dataesr/scientific-paragraphs-categorization huggingface dataset:
While middle-income economies, such as China and India, are building domestic CCMT industries, other low-and middle-income countries are often reluctant to bear the additional cost of CCMT compared to "brown" alternatives  4, 7. Within the United Nations Framework Convention on Climate Change (UNFCCC), several instruments -such as the UN Technology Transfer Mechanism and the Clean Development Mechanism -have attempted to address this challenge and to encourage international CCMT transfer  8. While existing research has provided a clear picture of CCMT invention and diffusion from 1978-2005  5  , there is a lack of a recent and comprehensive global overview. Using data from the Worldwide Patent Statistical Database (PATSTAT)  9  maintained by the European Patent Office (EPO), we examine high-value inventions in the seven climate-change mitigation technologies identified under the Y02 classification (Table  1 ), which provides the most comprehensive and standardised low-carbon patent classification and covers most technology fields (buildings, carbon capture and storage (CCS), energy, information and communication technology (ICT), manufacturing, transportation, and waste management).
13 ce, 2017 is the last reliable year in our patent database. Secondly, we use the same data on international patent families to investigate trends in international technology diffusion. [Table  1 here]  We find an average increase of 10% in annual high-value CCMT invention rates from 1995 to 2012. Yet, from 2013 to 2017, high-value CCMT invention rates have fallen by around 6 percent annually, likely driven by declining fossil fuel prices and technology-specific drivers, such as maturity. CCMT invention is highly concentrated geographically: Germany, Japan, and the US account for more than half of global inventions and the top 10 countries for almost 90%, while the contribution of most middle-income economies remains negligible. The concentration in the top-10 countries has remained largely stable over the last two decades.
Global diffusion trends To adopt CCMT technologies, countries can invent them or import them from foreign countries. We measure the transfer of inventions by counting patent families filed outside of the inventor's country. If the patent family is filed in multiple countries, we divide the fractions evenly across all receiving countries. To corroborate our metric of CCMT diffusion, we correlate our approach with the most important technology transfer channels  44  , namely Foreign Direct Investment (FDI) and trade. Data on licensing -another important transfer channel -is limited. Yet, as licensing agreements are commonly based on locally filed or transferred patents, our indicator also captures that transfer channel. Overall, our approach correlates well with FDI and moderately well with trade.
Patent data To measure the invention and transfer of technologies, we use patent data from the Worldwide Patent Statistical Database PATSTAT (2019, Autumn version)  9  , a database maintained by the European Patent Office (EPO). The database includes more than 100 million patents. The relevant patent codes used in our study can be found in Table  1. scheme, but until the introduction of Y02, a single classification approach was missing. The complexity of CCMTs is mirrored in other emerging technological sectors (e. g. , nanotechnology) and is partly due to the increasing complexity of inventions (covering different sectors), interoperability between technologies, and the rise of digital technologies (e. g. , peerto-peer energy trading)  50. Hence, merely relying on either the CPC or the IPC classification scheme (or both) for selecting CCMTs can lead to omissions or too many irrelevant inventions.
The resulting outputs are then perused by EPO experts to guarantee a high-quality standard across all Y02 codes. The EPO selects 150 random documents from the outputs and strives for an error rate below 7%. As the PATSTAT database contains more than 100 million documents, this approach enables the tagging of the entire database. In contrast to the abovementioned example, the tag Y02C 10/00 only includes CO2 capture or storage (and not irrelevant patents on carbon monoxide capture) and in addition to biological and chemical separation also includes capture by absorption, adsorption, membranes, or diffusion as well as rectification and condensation. The Y02 classification, therefore, contains fewer irrelevant patents and a greater variety of relevant patents. EPO experts identified seven categories of mitigation technologies.
Climate Scenarios As stated above, we consider CLC as exogenous in our model. We thus disregard the endogeneity of CLC, which involves uncertain links between economic activities and policies, CO 2 emissions, and the resulting changes in temperature. Projections of temperature. We follow three steps to construct our projections of global temperature distribution. In a first step, we collect raw data on monthly temperature levels and projections from the Climate Change Knowledge Portal (CCKP) of the World Bank Group. Our variable of interest is the near-surface monthly mean air temperature level. In Appendix A. 1, we discuss how raw temperature levels are adjusted to account for within-country disparities in temperature and population density and how climate windows are linked to the time periods of our overlapping generations model.
We use the NASA database and estimate of the fraction of land that could be flooded. Using population and land data, we compare the density of people in flood-risk areas and in the rest of the region and assume, in line with  Desmet and Rossi-Hansberg (2015) , that disparities in population density reflect disparities in total factor productivity. For each country, we produce region-specific estimates of the productivity loss caused by the sea level rise. These productivity losses are small (in countries with access to the sea, we obtained an average loss of 1. 2% in urban regions and of 0. 7% in rural regions), either because the share of population located in flooded areas is small, or because productivity differences are small. For this reason, this mechanism is not included in the model.
The maximal productivity level is normalized to one. To estimate the quadratic relationship in the non-agricultural sector, DRH use data on population density (a proxy for economic development) by latitude. They consider 1,000 bands of 9. 6km each, and estimate the relationship between (smoothed) levels of population density and temperature. They obtain (g 0n , g 1n , g 2n ) = (0. 3, 0. 08, -0. 0023), which gives an optimal temperature T * n of 17. 4 • Celsius. The quadratic relationship is compatible with the findings of  Dell et al. (2014) , who show that industrial output decreases by 2% for a 1 • Celsius increase in temperature; it is also compatible with In the bottom panels, latitude (geographic coordinate) is measured on the Y-axis, and sectorspecific productivity changes between 2010 and 2100 are measured on the X-axis (as % of deviation from the 2010 level).
To proxy the number of people affected by SLR, we need to determine the fraction of the population living in low-elevation coastal areas. We use the NASA database on the distribution of the population by elevation, by country, and by region type (urban versus rural). 17  In OECD countries, we assume that sea walls or other solutions will be constructed to protect vulnerable populations from rising sea levels. On the contrary, SLR will induce forced displacements in non-OECD countries. For each non-OECD country, we produce region-specific estimates of the fraction of population living at an elevation under 1. 1m or under 1. 3m using a third order polynomial of the NASA data. Figure  3  focuses only on those countries in which the share of population living in low-lying areas exceeds 4%. Bars represent the percentage of the population by country living in low-lying areas in 2010.
The extensive and intensive margins (i. e. , frequency and intensity) of these fast-onset weather shocks are highly uncertain. However, their frequencies are likely to be correlated with higher moments of the regional temperature distribution, including the weights of upper tails of these distributions, which are expected to deviate significantly in the future. We consider three additional sources of climate-driven costs and model them as a decrease in disposable income (which is equivalent to a tax on income). Firstly, we assume that weather-related natural disasters (i. e. , floods, storms/hurricanes, and droughts) induce direct income losses. Using the EM-DAT database, which collects information on human and material losses generated by disasters over the last six decades, we regress the average shares of GDP lost due to extreme weather events on the annual mean level of temperature, controlling for a time trend.
Parameterization We summarize our parameterization strategy for 145 developing countries and for the entire set of 34 OECD countries. 23  More details can be found in the Appendix (Section A). In particular, Table  A. 1 summarizes the calibration outcomes. Our strategy is such that our intermediate CLC scenario matches socio-demographic and economic data for 1980 and 2010, as well as reasonable socio-demographic prospects for 2040. Economic data sources. We collect data on socio-demographic and economic characteristics of 179 countries in 1980 and 2010. We use data on Gross Domestic Product (GDP) from the United States Department of Agriculture (USDA) and the agriculture share in value added from the Food and Agriculture Organization of the UN (FAOSTAT). As for the structure of the resident labor force by education level and by sector, we use the estimates described in  Burzyński et al.
(2019). Data on wages by education level are obtained from  Biavaschi et al. (2019)  for the non-agricultural sector and from the Gallup World Polls for the agricultural sector. We model international migration to OECD countries only. From the Database on Immigrants in OECD and non-OECD countries (DIOC), we extract the number of emigrants by education level for 2010. Within each country, we split the number of emigrants to OECD by region of origin and education level, assuming that the structure of migration aspirations (obtained from the Gallup World Polls) is identical to the structure of actual emigration stocks. To proxy the average fertility rate, we divide the total native population of adults in 2010 by the resident population of adults in 1980, both taken from the UN Population Division.
We use the Gallup World Polls and extract the average number of children per household in urban and rural regions by skill level for 2010. Technological parameters. For each region, we collect the data on cross-country income gaps between college graduates and the less educated. We calibrate the elasticity of substitution between college graduates and less educated workers, relying on existing studies: For the non-agricultural sector we follow  Ottaviano and Peri (2012) , who suggest setting the elasticity close to 2, whereas for the agricultural sector, it is usually assumed that the substitution is perfect (e. g. Lucas, 2009; Vollrath, 2009). We then calibrate the skill-bias term, η r,t , so as to match the observed income ratio between skill groups. Magnitudes of technological externalities are calibrated using constructed data on labor supply, region-specific incomes, and wage gaps.
Conclusion This paper provides evidence that climate change (CLC) will increase the income gap between the richest and poorest countries by at least 25% over the course of the 21st century. It will also increase extreme poverty at both the extensive and intensive margins and force millions of adults to flee their flooded areas of residence. These CLC-related developments create favorable conditions for increasing the internal and international mobility of workers. In this paper, we endogenize the migration responses to CLC at various spatial scales under current migration laws and policies. Our model relies on consensus micro-foundations and is calibrated to match international and urbanization data of the last 30 years, with a focus on long-haul migration from developing countries towards OECD destinations.
Overall, all models under RCP-4. 5 predict an increase in temperature levels. We proceed to two adjustments before plugging the temperature data into our model: Firstly, the climate literature suggests that aggregate (unweighted) country levels of temperature may not reflect accurately the impact of CLC  (Dell et al. , 2014). Particularly in large countries with regions of heterogeneous population densities, the aggregate measure poorly captures the intensity of the phenomenon. Hence, we weight the monthly future temperature levels by population. To do this, we extract from the CCKP the monthly mean air temperature levels for the climatological window for 1991 to 2015. We weight equally the monthly observations to obtain a yearly temperature level for each country. Furthermore,  Dell et al.
We assimilate the 2040-2059 climatological window to 2040, the 2060-2079 climatological window to 2070, and the 2080 to 2099 climatological window to 2100, respectively. In this way we obtain monthly populationweighted levels of temperature for the 179 countries in our data set. When averaged over all countries and months, our baseline temperature data predicts an increase in global temperature of 2. 09 • C by the end of the 21st century. 29 resident labor force by education level and by sector, we use the estimates described in  Burzyński et al. (2019). Data on wages by education level ( r,2010 ) are obtained from  Biavaschi et al. (2019)  for the non-agricultural sector, and from the Gallup World Polls for the agricultural sector. We model international migration to OECD countries only. From the Database on Immigrants in OECD and non-OECD countries (DIOC), we extract the number of emigrants by education level to OECD countries for all countries in our sample and for 2010.
33  Moreover, our calibration requires data on the skill-and region-specific fertility for each country. By construction, we have n t ≡ r,s L r,s,t n r,s,t / r,s L r,s,t. We use the Gallup World Polls and extract the Gallup-based average number of children per household in urban and rural regions by skill level for 2010. We compute the fertility of the college educated workers by fitting the sector-specific, low/high-skilled fertility differentials from the Gallup database. In this way, we obtain the fertility rates for each country for 1980. From 2010 onward, the number of children is endogenous. Once skill-specific fertility rates are calibrated, we use (10) to compute the employment levels ( r,s,2010 ) and the skill ratio (z r,2010 ) in all regions. Technological parameters. -Output in each sector depends on the size and skill structure of employment.
e. , κ n = 0. 19), and we calibrate η n as a residual from (5) in each region. We find that this scale factor is positively associated with the share of researcher in the labor force. In the second step, we use data on income by sector in 2010 and identify the TFP levels (A r,t ) as a residual from Equation (1). There is a clear positive relationship between TFP and the skill ratio in both sectors. Indeed, regressing the log of A r,t on the log of z r,t gives a coefficient of 0. 57 in the non-agricultural sector, and 0. 66 in agriculture. We assume that half the correlation between TFP and the share of college-educated workers is due to the schooling externality (i. e. , n = 0. 28 and a = 0. 33). We calibrate A n as a residual from (4). This scale factor exhibits realistic correlations with proxies for infrastructure and with the cost of starting a business.
* The authors thank the  Agence Française de Développement  for its financial support (convention  IRS/ECO/437-2017 ). Jaime de Melo  acknowledges support from the  French National Research Agency  under program  ANR-10-LABX-14-01. This paper benefitted from helpful suggestions and comments from  Catia Batista ,  Michel Beine ,  Simone Bertoli ,  Carlo.
Killer whale abundance We used data collected under the French Southern Ocean Fishery Observer Program  (Gasco 2011 ) by fishery observers from 2007 to 2018. Fishery observers are assigned on board 100% of the fishing vessels that are allowed to operate in the Crozet EEZ and collect fishery and scientific data on target and non-target species, as well as data on the occurrence and abundance of killer whales involved in depredation events. These data were extracted from the 'PECHEKER' database hosted and managed by the French National Museum of Natural History  (Martin & Pruvost 2007 , Pruvost et al. 2011). Trained observers conducted visual surface observations during hauling, which is when killer whales come into the vicinity of vessels to feed on catches being brought up by fishers. This observation effort was systematic when light and weather conditions were suitable and covered on average 90 ± 1% (SE) of the longline sets per year (n = 12 yr; on average, 1005 ± 81 longlines yr -1 are deployed).
As killer whales rarely leave fish remains on hooks, the occurrence of depredation on a given longline set was confirmed when whales were present and observed repeatedly diving towards the longline being hauled, being surrounded by seabirds and leaving fish oil slicks in the water when coming back to the surface. For all longline sets with confirmed killer whale depredation, observers systematically recorded the number of killer whales present from visual surface observations. Observers were trained to provide minimum and maximum abundance estimates for each depredated set. Observers were also trained to collect photo-identification data and took photographs of killer whale individuals present using DSLR cameras fitted with 100-400 mm telelenses following a standardized protocol  (Gasco et al.
We considered that double counting a whale depredating on different vessels on the same day was negligible since it has only been seen a few times since 2003 (P. Tixier pers. obs. ). Observers did not differentiate between the 2 forms of killer whales when visually estimating abundances per depredated set and therefore, daily estimates may have included individuals from both the Crozet and the Type-D form. However, the frequency of occurrence of Type-D killer whales during depredation events was assumed minor compared to that of the Crozet killer whales based on a previous assessment (11 vs. 89% of depredated longline sets for the 2 forms, respectively;  Tixier et al. 2016). Abundance estimates retrieved from photo-identification data were used for bioenergetic analyses conducted at monthly and yearly scales.
At Crozet, photo-identification (from fishing vessels, but also from the shore of Possession Island) allows for high detection rates of the Crozet killer whales (> 0. 99,  Tixier et al. 2017)  but is too sporadic for Type-D killer whales to representatively capture all individuals from this form. Because of low detection rates, Type-D killer whales were excluded from analyses at the monthly and yearly scales. Photographs were used to identify and count depredating individuals based on an existing long-term photo-identification database and catalogue  (Tixier et al. 2021). From these data, and for each year, individuals were differentiated into calves (individuals < 2 yr old, considered unweaned), juveniles (≥ 2 and <10 yr old) and adults (≥ 10 yr old). For killer whales, sex could only be assigned to adults based on secondary sexual characteristics not being visible from the surface until the whale was ≥ 10 yr of age (hereafter 'monthly' and 'yearly depredating individuals;' Tables  1  & 2 ).
The amount of toothfish depredated by killer whales was estimated as the difference between the amount of toothfish expected to have been landed on vessels if whale depredation had not occurred (predicted catch in the absence of whales) and the amount of toothfish expected to have been landed if whale depredation had occurred (predicted catch in the presence of whales) depending on the number of depredating individuals present. Estimates of depredated toothfish (in t, with 95% confidence intervals, CIs) were obtained for each longline set for which the occurrence of killer whale depredation was confirmed by fishery observers, and summed by day, month and year for the analyses (Tables 1 & 2). However, when fishery observers could not conduct visual observations, the dataset included longline sets for which the occurrence of killer whale depredation was unknown.
Contribution of depredated toothfish to the energetic requirements of killer whales The contribution of depredated toothfish to the energetic requirements (C i, Eqs. 1 & 5) of killer whales was assessed at 3 temporal scales. Annual assessments were conducted over the period 2007-2018, and daily and monthly assessments were performed for the year 2009 only. This specific year was representative of the time series in terms of both the extent and the temporal distribution of the fishing effort (Table  1 ). It was also chosen because 2 fishing trips had observers dedicated to collecting data on killer whales, resulting in extensive photo-identification effort and increased accuracy of abundance estimates. C i was estimated at different temporal scales of i (day, month, year) using several parameters: one was fixed (the energetic content of Patagonian toothfish; ED) and the others varied depending on the time scale i considered: the depredated biomass (Q i in t), the number of killer whales, excluding calves (Nb kw,i ), the number of days (fishing days in daily analysis and all days for monthly and yearly analyses; Nb days,i ) and the daily prey energetic requirements (DPER year ): (1) The energy intake from depredation was calculated from the ED (in kJ g -1 ) and Q i.
2008 , Spitz et al. 2018 , Lefort et al. 2020). As BMR is a function of individual body mass (M in kg), and measuring individual body mass is almost impossible from field studies on wild cetacean populations, we used a length-mass regression for killer whales of the South African subregion: M g = 8. 6791 × L g 3. 2769 , where g is the class of individuals (adult female [f], adult male [m] or juvenile [j];  Best 2007 , Reisinger et al. 2011a ). BMR g (Eq. 3) was estimated for each class g of the population, where L g was 6. 75 and 7. 75 m for adult females and adult males, respectively  (Best et al. 2010 ), which appears to be consistent with the few data from individuals stranding in Crozet (French Marine Mammal Stranding Database, www. observa toire-pelagis. cnrs. fr/ pelagis-2/ bases-de-donnees/), and 4 m for juveniles  (Esteban et al.
Contribution of the depredated toothfish to daily prey requirements Data from a total of 113 fishing days during which depredation occurred in 2009 (51% of all days with fishing and 66% of all fishing days with depredation) were used to assess the contribution of depredated toothfish to killer whale energetic requirements (C i ) at the daily scale. During these 113 days, the mean daily toothfish biomass depredated by killer whales was 1. 2 ± 0. 08 t (SE), ranging from 0. 1 to 4. 9 t, and the mean number of daily depredating individuals was 9. 3 ± 0. 5, ranging from 3 to 30 individuals. Consequently, the median daily intake was estimated to 103 kg (14-418 kg) of depredated toothfish per individual involved in depredation. With killer whale DPER 2009 estimated at 995 MJ d -1 (see Table  S1 ), the daily C i during days when depredation occurred was estimated to be 94.