# Economic activity and climate change.111Financial Support from La Caixa
Fundation, grant LCF/PR/SR20-52550012-Climate Change and Economic Challenges
for the Spanish Society (ECHASS), is gratefully acknowledged by all the
authors. Esther Ruiz also acknowledges financial support from Project
PID2019-108079GB-C21 while Pilar Poncela and Arantxa de Juan are supported by
Project PID2019-108079GB-C22, both from the Spanish Government. We are also
very grateful to Carlos Cuerpo for invaluable comments that help us to focus
this paper on relevant problems for policy makers. Any remaining errors are
obviously only our responsibility.

Aránzazu de Juan  Department of Economic Analysis Quantitative Economics,
Universidad Autónoma de Madrid  Pilar Poncela  Department of Economic Analysis
Quantitative Economics, Universidad Autónoma de Madrid  Vladimir Rodríguez-
Caballero  ITAM (Mexico) and CREATES (Aarhus University)  Esther
Ruiz222Corresponding author. e-mail<EMAIL_ADDRESS>Address: Dpto.
de Estadística, Universidad Carlos III de Madrid, C/ Madrid, 28903 Getafe
(Spain). Department of Statistics, Universidad Carlos III de Madrid

(14th May 2022)

###### Abstract

In this paper, we survey recent econometric contributions to measure the
relationship between economic activity and climate change. Due to the critical
relevance of these effects for the well-being of future generations, there is
an explosion of publications devoted to measuring this relationship and its
main channels. The relation between economic activity and climate change is
complex with the possibility of causality running in both directions. Starting
from economic activity, the channels that relate economic activity and climate
change are energy consumption and the consequent pollution. Hence, we first
describe the main econometric contributions about the interactions between
economic activity and energy consumption, moving then to describing the
contributions on the interactions between economic activity and pollution.
Finally, we look at the main results on the relationship between climate
change and economic activity. An important consequence of climate change is
the increasing occurrence of extreme weather phenomena. Therefore, we also
survey contributions on the economic effects of catastrophic climate
phenomena.

Keywords: Catastrophic weather, Energy consumption, Environmental Kuznets
Curve, Global Warming, Greenhouse Gases, Temperature trends.

##  1 Introduction

Measuring the interactions between economic growth and climate change could
give an answer to whether it is socially beneficial for present and near
future generations to sacrifice their own consumption to mitigate the latter
in favour of generations yet to come and, consequently, it is critical for the
design of economic and environmental policies. As Auffhammer (2018) states,
“optimal policy design in the context of addressing the biggest environmental
market failure in human history requires an understanding of the external cost
imposed by additional emissions of greenhouse gases.” In this direction, the
European Union (EU) has designed a new strategy for growth directed towards
its transformation into a fair and prosperous society with a modern, resource-
efficient and competitive economy. As a result of this strategy, in December
2019, the EU launched the European Green Deal plan. Furthermore, as a response
to the recent COVID-19 pandemic, the EU has put forward an ambitious and
innovative budget for European recovery, the “Next Generation EU” with a
budget of 750.000 million Euro. This plan is a game changer with respect to
the discussion about climate change as member states receiving financial
support should direct one third of their budgets towards the Green Economy and
the ecological transition. Relevant policy recommendations need to be based on
correct forecasts of the externalities caused by climate change and its
mitigation costs. Robust measures of the interactions between environmental
variables and the economy are crucial to formulate suitable economic
development paths; see Tol (2009, 2014), who points out three of the big
unknowns related to climate change, namely, extreme climate scenarios, the
very long-term scenarios, and the potential economic and social effects of
climate change. In this direction, the European Commission has recognized the
need of designing robust quantitative systems for monitoring and forecasting
key variables related with the Climate and Energy Framework objectives in
order to understand whether the steps taken within each of EU members will
produce the desired effects in terms of the European Green Deal plan. It is
important to remark that all member states will simultaneously carry out their
policies within the “Next Generation EU” plan and, by that, they will generate
potential externalities that need to be measured.

Given the implications mentioned above, it is not surprising that, during the
last decades, there has been an increasing interest among policy-makers,
academics and the society in general, in knowing about the negative
externalities of macroeconomic growth, with climate change being among the
most relevant and dangerous of these externalities; see, for example, the
discussions by Pindyck (2013), Stern (2008) and Stock (2019). Knowing the
extent to which climate change affects important macroeconomic magnitudes,
and, in particular, growth, has straightforward implications for the design of
resilience economic policies. Economic growth based on burning of fossil or
carbon-based fuels leads to pollution in the form of emissions of Greenhouse
Gases (GHG). Higher atmospheric concentrations of GHG warm the land and oceans
with impacts on temperatures, precipitation patterns, storm location and
frequency, river run-off and water availability, among others. These long-run
permanent changes in (short-run) weather is what is known as climate change.

The relationship between economic activity and climate change is not
unidirectional as the latter may also have an impact on economic magnitudes.
In a series of works, Rezai, Foley and Taylor (2012), Taylor, Rezai and Foley
(2016) and Rezai, Taylor and Foley (2018) show that climate change generates
externalities that may affect important economic variables as, for example,
employment, income distribution and growth; see also Weitzman (2009) for a
theoretical model of the economic effects of catastrophic climate change and
Tanoue et al. (2020) for the effects of floods. There are also several
“empirical” popular models proposed to measure climate change and its economic
impact. Among them, the most remarkable contribution of Nobel laureate
Nordhaus is the proposal of the Dynamic Integrated model of Climate Change and
the Economy (DICE), an integrated assessment model (IAM), which is a
constrained non-linear dynamic optimization model with an infinite horizon;
see, Nordhaus (2019) for a summary of his contributions on the economics of
climate change, for which he received in 2018 the Nobel prize in Economics,
and Dietz et al. (2021) for a recent analysis of the climate policy
implications of different representations of the climate system based on the
DICE model. In November 2006, the British Government presented a comprehensive
study based on IAMs, The Stern Review on the Economics of Climate Change,
according to which “…if we don’t act, the overall costs and risks of climate
change will be equivalent to loosing at least 5% of global GDP each year, now
and forever. If a wider range of risks and impacts is taken into account, the
estimates of damage could rise to 20% of GDP or more…”; see Stern (2007) for
the full text of the Review and Nordhaus (2006) and Tol (2006) for enlighting
comments on it. Barnett, Brock and Hansen (2021), Hansen (2022), Pindyck
(2013, 2017), Stern (2008, 2013, 2016) and Weitzman (2007, 2015) discuss the
limitations of models as DICE, which can give an illusory and misleading
perception of knowledge and precision. Uncertainty is the “Achilles’ heel” of
IAMs of climate change, being of little value for a prudent current policy;
see, for example, Hansen (2022), who represents the histogram of temperature
projections obtained across various sets of 144 models, reflecting the
substantial amount of cross-modelling uncertainty that could emerge from
climate IAMs. Recently, Ikefuji et al. (2020) propose to address some of these
concerns by proposing a stochastic DICE model and introducing uncertainty
about future climate change and economic changes into it.

Instead of focusing on theoretical models or on deterministic models, as those
described above, in this paper, we survey over 250 empirical contributions to
the literature on measuring the relationship between climate change and
economic activity based on using data analysis and econometric models. The
complexity of the problem makes it difficult to know where to start. Due to
the huge related literature, this is a very ambitious and endless objective.
Consequently, we restrict ourselves to survey the main conclusions and
econometric issues faced in measuring this relationship, focusing on
contributions appearing in the literature over the last decade. However, to
put recent work in a broader context, in some cases, we need to briefly cover
some earlier development, overlapping with some portions of available surveys.
We want to apologise in advance to the many authors who have contributed to
this huge literature and have not been cited in this survey.

The global picture of the channels through which climate change and the
economy may be related is well understood. First, economic activity is related
with an increase in energy consumption, which is related with pollution.
Larger pollution is related with climate change; see, among others, Kaufmann,
Kauppi and Stock (2006), Kaufmann et al. (2011), Agliardi, Alexopoulos and
Cech (2019), Estrada, Perron and Martínez-López (2013), Chang et al. (2020),
Miller and Nam (2020) and Eroğlu, Miller and Yiğit (2022), for some evidence
about the long-run relationship between anthropogenic pollution and
temperatures. Finally, climate change is itself affecting the economy; see
Figure 1 for a chart summarizing these interactions. The first issue found
when measuring and testing for the relationships described in Figure 1, is
that of defining and measuring the variables involved in the different
channels. The first variable that should be defined and measured is climate
change itself. In the most recent literature, most measures of climate change
are based on temperatures and precipitations. Given the relationship between
temperatures and pollution, there is also an interest in measuring pollution,
with CO2 emissions being among the most popular measures because of its
relevance and weight in total emissions. Finally, one should decide about the
economic variables of interest to analyse the impact of climate change. In
choosing the variables to focus the analysis on, one should not only choose
the magnitudes to be measured but also need to decide about their spatial and
temporal coverage and the frequency of observation. For example, one can carry
out the analysis considering cross-sectional, panels or time-series data and,
in any case, looking at global or local variables observed at different levels
of aggregation and at different frequencies.

Figure 1: Chart on the channels of the relationship between climate change and
the economy.

The second important issue involved in measuring the interactions between
climate change and the economic activity is to decide the econometric
methodology (models, estimators and tests) to be implemented, which obviously
depends on the particular data under analysis; see Petris and Hendry (2013),
who outline important hazards that can be encountered when modelling climate-
rated time-series data and Auffhammer (2018), who describes several concerns
in the empirical analysis of the economic cost of climate change. Here, we
list the most relevant:

  1. i.

Endogeneity due to the potential bi-directional causality between climate
change and the economy. Auffhammer et al. (2013) point out that often studies
try to estimate the economic impact of climate change under a climate
influenced by human activity. For example, agriculture is highly exposed to
climate change because its activities directly depend on climatic conditions
and, simultaneously, agriculture contributes to climate change through GHG
emissions; see, Agovino et al. (2019). Very recently, in a very important
work, Petris (2021) discuss several issues related with the exogeneity
assumption and propose a novel methodology to overcome them.

  2. ii.

Non-linear relations. For example, non-linear relationships appear in the
Environmental Kuznets Curve (EKC), which postulates that the relationship
between economic growth and environmental degradation follows an inverted U
curve. The seminal and highly influential contribution of Grossman and Krueger
(1995) concludes that there is an increase of environmental degradation and
pollution in early stages of economic growth, but, beyond some level of income
per capita, this relationship reverses with additional income growth leading
to environmental improvement.333A number of elements related to growth as, for
example, changes in the economic structure, technological progress, changes in
preferences and increased environmental awareness, would be at the basis of
such a relationship; see the survey by Dinda (2004), who summarizes the
factors responsible for the shape of the EKC. When the economy is mainly
devoted to the agricultural activity, the environmental quality is higher than
when it is mainly driven by industrial production, while when the service
sector starts its development, then the environmental quality improves. The
long-run relationship between economic growth and environmental degradation
could thus be described as an inverted U-shaped curve, known after Kuznets
(1955)444Note that Kuznets (1955) postulates the inverted U-shape for the
relationship between income and income inequality. as EKC.

  3. iii.

Non-stationarity, with potential different relations in the short or the long
run; see, for example, Petris and Allen (2013), who point out that a
difficulty with the statistical analysis of the relationship between
temperature, GHG concentrations and the economic drives responsible for GHG
changes, lies with the fact that each of these time series is non-stationary.
In many works, non-stationarity is dealt with by transforming non-stationary
data to stationarity. However, in doing this transformation, the information
about the long run is lost; see the discussion on non-stationarity of climatic
variables by Castle and Hendry (2020). Given that some of the GHGs have an
atmospheric lifetime measured in tens of thousands of years, climate change is
a long-run phenomenon; see Tol (2009). Therefore, measuring the long-run
relationships between climate change and the economy is one the most relevant
aspects of the analysis.

  4. iv.

Another econometric aspect to take into account when measuring the relations
between climate change and the economy is related with the potential presence
of structural breaks and/or outliers; see the discussion by Castle and Hendry
(2020). To mention a recent example, consider the economic havoc observed
during the present recession due to COVID-19, which will affect the state of
the business cycle. The COVID-19 pandemic has caused most of the world
economies to sink at unprecedented growth rates. COVID-19 has also affected
the demand of energy with reductions in energy demand producing large short-
run reductions in GHG emissions and uncertain long-run effects; see, for
example, Gillingham et al. (2020).

  5. v.

An appropriate econometric model should also take into account the
externalities due to the relationships among different countries; see, for
example, the results in Munir, Lean and Smyth (2020) on the between-country
interactions when looking at the relationships between CO2 emissions, energy
consumption and economic growth.

  6. vi.

A good econometric model should take into account the uncertainty associated
with the measures of climate change and its economic implications: see
Weiztman (2020), who points out that deep structural uncertainty lies at the
heart of climate change economics and Calel et al. (2020), who show that
uncertainty plays a major role in computing the economic cost of climate
change. Stern (2008, 2016), Pindyck (2014) and Convery and Wagner (2015)
discuss that, by necessity, standard climate-economy models focus on what is
known and can be quantified and, consequently, they convey a false sense of
precision. Tol (2009, 2014) also argues that the level of uncertainty about
the economic effects of climate change is large and understated, especially in
terms of capturing downside risk. Petris et al. (2018) remark the importance
of taking into account uncertainty when looking at the economic effects of
rising temperature. Finally, an appropriate measure of uncertainty is also
important because, when looking at the economic impacts of climate change,
analysts and policy-makers are mainly interested not in the average effect but
in the left tail of the impact; see Weitzman (2009). In this context, Burke,
Hsiang and Miguel (2015) and Petris and Roser (2017) point out that failing to
account for climate uncertainty greatly understates the severity of the worst-
case scenario.

The rest of the paper is organized as follows. Section 2 revises the measures
of climate change usually used in econometric studies. Sections 3, 4 and 5 are
devoted to describing the main econometric contributions related to measuring
the relationships between economic activity and energy consumption, energy
consumption and pollution, and pollution and climate change, respectively.
Section 6 reviews the economic effects of catastrophic phenomena related with
weather change. Finally, section 7 concludes.

##  2 Weather variables to measure climate change

In order to measure the relationships between climate change and the economy,
it is crucial to start by defining and measuring climate change. This section
is devoted to describing the variables often used in the empirical analysis of
climate change.

Climate is usually defined as the long-run average of weather in a given
location. As stated by Auffhammer et al. (2013), "the difference between
weather and climate is basically a matter of time". The choice of weather
versus climate as the variable of interest affects the interpretation of the
estimated econometric models as measuring short or long-run phenomena. Hsiang
and Koop (2018) define climate change as the long-run variation in the joint
probability distributions describing the state of the atmosphere, oceans, and
fresh water including ice.

In many studies analysing the relationships between climate and economic
activity, the measures used for climate change are observations of weather
variables, often temperatures and precipitations, obtained from weather
stations; see Auffhammer et al. (2013) for a discussion on some common
pitfalls that empirical researchers should be aware of when using historical
weather data as explanatory variables for the economy. However, one of the
most important and popular aspects of climate change is Global Warming (GW),
which can be described by the evolving distribution of temperature; see the
reports by Intergovernmental Panel on Climate change, IPCC (2014, 2022). Dell,
Jones and Olken (2014) also point out that the second most analysed variable
for climate change is precipitation. Several important works also analyse ice
levels: see Castle and Hendry (2020), Diebold and Rudebush (in press) and
Diebold et al. (2021), among others. Other less popular variables are relative
humidity, solar radiation, wind speed and direction and atmospheric pressure.

Given that our goal in this paper is to survey the literature analysing the
relationships between climate and the economy, we focus on studies of climate
data observed at the frequencies and for the time spans for which economic
data are usually available, namely, monthly, quarterly or yearly data observed
since the XX century.555Alternatively, many important studies of climate
change consider paleo-climate observations. For example, Schmidt et al. (2014)
model paleo-climate changes for the Last Glacial Maximum, the mid-Holocene and
the Last Millennium, while Castle and Hendry (2020) model climate variability
over the Ice Ages.

As mentioned above, one of the main characteristics of climate change is GW
and, consequently, we will mainly focus on describing how GW has been modelled
in the related literature. One popular methodology to obtain future
projections of climate is to simulate data by General Circulation Models, also
known as Global Climate Models (GCMs), which are detailed computer models that
numerically approximate fundamental physical laws for representing global
climate (temperatures and precipitations) in a given grid in the space; see,
for example, IPCC(2014, 2022) for a description and Hsiang et al. (2017) for a
recent implementation. GCMs forecast future climate assuming heightened
atmospheric concentrations of GHGs. However, there are large discrepancies
across point projections of GCMs, without evidence of any model being superior
than others for long-term forecasts; see, for example, Beenstock, Reingewertz
and Paldor (2016) for a comparison among models and Fernández et al. (2019)
for an analysis of projections made by a large number of models for the
Iberian peninsula. Furthermore, the majority of GCM projections are point
projections without associated measures of their uncertainty; see, Burke,
Hsiang and Miguel (2015), who survey the literature on the impacts of climate
change and show that the vast majority of estimates of the effects of climate
on economic outcomes fail to account for the uncertainty in future
temperatures and rainfall. Moreover, they argue that incorporating uncertainty
into future economic impact assessments will be critical for providing the
best possible information on potential impacts. Finally, Auffhammer et al.
(2013) also point out that severe biases due to spatial average can often
occur when GCMs are used to simulate future climate; see Fowler, Bleukinsop
and Tebaldi (2007) for bias corrections based on regressions between the GCM
outputs and observed variables.

Alternatively, instead of using GCMs, many studies on GW focus on analysing
the temporal evolution of particular characteristics of temperatures, hot and
cold spell duration, frost days, growing season length, ice days, heating and
cooling degree days, and start of spring dates; see, for example, the
references cited in Diebold and Rudebush (2022). Regarding how data are
collected or generated on these analysis, Dell, Jones and Olke (2014) review
four types of input weather variables usually found in empirical analysis of
climate change: ground station data, recorded in situ in weather stations,
gridded data, which interpolate data among ground stations, satellite data
with very broad coverage although less precise and reanalysis data, which use
a climate model to combine all previous types of data to enlarge the coverage
of the databases. Of course, the level of temperature, i.e. the central
tendency of the temperature distribution, has attracted the most attention,
with many studies finding an upward trend in average daily temperatures; see,
for example, Deng and Fu (2019), who compare several methods for extracting
cycles from daily temperatures.

##  3 Economic activity and energy consumption

Kyoto Protocol and Paris Climate Accords have urged that sustainable
development has been a critical part of the political climate agenda in the
last decade. Thanks to climate agreements, governments have been forced to
seek successful energy policies that can help to reduce environmental damage
without affecting the economic growth path.666While the Kyoto protocol adopted
in 1997 set top-down legally binding emission reduction targets and sanctions
only for developed nations, the Paris Agreement of 2015 requires that all
developed and developing countries reduce greenhouse gas emissions. Gaast
(2017) describes the three main climate negotiation phases between 2005 and
2015.

To establish successful green policies, there has been a growing interest in
issues related to economic growth through energy consumption, both from a
political and academic perspective. The relationship between energy and
economic activity has been analysed for a long time. The early seminal short
note of Kraft and Kraft (1978) introduced the necessity of studying the causal
relationship between energy consumption and economic growth in the US. Since
then, many papers have analysed this conundrum, including different economic
mechanisms by which energy consumption and economic growth can be interlinked
over time. Literature surveys by Ozturk (2010) and Payne (2010a, 2010b)
provide the reader a first look at the literature using standard econometric
methods on the original nexus. Tiba and Omri (2017) analyse the literature
behind energy- environment-growth nexus. Waheed, Sarwar and Wei (2019) examine
the survey of earlier literature that deals with economic growth, energy
consumption, and carbon emission. Finally, Mutumba et al. (2021) provide the
reader with an extensive meta-analytic investigation of energy consumption and
economic growth.

The goal of studying the relationship between economic activity and energy
consumption is to identify their causal direction, whose consequences in the
design of energy policies can be completely opposed: see the discussions by
Apergis and Payne (2011) and Chen, Xie and Liao (2018) on different theories
about the relationship between energy use and economic growth. The main
hypotheses are described below:

  * •

The Growth Hypothesis, which is supported if there exists unidirectional
causality running from energy consumption to economic growth. This case is
perhaps the more sensitive for energy policies due to possible direct shocks
to the economic growth. This causal relationship has generated intensive
participation in the global warming debate regarding the level of carbon
emissions and their relationship to economic growth.

  * •

The Conservation Hypothesis, which demands a unidirectional causality from
economic growth to energy consumption. This hypothesis would indicate to
governments that reducing energy consumption would not affect the growth path
and give them the breath to reach climate agreements.

  * •

The Neutrality Hypothesis that indicates the absence of a causal relationship
between energy and growth. As in the previous case, energy conservation
policies should be encouraged.

  * •

The Feedback Hypothesis, which requires evidence of bidirectional causality,
meaning that energy consumption and growth are jointly determined. Therefore,
along with the Growth Hypothesis, energy conservation policies need to be
defined with the caution of avoiding to shock negatively the economic growth.

Although the literature is vast, there is still no consensus due to the
heterogeneity in climate conditions, the econometric methodology employed, or
even if the analysis considers only one country or a panel of them, among many
other circumstances. However, as mentioned above, the common objective of this
literature is clarifying the type of hypothesis supported.

The nexus between energy consumption and economic growth has been analysed
using a large variety of procedures. First, it may exhibit strong cross-
sectional dependence due to the high economic and financial integration among
countries and regions. Consequently, panel data models have been often used in
this context; see, for example, Damette and Seghir (2013), Jalil (2014),
Liddle and Lung (2015), Kais and Sami (2016) and Acheampong et al. (2021).
Many authors also consider the temporal dependence in the panel. For example,
Apergis and Payne (2011) fit a panel VECM to 88 countries categorized into
four panel according to their income level. They found bidirectional causality
in both the short- and long-run for the high and upper-middle income
countries. However, in the lower-middle income countries, the causality is
unidirectional from electricity consumption to economic growth in the short
run while it is bidirectional in the long-run. Finally, they find
unidirectional causality from electricity consumption to economic growth for
the low income countries. More recently, Lin and Benjamin (2018) also fit a
fixed effect panel VECM to represent the relationship between energy
consumption, foreign direct investment and economic growth for the MINT
countries (Mexico, Indonesia, Nigeria and Turkey). Their conclusions about the
directionality of the effects between energy consumption and growth are not
uniform across all countries; see also Apergis and Payne (2009) and Kasman and
Duman (2015) for applications using panel cointegration and Antonakakis,
Chatziantoniou and Fillis (2017) for panel VAR.777Other proposals based on
panel data take into account non-linearities as, for example, the panel
quantile regression proposed by Zhu et al. (2016)) or the nonlinear panel
model proposed by Wang and Wang (2020).

It is important to note that many papers involved in the study of the causal
link between economic growth and energy consumption (even controlling for
other variables such as CO2 emissions) use Granger Causality (GC) as a vehicle
to study the causal relationship. For example, Chen, Xie and Liao (2018)
implement the panel Granger causality analysis proposed by Emirmahmutoglu and
Kose (2011) to examine the causal relationship between energy consumption and
energy growth for 29 Chinesse provinces. They conclude that there is a
unidirectional causal link running from real output to energy use. However,
such tests cannot be considered neither a necessary nor a sufficient condition
for causality since it does completely characterize the notion of “cause”; see
the discussion by Hendry (2004). In contrast, to analyse the causal link
between economic growth and energy consumption and further control variables,
Rodríguez-Caballero and Ventosa-Santaularia (2017) propose going back to the
original ideas of exogeneity emboided in Engle, Hendry and Richard (1983) and
study a causal link through the statistical concepts of weak and super
exogeneity.

Very recently, Rodríguez-Caballero (2022) proposes a non-stationary panel data
model with multi-level cross-sectional dependence to study the long-run
relationship between economic growth and energy consumption in 69 countries.
In his model, the commonality between countries is driven by unobservable
common factors, which can gather relevant variables affecting all countries
such as international shocks generating financial crisis, the sharp decrease
in oil prices, or by climate conditions.

Second, in the context of single-equation time series, Shahbaz et al. (2018)
propose dealing with potential non-linearities in the nexus between energy use
and economic growth by using the quantile-on-quantile (QoQ) methodology
recently proposed by Sim and Zhou (2015). QoQ combines quantile regression and
non-parametric estimation to regress a quantile of GDP on a quantile of energy
consumption and vice-versa. After fitting QoQ to top ten energy-consumption
countries, they find a weak effect of economic growth on energy consumption
for lower quantiles in some countries and for upper quantiles in others.
Furthermore, the effect of energy consumption on energy growth is, in general,
even weaker. However, it is important to point out that, although the QoQ is
an interesting way of dealing with non-linearity, Shahbaz et al. (2018) do not
take into account the non-stationarity of both energy consumption and economic
growth and, consequently, their results may be spurious.

Finally, note that climate change can alter energy generation potentials and
needs, adding to the route that energy-growth conundrums could follow; see
Figure 1. Naturally, climate change could excessively increase the electricity
demand for cooling during heat waves. New literature is emerging intending to
analyse the direct impact of climate change on energy and vice versa, through
renewable energy; see, for example, Cronin, Anandarajah and Dessens (2018),
Quaschning (2019), Van Ruijven, de Cian and Wing (2019) and Olabi and
Abdelkareem (2022). A review of climate change impacts on renewable energies
is provided by Solaun and Cerdá (2019).

##  4 Economic activity and pollution

GHG emissions are a major cause of climate change. They have a strong
relationship with energy consumption, which, as described in the previous
section, is closely related with economic activity. Since the seminal works of
Selden and Song (1994), Grossman and Krueger (1995) and Holtz-Eakin and Selden
(1995), the econometric analysis of the relationship between economic activity
and pollution has been the focus of an extremely large literature; see the
relatively recent surveys by Al-Mulali, Saboori and Ozturk (2015), Moutinho,
Varum and Madaleno (2017), Stern (2017), Shahbaz and Sinha (2019), Waheed,
Sarwar and Wei (2019), Purcel (2020) and Ul Husnain, Hider and Khan (2021),
and the references therein. The number of works is so large that trying to
survey this literature seems an impossible mission. Consequently, in this
section, we just summarize some of the main findings of the literature on the
nexus between economic activity and pollution. Most works assume that the
former variable is exogenous and represent the EKC by the following static
(long-run) model

$e_{it}=\beta_{0i}+\beta_{1i}y_{it}+\beta_{2i}y_{it}^{2}+\beta_{3i}y_{it}^{3}+u_{it},$
(1)

where $e_{it}$ represents the logarithmic transformation of the environmental
quality proxy in country $i$ at time $t$, and $y_{it}$ is the corresponding
logarithmic transformation of the economic measure. For each $i$, $u_{it}$ is
assumed to be an independent white noise with variance $\sigma^{2}_{u_{i}}$.
If $\beta_{1i}>0,\beta_{2i}<0$ and $\beta_{3i}=0$, then (1) represents an
inverted-U shape in the relationship between pollutants and economic activity.
Furthermore, when $\beta_{3i}\neq 0$, the relationship between pollution and
per capita GDP has an N-shaped relationship (which can be inverted depending
on the sign of the parameters in (1)); see Moosa (2017) for an interesting
discussion on the shape of the relationship between pollution and GDP. For a
given country $i$, if $\beta_{3i}=0$, the turning point of the per capita GDP
in which the environmental quality measure, $e_{it}$, begins to improve is
$y_{i}^{\ast}=-\frac{\beta_{1i}}{2\beta_{2i}}$.

Next, we describe the variables that have been usually chosen to represent
environmental quality and economic activity as well as the methods implemented
to estimate model (1).

###  4.1 Variables to measure pollution and economic activity

With respect to the variables used to measure pollution, empirical studies of
the EKC consider at least one of eight possible broad types of pollution,
namely, air pollution, water pollution, land pollution, radioactive pollution,
noise pollution, light pollution, thermal pollution and ocean or marine
pollution.888Dinda (2004) points out that EKC cannot be generalized for all
types of pollutants, being not valid, for example, for industrial water
pollution or toxic pollution. However, one of the most enduring sources of GW,
and consequently of climate change, is the burning of fossil or carbon-based
fuels as coal, oil and natural gas, which leads to emissions of GHG, with
carbon dioxide (CO2) being among the most important. Consequently, the
analysis of the effects of economic growth on the environment has been mainly
done considering air pollution, and more specifically CO2 emissions, which
remain accumulated in the atmosphere for a long time; see, for instance, the
references listed in Tables 1 and 2 in which we summarize empirical results on
the EKC and Al-Mulali, Saboori and Ozturk (2015) and Ul Husnain, Haider and
Khan (2021) for similar tables. Very few empirical investigations about the
EKC have been done using sulphur dioxide (SO${}_{2})$ emissions or other air
pollutants; see, for example, Stern and Common (2001) and Taguchi (2013).
Finally, it is important to mention that, in a recent study, Altintas and
Kassouri (2020) consider two indicators of environmental degradation, namely,
ecological footprint and CO2 emissions, and find evidence for the sensitivity
of the EKC hypothesis to the type of environmental degradation proxy used and
conclude about the relevance of the ecological footprint as an appropriate
environmental tool. Gill et al. (2017) and Rao and Yan (2020) also point out
that the EKC has only been proved for a subset of pollutant indicators.
Recently, Haider, Bashir and Ul Husnain (2020) find strong support for the EKC
using NO2 as pollutant and Pandey and Mishra (2021) obtain a similar result
using SO2 and NO2 considering 21 states of India.

With respect to the variables used to represent economic activity, most works
usually consider per capita income or Gross Domestic Product (GDP), although
alternative explanatory variables have also been added to explain pollution.
For example, Pablo-Romero, Cruz and Barata (2017) test the EKC using transport
as a proxy for economic activity. Furthermore, according to the Pollutant
Heaven Hypothesis, developed countries look for the cheapest options in terms
of resources and labour and often set up factories abroad. In this case, even
if their economic activity increases, they may not have increases in their
pollutant emissions. However, the developing countries that recieve the
factories may show larger emissions without having larger GDP. For this
reason, many studies of the EKC control for variables related to trade; see,
among others, Jebli, Youssef and Ozturk (2016), Tiwary, Shahbaz and Hye
(2013), Lau, Choong and Eng (2014), Onafowara and Owoye (2014), Dogan and
Turkekul (2016), Anser et al. (2020), Adebayo, Awosusi an Adesola (2020), Khan
and Eggoh (2021) and Bidi and Jamil (2021), for recent references.

Alternatively, many works explore the link between the economy and CO2
emissions through the energy channel; see, inter alia, Ang (2007), Acaravci
and Ozturk (2010), Shahbaz, Mutascu and Azim (2013), Smiech and Papiez (2014),
Acheampong (2018), Chen, Wang and Zhong (2019), Diaz et al. (2019), Usman,
Larember and Olanipekun (2019), Munir, Lean and Smyth (2020), Hasmi et al.
(2021), Altintas and Kassouri (2020), Saidi and Omri (2020), Khattak et al.
(2020), Erdogan (2020), Raza, Shah and Khan (2020) and Cheikh, Zaied and
Chevallier (2021). Other authors have also controlled for other variables as
Hailemariam, Dzhumashev and Shahbaz (2020), who consider the effect of income
inequality, Balaguer and Cantavella (2016), who add fuel prices as an
indicator of fuel energy consumption, Hasmi et al. (2020), who consider the
effect of geopolitical risk or Usman, Larember and Olanipekun (2019), who
consider the effect of democratic regime. Finally, Hipólito Leal and Cardoso
Marques (2020) analyse the repercussion of globalization on the environment.

The potential set of variables that drive emissions of CO2 is very large.
Furthermore, it is also important to analyse the functional form in which
these variables enter the EKC; see the very extensive analysis by Auffhammer
and Steinhauser (2012), who compare over 27000 specifications arising from
possible permutation of a very limited set of explanatory variables.

Finally, there are authors considering variables that are not properly
economic although they are closely related. For example, Wang et al. (2021)
analyse the long- and short-run relationships between urbanization and three
carbon emission dimensions in OECD high income countries. They conclude that
developed countries tend to have the same negative impact of urbanization on
carbon emissions, although there are differences on the endowments of
different countries; see also Adebayo and Odugbesan (2021), who also consider
the role of urbanization on CO2 emissions. Chen, Huang and Lui (2019) consider
the effects of emissions on environmental awareness. Beyene and Kotosz (2020)
and Aziz et al. (2021) analyse the effect of globalization on CO2 emissions
and Anset et al (2020), Aller et al (2020), Lee, Chen and Wu (in press) and
Nosheen et al (2021) emphasize the effect of tourism on pollutant emissions.

###  4.2 Estimators of the EKC

In this subsection, we survey different procedures implemented to estimate the
EKC, clasifying them into panel, time series and regression estimators.

####  4.2.1 Panel data

Estimation of model (1) is usually carried out using panel data and the fixed
effects estimator; see, for example, the meta analysis carried out by Sarkodie
and Strezov (2019) on the estimation of the EKC. Very recently, instead of the
fixed effects estimator, Aller, Ductor and Grechyna (2021) propose using
Bayesian Model Averaging and Cluster-LASSO to estimate all possible
combinations of the regressors, taking afterwards a weighted average over the
candidate models. They conclude that CO2 emissions in high- and medium-income
economies are affected by the share of industry in GDP, political polarization
and tourism, while, in low-income countries, they are positively affected by
foreign direct investment, the level of democracy and corruption. It is
important to point out that the relationship in model (1) may depend on the
level of economic development of the countries included in the analysis with
low developed countries in the first increasing part of the inverted-U. To
account for this heterogeneity, Stern and Common (2001) and Galeotti, Lanza
and Pauli (2006) consider specific intercepts and found support of the EKC
when applied to a panel with a large number of countries. Moutinho, Varum and
Madaleno (2017) also use panel data on different sectors to investigate the
EKC in Portuguese and Spanish economic activity sectors.

Estimates of model (1) could be affected by simultaneity biases resulting from
the fact that pollutants could also cause economic activity; see the surveys
by Tiba and Omri (2017) and Waheed, Sarwar and Wei (2019) on the causal
directions between economic growth, energy and pollution. To control for the
potential endogeneity of growth, Lin and Liscow (2013) propose an instrumental
variable (IV) estimator, which is implemented to a panel of OECD and non-OECD
countries. Using several measures of water pollution as pollulants, and
controlling for the indices on political rights and civil liberties from
Freedom House, Lin and Liscow (2013) find empirical support of the EKC
hypothesis and conclude that controlling for variables representing social
issues is important in this relationship.

Due to the strong persistence often observed in the variables involved in the
estimation of the EKC, panel models have been estimated using procedures
designed to deal with this characteristic. Acheampong (2018) proposes an
integrated framework to analyse Granger causality between economic growth,
energy consumption and carbon emissions, based on a panel Vector
Autoregression model estimated using the system-GMM of Blundell and Bond
(1998) that takes into account strong persistence. In a very recent work,
Munir, Lean and Smyth (2020) propose testing for Granger causality among CO2
emissions, energy consumption and economic growth using the estimator of
Westerlund (2007), taking into account not only cointegration but also
heterogeneity and cross-sectional dependence. They show that panel unit root
and cointegration tests that do not accommodate cross-sectional dependence
give mixed and inconclusive results. After implementing the model to the five
main countries of the Association of Southeaster Asian Nations (ASEAN-5) over
the period 1980-2016, they conclude that in three countries, Indonesia,
Malaysia and Thailand, there is unidirectional causation running from GDP to
energy consumption. In these countries, energy conservation is unlikely to
have much impact on economic growth. For four countries, Malaysia,
Philippines, Singapore and Thailand, they find unidirectional causality
running from GDP to CO2. They also conclude that three of these countries,
Malaysia, Philippines and Thailand, have not yet reached the income turning
point suggested by the EKC. For these countries, economic growth can be
expected to adversely affect the environment until the turning point is
reached. The Westerlund’s (2007) panel cointegration method has also been used
in Zafar et al. (2020) to verify long-run relationship between CO2 emissions
and GDP, controlling for energy consumption, urban population and
industrialization in a panel regression model for 46 countries covering the
period 1980-2017. Another application of this methodology can be found in
Altintas and Kassouri (2020), who analysed the EKC relationship using a
heterogeneous panel for 14 European countries over the period 1990-2014.
Alternatively, several authors have implemented the cointegration tests for
panel data proposed by Pedroni (2004); see, for example, Erdogan (2020), Lazar
et al. (2019), Saidi and Omri (2020), Adebayo, Awosusi and Adesola (2020) and
Dogan and Inglesi-Lotz (2020).

Finally, several authors analyse the EKC hypothesis using the panel extension
of the Autoregressive Distributed Lag (ARDL) procedure of Pesaran, Shin and
Smith (1999, 2001)999Shin, Yu and Greenwood-Nimmo (2014) propose non-linear
ARDL tests. More recently, McNown, Sam and Goh (2018) propose a bootstrap
correction of the ARDL tests with better properties.; see Hanif et al. (2019),
who support the EKC for a panel of 15 Asian developing countries, observed
over the period 1990 - 2013 and Waqih et al. (2019), who also find support for
the EKC in a panel of four countries of the South Asian Association for
Regional Cooperation (SAARC), observed during the period 1986-2014 based on
combining the ARDL approach with the Full Modified OLS.

Given that the channel of the relationship between economic activity and
pollution is energy consumption, Marrero (2010) studies the simultaneous
relationship between emissions, growth and energy based on a dynamic panel of
24 European countries observed from 1990 to 2006. He concludes that the
elasticity between aggregate energy consumption and emissions is significantly
greater than zero and below unity. However, he does not find evidence in
favour of the EKC hypothesis.

In the context of panel data, several authors have estimated model (1) using
procedures from spatial econometrics; see, for example, Kang, Zhai and Yang
(2016), who consider a panel of Chinese provinces over the period 1997-2012
and find an inverted-N shaped relationship and Hao et al. (2016), who consider
coal consumption as the endogenous variable and find a bell shape
relationship. Li and Wang (2019) also apply spatial econometrics to a panel of
30 Chinese provinces. They use carbon intensity of human well-being as the
pollutant measure and find an inverted-N shaped relationship.

Table 1: Summary of selected works testing the EKC using panel data methods.

Authors | Dependent Variable | Independent Variables | Countries | Sample | Conclusion | Method
---|---|---|---|---|---|---
Adebayo et al. (2020) | CO2 | EG, Energy use, trade, urbanization | MINT economies | 1980 - 2018 | Cointegration CO2 and determinants | Panel Cointegration, ARDL-MMG
Altintas and Kassouri (2020) | Ecological Footprint, CO2 | Renewable energy, Fossil fuel consumption | 14 European Countries | 1990 - 2014 | Depend | Heterogenous panel
Anser et al. (2020) | CO2 | International tourism, social distribution, FDI | G7 countries | 1995 - 2015 | Support EKC, PHH and REH | Panel random Effect and Panel
Beyene and Kotosz (2020) | CO2 | Globalization, FDI, Pop. density, Pol.stability | 12 East African countries | 1990 - 2013 | Support EKC in the short run | Pedroni’s test, Pooled Mean Group
Cai et al. (2020) | Different pollutants | EG | China | 2003 - 2017 | Different types of EKC | Locally weighted smoothed
Dogan and Inglesi-Lotz (2020) | CO2 | EG, Economic Structure | 7 European countries | 1980 - 2014 | Support EKC | Panel UIR, Panel CI tests, FMOLS
Erdogan (2020) | Ecological Footprint, CO2 | Renewable energy, fossil fuel, consumption | Europe | 1990 \- 2014 | Sensitivity of the EKC to the pollutant | Cross-section and slope heterogeneity, Cl
Haider et al. (2020) | NO2 | Agricultural land use, exports, EC | Developed, developing countries | 1980 - 2012 | Support EKC | Pooled mean group approach
Jiang et al. (2020) | Air pollution | EG | China and South Korea | 2003 - 2017 | Support EKC | Panel data
Khattak et al. (2020) | CO2 | Innovation, renewable energy consumption | BRICS countries | 1980 - 2016 | Support EKC | Johansen + Fisher, Kao’s CI tests
Leal and Marques (2020) | CO2 | Degree of Globalization | 20 OECD countries | 1990 - 2016 | Support EKC | Driskoll - Kraay estimator
Munir et al. (2020) | CO2 | Energy consumption, EG | ASEAN-5 | 1980 –2016 | Inconclusive results | Panel VAR
Raza et al. (2020) | Residential energy | EG, renewable energy, development | NEXT11 and BRICS countries | 1990 - 2015 | Support EKC | FMOLS, Pedroni test,Westerlung (2007)
Saidi and Omri (2020) | CO2 | Renewable energy, EG | 15 major renewable energy consuming | 1990 - 2014 | Support EKC in the short run | Pedroni’s test, FMOLS
Akadin et al. (2021) | CO2 | EG, Economic freedom | BRICS countries | 1995 - 2018 | Support EKC in the long-run | Pooled mean Group estimation
Aller et al. (2021) | CO2 | % ind. in GDP, Pol. polarization, Tourism, FDI | High and medium income countries | 1995 - 2014 | Support EKC | BMA / and Cluster/LASSO
Aziz et al. (2021) | CO2 | Nat. ressources, renewable energy and globalization | MINT countries | 1995 - 2018 | Not Support highest quantiles | Pedroni’s test, FMOLS, DOLS, FE-DOLS
Bakhsh et al. (2021) | CO2 | Foreign investment inflows | 40 Asian countries | 1996 - 2016 | FDI positive impact on CO2 | Panel data
Bidi and Jamil (2021) | CO2 | GDP, Trade, FDI, financial and institutional Quality | Latin American, Caribbean, sub-Saharian | 2000 - 2018 |  Support EKC except for sub-Saharian | Panel data econometric models
Cheikh et al. (2021) | CO2 | Energy consumption, GDP Growth | MENA regions | 1980 - 2015 | Support EKC | Panel smooth transition modeling
Khan and Eggoh (2021) | CO2 | EG, trade, financial development, FDI. | 146 countries | 1990 - 2016 | Support EKC | Smooth threshold Regression model
Pandey and Mishra (2021) | SO2 and NO2 | Net state domestic product, social expenditure | 21 Indian states | 2001 - 2018 | N-shaped EKC | Panel unit root, cointegration, DOLS
Wang et al. (2021) | CO2 | Urbanization | OECD countries | 1960 - 2014 | Support EKC | Dynamic Panel ARDL model
Beyene (in press) | Environment quality | Decomposed growth | 108 countries | 2000 - 2018 | Not support EKC | Panel mean and quantile regressions
Lee et al. (2022) | 6 ecological footprints | Tourism development | 99 countries | 2000 - 2017 | Support EKC | Quantile regression approach
Zhong (2022) | SO2 and CO2 | Income growth and inequality, industrial structure | China | 2011 - 2015 | Support EKC | Panel data methods

####  4.2.2 Time series models

Time series models are also very popular in the context of estimation of the
EKC for a given country or territory. A large part of works using time series
is based on single-equation models while some of them use multi-equation
models; see the selected works reported in Table 2.

As mentioned above, given that the EKC is a long-run phenomena, model (1)
could be estimated with the variables in levels after taking into account
possible cointegration relationships between $e_{it}$ and $y_{it}$; see, for
example, Waheed, Sarwar and Wei (2019), who survey several papers using
Johansen cointegration tests and Umar et al. (2020), who employ the combined
cointegration and wavelet coherence approaches over the period 1971-2018 to
explain the long run and causal effects of innovation, financial development
and transportation infrastructure on CO2 emissions in China. Alternatively, a
very popular way of dealing with cointegration in the context of testing the
EKC hypothesis is based on the ARDL bounds test of Pesaran, Shin and Smith
(2001), which allows the possibility of including variables with different
order of integration ($I(1)$ or $I(0)$), allowing for long- and short-run
relationships and potential bi-directional relations; see the references
reported in Table 2 for several examples.

However, it is important to note that the possibility of non-stationarity of
the variables involved in the estimation of the EKC opens the door to the
estimation of spurious relations; see Wagner (2008), who shows that most
studies estimating EKC gave spurious results. For example, Paruolo, Murphy and
Janssens-Maenhout (2005) analyze the relation between income and emissions in
all countries in the world over the period 1970-2008 based on a VEC model.
They conclude that income and emissions seem to be driven by unrelated random
walks plus drift.

Furthermore, it is important to point out that standard cointegration
procedures may face problems when implemented in a non-linear context as that
encountered when dealing with the EKC. Müller-Fürstenberger and Wagner (2007)
show that, if $\ln(y_{it})$, where $y_{it}$, is the per capita GDP in region
$i$ at time $t$, is a unit root process, its square is not an integrated
process of order 1. Therefore, some previous findings obtained using standard
unit-root and cointegration techniques both in panel or time series data may
be questionable; see Perman and Stern (2003) for a discussion.

Recently, within the context of time-series regressions, some authors have
shifted the focus away from the regression in levels in (1) to factor-
augmented regressions in which the growth rate of GHG emissions in a given
country is modelled as a function of factors extracted from a large
(stationary) system of economic variables.101010Note that short-run relations
between emissions and the economy are also of interest because some policy
instruments, such as emissions trading schemes, use economic incentives to
control emissions over a short time spam, with the Regional Greenhouse Gas
Initiative in US being an example. Fosten (2019) fits the following diffusion
index model to analyse the effects of economic activity on CO2 emissions

$\varepsilon_{t}=\alpha+\delta^{\prime}\tilde{f}_{t}+\nu_{t}$ (2)

where $\varepsilon_{t}=\bigtriangleup logE_{t}$ with $E_{t}$ being yearly CO2
emissions,111111Fosten (2019) also considers emissions by source and non-
linear specifications including polynomials of the factors.. $\tilde{f}_{t}$
is the $r\times 1$ vector of Principal Component (PC) factors extracted from a
large set $X_{t}$ of macroeconomic variables and $\nu_{t}$ is assumed to be a
white noise. Finally, $\alpha$ and
$\delta=\left(\delta_{1},...,\delta_{r}\right)^{\prime}$ are parameters.
Fosten (2019) also carries out a multi-resolution analysis of $y_{t}$ and
$\tilde{f}_{t}$ based on discrete wavelet transformations, which decomposes
the original monthly series into $J$ different time scales plus a smooth term.
After extracting one single factor from $10$ selected variables of the data
base described by McCracken and Ng (2016) and based on seasonally adjusted
monthly data for U.S. observed from April 1978 to December 2018, Fosten (2019)
concludes that emissions are not linked to economic activity in the short-run
but there are strong linkages when looking at medium-run cycles of around one
to three years.121212Wavelet analysis has also been implemented by Jammazi and
Aloui (2015), who analyse the relationship between energy consumption and
economic growth using wavelet windowed cross-correlation for six oil exporting
countries from the Gulf Cooperation Council (GCC) region, finding a bi-
directional relationship. Kalmaz and Kirikkaleli (2019) also use a wavelet
coherence approach to analyse the causal effects between CO2 emissions and
energy consumption, trade openness, urbanization and economic growth in
Turkey. Finally, another study using wavelet techniques applied to US data is
Raza, Shah and Sahrif (2019), who analyse several wavelet measures and
conclude that in the short, medium and long-run energy consumption has
positive influence over CO2 emissions. They also findd unidirectional
causality running from energy consumption to CO2 emissions.

Similarly, Mamipur, Yahoo and Jalalvandi (2019) extract factors from Iranian
systems of “social”, “economic” and “environmental” variables and fit the
following VAR model to the first differenced factors allowing for bi-
directionality in the relations between the three systems

$\triangle\tilde{F}_{t}=\Phi\triangle\tilde{F}_{t-1}+a_{t}$ (3)

where
$\tilde{F}_{t}=\left(\tilde{f}_{1t},\tilde{f}_{2t},\tilde{f}_{3t}\right)^{\prime}$
is the vector of PC factors extracted from each of the systems.

In the same fashion, Bennedsen, Hillebrand and Koopman (2021) propose the
following collapsed Structural Augmented Dynamic Factor Model (SADFM) for US
CO2 emissions

$\begin{pmatrix}\varepsilon_{t}\\\ x^{*}_{t}\\\
\tilde{f}_{t}\end{pmatrix}=\begin{pmatrix}\alpha\\\ 0\\\
0\end{pmatrix}+\begin{pmatrix}\delta^{\prime}+\beta^{\prime}\Lambda^{*}\\\
\Lambda^{*}\\\ I_{r}\end{pmatrix}f_{t}+\begin{pmatrix}\gamma^{\prime}\\\ 0\\\
0\end{pmatrix}z_{t}+\begin{pmatrix}u_{t}\\\ v_{t}\\\ e_{t}\end{pmatrix}$ (4)
$f_{t+1}=\Phi f_{t}+\eta_{t+1}$ (5)

where $\varepsilon_{t}=\bigtriangleup e_{t}$ with $e_{t}$ being the log-
transformation of per capita CO2 emissions observed yearly from 1960 to 2017,
$x^{*}_{t}$ is a subvector of $X_{t}$, with $X_{t}$ containing the economic
variables of special relevance, and $\tilde{f}_{t}$ is defined as in (2).
Finally, $z_{t}$ are dummy variables that represent outliers; see Bennedsen,
Hillebrand and Koopman (2021) for details about the estimation of the
parameters, which is related with the two-step iterative procedure of Doz,
Giannone and Reichlin (2012). They also consider a model for CO2 emission
rates depending on IP variables with time-varying parameters. In an out-of-
sample exercise with forecast horizon $h=1$, their results favour the SADFM
with $\delta=0$ over competing alternatives, and forecast decreases in
emissions for 2018 and 2019.131313Note that all variables in the SADFM are
previously transformed to stationarity and, consequently, the diffusion index
and SADFM models capture short-run relationships.

Wagner, Grabarczyk and Hong (2020) deal with estimation and testing of the EKC
for CO2 emissions. The latter postulates an inverted U-shaped relationship
between the level of economic development and CO2 emissions, which in a time
series setup implies a polynomial cointegration relationship between the
emissions and the log of per capita gross domestic product. Typically, time
series panels with small cross-sectional dimension for a limited number of
countries are available, and it is quite natural to consider the country-
specific EKCs as a system of seemingly unrelated relationships, including
deterministic variables, integrated processes, and integer powers of
integrated processes as explanatory variables. The authors develop two fully
modified ordinary least squares type estimators for systems of seemingly
unrelated cointegrating polynomial regressions, which account for serial
correlation and for the endogeneity resulting from the existence of a
cointegrating relationship. The paper derives the limiting distribution of the
two estimators, in terms of a zero mean Gaussian mixture, and asymptotically
chi-square Wald tests of linear restrictions, allowing to test for various
forms of pooling. Poolability leads to estimation efficiency. Monte Carlo
simulations illustrate the finite sample performance of the estimators and the
tests. The empirical application considers a panel of 19 industrialized
countries for the period 1870–2013 and finds evidence for quadratic
cointegrating EKCs for a subset of six countries composed of Austria, Belgium,
Finland, the Netherlands, Switzerland and the United Kingdom. The specific
form of the EKC differs across these countries, but some group-wise pooling is
supported by the tests, leading to sizeable parsimony and greater estimation
efficiency. On the contrary, global pooling is strongly rejected.

####  4.2.3 Regression models

Due to the reduced sample and the heterogeneity of the countries included in
the analysis, there are not so many studies using cross-sectional data as
those using panel data or time series. For example, Magnani (2001) considers
152 countries in 1970, 1980 and 1990 and analyses the EKC relationship
considering three pollutants (CO2, SO2 and nitrous oxide emissions), fitting
model (1) in each of the three years. She finds different results depending on
the degree of development of the economies. Similar analysis is done in Hill
and Magnani (2002), using a sample of 156 countries and the same three
previous pollutants. More recently, Chow and Li (2014) estimate the EKC
relationship using 132 countries, considering a sample from 1992 to 2004. They
estimate the EKC for each year separately and find strong support of the EKC.
Finally, Atwi et al. (2018) consider 182 countries and a sample from 1992 to
2011. They estimate the EKC relationship considering both a panel approach and
a cross-section approach estimating model (1) for each year. They find support
of the EKC when using panel data approach but not when considering cross-
sectional regressions.

Table 2: Summary of selected works testing the EKC using time series methods.

Authors | Dependent Variable | Independent Variables | Countries | Sample | Conclusion | Method
---|---|---|---|---|---|---
Adejumo (2020) | CO2 | EG | Nigeria | 1970 - 2014 | Bidirectional relation environment and EG | ARDL
Koondhar et al. (2020) |  CO${}_{2},$ NO2 | Agricultural GDP, fertilizers, energy cons., grains | China | 1998 - 2018 | Support EKC | ARDL
Pata and Aydin (2020) | CO2 | Hydropower energy cons., EG, | 6 hydropower cons. | 1965 - 2016 | Not support EKC | Fourier boostrapp ARDL .
Rahman (2020) | Ene. cons., CO2 | EG, Pop. density, trade | India | 1971 - 2011 |  Different effects of EG on energy and CO2 | ARDL
Rahman and Wu (2020) | CO2 | Rew. Energy Cons., EG, trade, urbanizastion | Australia and Canada | 1960 - 2015 | Different results LR and SR for each country | ARDL, VECM
Rao and Yan (2020) | Different pollutants | EG | Wuhan (China) | 1996 - 2015 | Not support EKC for all pollutants | LARS/LASSO model
Rasool et al. (2020) | CO2 | Ene. cons., EG, inancial development | India | 1971 - 2014 | Validate conventional EKC | ARDL, VECM
Sethi et al. (2020) | CO2 | Globalization, EG, Financial development | India | 1980 - 2015 | Financial development not contribute to CO2 | ARDL, VECM
Zmani and Ben-Salha (2020) | CO2 | Pop., Affluence and Technology, urbanization, Energy Cons. | Gulf cooperation Council | 1980 - 2017 | Determinants of CO2/ volatility of EKC | PMG-ARDL approach
Mehmood (2021) | CO2 | Globalization | Singapore | 1970 - 2014 | Support EKC in Singapore | ARDL
Mele and Magazzino (2021) |  PM${}_{2.5},$CO${}_{2},$ NO2 | EG | India | 1980 - 2018 | Unidirectinal causality EG and pollution | Yamamoto test
Murshed (2021) | CO${}_{2},$NO${}_{2},$CH4 | LPG cons. FDI, trade | 6 Asian Economies | 1980 - 2016 | Support EKC India, Banglaseh, Sri Lanka, Bhutan | ARDL
Naeem et al. (2021) | GDP per capita | Energy and non-Energy factors on EC | Pakistan | 1985 - 2018 | Substitution of factors to improve EG | Ridge Regression
Ougan et al. (2021) | CO2 | Income pc increases, Income pc decreases | US | 1990.01 - 2019.07 | EKC supported for decomposed model | ARDL
Patiño et al. (2021) | CO2 | Sectorial decomposition of GDP | Colombia | 1971 - 2017 | Additive and Multiplicative decomposition | DSGE models
Rehman et al. (2021) | Economic progress | Urbanization, energy use, fossil fuel energy and CO2 | China | 1975 - 2017 | Asymmetric influence | Non linear ARDL
Sahbahz et al. (2021) | CO2 | Income pc, energy use, trade, oil price | India | 1980 - 2019 | Asymmetric long term impact on CO2 | Non linear ARDL
Solarin et al. (2021) | Ecological footprint | EG, urbanization, FDI, trade | Nigeria | 1977 - 2016 | Only EG contributes to disminish environment | ARDL
Villanthenkodath et al. (2021) | CO2 | EG and its components | India | 1971 - 2014 | Conventional EKC hypothesis does not hold | ARDL
Xiang and Xu (2021) | GDP annual growth | Electricity prod. from different fonts, CO2emissions | China | 1995 - 2015 | Ren. ene prod.contributes to increase in GDP | ARDL
Can Genç et al. (2022) | CO2 | Volatility of EG | Turkey | 1980 - 2015 | Support EKC | ARDL
Hashmi et al. (2022) | CO2 | GDP, Geopolitical risk and energy | Global | 1970 - 2015 | Support EKC | ARDL
Kilinc-Ata and Likhachev (in press) | CO2 | EG, ene.cons., pop., trade and financial development | Russia | 1990 - 2020 | EKC supported | ARDL
Liu et al. (2022) | Ecological footprint | tourism, EG, ene. cons., trade, FDI | Pakistan | 1980 - 2017 | EKC depends on the independent variables | ARDL, Bayer and Hanck
Massagony and Budiono (in press) | CO2 | Income, fossil and rene. ene cons.,forest restoration policies | Indonesia | 1970 - 2017 | EKC supported | ARDL
Seri and de Juan (in press) | CO2 | GDP and other control variables | Latin American countries | 1970 - 2018 | Support EKC depending on country | ARDL VECM

EG= Economic Growth, FDI=Foreign Direct Investment; Renew Ene: renewable
energy; ene. cons. = energy consumption; Pop = population; pc = per capita;.

###  4.3 Main conclusions

In general, the results described above are mixed and inconclusive with many
open econometric issues; see, for example, the survey by Ul Husnain, Haider
and Khan (2021). The empirical studies described in this section produce
results that differ significantly depending on the period, the set of
countries, the variables included, or the method of analysis. In general,
regardless of the estimator implemented, most studies are based on annual data
that is observed over a relatively short spam of time. Recently, many works
estimate the EKC using single-equation time series models and the ARDL
methodology. Consequently, one could expect weak results when dealing with
non-stationary data. Upon the basis of the studies reported in Tables 1 and 2,
we can conclude that there is not a strong support to the EKC. Evidence of an
EKC is weak when we look at the worldwide level.

Finally, it is important to remark that most models in the literature focus on
in-sample predictions of per capita CO2 emissions. However, Auffhammer and
Steinhauser (2012) argue that the interest is on out-of-sample forecasting of
aggregate emissions. They argue the need for a shift in the emission
forecasting literature toward criteria based on out-of-sample ability to
predict total emissions versus the standard approach of selecting per capita
models based on in-sample fit.

##  5 Economic activity and climate change

In this section, we survey the contributions looking at the (potential)
effects of climate change on the economy and, in particular, on GDP. Several
recent reviews are available. Tol (2009, 2014) describes the vast amount of
research devoted to this topic. Focusing on GDP, he summarizes the results of
several studies that use different methodologies to estimate the welfare cost
(as percentage of GDP) of an increase on average temperature, where the main
conclusions are that, on average, the effect is negative, being more
pronounced in countries in Africa, Asia and South America. However, this
effect is found to be positive for some European countries. Dell, Jones and
Olken (2014) revise the literature of the effects of different climate
variables (temperatures, precipitation and wind-storms) over economic
outcomes, pointing out how the temporal dimension (exploited using panel data
models) helps in identifying their effects. Apart from economic growth, Dell,
Jones and Olken (2014) also review the effect on different outcomes as energy,
labour productivity, industrial output and agriculture, among others. Burke,
Hsiang and Miguel (2015) review the literature on the impacts of climate
change on agricultural productivity and economic growth. The survey by Hsiang
(2016) points out the main econometric issues in order to detect the effects
of climate in social and economic outcomes. Hsiang and Kopp (2018) explain the
physical science of climate change and presents evidence towards the rise of
temperatures being due to human emissions. Finally, Nordhaus (2019) addresses
climate change as externalities and describes the policy tools that are used
in order to predict the effect of climate change in the economy. In this
section, we summarize the main conclusions from these reviews and update them
with the most recent contributions to the literature.

###  5.1 GHG and Global Warming

First, we survey works analysing the relationship between GHG emissions and
GW. It is important to note that GW consists of two components: the greenhouse
effect and the solar radiation effect, with both working in opposite
directions. While an increase in GHG contributes to GW, more pollution causes
an increase in aerosols (small particles that reflect and absorb sunlight in
the atmosphere), so that less sunlight reaches the Earth. This phenomena is
known as global dimming. Note that, in spite of its name, Magnus, Melenberg
and Muris (2011) point out that global dimming is primarily a local (or
regional) effect. Because of the dimming the Earth becomes cooler: the solar
radiation effect. In practice, only the sum of the GHG and the solar radiation
effects is observed. Magnus, Melenberg and Muris (2011) gather data from a
large number of weather stations around the world for the period 1959–2002 and
use dynamic panel data methods to separate both effects. They decompose the
estimated temperature change of $0.73^{\circ}$C (averaged over the weather
stations) into a greenhouse effect of $1.87^{\circ}$C, a solar radiation
effect of $-1.09^{\circ}$C, and a small remainder term. Similarly, Phillips,
Leirvick and Storelvmo (2020) use dynamic panel data methods to analyse the
sensitivity of Earth’s climate to a given increase in atmospheric GHG
concentrations. Their analysis is based on spatio-temporal data of annual
global surface temperatures, solar radiation and GHG concentrations observed
over the last half century to 2010. They conclude that atmospheric aerosol
effects masked approximately one-third of the continental warming due to the
increasing GHG concentrations over this period; see also Storelvmo et al.
(2016) for similar conclusions. Petris and Hendry (2013) also conclude that
there is a relationship between CO2 and temperature supporting the
anthropogenic forcing of climate change; see also Petris and Allen (2013),
Estrada and Perron (2014), Stock (2019), Castle and Hendry (2020), Kim et al.
(2020) and Estrada, Kim and Perron (2021a, 2021b), among many others. Very
recently, Chen, Gao and Vahid (in press) use the common features perspective
and stablish that global temperatures and GHG share a common trend without
conditioning on a particular type of trend and considering the possibility of
endogeneity.

###  5.2 Temperatures and economic activity

Many studies analyse the effect of climate change on a particular sector or
characteristic of the economy, which in the medium or long-run would affect
growth. For instance, many papers focus on the effects of climate change on
agriculture (Schlenker and Roberts, 2009, Burke and Emerick, 2016, Iizumi and
Ramankutty, 2016, Hsiang et al., 2017 and Gallic and Vermandel, 2020), exports
(Jones and Olken, 2010), labour productivity (Seppänen, Fisk and Lei, 2006),
energy demand (Auffhammer and Mansur, 2012), health (Deschênes and Greenstone,
2011, and Deryugina et al., 2019), conflict (Miguel, Satyanath and Sergenti,
2004, Hsiang, Burke and Miguel, 2013, Hsiang and Burke, 2014) and crime and
aggression (Miguel, 2005), among other outcomes; see also Carleton and Hsiang
(2016) for various social and economic impacts of climate.

Earlier studies rely on cross-sectional regressions of several regions or
countries observed in a given moment of time. The main finding of these early
studies is the negative correlation between temperature and income per capita
while there is no so clear connection between precipitation and income; see
Dell et al. (2014). Dell, Jones, and Olken (2009) show at country level that
in 2000 an increase in $1^{\circ}$C translates into 8.5% lower income per
capita. Using municipal data for 12 American countries, they find that this
drop is reduced to 1-2% for municipal or province data, pointing out that
other differences among countries besides climate change might be also
responsible for the larger differences found among countries. However, even
controlling for country fixed effects, Nordhaus (2006) concludes that 20% of
the income differences between rich industrial regions and Africa can be due
to geographical variables, among them, temperature and precipitation.

The same conclusion still holds in panel data analysis, the negative
correlation between temperature and income per capita, although sometimes this
is only found statistically significant for poorer countries. For instance, in
a very well known work, Dell, Jones and Olken (2012), using a panel of
countries observed from 1950 to 2003, conclude that there is a strong negative
relationship between economic growth and warmer-than-average temperatures in
poor countries while in rich countries this relationship is not significant.
They also conclude that the effect of an increase of $1^{\circ}$C in annual
temperature reduces income per capita growth rate by 1.4%. Hsiang (2010) finds
a more negative effect for Caribbean countries, with national output falling
by 2.5%. Moreover, when looking at temperature shocks, the effect turns out to
be statistically significant only in the hottest season. However, some authors
also find a negative effect of warm temperature on economic growth in rich
countries. Burke, Hsiang and Miguel (2015) unify the seemingly contradictory
results by accounting for non-linearity. They show that overall economic
productivity is non-linear in temperature for all countries considered, with
productivity picking at an annual average temperature of $13^{\circ}$C and
declining strongly at higher temperatures. In spite of these results, there is
no agreement in the literature on how the relation between GDP and temperature
should be modelled. Newell, Prest and Sexton (2021) estimate more than 800
models!, depending on whether the effect of the temperature is linear or
quadratic, GDP should be considered in levels or growth rates and the nature
of some additional terms (trend, country fixed effects, time fixed effects,
interaction fixed effects) to be included in the model, with mixed results in
the outcomes pointing out the great uncertainty regarding model selection.
Moreover, in an out of sample forecasting exercise, the effect of projections
of unmitigated warming on GDP by 2100 also ranges from negative to positive
depending on the model.

As regards precipitation, the conclusion is even less clear. Dell, Jones and
Olken (2012) conclude that fluctuations in mean precipitation do not affect
income per capita growth rate. However, Barrios, Bertinelli and Strobl (2010)
found that, using standardized variables (the so-called weather anomalies) to
account for climate fluctuations, higher rainfall is associated with higher
growth rates in poor countries but not in rich ones; see, also, the literature
review in their paper that suggests the inconclusive results regarding the
effects of precipitation on growth. The reasons might be the type of
precipitation variable used, the additional regressors or control variables
introduced in the analysis and the sample used.

Recently, Kahn et al. (2021) study the long-term impact of climate change on
economic activity across countries using a panel model in which productivity
is affected by deviations of temperature and precipitations from their long-
term moving average historical norms. They analyse a panel of 174 countries
observed annually from 1964 to 2014 and estimate the parameters using the
half-panel jacknife fixed effects estimator of Chudick, Pesaran and Yang
(2018), which allows to deal with biases due to temperature only being weakly
exogenous. Also they allow for dynamics and feedback effects in the
interconnections of climatic and macroeconomic variables, distinguishing
between level and growth effects. They conclude that per-capita real output
growth is adversely affected by persistent changes in temperature above and
below its historical norm, but they do not obtain any statistically
significant effect for changes in precipitation. Their empirical findings
pertain to poor or rich, and hot or cold countries alike. However, the effects
vary significantly across countries. One of the reasons that cold countries
are also affected by climate change is the faster pace with which temperatures
are rising in these regions when compared with that in hot countries. Also,
the marginal effects of weather shocks are larger in low-income countries
because they have lower capacity to deal with the consequences of climate
change.

Another branch of the literature with estimations relating climate variables
and GDP growth is when climate variables are used as instruments of growth in
first stage estimations using instrumental variables. Within this type of
estimations, we also find mixed evidence of the relation between
precipitations and economic growth. For instance, Miguel, Satayanath and
Sergenti (2004) and Miguel and Satyanath (2011) found that variations in
precipitations are positively related to GDP growth rate in panel studies
although the relation seems to be weakening with time, while Burke and Leigh
(2010) found negative correlation between precipitation and per capita income
growth. In all these estimations the $R^{2}$ is quite low.

In the context of time series regressions, evidence for common breaks in
temperature and radiative forcing could be consistent with the attribution of
climate change to economic activity; see the editorial by Hillebrand, Petris
and Proietti (2020).

##  6 Economic effects of catastrophic weather phenomena

Climate change is likely to affect economies not only through warming, but
also via an increase in extreme events like cold snaps and heat waves, floods
and droughts, hurricanes, higher sea levels, and so on. Figure 2 plots the
number of annual weather and climate related disasters in US with a cost over
one billion-dollar to illustrate this increase.141414This plot was obtained
from https://www.ncei.noaa.gov/access/monitoring/billions/time-series on 17th
April 2022. This figure also plots the direct costs adjusted for inflation
using the Consumer Price Index (CPI), together with 95% confidence intervals
as well as the 5-year average costs. See Smith and Katz (2013), who describe
the methodology used to estimate the US billion-dollar disaster loss, and
conclude the costs are underestimated in roughly 10-15%, and Smith and
Matthews (2015) for a description of the Monte Carlo simulations used to
obtain the confidence intervals. Figure 2 suggests that increasing trends in
both the annual frequency of billion-dollar events and in the annual aggregate
loss from these events.

Figure 2: US billion-dollar weather and climate disasters from 1980 to 2021
(CPI adjusted). The red line represents the combined disaster cost and the
grey lines are 95% confidence intervals. The black line represents 5-year
average costs. Source: National Oceanic and Atmospheric Administration.

Hoeppe (2016) shows that direct losses are generally estimated using
catastrophe models and measured using empirical data on losses. Most estimates
of the economic impacts of natural disasters are based on regressions of
aggregate variables (measured at the country level) on some measure of
disasters, such as their number, the monetary damages or the number of
fatalities.

The question of whether natural disasters affect economic growth is ultimately
an empirical one; see the arguments in Cavallo et al. (2013). The economic
losses of natural disasters have been increasing over the last few decades,
with the number of natural disasters causing substantial losses increasing by
a factor of three since 1980; see Pindyck (2013), who stresses the importance
of measuring the effects of climate change including the possibility of
catastrophic outcomes, Schewe et al. (2019), who warn against the under-
estimation of the impacts of climate extremes by global models, and the
reviews by Cavallo and Noy (2011), Botzen, Deschenes and Sanders (2019) and
Hoeppe (2016) or the meta-analysis by Klomp and Valckx (2014).151515Mittnik,
Semmler and Haider (2020) analyse the link between CO2 emissions and the
frequency of climate related disasters.

Most of this literature focus on short-run analysis. An exception is Cavallo
et al. (2013), who investigate the sign and size of the short- and long-run
effects of large natural disasters on growth using event study with synthetic
control groups. They conclude that, once one control for political changes,
even extremely large disasters do not play any significant effect on economic
growth.

The literature considers different types of extreme events as inputs for the
models in order to estimate their economic effect. As in Felbermayr et al.
(2022), we focus only on weather related events (windstorms, intense
precipitation, droughts and cold spells) as input variables.

When using windstorms as input variable and based on a sample covering the
period 1950-2008, Nordhaus (2010) finds that, for the US, windstorms cost on
average 0.07% of GDP. However, Hsiang (2010) finds no average significant
impact of cyclones on income using data of 28 Caribbean countries, although
the effect can be positive in some sectors (construction) while negative in
some others (tourism). Moreover, Strobl (2011), after proposing a novel
hurricane destruction index, estimates the impact of hurricanes in US and
concludes that they have at least a negative effect of 0.45 percentage points
in coastal counties. The effect of windstorms in rich countries seems to be
temporary and compensated through government transfers. For instance, at the
county level in the US, Deryugina (2017) shows that 10 years after the
hurricane, there is no effect on county income due direct and indirect
government transfers in the form of unemployment insurance and public medical
payments pointing out that the fiscal costs of natural disasters have been
significantly underestimated due to these indirect transfers. In the
particular case of hurricane Katrina in the US, Deryugina, Kawano and Levitt
(2018) use a panel of tax return data and find that the effect of the
hurricane on income was small and transitory, although it had a strong effect
on where people live. Camargo and Hsiang (2016) survey the literature about
the link between tropical cyclones and climate and also analyse their socio-
economic impact, concluding that the direct and secondary economic impact of
tropical cyclones is larger than previously thought.

Another set of studies use night-light emissions as proxy for economic
activity and then the relation between night-light emision and economic
growth, (see Bertinelli and Strobl, 2013 for the Caribbean, Elliot, Strobl and
Sun, 2015, for China or Fellbermayr et al., 2022, for the whole globe) and
then translate these results into GDP (for instance, Fellbermayr et al., 2022,
use an elasticity of 0.37 of lights to GDP). The reasons for using night-light
emissions are that they are available for the whole globe at the appropriate
granularity (spatial resolution and time frequency), they also account for the
informal economy and their measurement error is not correlated with the level
of income per capita; see Felbrmayr et al. (2022). The three analyses
previously mentioned for the Caribbean, China and the whole globe, find
negative effects of windstorms on night-light emission although Felbermayr et
al. (2022) find positive small spatial spillover effects.161616Although the
three studies look for spatial spillover effects, only Felbermayr et al.
(2022) include them directly in the model and find strong evidence of them
with one lag.

Willner, Otto and Levermann (2018) show that the absence of enough adaptation
to pluvial floods may provoke economic looses of around 17% in the global
trade network with China being the country with the most serious looses.
However, Duan et al. (2022) find that in China an additional day of heavy
rainfall (more than 50 mm accumulated in 24 h) is associated with a 0.0092
percentage point increase in output. The rationale behind this contradicting
result is as follows: Although there are clearly risks to floods and
landslides from frequent precipitation in a short period, heavy rain helps
economic development and the storage of water resources. There are also
studies that use night-light emissions in order to see the economic effects of
floods. Felbermayr et al. (2022) use night-light emissions and, contrary to
Duan et al. (2021), find a negative relation between extreme precipitation and
local income growth using regression fixed effects models. They also find that
local income growth rebounds. Kokornik-Mina et al. (2020) also relate floods
with night-light emissions although they focus on cities. They find that
economic activity is disrupted only briefly after a flood, returning to
previous levels quickly.

Regarding extreme temperatures, there are analyses that focus on droughts or
the effect of too high temperatures while other study cold spells. For the
first one, Felbermayr et al. (2022) built a Standardized Precipitation-
Evapotranspiration Index, SPEI, and find that a unit increase of this index
results in an increase of 0.8 percentage points in local night-light emission,
with negative spillover effects resulting in a 0.44 decrease. For China, Duan
et al. (2022) study the effect of extreme heat (defined as the number of days
that the mean temperature is higher than $32^{\circ}$C) and conclude that an
additional day of extreme heat reduces GDP by 0.0006 percentage points with a
heterogeneous impact across sectors, being the highest impact on industry,
where an additional day of extreme heat implies that GDP loss increases 0.45%;
see Zhang et al. (2018).

For cold spells, Felbermayr et al. (2022) find that their cold anomaly measure
is negatively related to lights growth contemporaneously and in the next
period. However, Duan et al. (2022) study the effect of extreme cold for China
(defined as the number of days that the mean temperature is lower than
$-12^{\circ}$C) and do not find a significant relation.

All in all, the main conclusion about the impact of natural disasters on
income or GDP seems to be negative for all extreme input variables but heavy
rain. However, this effect depends on the country being poor or rich. For the
latter, the effects seem to be small and transitory, probably, due to the
support of governments in form of transfers or other indirect insurance.

##  7 Conclusions

Relevant policy recommendations need to be based, among other considerations,
on correct forecasts of the economic externalities caused by climate change.
Consequently, robust measures of the interactions between environmental
variables and the economy are crucial to formulate suitable economic
development paths. In this paper, we survey over 250 econometric contributions
to the literature on measuring these interactions over the last decade.

Credible quantitative modelling of climate change and its relations with
economic activity is still in its very early stages. The steps forward taken
during the last decades faces important econometric issues as, for example,
endogeneity, non-stationarity, non-linearity and the importance of extreme
events. The contributions in this area are not only crucial because of their
implications for society, they are also a challenge for econometricians.
Previous quantitative analysis of the externalities of climate change on the
economy are limited in their conclusions because they only consider partial
aspects of the environmental and economic variables and their relationships.

The global picture of the channels through which climate change and the
economy may be related are well understood. First, economic activity causes an
increase in energy consumption, which causes pollution. Larger pollution is
related with climate change. Finally, climate change is itself affecting the
economy.

However, the first issue faced to begin with is defining climate change.
Climate is usually defined as the long-run average of weather in a given
location (or globally) and climate change as the long-run variation in the
joint probability distributions describing the state of the atmosphere,
oceans, and fresh water including ice. In many studies analysing the
relationship between climate change and economic activity, the measures used
for the former are observations of weather variables, often temperatures and
precipitations, obtained from weather stations. A majority of studies of
climate change measure it by looking at temperatures and mainly to average
temperatures.

With respect to the first channel, the nexus between energy consumption and
economic growth has been analysed since long based mainly on panel data.
Evidence on causality between energy consumption and economic growth still
remains ambiguous.

There is also a huge and still growing literature on the nexus between
economic activity and pollution. Most works focus on estimation of EKC based
on either panel data or time series models. However, the time span considered
in empirical studies is often relatively short while the variables considered
are non-stationary. Consequently, the results are mixed and inconclusive.

With respect to the works looking at the relationship between economic
activity and climate change, many papers focus on the effects of the latter on
particular sectors while other, looking at a more aggregate level, find a
negative correlation between temperature and income per capita (mainly in
poorer countries). However, once more, evidence is mixed.

Finally, we survey the literature on the economic effects of catastrophic
weather phenomena, which mainly focus on short-term effects. As before, the
results are mixed, however, the empirical evidence seems to point to a
negative relation between weather extreme events (but heavy pricipitation) and
economic growth in poorer countries.

On top of describing what is known about the nexus between climate change and
economic activity, this survey shows that a lot of work is required by
econometricians in this very socially relevant topic. This analysis involves
non-stationarity, endogeneity and non-linearity together with relatively short
spans of data, being an important challenge for econometricians. Furthermore,
the current approaches understated the large uncertainty around the economic
effects of climate change, in particular they do not capture the downside
risk. Very few empirical studies deal with the uncertainty problem around
climate change. Pindyck (2014) and Convery and Wagner (2015) already discuss
that standard climate-economy models fall victim of two important fallacies in
dealing with uncertainty: by necessity, they focus on what is known and can be
quantified and they convey a false sense of precision. According to our survey
the same problem can be pointed out after several years.

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