Publications | Competition Data Observatory

Identifying Roadblocks to Net Zero Legislation

Tue, 16 Mar 2021 00:00:00 +0000

In our use case we are merging data about Europe’s coal regions, harmonized surveys about the acceptance of climate policies, and socio-economic data. While the work starts out from existing European research, our retroharmonize survey harmonization solution, our regions sub-national boundary harmonization solution and iotables allows us to connect open data and open knowledge from other coal regions of the world, for example, from the Appalachian economy.

Policy Context

The Just Transition Platform aims to assist EU countries and regions to unlock the support available through the Just Transition Mechanism. It builds on and expands the work of the existing Initiative for Coal Regions in Transition, which already supports fossil fuel producing regions across the EU in achieving a just transition through tailored, needs-oriented assistance and capacity-building.

The Initiative has a secretariat that is co-run by Ecorys, Climate Strategies, ICLEI Europe, and the Wuppertal Institute for Climate. While the initiative is an EU project, it cooperates with other similar initiatives, for example, with the Coalfield Development social enterprise in the Appalachian economy.

Data Sources

Coal regions: Our starting point is the EU coal regions: opportunities and challenges ahead publication Joint Research Centre (JRC), the European Commission’s science and knowledge service. This publication maps Europe’s coal dependent energy and transport infrastructure, and regions that depend on coal-related jobs.
Harmonized Survey Data: The dataset of the Eurobarometer 91.3 (April 2019) harmonized survey. Our transition policy variable is the four-level agreement with the statement More public financial support should be given to the transition to clean energies even if it means subsidies to fossil fuels should be reduced (EN) and Davantage de soutien financier public devrait être donné à la transition vers les énergies propres même si cela signifie que les subventions aux énergies fossiles devraient être réduites (FR) which is then translated to the language use of all participating country.
Environmental Variables: We used data on pm and SO2 polution measured by participating stations in the European Environmental Agency’s monitoring program. The station locations were mapped by Milos to the NUTS sub-national regions.

Exploratory Data Analysis

Our coal-dependency dummy variable is base on the policy document Coal regions in transition.

readRDS(file.path("data", "coal_regions.rds"))

## # A tibble: 253 x 5
##    country_code_is~ region_nuts_nam~ region_nuts_cod~ coal_region is_coal_region
##    <chr>            <fct>            <chr>            <chr>                <dbl>
##  1 BE               Brussels hoofds~ BE10             <NA>                     0
##  2 BE               Liege            BE33             <NA>                     0
##  3 BE               Brabant Wallon   BE31             <NA>                     0
##  4 BE               Antwerpen        BE21             <NA>                     0
##  5 BE               Limburg [BE]     BE22             <NA>                     0
##  6 BE               Oost-Vlaanderen  BE23             <NA>                     0
##  7 BE               Vlaams Brabant   BE24             <NA>                     0
##  8 BE               West-Vlaanderen  BE25             <NA>                     0
##  9 BE               Hainaut          BE32             <NA>                     0
## 10 BE               Namur            BE35             <NA>                     0
## # ... with 243 more rows

Our exploratory data analysis shows that respondent in 2019, agreement with the policy measure significantly differed among EU member states and regions.

transition_policy <- eb19_raw %>%
  rowid_to_column() %>%
  mutate ( transition_policy: normalize_text(transition_policy)) %>%
  fastDummies::dummy_cols(select_columns: 'transition_policy') %>%
  mutate ( transition_policy_agree: case_when(
    transition_policy_totally_agree + transition_policy_tend_to_agree > 0 ~ 1, 
    TRUE ~ 0
  )) %>%
  mutate ( transition_policy_disagree: case_when(
    transition_policy_totally_disagree + transition_policy_tend_to_disagree > 0 ~ 1, 
    TRUE ~ 0
  )) 

eb19_df  <- transition_policy %>% 
  left_join ( air_pollutants, by: 'region_nuts_codes' ) %>%
  mutate ( is_poland: ifelse ( country_code == "PL", 1, 0))

Preliminary Results

Significantly more people agree where

there are more polutants
who are younger
where people are more educated

Significantly less people agree

in rural areas
where more people are older
where more people are less educated
in less polluted areas
in coal regions

A simple model run:

c("transition_policy_totally_agree" , "pm10", "so2", "age_exact", "is_highly_educated" , "is_rural")

## [1] "transition_policy_totally_agree" "pm10"                           
## [3] "so2"                             "age_exact"                      
## [5] "is_highly_educated"              "is_rural"

summary( glm ( transition_policy_totally_agree ~ pm10 + so2 + 
                 age_exact +
                 is_highly_educated + is_rural + is_coal_region +
                 country_code, 
               data: eb19_df, 
               family: binomial ))

## 
## Call:
## glm(formula: transition_policy_totally_agree ~ pm10 + so2 + 
##     age_exact + is_highly_educated + is_rural + is_coal_region + 
##     country_code, family: binomial, data: eb19_df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7690  -1.0253  -0.8165   1.2264   1.9085  
## 
## Coefficients:
##                      Estimate Std. Error z value Pr(>|z|)    
## (Intercept)        -0.1975096  0.0921551  -2.143 0.032095 *  
## pm10                0.0068505  0.0017445   3.927 8.60e-05 ***
## so2                 0.1381994  0.0405867   3.405 0.000662 ***
## age_exact          -0.0075018  0.0007873  -9.529  < 2e-16 ***
## is_highly_educated  0.2953905  0.0311127   9.494  < 2e-16 ***
## is_rural           -0.1277983  0.0313321  -4.079 4.53e-05 ***
## is_coal_region     -0.2624005  0.0640233  -4.099 4.16e-05 ***
## country_codeBE     -0.3290891  0.0916117  -3.592 0.000328 ***
## country_codeBG     -0.6470116  0.1125114  -5.751 8.89e-09 ***
## country_codeCY      0.8471483  0.1273306   6.653 2.87e-11 ***
## country_codeCZ     -0.5754008  0.0965974  -5.957 2.57e-09 ***
## country_codeDE      0.0106430  0.0856322   0.124 0.901088    
## country_codeDK      0.0577724  0.0925391   0.624 0.532429    
## country_codeEE     -0.8041188  0.0989047  -8.130 4.28e-16 ***
## country_codeES      1.1266903  0.0941495  11.967  < 2e-16 ***
## country_codeFI     -0.2617501  0.0946837  -2.764 0.005702 ** 
## country_codeFR      0.0130239  0.1639339   0.079 0.936678    
## country_codeGB      0.2454631  0.0891845   2.752 0.005918 ** 
## country_codeGR      0.2169278  0.1209199   1.794 0.072816 .  
## country_codeHR     -0.1632727  0.1001563  -1.630 0.103064    
## country_codeHU      0.5779928  0.1020987   5.661 1.50e-08 ***
## country_codeIT     -0.1427249  0.0940144  -1.518 0.128985    
## country_codeLU     -0.3111627  0.1140426  -2.728 0.006363 ** 
## country_codeLV     -0.6246590  0.0963526  -6.483 8.99e-11 ***
## country_codeMT      0.3303363  0.1228611   2.689 0.007173 ** 
## country_codeNL      0.1707080  0.0902189   1.892 0.058470 .  
## country_codePL     -0.2843198  0.1228657  -2.314 0.020664 *  
## country_codePT      0.1447295  0.0899079   1.610 0.107452    
## country_codeRO     -0.0479674  0.0930433  -0.516 0.606177    
## country_codeSE      0.4865939  0.0922486   5.275 1.33e-07 ***
## country_codeSK     -0.2427307  0.0964652  -2.516 0.011861 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 30568  on 22401  degrees of freedom
## Residual deviance: 29313  on 22371  degrees of freedom
##   (5253 observations deleted due to missingness)
## AIC: 29375
## 
## Number of Fisher Scoring iterations: 4

summary( glm ( transition_policy_agree ~ pm10 + so2 + age_exact +
                 is_highly_educated + is_rural, 
               data: eb19_df, 
               family: binomial ))

## 
## Call:
## glm(formula: transition_policy_agree ~ pm10 + so2 + age_exact + 
##     is_highly_educated + is_rural, family: binomial, data: eb19_df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1970   0.5035   0.5803   0.6495   0.8465  
## 
## Coefficients:
##                     Estimate Std. Error z value Pr(>|z|)    
## (Intercept)         1.807823   0.079297  22.798  < 2e-16 ***
## pm10                0.005092   0.001239   4.108 3.99e-05 ***
## so2                 0.003274   0.051410   0.064  0.94922    
## age_exact          -0.009781   0.000988  -9.900  < 2e-16 ***
## is_highly_educated  0.396743   0.039735   9.985  < 2e-16 ***
## is_rural           -0.107448   0.037953  -2.831  0.00464 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 20488  on 22401  degrees of freedom
## Residual deviance: 20250  on 22396  degrees of freedom
##   (5253 observations deleted due to missingness)
## AIC: 20262
## 
## Number of Fisher Scoring iterations: 4

Next Steps

After careful documentation, we will very soon publish all the processed, clean datasets on the EU Zenodo repository with clear digital object identification and versioning.
We will seek contact with the Secretariat of the Initiative for Coal Regions in Transition to process all the data annexes in the EU coal regions: opportunities and challenges ahead report.
With our volunteers we want to include coal regions from the United States, Latin America, Australia, Africa first – because we have harmonized survey results – and gradually add the rest of the world.
We will ask political scientists and policy researchers to interpret our findings.

Ensuring the Visibility and Accessibility of European Creative Content on the World Market: The Need for Copyright Data Improvement in the Light of New Technologies

Sat, 13 Feb 2021 11:00:00 +0000

In the European Strategy for Data, the European Commission highlighted the EU’s ambition to acquire a leading role in the data economy. At the same time, the Commission conceded that the EU would have to increase its pools of quality data available for use and re-use. In the creative industries, this need for enhanced data quality and interoperability is particularly strong. Without data improvement, unprecedented opportunities for monetising the wide variety of EU creative and making this content available for new technologies, such as artificial intelligence training systems, will most probably be lost. The problem has a worldwide dimension. While the US have already taken steps to provide an integrated data space for music as of 1 January 2021, the EU is facing major obstacles not only in the field of music but also in other creative industry sectors. Weighing costs and benefits, there can be little doubt that new data improvement initiatives and sufficient investment in a better copyright data infrastructure should play a central role in EU copyright policy. A trade-off between data harmonisation and interoperability on the one hand, and transparency and accountability of content recommender systems on the other, could pave the way for successful new initiatives.

The published article: https://www.jipitec.eu/issues/jipitec-13-1-2022/5515

Preprint version

The earlier preprint version on SSRN our for direct download here on Data & Lyrics. Senftleben, Martin and Margoni, Thomas and Antal, Daniel and Bodó, Balázs and Gompel, Stef van and Handke, Christian and Kretschmer, Martin and Poort, Joost and Quintais, João and Schwemer, Sebastian Felix, Ensuring the Visibility and Accessibility of European Creative Content on the World Market - The Need for Copyright Data Improvement in the Light of New Technologies and the Opportunity Arising from Article 17 of the CDSM Directive (February 12, 2021). Available at SSRN: https://ssrn.com/abstract=3785272 or http://dx.doi.org/10.2139/ssrn.3785272

Can scholarly pirate libraries bridge the knowledge access gap? An empirical study on the structural conditions of book piracy in global and European academia

Thu, 03 Dec 2020 11:00:00 +0000

PLOS One is the fourth most influential multidisciplinary journal after Nature, and Science, and Proceedings of the National Academy of Sciences of the United States of America (based on H index.) On December 3, 2020 it published a paper co-authored by Dr. Balazs Bodo, associate professor at the Institute for Information Law (IViR), Daniel Antal (Reprex, Demo Music Observatory), a data scientist interested in reproducible research, as an independent researcher, and Zoltan Puha, a Data Science PhD at Tilburg University, JADS. PLOS (Public Library of Science) is a nonprofit Open Access publisher, empowering researchers to accelerate progress in science and medicine by leading a transformation in research communication.

The article utilizes the our reproducible datasets created with our regions package, and builds on many years of expertise in empirical research on the field of music and audiovisual piracy, home copying and private copying compensation (see for example Private Copying in Croatia.) Our aim is to provide reliable, high quality indicators for the creative industries not only on national, but provincial, state, regional and metropolitan area level, too, because these levels are often more relevant for creators, performers and policy-makers.

The topic of the paper is Library Genesis (LG), the biggest piratical scholarly library on the internet, which provides copyright infringing access to more than 2.5 million scientific monographs, edited volumes, and textbooks. The paper uses advanced statistical methods to explain why researchers around the globe use copyright infringing knowledge resources. The analysis is based on a huge usage dataset from LG, as well as data from the World Bank, Eurostat, and Eurobarometer, to identify the role of macroeconomic factors, such as R&D and higher education spending, GDP, researcher density in scholarly copyright infringing activities.

We created a global and a far more detailed European model for pirate book downloads.

The main finding of the paper is that open access, even if it is radical, is not a panacea. The hypothesis of the research was that researchers in low-income regions use piratical open knowledge resources relatively more to compensate for the limitations of their legal access infrastructures. The authors found evidence to the contrary. Researchers in high income countries and European regions with access to high quality knowledge infrastructures, and high levels of funding use radical open access resources more intensively than researchers in lower income countries and regions, with less resourceful libraries. This means that while open knowledge is an important resource to close the knowledge gap between centrum and periphery, equality in access does not translate into equality in use. Structural knowledge inequalities are both present and are being reproduced in the context of open access resources.

The paper is unique not just because of the data it is based on. It also sets new standards in interdisciplinary legal research by publishing the paper, the data and the software code in the same time in open access repositories, following reproducible research best practices — the practices that we want to promote in our Digital Music Observatory and further data observatories to serve business, evidence-based policy and scientific research.

Our research was funded from the Horizon Europe 2020 Research grant #710722 “OPENing UP new methods, indicators and tools for peer review, dissemination of research results, and impact measurement”.