dataesr/scientific-paragraphs-categorization huggingface dataset:
The integral SSI at the wavelengths of the conventional Voronoi model accounts for 84. 7% of the accurate SSR (Fig. 3a ). This implies that the number of wavelengths utilized by this Voronoi model is not enough to calculate the surface SSR. In the present study, we enlarged the number of spectral wavelengths to calculate the singlescattering properties of the Voronoi ISC model from 86 to 100 and installed them in the RSTAR model as an ice scattering kernel. From Fig. 3b , we can confirm that the solar irradiation over the selected 100 wavelengths (red line) of the new Voronoi ISC model is highly consistent with the accurate data (black line), i. e. the calculated solar irradiation produced by the new Voronoi model accounts for 99. 87% of the accurate total solar irradiance. With the extended ICS database, the RSTAR is used to construct the LUT of the SSR estimation.
Cloud parameter retrieval A LUT algorithm was developed using the "Comprehensive Analysis Program for Cloud Optical Measurement" (CAPCOM) to retrieve the cloud properties (e. g. COT and CER). CAPCOM is a comprehensive cloud property retrieval package  (Nakajima and Nakajma, 1995; Kawamoto et al. , 2001). One component included in the package is a forward model called CAPCOM-fwd, which is essentially the same as RSTAR. The RSTAR module is a comprehensive radiative transfer package designed for multiple purposes. The CAPCOM-fwd is more convenient for constructing the LUT of the cloud property retrievals. As shown in Fig. 2 , a cloud screening algorithm is used to identify cloudy and clear pixels from AHI L1B data  (Letu et al. , 2014 (Letu et al. , , 2018;; Shang et al. , 2017; Ishida et al.
LUT for SSR calculation The RSTAR model is used to calculate the SSR from AHI data-derived cloud and aerosol properties. The RSTAR is a narrow-band radiative transfer model, and therefore the SSR is calculated by integrating the radiative flux over the spectral wavelengths. In this study, the Voronoi new scattering database with 100 spectral wavelengths was installed in the RSTAR to calculate the radiative flux for ice clouds. These wavelengths were determined by the characteristics of the refractive index and SSI in the spectral wavelength when developing the scattering database. The calculation of the radiative flux from AHI measurements by the RTM requires a large amount of computational time. Moreover, the AHI data contains a large number of pixels due to its high spatial-temporal resolution.
0 cloud property product at 02:50 UTC, on November 12, 2018. Fig. 6a  shows the cloud distribution in the selected area. The area is almost completely occupied by water clouds (Fig. 6b ). The spatial distribution of the COT from MODIS C6 (Fig. 6c ) and AHI data (Fig. 6d ) are comparatively consistent. The distribution of CERs from the AHI data (Fig. 6f ) is also consistent with MODIS C6 (Fig. 6e ). Fig. 7  illustrates the comparisons of COT and CER by showing the scattering plot and histogram in the area as presented in Fig. 6. The COT from the AHI data is also consistent with results from MODIS C6, with a coefficient of determination (R 2 ) of 0. 59, mean bias error (MBE) of 0. 2 and rootmean-square error (RMSE) of 3. 47. Similarly, CER from the AHI data is also well correlated with MODIS C6 results.
The R 2 , MBE and RMSE are 0. 15, 1. 7 μm and 7. 98 μm in the selected areas, respectively. A possible reason for this is the radiance calibration error for the AHI measurement, as well as the differences in the single-scattering properties of the assumed ice particle models used in the AHI and MODIS cloud products. A study by  Okuyama et al. (2018)  pointed out that the radiance calibration error of the AHI data has little effect on COT and CER retrievals. It can be inferred that the different ice scattering models used in AHI and MODSI Fig. 10. Comparison of single scattering albedo, volume-normalized extinction coefficient and asymmetry factor for bulk-scattering properties of ice particle models at the wavelengths of 0. 64 μm and 2. 3 μm as a function of the parameter size. cloud products have resulted in these differences.
Validation of instantaneous SSR estimates First, we show the spatial distribution of SSR in a severe weather condition. Fig. 11  presents SSR estimation results from H-8 data with near full disk regions for 03:00 UTC, August 8, 2016. In the true color image (Fig. 11a ), a typhoon near Japan can clearly be seen. Fig. 11b  and c  show the spatial distribution of AOT and COT in the full disk region. Fig. 11d  illustrates the estimated SSR. It is indicated that SSR decreases drastically with increasing COT; high values of SSR with lower SZA can also be seen. H-8/AHI has a high temporal resolution of 10 min, and thus here we adopted 10 minute-averaged SSR observations to evaluate the instantaneous SSR from the satellite data. Since CMA stations do not have instantaneous observation data, we evaluated AHI-derived SSR using the 33 non-CMA stations.
Fig. 12a  presents comparisons of instantaneous SSR between AHI estimated results and ground observations at 33 non-CMA stations in 2016 except for the months of January, February, and June. The instantaneous SSR from the AHI data are highly consistent with the ground-based observation data, with R 2 of 0. 85. The MBE and RMSE values at these 33 sites are 11. 86 Wm -2 and 119. 07 Wm -2 , respectively. Fig. 12b  shows the probability density function (PDF) of the SSR in Fig. 12a. From the figure, we can confirm that the PDF of the estimated SSR (red line) is consistent with the result of the ground-based SSR. Fig. 13  shows comparisons of instantaneous SSR at four randomly selected stations. Clearly, high temporal variability of SSR is retrieved well.
17a ), but the difference is considerably large under the cloudy sky (Fig. 17b ). Fig. 18  shows the comparison of daily SSR data at this station in March 2016 among the observations, the 10-min retrieval mean and the 60-min retrieval mean. It can be found that the degree of agreement between the ground observation data and SSR_10min is high, with MBE, RMSE and R 2 of 7. 1 Wm -2 , 20. 0 Wm -2 and 0. 94, respectively (in Table  3 ). These error metrics indicate much better accuracy than the SSR_60min that yields MBE, RMSE and R 2 of 20. 0 Wm -2 , 25. 0 Wm -2 , and 0. 77, respectively. Therefore, the AHI high temporal resolution is of great significance for improving the SSR retrieval accuracy.
Conclusions In this paper, surface solar radiation (SSR) was estimated from H-8/ AHI new-generation geostationary satellite measurements produced by the LUT-based algorithm. Cloud properties derived from AHI measurements were used to calculate the SSR from the proposed algorithm. However, the accuracy of calculated SSR depends largely on the reliability of cloud parameters. The calculation process of the SSR includes two major steps. The first step was the retrieval of cloud properties. A cloud property retrieval algorithm was established to calculate the COT and CER for water and ice clouds as input data to the SSR calculation algorithm. The Voronoi ice crystal scattering database was improved to retrieve the AHI ice cloud properties. The retrieval results of the COT and CER from the AHI data were validated by comparing them with cloud properties from the MODIS C6 product.
As a result, the AHI-derived COT and CER for water cloud were correlated well with MODIS C6, with correlation coefficients of 0. 77 and 0. 82, respectively. The COT of the ice cloud also shows good consistency with a correlation coefficient of 0. 85. It was confirmed that the CER from the AHI data is significantly larger than that of the MODIS C6. The R 2 , MBE and RMSE of the ice cloud CER from the two sets of satellite data are 0. 38 μm, 3. 98 μm and 14. 03 μm in the selected areas, respectively. The reason for the lower correlations is considered to be caused by the differences in the shapes of assumed ice particle models as well as the different single-scattering properties assumed for the retrievals from the two sets of satellite data. The second step was the estimation of the SSR, with the input of cloud parameters derived in the first step and the AHI official aerosol    (2016).
Finally, the importance of high observation frequency SSR data is discussed. It is confirmed that SSR with 10-minute observation frequency from the AHI data is useful to improve the accuracy of SSR estimation, which is used to drive the land surface models. In summary, high accuracy of the SSR product can be obtained through new-generation satellite sensor AHI, compared to conventional satellites. These high-resolution SSR data are important for solar energy applications and for the driving of land surface and hydrological models.
Acknowledgments This work was supplied by  National Key R&D Program of China , Grant Award Number: 2018YFA0605401;  Second Tibetan Plateau Scientific Expedition and Research Program (STEP) , Grant No. 2019QZKK0206 ;  National Natural Science Foundation of China , No:  41771395  and  JST CREST  Grant Number  JPMJCR15K4 ,  Japan. The Himawari-8 cloud property products used in this paper was supplied by the  P-Tree System, Japan Aerospace Exploration Agency (JAXA).
, 2010). However, the effects of the institutional environment on emerging industries can be highly heterogenous as it can both encourage new venture creation through, for example, market design incentives, but can also hinder new founding rates through an overly prescriptive and/or restrictive regulation  (Bruton et al. , 2010). So far, studies approaching green entrepreneurship from a formal institutional angle have linked environmental policy with new venture creation mainly through an empirical test linking the presence, but not the stringency, of environmental policies with green start-up creation or financing  (Criscuolo and Menon, 2015; Sunny and Shu, 2017; York and Lenox, 2014). Until recently, the unavailability of cross-country environmental stringency datasets means that the literature provides qualitative assessments on how green entrepreneurs perceive environmental policy  (Zietsma et al.
g. through market taxes or emissions trading schemes), do not necessarily translate into entrepreneurship opportunities, but rather these need to be followed by additional policies which specifically target green technologies (e. g. feed-in-tariffs). This paper is structured as follows. In Section 2, we provide the theoretical background that led us to framing our main research questions. Section 3 discusses our empirical model, data collection and preparation. Section 4 provides descriptive statistics of our panel dataset, while Section 5 outlines the results of our empirical investigation. Section 6 discusses our results in relation to theory and provides implications for theory development as well as practice.
software start-ups) although they may rely on natural resources. Finally, "brown" start-ups are those that are likely to be heavily impacted by more stringent environmental policies due to their reliance on natural resources and environmental externalities that they produce (e. g. oil and gas entrepreneurs). We explain in more depth in Section 3 how the sub-sector green classification has been used to arrive at our start-up dataset. 2 The term "environmental policy stringency" is defined as both: "a higher, explicit or implicit, cost of polluting or environmentally harmful behaviour  […]  for instruments like taxes"  (Botta and Koźluk, 2014, pp. 14)  as well as incentives for the development of environmental technologies and processes such as feed-in-tariffs or R&D subsidies (Appendix A3).
In addition, a measure based on counts does not necessarily imply the quality or growth potential of entrepreneurship in a given region, hence, one requires additional measures for entrepreneurship outcomes. For this reason, we select VC-backed or VC seeking start-ups proxied by their appearance in the Crunchbase dataset, based on which we build a regional count of green entrepreneurs by manually checking the websites of those start-ups that Crunchbase tags as related to green energy as well as from additional datasets. Next, we complement it with a second measure, regional VC financing, which further enhance our understanding about which regions have the start-ups with the highest perceived growth potential. In order to link these two entrepreneurship measures with environmental knowledge, we also require a new knowledge creation proxy which can be reliably measured across our 24 countries and 293 regions.
New regional VC-backed and VC-seeking technology start-up ventures The dependent variable is comprised of the yearly count of new high technology ventures across 293 regions in 24 OECD countries founded between 2001 -2013. To illustrate how different entrepreneurship sectors are impacted by different government policies, we study new entries in green, brown and gray sectors. We use a paid database subscription to Crunchbase 4  , a leading provider of intelligence for investors in technology start-ups around the world, to map the high-tech, VCbacked start-ups founded between 2000 -2016 (176,994 in total) by region and sector. To identify the sectors of interest that new entrants operate in, we mapped the technology sector provided by Crunchbase with the Sustainable Industry Classification System (SICS) provided by the Sustainability Accounting Standards Board (SASB).
We have also reviewed the green sub-sector classifications used in literature  (BNEF, 2019; Criscuolo and Menon, 2015; Gaddy et al. , 2017; Vedula et al. , 2018)  as well as those from specialised research providers such as the Cleantech Group 5  , and we narrow the definition of green start-ups as those operating in any of the following 8 green technology subsegments: battery storage and fuel cells, biofuels, environmental data, energy efficiency, electric vehicles and low carbon mobility solutions, low carbon grid infrastructure, renewable energy generation and low carbon materials R&D). We provide further detail in Table  1. To further enhance the dataset, we included a database of 6,831 corporations which have environmental and social aims in their articles of incorporation (list of B corporations and public benefit corporations provided by B Lab 6  ) which we used to further identify companies in the green sector in Crunchbase.
This process resulted in the identification of 4,201 potentially green start-ups, founded between 2000 -2016. We manually classify these 4,201 companies through researching their websites, news and Crunchbase profiles and restrain the sample over the period 2001 -2013, over 24 countries for our proposed statistical analysis. In cases where the start-ups may have been closed or acquired and hence had valid website, we investigated historical business registry data from different countries or investigated their archived websites at a given point in time using the Internet Archive Wayback Machine 7  , a collection of over 357 billion web pages saved over time. By narrowing the sample over the 2001 -2013 period and categorising green start-ups over the 8 green sub-markets, the resulting dataset consists of 2,468 green start-ups.
We use the yearly count of new entrants by technology and region. The mapping of all the VC-backed or VC-seeking start-ups by region was done manually using a company's incorporation address and a mapping tool provided by the OECD which allows us to match company addresses with Territorial Level 2 or TL2 regions of OECD countries  (OECD, 2018). This allows for comparability internationally given that national statistical offices use the OECD classification to collect different policy relevant datasets and governments use these to set regional policies.
Independent variables The variables below seek to capture new environmental knowledge per region (as measured by patent applications) and environmental policy stringency (as measured by OECD environmental indices). Environmental knowledge We use regional patent applications as a proxy for new regional environmental knowledge. Regional green patent data was collected from the OECD REGPAT database which allows for quantification of the innovation output over more than 2,000 TL3 regions in the OECD and across several technologies including environmental technologies  (Maraut et al. , 2008). We aggregate these to the TL2 level to match the start-up dataset. The OECD employs robust patent search methodologies to identify all the patent applications to the EPO, USPTO and JPO as well as those filed under the Patent Cooperation Treaty (PCT)  (OECD, 2016).
We study the effects of country level environmental policies on regional entrepreneurship given that environmental policy decision making is made at the national level whereas entrepreneurship is highly dependent on clusters and regional characteristics. For federal countries in our dataset (e. g. US States or Canadian Provinces), the country level EPS at different levels is obtained as a weighted average of state-level EPS based on a state's share of a country's total energy generation. In this way the index also takes into account the relative influence of regions where these do have a real influence on regional environmental policy. The indices are useful for comparing their relative effects across countries but are not built to reflect the relative stringency across different types of policy instruments (e.
Model specification We use two different models to account for regional venture entry vs. regional venture investment. For all the models, regressors are lagged one year to alleviate some potential concerns with reverse causality. Although our start-up dataset extends to 2016, one of our key independent variables, environmental policy stringency is only available to 2012, and our regional patent dataset is available to up to 2013. Hence, our analysis is conducted at the regional level, with data organised in a balanced panel between 2001 -2013.
3  and 4 ). At the country level, between 2001 -2013, the US claims over 65% of the total number of new VC-backed green start-ups and gray start-ups and c. 56% of new fossil fuel start-ups across the 24 OECD countries in our dataset. New green start-ups in California represent over 33% of the US total new green cohort, as well as 22% out of the OECD's new green ecosystem. This is a consequence of California's dominance in the technology and entrepreneurship space for a long time and the willingness of many venture capitalists to finance the first movers in the green venture space. The UK follows with 215 new green start-ups which is just below 9% of the cumulative number of green start-ups across the OECD, with Greater London and South-East England are the first and third regions by number of new green entrepreneurs across the OECD excluding the US.
GRID (Global Research Identifier Database) and significantly related to the emergency of gray, green and brown ventures across the board (models 1a-f). Table  5  also offers preliminary insights with respect to the impact of environmental policy on start-up entry. We find that the aggregate environmental policy stringency measure is not statistically related to the emergence of new green ventures, but negatively related with the emergence of both gray (β = -0. 327, p < 0. 001, model 1a) and brown ventures (β = -0. 792, p < 0. 01, model 1e). Models 2a-c (Table  6 ) illustrate that new environmental knowledge production in a region is still beneficial for the financing of gray startups (β = 0. 057, p < 0. 05, model 2a), but statistically insignificant for either green or brown start-ups. Surprisingly, the aggregate environmental policy stringency index is positively related to gray (β = 1.
While we manage to provide evidence at the granular policy instrument level, and how it affects new rates of entrepreneurship, we are unable to distinguish between the relative coherence, credibility or comprehensiveness of the policy instruments at the country level which ultimately influence the environmental and economic outcomes of the policy  (Georgallis et al. , 2018; Rogge and Reichardt, 2016). Furthermore, policy instruments interact with each other and may enhance or diminish the overall strength of a policy regime. Further research would need to be conducted to explore these aspects. Another limitation has to do with the dataset which allows us to study environmental knowledge and policy effects on entry and financing of VC-backed or VC seeking start-ups only. Seeking to answer questions, such as does environmental policy affect the revenue growth of start-ups or do start-ups meaningfully contribute to the reduction of environmental degradation can provide complementary views to our research angle.
To alleviate potential concerns for reverse causality, all independent variables are lagged one year. It is possible however that as the green start-up pool receives more funding, it will generate more green related knowledge which in turn influences green entrepreneurship. However, in this case, the start-up producing green knowledge may be considered itself an incumbent or competitor for the next generation of green start-ups, so to that extent, that may ease some endogeneity concerns. Additional bias may be introduced in our study since we were able to only consider green patents published in English only. The research institutes control variable partly addresses this issue for non-patentable green knowledge production, but not entirely. Our measure of green venture entry and financing, while uses a robust dataset (which compares in coverage to other research providers such as Preqin), relies on self-disclosed data from companies and venture capitalists which is prone to bias.
As much as possible, this has been complemented with additional datasets (e. g Blab data) to ensure a representative sample of green, gray and brown VC-backed start-up ventures across our 293 regions and 24 countries. ***p < 0. 001, **p < 0. 01, *p < 0. 05, +p < 0. 1. New start-up financing log-normal OLS models. Dependent variable is the natural logarithm of the total regional green start-up funding in a given year in $ million, including across green sub-sectors. Dataset is a balanced regional panel over 2001 -2013 and includes year and regional fixed effects. Independent variables are lagged 1 year. Panel corrected standard errors (PCSE) in parentheses.
Acknowledgments We are very grateful to the editor,  Prof. Maryann Feldman , and two anonymous reviewers whose comments and guidance have significantly improved the quality of our paper. We acknowledge that this work has been supported by funding from the  Smith School of Enterprise  and the Environment, the  IRC  and the  EU Horizon   2020 Marie Sklodowska-Curie  grant agreement No. 713279  and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 681337 ) -CityNet Project. We are also grateful for the useful comments of  Dr. Aoife Brophy Haney ,  Dr. Alex Money ,  Sergio Collaco de Carvalho  and  Dr. Stefania Innocenti  and thankful to the participants of the  Irish Academy of Finance  (  2019 ), of the  Global Conference on Economic Geography (Cologne ,  2018 ) and the  MIT/YSI Workshop in Innovation ,  Economic Complexity and Economic Geography (Boston ,  2018 ).
428 ⁎⁎⁎ 0. 286 ⁎⁎⁎ 0. 114* 0. 220 ⁎⁎ (0. 118) (0. 082) (0. 050) (0. 080) R&D Subsidies 0. 098 0. 017 -0. 021 0. 081 + (0. 108) (0. 060) (0. 036) (0. 045) Environmental 0. 015 0. 020 -0. 002 0. 014 Knowledge (0. 022) (0. 014) (0. 007) (0. 009) Control Variables YES YES YES YES Num. obs. 3809 3809 3809 3809 Adj. R 2 0. 541 0. 378 0. 131 0. 275 ***p < 0. 001, **p < 0. 01, *p < 0. 05, +p < 0. 1. New start-up financing log- normal OLS models. Dependent variable is the natural logarithm of the total regional green start-up funding across different start-up funding stages in a given year (in $ million). Dataset is a balanced regional panel over 2001 -2013. Independent variables are lagged 1 year. Two-way cluster (region and time) robust standard errors in parentheses.