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- No OECD German list 2009 147 The list has been compiled by Germany’s statistical office in accordance with Eurostat’s criteria of environmental protection and resource management. No German National Statistical Office Notes: Authors’ elaboration on PRODCOM data. For each list we report its name, the year in which it was compiled, the number of PRODCOM codes it contains, a brief description of the list, whether it is the outcome of trade negotiations and which organization has compiled it. All lists in the table are based on the HS product classification, except for Germany’s list that is compiled with PRODCOM product codes. To obtain the number of products for each list we have relied on crosswalks between HS and Eurostat’s Combined Nomenclature (CN) and between PRODCOM and CN, provided by Eurostat.
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- No OEDC WTO Friends 2009 604 This list has been negotiated by a smaller group of high-income economies within the WTO Yes WTO PEGS 2010 470 The list has been compiled by OECD with a focus on renewable energies No OECD APEC 2012 206 Countries member of APEC have negotiated this list agreeing to reduce tariffs on the products included down to at least 5% Yes APEC WTO Core 2011 78 This is more restrictive list that has been negotiated within WTO during negotiations towards a comprehensive free trade agreement on environmental goods.
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- Non-green RCA 0.000 0.201 0.645 0.926 1.139 20.324 1.541-0.037 Non-green RCA (log) -9.210-1.603-0.438-1.456 0.130 3.012 2.855 0.085 Number of green products with RCA 0.000 0.000 1.000 1.098 1.000 21.000 2.168 0.036 Number of green products with RCA (log) 0.000 0.000 0.693 0.510 0.693 3.091 0.596 0.005 Number of non-green products with RCA 0.000 0.000 2.000 3.324 4.000 33.000 4.424-0.100 Number of non-green products with RCA (log) 0.000 0.000 1.099 1.101 1.609 3.526 0.836 0.001 Notes: Authors’ elaboration on PRODCOM data. The table reports the distribution of the key variables from equation 6, i.e. only for the period 2005-2015 and only high-green potential industries, as defined in Table 3. Table E.2: Descriptive statistics of specialisation variables. Variables Min 1st Qu. Median Mean 3rd Qu. Max St. Dev.
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- Notes: Authors’ elaboration on PRODCOM data and OECD for the index of environmental policy stringency (EPS) for market-based policies. We plot countries’ green RCA and EPS, developed by the OECD. Green RCA is based on green production from CLEG list. Production values are deflated to have data at constant prices, with 2010 as base year. The RCAs are computed following formula 3 are made symmetrical around 0 and bounded between-1 and 1, the value of 0 indicates therefore whether a country has successfully specialised in green production. We also report the coefficient of a regression of green RCA on the EPS index for each year. Figure D.6: Green and polluting RCA across countries and over time, using green production from all green industries.
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- Notes: Authors’ elaboration on PRODCOM data and OECD for the index of environmental policy stringency (EPS) for market-based policies. We plot countries’ green RCA and the EPS, developed by the OECD. Green RCA is based on green production from all green industries, as identified in Table 3. Production values are deflated to have data at constant prices, with 2010 as base year. The RCAs are computed following formula 3 are made symmetrical around 0 and bounded between-1 and 1, the value of 0 indicates therefore whether a country has successfully specialised in green production. We also report the coefficient of a regression of green RCA on the EPS index for each year. Figure D.3: Green and polluting RCA across countries and over time, using green production from all green industries.
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- Notes: Authors’ elaboration on PRODCOM data. We plot countries’ green and polluting RCA. Green RCA is based on green production of CLEG list. Polluting production is total production from polluting industries identified in Table 2. Production values are deflated to have data at constant prices, with 2010 as base year. The RCAs are computed following equation 3 and are made symmetrical around 0 and bounded between-1 and 1, the value of 0 indicates therefore whether a country has, on average, successfully specialised in green production. We also report the coefficient of a regression of green RCA on polluting RCA for each year.
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- Production values are deflated to have data at constant prices, with 2010 as base year. The RCAs are computed following formula 3 are made symmetrical around 0 and bounded between-1 and 1, the value of 0 indicates therefore whether a country has successfully specialised in green production. We also report the coefficient of a regression of green RCA on the EPS index for each year. Figure 4: Green and polluting RCA across countries and over time.
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- Tables and Figures Table 1: Correlation table among green product lists. (1) (2) (3) (4) (5) (6) (7) (8) (9) CLEG WTO 2009 PEGS PRODCOM (favourite) APEC German list Core (WTO + CLEG) WTO Core CLEG Core CLEG 1 WTO 2009 0.84** 1 PEGS 0.73** 0.47** 1 PRODCOM (favourite) 0.49** 0.31** 0.58** 1 APEC 0.46** 0.49** 0.41** 0.31** 1 German list 0.16** 0.15** 0.14** 0.12** 0.17** 1 Core (WTO + CLEG) 0.37** 0.37** 0.35** 0.45** 0.44** 0.13** 1 WTO Core 0.29** 0.25** 0.27** 0.3** 0.16** 0.04** 0.77** 1 CLEG Core 0.23** 0.28** 0.24** 0.35** 0.51** 0.16** 0.65** 0.03** 1 Number of goods 819 604 470 221 206 147 123 78 47 Notes: authors’ own calculation on PRODCOM data. The table reports correlation coefficients of dummy variables indicating the presence of a certain product in a given list across different lists. The last row reports the number of PRODCOM product codes within each green product list. For further details about the lists of green goods, see Appendix A. *p<0.05 ** p<0.01.
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- two non-missing observations. The issue remains unfortunately for trailing and leading missing values (i.e. those country-product-year combinations for which we have no non-missing observations either before or after). This is however mitigated by the fact that our analysis is carried out at 4-digits NACE rev. 2. Unless all products underlying a given NACE 4-digit code are all missing (as it is the case, for example for Poland before 2003) we perform our aggregations treating the missing values as zeros. A.2 Lists of green products In this Appendix, we provide additional information on the lists used to identify our favourite PRODCOM list and for the validation analysis of Section 2.4. As we detail in Section 2 our universe of potential lists is the union of the CLEG list and German list. CLEG is the result of the union of three broader lists of the Asia and Pacific Economic Cooperation (APEC) forum, WTO Friends’ list and Plurilateral Environmental Goods and Services (PEGS).
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- Whenever possible we impute these by applying the average growth rate to fill the years between 23 Eurostat provides a crosswalk between the two versions of NACE. However, such crosswalk is imperfect as it entails many-to-many correspondences with some NACE rev 1 industries splitting and/or merging into NACE rev 2 industries. 24 We match our data to the EUKLEMS dataset, which contains industry-country specific price deflators at 2-digits NACE classification. We use these deflators to obtain production values at constant price. We use 2010 as base year. 25 Among the products that we identify as green, which are explained in details in section 2.3, 82 out of 221 green products are affected by this issue.
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