarli,
2019) – are not uncontroversial. First, the future ability of emerging
countries – like China and Brazil – to decouple economic growth from GHG
emissions is all but certain. This casts a doubt on whether we can expect low-
and middle-income countries to go down the same path as currently high-income
countries. Even if this will be the case, it must be taken into account that
the polluting industries in which countries were once specialized may be
simply outsourced to other countries, lending support to the pollution haven
hypothesis151515The pollution haven hypothesis states that regulations in
high-income countries aiming at reducing GHG emissions may lead firm to
relocate in countries with looser environmental regulations, thus leading to
an outsourcing of CO2-intensive activities rather to their effective reduction
worldwide. For a case linking EC to the pollution haven hypothesis, see Dong
et al., (2020). (Cole, 2004). Moreover, the right part of the EKC – where the
decoupling of economic growth and CO2 emissions is observed – is populated
only by a few developed countries (Csereklyei et al., 2016). Even if the same
relationship was to hold also for developing countries in the future, waiting
for all countries to move beyond the plateau may irremediably compromise the
environment, as highlighted by the IPCC 2022.

Moreover, as noted by Stern, (2004) in his critical review, the EKC literature
tends to tread GDP per capita as an exogenous variable, ruling out any
possible feedback from the environmental dimension to the productive sphere.
Additionally, focusing on individual pollutants or GHG emissions has neglected
the changing composition of emissions associated to the change in the
productive structure. More recently, it has been noted that convergence
approaches may be more relevant in describing the relationship between
economic development and environmental impact, accounting also for non-
industrial GHG emissions (Stern, 2017).

Another issue worth discussing revolves around the data used in the empirical
analyses discussed above. While most of the empirical literature reviewed in
this section relies on exported products data, the trade dimension is not the
only relevant aspect in terms of environmental sustainability. As shown by
Stojkoski et al., (2023), the CO2 intensity across countries is explained more
fully by a combination of trade, technological, and scientific complexity,
computed respectively using data on exported products, patent applications,
and scientific publications. Moreover, the authors show that not all
complexity algorithms work equally well in predicting CO2 intensity, showing
that the Fitness algorithm (Tacchella et al., 2012) outperforms competing
measures. As shown in Fig. 1, however, only a small minority of authors opts
for the EFC algorithm (in this review, Boleti et al., 2021, You et al., 2022).

Finally, we would like to raise some methodological issues regarding the
evidence canvassed in this section. Apart from rare exceptions (i.e Can and
Gozgor, 2017), the empirical papers that examine the relationship between EC
and indices of environmental performance, use a measure – the Economic
Complexity Index (Hidalgo and Hausmann, 2009) – which should only be treated
as relative within each year (as explained in Section 2.2.1). As the term of
reference for the measure changes every year, changes in ECI over time do not
have any longitudinal interpretation, as the scale with which the index is
measured changes every year. In order to address this, the use of EC metrics
within longitudinal regressions should rely on the projection of product
complexity in a given year of the series, upon countries’ Revealed Comparative
advantages in every year (Sbardella et al., 2018a , Operti et al., 2018) –
i.e. an ”exogenous” complexity, as explained at the end of Section 2.2.1.
Alternatively, comparability over time is ensured by measures that maintains
an invariance of scale, as suggested by Mazzilli et al., (2024) and explained
in this article after Eq. 4.

One of the advantages offered by complexity methods in studying the
sustainability transition is that these allow to observe productive structures
and trajectories at a finely disaggregated level, for instance identifying
single products or technologies towards which diversification should be
steered in order to facilitate the transition towards greener activities. The
role of products and technologies will be discussed in the following two
subsections.

Table 3: Literature on the relationship between EC and environmental variables Article | Dep. var. | Indep. var. | Geo | Rel. EC $\rightarrow$ Env.
---|---|---|---|---
Neagu, (2019) |  CO2 emissions |  ECI, Energy intensity |  25 EU countries |  Non-linear: ECI-based EKC
Dong et al., (2020) |  Coal consumption |  Industrial Complexity Index (ICI) and Relatedness density (ECI-based); Energy-saving policies |  Chinese provinces |  Linear: $\uparrow$ ICI, $\downarrow$ Coal cons.; energy-saving policies associated to pollution haven hypothesis
Can and Gozgor, (2017) |  CO2 emissions |  Exogenous ECI, Energy consumption |  France |  Linear: $\uparrow$ ECI, $\downarrow$ CO2; GDP-based EKC (controlling for ECI)
Romero and Gramkow, (2021) |  GHG intensity, GHG per capita, Product Emission Intensity Index |  ECI |  67 countries |  Linear: $\uparrow$ ECI, $\downarrow$ GHG $\downarrow$ GHGpc $\downarrow$ PEII; centre of the product space has lower emissions
Akadiri et al., (2022) |  Ecological footprint (composite index) |  ECI, Renewable and non-renewable energy use, Economic growth |  China |  Linear and two-way: $\uparrow$ ECI, $\uparrow$ Footprint $\uparrow$ Energy cons.
Pata, (2021) |  Ecological footprint (composite index) |  ECI, Economic growth, Energy consumption, Globalisation |  US |  Non-linear: ECI-based EKC
Dordmond et al., (2021) |  Green Jobs Index (GJI) |  ECI |  Brasilian states |  Linear: $\uparrow$ ECI, $\uparrow$ GJI
Zheng et al., (2021) |  CO2 emissions |  ECI, Economic growth, Renewable energy consumption |  16 leading exporters |  Linear and two-way: $\uparrow$ ECI, $\downarrow$ CO2; GDP-based EKC
Martins et al., (2021) |  CO2 emissions |  ECI, Economic growth, Energy consumption, Globalisation |  7 top ECI countries |  Linear: $\uparrow$ EC, $\uparrow$ CO2; GDP-based EKC
Rafique et al., (2022) |  Ecological footprint (composite index) |  ECI, Human capital, Renewable energy, Urbanization, Economic growth, Export quality, Trade |  Top 10 ECI countries |  Linear: $\uparrow$ ECI, $\uparrow$ Footprint
Boleti et al., (2021) |  Environmental Performance Index (EPI) |  ECI/Fitness |  88 low and high income countries |  Linear: $\uparrow$ ECI/Fitness, $\uparrow$ EPII; GDP-based EKC
Dogan et al., (2021) |  CO2 emissions |  ECI, Renewable energy consumption, Non-renewable energy consumption |  OECD Countries |  Linear: $\uparrow$ ECI, $\downarrow$ CO2
You et al., (2022) |  CO2 emissions |  ECI/Fitness, Economic growth |  95 low, middle and high income countries |  Linear: $\uparrow$ ECI/Fitness, $\downarrow$ CO2 (high-income); $\uparrow$ ECI/Fitness, $\uparrow$ CO2 (low- and middle-income)
Yilanci and Pata, (2020) |  Ecological footprint (composite index) |  ECI, Energy consumption, Economic growth |  China |  Linear: $\uparrow$ ECI, $\uparrow$ Footprint; ECI-based EKC rejected
Swart and Brinkmann, (2020) |  Deforestation, Forest fires, Solid waste generation, Air pollution |  ECI |  Brazilian metropolitan regions |  Linear: $\uparrow$ ECI, $\uparrow$ Fires $\downarrow$ Waste; GDP-based EKC
Stojkoski et al., (2023) |  GDP growth, Income inequality, CO2 intensity |  ECI/Fitness |  World countries |  Predicting CO2 intensity: Multidimensional EC (trade + technologies + publications) outperforms trade-based; Fitness outperforms ECI

###  3.2 Economic Complexity for assessing green productive capabilities

Product-level data is the most commonly used data source in the EC framework.
However, it is less frequently employed in studies linking EC and the
sustainability transition, as compared to data on green patenting activity.
This is mainly due to the difficulty of defining the ’green products’ outlined
in Section 2.1. Table LABEL:tab:green_prod summarizes our selection of
articles for this line of research.

Few attempts have been made to assess the green complexity/potential of
national industrial systems. So far, the literature focused on the following
research questions: Are green products more complex? How related to new green
products is a given productive structure? Is there a significant difference in
the dynamics of the product space for green and non-green products? Thus, on
the one hand, scholars have tried to characterize green products as a
particular subset of products in international trade networks by using
standard EC tools. On the other hand, the main question – ’how prepared is a
country to a green transition?’ – is tackled mainly via network tools such as
the products space, not introducing specific tools or frameworks for green
commodities. Most contributions in the literature in fact appliy the standard
EC methodology and restrict the analysis to the subset of green products.

For instance, Fankhauser et al., (2013) assess the starting point of the green
race for eight countries (China, Germany, UK, USA, France, Italy, Japan, and
South Korea) for the period 2005-2007. They analyze three main factors of the
green transition: green conversion, estimated using the Green Innovation Index
(GII), an index constructed using patent data; change in RCAs; and green
production at the outset, which, due to lack of specific data on this
dimension, is assumed to be proportional to total production. Their findings
reveal different areas of competitiveness among countries, detected from a
correlation analysis of their Green Innovation Index and changes in RCAs. They
find no actual correlation between patenting GII and RCA competitiveness,
which could mean a lack of policies for the green conversion in most of these
countries.

Hamwey et al., (2013) apply the product space technique to identify
opportunities for countries in green production by taking into consideration
an arbitrary set of eleven environmental groups of products (in the SITC 4
classification at 4 digits) drawn from the list proposed by the WTO Committee
on Trade and Environment (CTE) in 2011. In particular, their analysis focuses
on Brazil, finding few selected products as potential opportunity of green
diversification. However, the authors recognize the weakness of the green
product definition, classification, and selection, as well as the limitation
of neglecting the dynamics of the RCA time series they use to construct the
products space.

Building on Hamwey et al., (2013), Fraccascia et al., (2018) apply the product
space description for green products and measure the proximity between green
products – as defined by the environmental goods and service sector
classification by EUROSTAT (2009) – and non-green products in which world
countries display an RCA>1\. Using panel regression analysis, they find this
proximity to be significant for the development of the export of green
products in a 4-years horizon.

In Mealy and Teytelboym, (2022) two lists of green products are defined: a
broader list comprising 293 green products, obtained by merging the WTO Core
list, OECD lists, and the APEC list, and a shorter list of 57 renewable energy
products. Using all products available in COMTRADE, they first compute the PCI
of all products and construct a product space. Then, they focus on green
products extracting the Green Complexity Index (GCI), the sum of green
products’ complexity, and the Green Complexity Potential (GCP), estimated by
the proximity to green products in the product space. They reveal the
complexity of green products to be significantly higher than the entire
products baskets. Notably, they find a negative correlation between GCI and
CO2 emissions per capita, as well as a positive correlation between GCP and an
increase of export in green products.

Pérez-Hernández et al., (2021) follow Mealy and Teytelboym, (2022) and apply
GCP and GCI at a sub-national level, studying the green potential of Mexican
regions. They use an adapted version of the CLEG classification to
characterize the green product space of regions. While their findings confirm
an overall positive correlation between GCI and GCP, some interesting outliers
arise are highlighted. For example, the state of Jalisco shows a high GCP and
a comparative advantage on few high complexity products, but a low
diversification in green products. This may be explained by some geographical
or fine-grained factors, not captured in these indexes, that are less
important at larger geographical scale.

Müller and Eichhammer, (2023) apply the product space methodology focusing on
the production of green hydrogen. They define a list of 36 goods ’that are
potentially relevant for the production of green hydrogen from solar and wind
power in a stand-alone configuration’. They also define a larger list by
selecting 434 products with a high proximity, in the product space, to the
first 36 goods. In agreement with the literature, they find higher PCI of
products in these two lists compared to the entire HS products classification.
Then they focus their study on European and Middle East and North African
(MENA) countries, analyzing how their GCI evolved in the period 1995-2019 and
assessing their readiness for the upcoming increasing demand of green
hydrogen, and their potential role in this specific supply chain.

Table 4: Literature for EC based green products classifications Article | EC indicator | EC methods | Geo | Main Results
---|---|---|---|---
Fankhauser et al., (2013) |  Green Innovation Index |  Descriptive statistics |  China, Germany, UK, USA, France, Italy, Japan, South Korea |  Characterization of areas of green potential/weakness
Hamwey et al., (2013) |  Products proximity |  Product space |  Brazil |  Full description of Brazilian product space and opportunities for new green production
Fraccascia et al., (2018) |  Green product proximity |  Product space and linear regression |  141 countries |  Products proximity is significant for new new green product export at 4-year horizon
Pérez-Hernández et al., (2021) |  Green Complexity Index and Potential |  Product space |  Regions of Mexico |  Full characterization of Mexican green potential. Confirmed positive correlation between GCI and GCP. Possible scale-variant effects.
Mealy and Teytelboym, (2022) |  ECI, Green Complexity Index and Potential |  Product space |  141 countries |  Green products are more complex, negative correlation between GCI and CO2/cap emission
Müller and Eichhammer, (2023) |  PCI, GCI |  Product space |  141 countries, special focus on EU and MENA regions |  Extended list of product relevant for green hydrogen production via proximity in the product space. Measure of GCI for green hydrogen-related product of EU and MENA countries

###  3.3 Economic Complexity for assessing green technological capabilities

In recent years, the EC literature exploring the intricate relationship
between green technological development, regional specialization dynamics, and
policies has grown substantially. By drawing insights from evolutionary
economic geography, these studies have revealed valuable insights into the
dynamics and drivers of green technology advancement. This has been pursued by
looking at the preparedness of European NUTS2 regions or national economic
structures to the green technology race, and by highlighting the potential
complementarities between green and non-green knowledge bases and/or
productive structures.

The majority of the papers reviewed in this section rely on climate change
mitigation and adaptation patent data as a proxy for low-carbon innovation,
using mainly the Env-Tech classification and the Y02/Y04S tagging scheme
presented in Section 2.1. This is increasingly becoming the gold standard to
measure green innovative activities. While the limits of using patent data for
the study of technology development should be acknowledged (see e.g., Arts et
al., 2013, Griliches, 1998, Lanjouw et al., 1998), information on patents is
widely available, and it can provide an array of quantitative information on
the nature of inventions and their applicants or inventors, including their
geographical location, allowing to easily geo-localise patents both at country
and local levels (Dechezleprêtre et al., 2011). Moreover, patent data can be
disaggregated into increasingly fine-grained technological areas, allowing
very specific green technologies to be identified (Haščič and Migotto, 2015).
This granularity is particularly helpful in the use of EC techniques to study
technological specialization (Boschma et al., 2015, Napolitano et al., 2018,
Pugliese et al., 2019a ). The application of EC approaches to reveal
technological advantages in each technology field rests on the idea that the
criteria to assign patent applications to specific domains are based on the
identifying characteristics of the expertise that is necessary to introduce
successful inventions. As a matter of fact, complex technologies appear almost
exclusively in the portfolios of high-complexity countries and less
diversified countries operating in less complex sectors.

As can be appreciated in Table LABEL:tab:green_tech, where we summarize the
main results, methodologies, and data employed in the papers included in this
section, some of these contributions focus on the interplay between green
capabilities and the maturity of green technologies in shaping the way in
which the technological portfolios of regions or countries grow and evolve
over time. Other contributions explore instead the relation between income
inequality and innovative capacities (both green and non-green) at the country
level, revealing the influence of socioeconomic and policy factors (e.g. smart
specialization strategies, political support, digital literacy) . Overall,
these contributions offer a cohesive narrative, that emphasizes the important
role played by local capabilities and relatedness to non-green knowledgge
bases or productive capacity in fostering new green technological innovation,
providing valuable guidance for policymakers seeking to promote sustainable
models of economic development.

Perruchas et al., (2020) investigate the relationship between country
characteristics and knowledge structures in the progression of green Env-Tech
patenting, and national specialization or diversification patterns from the
end of the 1970s. The authors focus on the life cycle of green technologies
and propose a ’ladder of green technology development’. Their evidence
emphasizes that not only countries diversify towards green technologies
related to their existing competencies, but also that green specialization
follows a cumulative path towards more mature technologies. Technology
maturity appears to be more relevant than a country’s economic development,
while technology complexity – computed through the ECI algorithm introduced in
Eq. 3 applied to green patent data – does not prevent further specialization.
By focusing on a panel of US states from the early 1980s, the same authors
(Barbieri et al., 2020) examine the role of related/unrelated variety in green
technology development and its heterogeneous effect over the technology life
cycle. The authors observe that unrelated variety plays a positive role in
fostering green innovation at early stages of the technology life cycle, while
as technologies mature, related variety becomes a more important driver.

Sbardella et al., 2018a  introduce Green Technological Fitness (GTF), a
measure of green innovative potential based on green patenting as defined in
Eq. 4 – here identified within PATSTAT through the Env-Tech catalogue. Taking
a geographical approach, the authors identify heterogeneous global patterns in
green technological competitiveness, with the United States, France, and
Germany as stable leaders; Eastern and Southern European countries gradually
gaining importance; and East Asian countries starting from the periphery and
rapidly establishing themselves as key actors. Finally, by analyzing the
distribution of countries’ innovation capacity across areas of specialization,
they document that innovation in green technology has become more horizontal,
with bigger efforts being observed in cross-domain, or enabling technologies.

Finally, focusing only on the technological dimension and not looking at
geographical dynamics, de Cunzo et al., (2022) explore the connection between
green technological innovation capacity and productive capabilities. In order
to do so, the authors define a statistically validated network – as defined in
Eq. 6 – connecting comparative advantages in Y02 green technologies to the
contemporaneous or subsequent comparative advantages in exported products.
When looking at same-year co-occurrences of single products at technologies,
their findings emphasize a large number of significant links between green
technologies and raw materials, especially critical minerals such as cobalt.
In contrast, when selecting ten-year time-lagged green technology-product
links, they suggest that green technology is better integrated into
manufacturing, and that more complex spillover effects emerge with new
additional links of products and technologies with higher complexity.

Evidence of different dimensions of complementarity between green technologies
and non-green pre-existing innovative capacity in European NUTS2 regions is
investigated by five articles (Bergamini and Zachmann, 2020, Montresor and
Quatraro, 2020, Barbieri et al., 2022, Sbardella et al., 2022, Grashof and
Basilico, 2023) using different approaches to assess relatedness. Firstly,
Bergamini and Zachmann, (2020) use regional patent data for Europe sourced
from the REGPAT database (Maraut et al., 2008) to predict the potential of
European regions to acquire (or maintain) competitive advantages in developing
green technologies. To this aim, the authors first leverage a network-based
measure of relatedness (as in Eq. 5) between non-green technologies to
estimate the potential technological advantage (RTA) in green technologies of
each region. In a second stage, they identify a set of socioeconomic variables
that hold a statistically significant association with observed/estimated RTAs
and derive possible policy implications.

Secondly, Montresor and Quatraro, (2020) explore the relationship between Env-
Tech green patenting and smart specialization strategies in European regions,
paying attention to the role of key enabling technologies.161616 The term key
enabling technologies denote six crucial technological domains identified by
the European Commission: industrial biotechnology, nanotechnology, micro- and
nanoelectronics, photonics, advanced materials, and advanced manufacturing
technologies. With the aim of understanding if the development or acquisition
of new sustainable technologies is related to regional technological
innovative capacity, the study shows that there is a strong connection between
green technologies and both green and non-green pre-existing knowledge bases.
Moreover, key enabling technologies support the shift towards green
technologies also mitigating the impact of relatedness to pre-existing
technologies.

Thirdly, Barbieri et al., (2022) and Sbardella et al., (2022) investigate the
nexus between non-green (A-H PATSTAT patents) and green (Y02-Y04S) innovative
capacity in and the green development potential of European regions, computing
measures of exogenous fitness as introduced in Section 2.2.1, regional Non-
Green Technology Fitness (NGTF), and Green Technology Fitness (GTF). Using the
statistically validated network approach introduced in Section 2.2.2, they
define a green potential metric quantifying the relatedness (as defined in Eq.
6) between non-green and green regional knowledge bases. The authors document
a heterogeneous but stable distribution of non-green and green innovative
competitiveness, and reveal a degree of complementarity between non-green and
green knowledge capabilities, albeit dependent on the patent portfolio
composition – especially for regions that have not fully developed the entire
set of Y02-Y04S technologies.

Finally, by investigating the nexus between EU NUTS2 regions’s knowledge base,
economic performance, and green technology specialization patterns (identified
through WIPO IPC Green Inventory) between 2000 and 2017, Grashof and Basilico,
(2023) argue that the sustainability transition will not widen regional
disparities. Their analysis, where a proximity-based relatedness is computed
according to Eq. 5, suggests that both high- and low-income regions can
diversify into green technologies. While high-income regions are more
specialized in technologies related to green innovations, they do not
necessarily have more entries in green technologies. Instead, related
technological capabilities appear to be more important for green
diversification than economic strength. Notably, low-income regions with high
green relatedness can successfully diversify into new, and even more complex,
green technologies.

Different contributions in this field are in agreement in suggesting that
relatedness is a driving force behind diversification in green technologies.
However, this new field of analysis has not yet fully emphasized the role of
the socioeconomic fabric and institutional set-up in sustaining the
sustainability transition. The sustainability transition literature has paid
greater attention to the policy and socioeconomic dimensions, however, putting
forward an important but not systematic collection of case studies, has often
failed to provide generalizable or scalable evidence on the role of
environmental policies or local characteristics in shifting towards a more
sustainable economy. The three following articles go in this direction and
analyze how green technological competitiveness, diversification in green
technologies, and/or relatedness to non-green knowledge interact with a number
of socioeconomic characteristics of countries or regions.

Looking at the regional dimension, Santoalha and Boschma, (2021) examines the
relationship between political support for environmental policies and the
diversification of green technologies in European regions. By employing a
measure of relatedness similar to the one encapsulated in Eq. 5, they find
evidence of a stronger relationship between related capabilities and green
diversification, as compared to regional policies. However, national political
support appears to mitigate the importance of capabilities.

Building on a similar metric for relatedness, Santoalha et al., (2021)
operationalize the notion of capabilities by studying whether and to what
extent digital literacy – as a proxy for the competencies embedded in ICT
infrastructures – foster diversification in green and non-green technologies
across European regions. The level of digital skills in the workforce has a
positive impact on a region’s ability to specialize in new technologies,
especially in green domains, with e-skills moderating the effect of
relatedness.

Shifting to a country-level analysis, Napolitano et al., (2022) investigate
whether and to what extent income inequality is a barrier to a country’s
environmental innovative capacity, proxied by a measure of Green Technological
Fitness based on Env-Tech technologies. To this end, and differently from
Sbardella et al., 2018a , they define a measure of sectoral fitness, to
provide a more realistic assessment of green technology complexity: first,
they account for the full technological spectrum in computing the EFC
algorithm (Eq. 4); then, they select only the complexities of Env-Tech
technologies in computing GTF. A negative and significant relationship between
income inequality and GTF is observed; by contrast, no significant association
is found when all technologies are considered. However, while for high-income
countries inequality does not appear to be a barrier, there is an income
threshold below which it is unlikely to develop a sufficient number of complex
technologies to achieve high green fitness. Low inequality reduces such
thresholds allowing middle-income countries to achieve greater green
innovative capacity.

##  4 Conclusions

In this review we have provided an encompassing account of the empirical
literature implementing EC methods and metrics to understand the
sustainability transition. First, the paper has summarized the most used
product- and patent-level data sources to compute EC metrics, with a
particular focus on green economic activities. Secondly, the review has
attempted to harmonize the most relevant methods adopted in the literature on
EC and environmental sustainability, in order to identify similarities and
differences across methods relying on a common framework and language. Third,
the three main blocks of empirical literature linking EC methods and metrics
have been reviewed, looking at i) the relationship between countries’ and
regions’ EC and the environment, and the role of ii) green products and iii)
green technologies in fostering the sustainability transition.

The growing literature on EC and the environment suggests that EC approaches
can be a useful lens to better understand how productive structures and
technological capabilities can be steered into the sustainability transition.
The empirical literature reviewed here offers some evidence that can be
summarized as follows. With respect to the first block of literature, which
examines how trade-based EC measures link to environmental outcomes such as
GHG emissions, ecological footprint, and environmental degradation, we
conclude that the evidence produced is mixed. On the one hand, high-income
countries – which are more specialized in knowledge-intensive activities –
seem to exhibit a positive association between the complexity of their export
basket and their ability to preserve the environment. However, the same
relationship cannot be observed in less mature, emerging economies, where
increasing complexity is associated to higher environmental impact. Empirical
test of the EKC hypothesis have shown that countries become able to decouple
economic growth from carbon emissions only after passing the industrialization
phase. Having this said, the extant literature on green EC has some important
limitations. First, the literature on country/regional complexity and
sustainability relies largely on aggregate measures of EC. This approach can
be limiting if confined to examining the relationship between complexity and
environmental variables, without taking advantage of the high level of
disaggregation made possible by EC methods. Secondly, as we have argued in
Section 3.1, the quest for empirical validation of the EKC hypothesis can be
of little normative utility, considering that not all countries will be able
to emulate the diversification and specialization patterns of today’s high-
income countries, some of which are specialized in knowledge-intensive
activities. Moreover, as shown by the IPCC, (2022), the current efforts to
curb carbon emissions also in high-income countries may be insufficient to
prevent a climatic catastrophe.

Looking at the second block of literature, one of the main goals of these
studies is to steer production systems toward a green, feasible, and just
transition. While the nature of the trade-off between these elements is left
to more theoretical studies, EC has the potential to identify, characterize,
and measure potential paths of such transformation. From the reviewed
attempts, it is clear that a greener production of commodities requires higher
capabilities. Green products show higher complexity and are intertwined to
non-green production. As stressed in the Data section of this paper, the
studies relying on green classifications for products are subject to the
problem of how to define a green product: different classifications based on
inconsistent definitions may undermine the replicability of the results. A
green sub-classification in the Harmonized System would be desirable for the
upcoming updates, so to have a single and exhaustive database for green
products. On the methodological front, most contributions in the EC literature
studying green products apply the well known product space description to
approach the problem of identifying the green potential of productive
structures. While the product space is indeed useful to visualize the
distances between the current production and green products, it has been shown
that this approach has low accuracy in quantitative forecasting of future
diversification (Tacchella et al., 2023).

The third block of studies on green technological development yields several
key conclusions. Firstly, there is evidence of countries engaging in a dual
strategy of diversification and specialization, entering green technologies
aligned with existing competencies while specializing in mature technologies
with accumulated experience.

Table 5: Literature on green technologies Article | Subtopic | Dep. var. | Indep. var. | EC def. |  | Relatedness
---
def.
Geo | Main Results |  Dataset; Geoloc.; Classification
Montresor and Quatraro, (2020) |  Green and non-green technology and key enabling technologies (KETs) |  New RTAs in green technologies |  Relatedness of green technology to all, non-green, green technologies; new RTAs in KETs, interaction between new RTAs in KETs and relatedness |  |  Proximity-based relatedness of tech. $z$ at time $t$ in region $i$ to all technologies at $t$; green or non-green technologies at $t-5$ |  EU NUTS2 regions |  Relatedness to non-green (rather than green) knowledge makes new green-tech specialisation more likely; KETs favour transition to green technologies and moderate relatedness to pre-existing technologies’ impact |  REGAPT (applicants); REGPAT NUTS3; ENV-TECH
Barbieri et al., (2020) |  Green technology life cycle, related and unrelated variety |  Green technology patent families (all and at different life cycle stages) |  Green technology related, semirelated, unrelated variety |  Technology ubiquity |  Related, semirelated, unrelated variety of green technologies computed with Shannon entropy if they share IPC code at different aggregation levels |  US states |  Unrelated variety positive predictor of green-tech specialisation; unrelated variety main driver of green technology development over the life cycle |  PATSTAT 2016 (applicants); GeoNames, Google Maps API; ENV-TECH
Perruchas et al., (2020) |  Green technological specialisation and life cycle across countries |  New RTAs in green technologies (all/only if country has RTA >1 at t-1) |  Relatedness, technology life cycle, environmental policy stringency |  ECI (green technology complexity) |  Proximity-based relatedness of green tech. $z$ in which country $i$ specialised at time $t$ to technologies in which $i$ already specialised at $t$ |  63 countries |  Diversification in green technologies related to pre-existing competences and more associated to green technology maturity than income, with green techn complexity not hampering further specialisation |  PATSTAT 2016 (applicants); GeoNames, Google Maps API; ENV-TECH
Santoalha et al., (2021) |  Green technology and digital skills |  New RTAs in green or non-green technologies |  Digital literacy, overall, green to non-green relatedness |  |  Proximity-based relatedness of green tech. $z$ in which region $i$ not specialised at time $t$ to technologies in which $i$ already specialised at $t$ |  EU NUTS2 regions |  Digital skills have a positive impact on a region’s ability to specialize in new technologies, especially in green domains, with e-skills moderating the effect of relatedness |  REGPAT (applicants); REGPAT NUTS3; ENV-TECH
Santoalha and Boschma, (2021) |  Green technology and national or regional environmental policy support |  New RTAs in green technologies |  National and regional environmental policy support, green to non-green relatedness |  |  Proximity-based relatedness of green tech. $z$ in which region $i$ not specialised at time $t$ to technologies in which $i$ specialised at $t$ |  EU NUTS2 regions |  Related technological capabilities more important than regional environmental policies, while national political support for environmental policies mitigates the importance of related technologies |  REGPAT (applicants); REGPAT NUTS3; ENV-TECH
Bergamini and Zachmann, (2020) |  Potential for green technology development and socio-economic regional characteristics |  RTAs in green technologies at time $t$ |  Potential technological advantage (Hausmann et al., 2022); labour market participation, STEM employment, education, R&D expenditures etc. |  |  Proximity-based relatedness of green tech. $z$ in which region $i$ not specialised at time $t$ to all technologies in which $i$ already specialised at $t$ |  EU NUTS2 regions |  Green techns RTAs concentrated in FR, DE, Northern IT, increase over time, but depend on technology type. Future regional green RTAs positively associated with labour market participation rate, employment duration, STEM employment, R&D exp., higher education |  REGPAT (inventors and applicants); REGPAT NUTS3; Fiorini et al., (2017) classif. based on Y02/Y04S
Sbardella et al., 2018a  |  Green innovative capacity across countries |  |  |  EFC (Green Technology Fitness) |  |  72 countries |  Global set of green technological capabilities is sticky with stable leaders, catch-ups (especially in East Asia) and laggards. Direct relationship between income and green innovative capacity, with complexity resonating with green technology life cycle |  PATSTAT 2016 (applicants); GeoNames, Google Maps API; ENV-TECH
Napolitano et al., (2022) |  Green innovative capacity and income inequality |  Green Technology Fitness |  Income inequality |  EFC (Green Technology Fitness) |  |  57 countries |  Green innovative capacity negatively correlated to income inequality, but inequality irrelevant for high-GDP countries, while for middle-income countries lower inequality favours green specialisation |  PATSTAT 2016 (applicants); GeoNames, Google Maps API; ENV-TECH
de Cunzo et al., (2022) |  Green technology and exported goods |  RCAs in exported products |  RTAs in green technologies |  EFC (Green Technology Complexity) |  Multi-layer based relatedness of green technology $z$ at time $t$ or $t-5$ to product $p$ at $t$ |  48 countries |  Strong connection between green technologies and exported products related to the processing of raw materials; with time, more complex green knowledge trickles down into more complex goods |  REGPAT (applicants); REGPAT countries; Y02/Y04S
Barbieri et al., (2022) |  Green and non-green know-how in EU regions |  Green Technology Fitness |  Non-green Technology Fitness, Non-green to green relatedness |  EFC (Green and Non-green Technology Fitness) |  Multi-layer based relatedness of non-green tech. $z$ in which region $i$ specialised at time $t-5$ to green tech. $z$ in which $i$ already specialised at $t$ |  EU NUTS2 regions |  Non-green and green competitiveness geographically heterogeneous and stable over time (persistent between Central-Eastern EU dichotomy). Complementarity between non-green and green tech know-how, but green specialisation depends on regional patent portfolios’ composition |  PATSTAT (inventors); PATSTAT inventor residence, de Rassenfosse et al., (2019)’s geocoding, EUROSTAT GIS NUTS 3; Y02/Y04S
Grashof and Basilico, (2023) |  Regional divergence, relatedness between brown and green know-how in EU regions |  New RTAs in green technologies (from time $t-5$ to $t$) |  Relatedness to green or brown technologies (at time $t$), economic strength ($=1$ if region $i$’s GDP per capita $\geq$ 75% of EU average, $=0$ otherwise) |  ECI (brown and green Technology Complexity) |  Proximity-based relatedness of brown/green technology $z$ in which region $i$ not specialised at time $t$ to technologies in which $i$ specialised at $t$ |  EU NUTS2 regions |  Related technological know-how more important for green specialisation than economic strength; high-income regions higher green relatedness, but low-income regions with high green relatedness can successfully diversify into complex green tech |  REGPAT (inventors); REGPAT NUTS2; WIPO IPC Green/Brown Inventory

Geographically, there is a mix of stable leaders and emerging players in green
technological competitiveness, with Eastern and Southern European countries
gaining prominence. Furthermore, economic factors – such as income inequality
– affect a country’s environmental innovative capacity. For instance, lower
income inequality in middle-income countries seems to lower barriers to the
successful development of complex green technologies. Finally, the
complementarity between non-green and green innovation capabilities, the
influence of regional factors and policies, and the significance of digital
literacy and e-skills in promoting green technology adoption are highlighted.
These findings provide valuable insights into the complex dynamics of green
technological development, emphasizing the interplay between capabilities,
regional factors, policies, and socioeconomic aspects.

Whilst the studies on technology-based EC can shed light on various aspects of
green technological development, it is important to acknowledge their
limitations. Firstly, the analyses often rely on patent data as a proxy for
technological innovation, which may not capture the full spectrum of green
technologies, or account for innovations that are not patented. This could
lead to a potential under-representation of certain sectors or technologies.
Secondly, the studies primarily focus on regional or national levels of
analysis, which may overlook the dynamics at the firm or individual levels.
The role of specific organizations or entrepreneurs in driving green
innovation is not extensively explored, potentially limiting the understanding
of micro-level factors influencing technological development. Lastly, the
studies primarily analyze the relationships between different variables and
identify correlations rather than establishing causal relationships. While the
associations observed are informative, further research is needed to delve
deeper into the underlying mechanisms and causality. These limitations
highlight the need for further research that encompasses a broader range of
data sources, considers multiple levels of analysis, and employs rigorous
methodologies to better understand the complexities of green technological
development and its implications for environmental sustainability.

The discussion on the empirical literature presented in Section 3 uncovers a
number of areas that require further exploration. First, the productive and
technological structures of countries are always accounted for separately in
relation to their linkage with environmental technologies. Whilst there have
been previous efforts linking the productive, technological and scientific
capabilities of countries with each other Pugliese et al., 2019a , and one
contribution focused on the green know-how-productive capacity nexus de Cunzo
et al., (2022), such effort should be extended to better understand which
specific capabilities are the most conducive to green specialization across
fields. Second, and related to the previous point, scientific capabilities
have not yet been included in the discourse on the sustainability transition
from a complexity perspective. Their relationship with the development of
green capabilities should be examined in depth. In fact, being cognizant of
the broad consensus on the key role of green technologies for the
sustainability transition, these cannot be viewed as a one-size fits all
solution, and should be integrated in a broad policy agenda targeting wider
dimensions of socioeconomic sustainability (Parkinson, 2010, Sarewitz and
Nelson, 2008).

Third, ensuring a green and fair transition will require to take into account
its interrelation with the process of structural change, labour reallocation,
and their distributive consequences. Future research will have to assess the
job creation/destruction potential of the green transition, and to identify
the transversal skills required to ensure a seamless reallocation of workers
across jobs. By the same token, not all territories and geographies are
equally well equipped with the necessary knowledge base to diversify away from
carbon-intensive production and technologies. It will be paramount to identify
the most viable ways for regions and countries to enter green economic
activities, bearing in mind the potentially negative (or positive) impact on
local labor markets.

Finally, green technologies and products may create further pressure on the
environment due to their dependency on natural commodities and minerals (Marín
and Goya, 2021), such as rare earth elements, lithium, cobalt, and others (de
Cunzo et al., 2023). Precisely, the World Bank (Hund et al., 2023) estimates
that meeting the 2°C scenario by 2050 for energy storage alone will require a
450 percent increase in the production of graphite, lithium, and cobalt.
Therefore, while pursuing the green transition may contribute to reducing
global dependence on fossil fuels, keeping up with current levels of energy
demand will shift the pressure towards the production and trade of raw
materials, neither of which is exempt from complications. For this reason,
future research will need to examine the production processes and recycling
potential associated to each green product, the CO2 production incorporated in
each value chain, its raw material content, the safety (also concerning
toxicity and pollutant exposure) and workplace condition of the labor force
employed in its production, and the environmental impact of production and use
(e.g. life cycle emissions, energy content, and waste management).

## Acknowledgments

DM, AP, and AS acknowledge the financial support under the National Recovery
and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.1, Call for
tender No. 104 published on 2.2.2022 by the Italian Ministry of University and
Research (MUR), funded by the European Union – NextGenerationEU– Project Title
"WECARE" – CUP 20223W2JKJ by the Italian Ministry of Ministry of University
and Research (MUR). DM, AP, and AS acknowledge the financial support under the
National Recovery and Resilience Plan (NRRP), Mission 4, Component 2,
Investment 1.1, Call for tender No. 1409 published on 14.9.2022 by the Italian
Ministry of University and Research (MUR), funded by the European Union –
NextGenerationEU– Project Title "Triple T – Tackling a just Twin Transition: a
complexity approach to the geography of capabilities, labour markets and
inequalities" – CUP F53D23010800001 by the Italian Ministry of Ministry of
University and Research (MUR).

## References

  * Akadiri et al., (2022) Akadiri, S. S., Adebayo, T. S., Asuzu, O. C., Onuogu, I. C., and Oji-Okoro, I. (2022).  Testing the role of economic complexity on the ecological footprint in China: a nonparametric causality-in-quantiles approach.  Energy & Environment, 0(0):0958305X221094573.
  * Angelucci et al., (2018) Angelucci, S., Hurtado-Albir, F. J., and Volpe, A. (2018).  Supporting global initiatives on climate change: The EPO’s “Y02-Y04S” tagging scheme.  World Patent Information, 54:S85–S92.
  * APEC, (2012) APEC (2012).  ANNEX C - APEC List of Environmental Goods.  Technical report, APEC.
  * Arts et al., (2013) Arts, S., Appio, F. P., and Van Looy, B. (2013).  Inventions shaping technological trajectories: do existing patent indicators provide a comprehensive picture?  Scientometrics, 97(2):397–419.
  * Atmar and Patters, (1993) Atmar, W. and Patters, B. (1993).  The measure of order and disorder in the distribution of species in fragmental habitat.  Oecologia, 96.
  * Balassa, (1965) Balassa, B. (1965).  Trade liberalisation and “revealed” comparative advantage.  The Manchester School, 33(2):99–123.
  * Balland et al., (2022) Balland, P.-A., Broekel, T., Diodato, D., Giuliani, E., Hausmann, R., O’Clery, N., and Rigby, D. (2022).  The new paradigm of economic complexity.  Research Policy, 51(3):104450.
  * Balland and Rigby, (2017) Balland, P.-A. and Rigby, D. (2017).  The geography of complex knowledge.  Economic geography, 93(1):1–23.
  * Barbieri et al., (2022) Barbieri, N., Consoli, D., Napolitano, L., Perruchas, F., Pugliese, E., and Sbardella, A. (2022).  Regional technological capabilities and green opportunities in europe.  The Journal of Technology Transfer, pages 1–30.
  * Barbieri et al., (2020) Barbieri, N., Perruchas, F., and Consoli, D. (2020).  Specialization, diversification, and environmental technology life cycle.  Economic Geography, 96(2):161–186.
  * Bergamini and Zachmann, (2020) Bergamini, E. and Zachmann, G. (2020).  Exploring EU’s regional potential in low-carbon technologies.  Sustainability, 13(1):32.
  * Bistline et al., (2023) Bistline, J., Mehrotra, N., and Wolfram, C. (2023).  Economic implications of the climate provisions of the inflation reduction act.  Working Paper 31267, National Bureau of Economic Research.
  * Boleti et al., (2021) Boleti, E., Garas, A., Kyriakou, A., and Lapatinas, A. (2021).  Economic complexity and environmental performance: Evidence from a world sample.  Environmental Modeling & Assessment, 26(3):251–270.
  * Boschma et al., (2015) Boschma, R., Balland, P.-A., and Kogler, D. F. (2015).  Relatedness and technological change in cities: The rise and fall of technological knowledge in US metropolitan areas from 1981 to 2010.  Industrial and Corporate Change, 24(1):223–250.
  * Boschma and Frenken, (2018) Boschma, R. and Frenken, K. (2018).  Evolutionary economic geography.  The new Oxford handbook of economic geography, pages 213–229.
  * Boschma and Frenken, (2006) Boschma, R. A. and Frenken, K. (2006).  Why is economic geography not an evolutionary science? Towards an evolutionary economic geography.  Journal of Economic Geography, 6(3):273–302.
  * Breschi et al., (2003) Breschi, S., Lissoni, F., and Malerba, F. (2003).  Knowledge-relatedness in firm technological diversification.  Research Policy, 32(1):69–87.
  * Bruno et al., (2023) Bruno, M., Mazzilli, D., Patelli, A., Squartini, T., and Saracco, F. (2023).  Inferring comparative advantage via entropy maximization.
  * Caldarelli et al., (2012) Caldarelli, G., Cristelli, M., Gabrielli, A., Pietronero, L., Scala, A., and Tacchella, A. (2012).  A network analysis of countries’ export flows: firm grounds for the building blocks of the economy.  PLoS ONE, 7.
  * Can and Gozgor, (2017) Can, M. and Gozgor, G. (2017).  The impact of economic complexity on carbon emissions: evidence from France.  Environmental Science and Pollution Research, 24(19):16364–16370.
  * Cimini et al., (2019) Cimini, G., Squartini, T., Saracco, F., Garlaschelli, D., Gabrielli, A., and Caldarelli, G. (2019).  The statistical physics of real-world networks.  Nature Review Physics, 1:58–71.
  * Cimoli and Dosi, (1995) Cimoli, M. and Dosi, G. (1995).  Technological paradigms, patterns of learning and development: An introductory roadmap.  Journal of Evolutionary Economics, 5(3):243–268.
  * Cole, (2004) Cole, M. A. (2004).  Trade, the pollution haven hypothesis and the environmental kuznets curve: examining the linkages.  Ecological Economics, 48(1):71–81.
  * Common and Stagl, (2005) Common, M. and Stagl, S. (2005).  Ecological economics: an introduction.  Cambridge University Press.
  * Cristelli et al., (2013) Cristelli, M., Gabrielli, A., Tacchella, A., Caldarelli, G., and Pietronero, L. (2013).  Measuring the intangibles: A metrics for the economic complexity of countries and products.  PloS one, 8(8):e70726.
  * Cristelli et al., (2015) Cristelli, M., Tacchella, A., and Pietronero, L. (2015).  The heterogeneous dynamics of economic complexity.  PloS one, 10(2):e0117174.
  * Csereklyei et al., (2016) Csereklyei, Z., Rubio-Varas, M. d. M., and Stern, D. I. (2016).  Energy and economic growth: the stylized facts.  The Energy Journal, 37(2).
  * de Cunzo et al., (2023) de Cunzo, F., Consoli, D., Perruchas, F., and Sbardella, A. (2023).  Mapping critical raw materials in green technologies.
  * de Cunzo et al., (2022) de Cunzo, F., Petri, A., Zaccaria, A., and Sbardella, A. (2022).  The trickle down from environmental innovation to productive complexity.  Scientific Reports, 12(1):22141.
  * de Rassenfosse et al., (2019) de Rassenfosse, G., Kozak, J., and Seliger, F. (2019).  Geocoding of worldwide patent data.  Scientific Data, 6(1):1–15.
  * Dechezleprêtre et al., (2011) Dechezleprêtre, A., Glachant, M., Haščič, I., Johnstone, N., and Ménière, Y. (2011).  Invention and transfer of climate change–mitigation technologies: A global analysis.  Review of Environmental Economics and Policy, 5(1):109–130.
  * Dogan et al., (2021) Dogan, B., Driha, O. M., Balsalobre Lorente, D., and Shahzad, U. (2021).  The mitigating effects of economic complexity and renewable energy on carbon emissions in developed countries.  Sustainable Development, 29(1):1–12.
  * Dong et al., (2020) Dong, Z., Chen, W., and Wang, S. (2020).  Emission reduction target, complexity and industrial performance.  Journal of Environmental Management, 260:110148.
  * Dordmond et al., (2021) Dordmond, G., de Oliveira, H. C., Silva, I. R., and Swart, J. (2021).  The complexity of green job creation: An analysis of green job development in Brazil.  Environment, Development and Sustainability, 23(1):723–746.
  * Dosi and Nelson, (1994) Dosi, G. and Nelson, R. R. (1994).  An introduction to evolutionary theories in economics.  Journal of Evolutionary Economics, 4(3):153–172.
  * European Patent Office, (2013) European Patent Office (2013).  EPO–Finding sustainable technologies in patents.
  * European Patent Office, (2018) European Patent Office (2018).  EPO worldwide patent statistical database data catalog, climate change mitigation technologies.
  * EUROSTAT, (2009) EUROSTAT (2009).  The environmental goods and services sector.
  * Fankhauser et al., (2013) Fankhauser, S., Bowen, A., Calel, R., Dechezleprêtre, A., Grover, D., Rydge, J., and Sato, M. (2013).  Who will win the green race? In search of environmental competitiveness and innovation.  Global Environmental Change, 23(5):902–913.
  * Fiorini et al., (2017) Fiorini, A., Georgakaki, A., Pasimeni, F., and Tzimas, E. (2017).  Monitoring R&I in low-carbon energy technologies.  Publications Office of the European Union: Luxembourg.
  * Fraccascia et al., (2018) Fraccascia, L., Giannoccaro, I., and Albino, V. (2018).  Green product development: What does the country product space imply?  Journal of Cleaner Production, 170:1076–1088.
  * Grashof and Basilico, (2023) Grashof, N. and Basilico, S. (2023).  The dark side of green innovation? green transition and regional inequality in europe.  Technical report, Utrecht University, Department of Human Geography and Spatial Planning ….
  * Griliches, (1998) Griliches, Z. (1998).  Patent statistics as economic indicators: A survey.  In R&D and productivity: The econometric evidence, pages 287–343. University of Chicago Press.
  * Grossman and Krueger, (1991) Grossman, G. M. and Krueger, A. B. (1991).  Environmental impacts of a north american free trade agreement.  Working Paper 3914, National Bureau of Economic Research.
  * Grossman and Krueger, (1995) Grossman, G. M. and Krueger, A. B. (1995).  Economic Growth and the Environment.  The Quarterly Journal of Economics, 110(2):353–377.
  * Hamwey et al., (2013) Hamwey, R., Pacini, H., and Assunção, L. (2013).  Mapping green product spaces of nations.  The Journal of Environment & Development, 22(2):155–168.
  * Hartmann et al., (2017) Hartmann, D., Guevara, M. R., Jara-Figueroa, C., Aristarán, M., and Hidalgo, C. A. (2017).  Linking economic complexity, institutions, and income inequality.  World Development, 93:75–93.
  * Haščič and Migotto, (2015) Haščič, I. and Migotto, M. (2015).  Measuring environmental innovation using patent data.  OECD Environment Working Papers 89, OECD Publishing, Paris.
  * Hausmann and Hidalgo, (2011) Hausmann, R. and Hidalgo, C. A. (2011).  The network structure of economic output.  Journal of Economic Growth, pages 309–342.
  * Hausmann et al., (2011) Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Chung, S., Jimenez, Juan Simoes, A., and Yildirim, M. A. (2011).  The Atlas of economic complexity: Mapping paths to prosperity.  Puritan Press, New Hampshire.
  * Hausmann et al., (2014) Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., and Simoes, A. (2014).  The Atlas of economic complexity: Mapping paths to prosperity.  MIT Press.
  * Hausmann et al., (2007) Hausmann, R., Hwang, J., and Rodrik, D. (2007).  What you export matters.  Journal of Economic Growth, 12:1–25.
  * Hausmann and Rodrik, (2003) Hausmann, R. and Rodrik, D. (2003).  Economic development as self-discovery.  Journal of Development Economics, 72(2):603–633.
  * Hausmann et al., (2005) Hausmann, R., Rodrik, D., Benavente, J. M., and Rodríguez, F. (2005).  Self-discovery in a development strategy for El Salvador [with comments].  Economía, 6(1):43–101.
  * Hausmann et al., (2022) Hausmann, R., Stock, D. P., and Yıldırım, M. A. (2022).  Implied comparative advantage.  Research Policy, 51(8):104143.
  * Hidalgo, (2009) Hidalgo, C. A. (2009).  The Dynamics of Economic Complexity and the Product Space over a 42 year period.  CID Working Paper Series, 189.
  * Hidalgo, (2021) Hidalgo, C. A. (2021).  Economic complexity theory and applications.  Nature Reviews Physics, 3(2):92–113.
  * Hidalgo and Hausmann, (2009) Hidalgo, C. A. and Hausmann, R. (2009).  The building blocks of economic complexity.  Proceedings of the National Academy of Sciences, 106(26):10570–10575.
  * Hidalgo et al., (2007) Hidalgo, C. A., Klinger, B., Barabási, A.-L., and Hausmann, R. (2007).  The Product Space Conditions the Development of Nations.  Science, 317(5837):482–487.
  * Hirschman, (1958) Hirschman, A. O. (1958).  The Strategy of Economic Development.  Yale University Press, New Haven.
  * Hund et al., (2023) Hund, K., La Porta, D., Fabregas, T., Laing, T., and Drexhage, J. (2023).  Minerals for climate action: The mineral intensity of the clean energy transition.  The World Bank Group, Washington, DC.
  * IPCC, (2022) IPCC (2022).  Climate Change 2022: Impacts, Adaptation, and Vulnerability.  Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK and New York, USA.
  * Jaffe, (1986) Jaffe, A. B. (1986).  Technological Opportunity and Spillovers of R&D: Evidence from Firms’ Patents, Profits and Market Value.  Working Paper 1815, National Bureau of Economic Research.
  * Kemp-Benedict, (2014) Kemp-Benedict, E. (2014).  An interpretation and critique of the method of reflections.  Working paper, Munich Personal RePEc Archive.
  * Kuznets, (1955) Kuznets, S. (1955).  Economic growth and income inequality.  The American Economic Review, 45(1):1–28.
  * Lanjouw et al., (1998) Lanjouw, J. O., Pakes, A., and Putnam, J. (1998).  How to count patents and value intellectual property: The uses of patent renewal and application data.  The Journal of Industrial Economics, 46(4):405–432.
  * Laudati et al., (2023) Laudati, D., Mariani, M. S., Pietronero, L., and Zaccaria, A. (2023).  The different structure of economic ecosystems at the scales of companies and countries.  Journal of Physics: Complexity, 4(2):025011.
  * Lewis, (1954) Lewis, A. (1954).  Economic Development with Unlimited Supplies of Labour.  The Manchester School, 22(8):139–191.
  * Li et al., (2019) Li, J.-F., Gu, A.-L., Ma, Z.-Y., Zhang, C.-L., and Sun, Z.-Q. (2019).  Economic development, energy demand, and carbon emission prospects of china’s provinces during the 14th five-year plan period: Application of cmrcge model.  Advances in Climate Change Research, 10(3):165–173.
  * Lin et al., (2011) Lin, J. Y., Krueger, A., and Rodrik, D. (2011).  New structural economics: A framework for rethinking development.  The World Bank Research Observer, 26(2):193–223.
  * Maraut et al., (2008) Maraut, S., Dernis, H., Webb, C., Spiezia, V., and Guellec, D. (2008).  The OECD REGPAT database: a presentation.  OECD Science, Technology and Industry Working Papers 2008/02, OECD Publishing, Paris.
  * Martins et al., (2021) Martins, J. M., Adebayo, T. S., Mata, M. N., Oladipupo, S. D., Adeshola, I., Ahmed, Z., and Correia, A. B. (2021).  Modeling the relationship between economic complexity and environmental degradation: Evidence from top seven economic complexity countries.  Frontiers in Environmental Science, 9.
  * Marín and Goya, (2021) Marín, A. and Goya, D. (2021).  Mining—the dark side of the energy transition.  Environmental Innovation and Societal Transitions, 41:86–88.  Celebrating a decade of EIST: What’s next for transition studies?
  * Mazzilli et al., (2024) Mazzilli, D., Mariani, M. S., Morone, F., and Patelli, A. (2024).  Equivalence between the fitness-complexity and the sinkhorn-knopp algorithms.  Journal of Physics: Complexity.
  * Mealy et al., (2018) Mealy, P., Farmer, J. D., and Teytelboym, A. (2018).  A new interpretation of the economic complexity index.  Alexander, A New Interpretation of the Economic Complexity Index (February 4, 2018).
  * Mealy et al., (2019) Mealy, P., Farmer, J. D., and Teytelboym, A. (2019).  Interpreting economic complexity.  Science advances, 5(1):eaau1705.
  * Mealy and Teytelboym, (2022) Mealy, P. and Teytelboym, A. (2022).  Economic complexity and the green economy.  Research Policy, 51(8):103948.
  * Montiel-Hernández et al., (2024) Montiel-Hernández, M. G., Pérez-Hernández, C. C., and Salazar-Hernández, B. C. (2024).  The intrinsic links of economic complexity with sustainability dimensions: A systematic review and agenda for future research.  Sustainability, 16(1):391.
  * Montresor and Quatraro, (2020) Montresor, S. and Quatraro, F. (2020).  Green technologies and Smart Specialisation Strategies: A European patent-based analysis of the intertwining of technological relatedness and key enabling technologies.  Regional Studies, 54(10):1354–1365.
  * Müller and Eichhammer, (2023) Müller, V. P. and Eichhammer, W. (2023).  Economic complexity of green hydrogen production technologies-a trade data-based analysis of country-specific industrial preconditions.  Renewable and Sustainable Energy Reviews, 182:113304.
  * Napolitano et al., (2018) Napolitano, L., Evangelou, E., Pugliese, E., Zeppini, P., and Room, G. (2018).  Technology networks: The autocatalytic origins of innovation.  Royal Society Open Science, 5(6):172445.
  * Napolitano et al., (2022) Napolitano, L., Sbardella, A., Consoli, D., Barbieri, N., and Perruchas, F. (2022).  Green innovation and income inequality: A complex system analysis.  Structural Change and Economic Dynamics, 63:224–240.
  * Neagu, (2019) Neagu, O. (2019).  The Link between Economic Complexity and Carbon Emissions in the European Union Countries: A Model Based on the Environmental Kuznets Curve (EKC) Approach.  Sustainability, 11(17).
  * Neagu and Teodoru, (2019) Neagu, O. and Teodoru, M. C. (2019).  The Relationship between Economic Complexity, Energy Consumption Structure and Greenhouse Gas Emission: Heterogeneous Panel Evidence from the EU Countries.  Sustainability, 11(2).
  * Nelson and Winter, (1982) Nelson, R. R. and Winter, S. G. (1982).  An Evolutionary Theory of Economic Change.  The Belknap Press of Harvard University Press, Cambridge, Massachussets and London, UK.
  * OECD, (1999) OECD (1999).  The Environmental Goods and Services Industry: Manual for Data Collection and Analysis.  OECD Publishing, Paris.
  * OECD, (2016) OECD (2016).  Patent search strategies for the identification of selected environment-related technologies (ENV-TECH).  https://www.oecd.org/environment/consumption-innovation/ENV-tech
  * OECD, (2020) OECD (2020).  Territorial grids.
  * Operti et al., (2018) Operti, F. G., Pugliese, E., Andrade Jr, J. S., Pietronero, L., and Gabrielli, A. (2018).  Dynamics in the Fitness-Income plane: Brazilian states vs World countries.  PloS one, 13(6):e0197616.
  * Parkinson, (2010) Parkinson, C. L. (2010).  Coming climate crisis? Consider the past, beware the big fix.  Lanham: Rowman & Littlefield Publishers.
  * Pata, (2021) Pata, U. K. (2021).  Renewable and non-renewable energy consumption, economic complexity, CO2 emissions, and ecological footprint in the USA: testing the EKC hypothesis with a structural break.  Environmental Science and Pollution Research, 28(1):846–861.
  * Patelli et al., (2023) Patelli, A., Napolitano, L., Cimini, G., and Gabrielli, A. (2023).  Geography of science: Competitiveness and inequality.  Journal of Informetrics, 17(1):101357.
  * Patelli et al., (2022) Patelli, A., Napolitano, L., Cimini, G., Pugliese, E., and Gabrielli, A. (2022).  The evolution of competitiveness across economic, innovation and knowledge production activities.
  * Penrose, (1959) Penrose, E. T. (1959).  The Theory of the Growth of the Firm.  John Wiley, New York.
  * Pérez-Hernández et al., (2021) Pérez-Hernández, C. C., Salazar-Hernández, B. C., Mendoza-Moheno, J., Cruz-Coria, E., and Hernández-Calzada, M. A. (2021).  Mapping the green product-space in mexico: From capabilities to green opportunities.  Sustainability, 13(2):945.
  * Perruchas et al., (2020) Perruchas, F., Consoli, D., and Barbieri, N. (2020).  Specialisation, diversification and the ladder of green technology development.  Research Policy, 49(3):103922.
  * Pietronero et al., (2017) Pietronero, L., Cristelli, M., Gabrielli, A., Mazzilli, D., Pugliese, E., Tacchella, A., and Zaccaria, A. (2017).  Economic complexity: "buttarla in caciara" vs a constructive approach.