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 (2009Tol ( , 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)  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 (2013Pindyck ( , 2017)), Stern (2008Stern ( , 2013Stern ( , 2016) ) and Weitzman (2007Weitzman ( , 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) 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; 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 CO 2 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 crosssectional, 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. 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: 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. 1 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)2 as EKC. 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 nonstationary. 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. 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 CO 2 emissions, energy consumption and economic growth. 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. 3As 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 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)  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 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

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 σ 2 u i . If β 1i > 0, β 2i < 0 and β 3i = 0, then (1) represents an inverted-U shape in the relationship between pollutants and economic activity. Furthermore, when β 3i = 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 β 3i = 0, the turning point of the per capita GDP in which the environmental quality measure, e it , begins to improve is y * i = -β 1i 2β 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). 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. 6 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 (CO 2 ) 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 CO 2 emissions, which remain accumulated in the atmosphere for a long time; see, for instance, the references listed in Tables 1 and2  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,

In this subsection, we survey different procedures implemented to estimate the EKC, clasifying them into panel, time series and regression estimators. 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. 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 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. 8 Fosten (2019) fits the following diffusion index model to analyse the effects of economic activity on CO 2 emissions

where ε t = △logE t with E t being yearly CO 2 emissions, 9 . ft is the r ×1 vector of Principal Component (PC) factors extracted from a large set X t of macroeconomic variables and ν t is assumed to be a white noise. Finally, α and δ = (δ 1 , ..., δ r ) ′ are parameters. Fosten (2019) also carries out a multi-resolution analysis of y t and ft 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. 10   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

where Ft = f1t , f2t , f3t ′ 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 CO 2 emissions

where ε t = △e t with e t being the log-transformation of per capita CO 2 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 ft 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 CO 2 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 δ = 0 over competing alternatives, and forecast decreases in emissions for 2018 10 Wavelet 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. and 2019. 11Wagner, Grabarczyk and Hong (2020) deal with estimation and testing of the EKC for CO 2 emissions. The latter postulates an inverted U-shaped relationship between the level of economic development and CO 2 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. 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)  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. 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 and2, 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 CO 2 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. 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 (2009Tol ( , 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  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. 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 be-  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 • 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 • 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 • 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 socalled 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. 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 The black line represents 5-year average costs. Source: National Oceanic and Atmospheric Administration. 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). 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. 12 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. 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 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. study the effect of extreme cold for China (defined as the number of days that the mean temperature is lower than -12 • 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, 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. Stern (2008Stern ( , 2016)),Pindyck (2014) andConvery 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 (2009Tol ( , 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. Stern (2008Stern ( , 2016)),Pindyck (2014) andConvery 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 (2009Tol ( , 2014) )

Stern (2008Stern ( , 2016)),Pindyck (2014) andConvery 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. Stern (2008Stern ( , 2016)),Pindyck (2014) andConvery and Wagner (2015)

Stern (2008Stern ( , 2016)),Pindyck (2014) and

Stern (2008Stern ( , 2016)),Pindyck (2014)

Stern (2008Stern ( , 2016)),

Stern (2008Stern ( , 2016))

1. Naturally, climate change could excessively increase the electricity demand for cooling during heat waves. New literature is emerging

seeWagner (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. seeWagner (2008)

see

A 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 byDinda (2004), who summarizes the factors responsible for the shape of the EKC. Note thatKuznets (1955) postulates the inverted U-shape for the relationship between income and income inequality. Alternatively, 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. While 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. Other 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). Shin, 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. Note 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) also considers emissions by source and non-linear specifications including polynomials of the factors. Note that all variables in the SADFM are previously transformed to stationarity and, consequently, the diffusion index and SADFM models capture short-run relationships. This plot was obtained from https://www.ncei.noaa.gov/access/monitoring/billions/time-series on 17th April 2022. Although 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.