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The impact of trade in environmental goods on pollution: what are we learning from the transition economies’ experience?

Environmental Economics and Policy Studies volume 20, pages 785–827 (2018)Cite this article

Abstract

We investigate the causal effects of trade intensity in environmental goods (EGs) on air and water pollution by treating trade, environmental policy, and income as endogenous. We estimate a system of reduced-form, simultaneous equations on extensive data, from 1995 to 2003, for transition economies that include Central and Eastern Europe and the Commonwealth of Independent States. Our empirical results suggest that, although trade intensity in EGs (pooled list) reduces CO2 emissions mainly through an indirect income effect, it increases water pollution because the income-induced effect does not offset the direct harmful scale-composition effect. No significant effect is found for SO2 emissions with respect to the list of aggregated EGs. In addition to diverging effects across pollutants, we show that results are sensitive to EGs’ classification, e.g., cleaner technologies and products, end-of-pipe products, environmentally preferable products, etc. For instance, a double profit—environmental and economic—is found only for “cleaner technologies and products” in the models explaining emissions of greenhouse gases. Interesting findings are discussed for imports and exports of various classifications of EGs. Overall, we cannot support global and uniform trade liberalisation for EGs from a sustainable development perspective. Either regional or bilateral trade agreements that take into account the states’ priorities could act as building blocks towards a global, sequentially achieved liberalisation of EGs.

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Notes

  1. Definition largely used by Eurostat, OECD, APEC, and WTO. Examples of EGs are parts for auxiliary plant for boilers, condensers for steam, vapour power units; solar power electric generating sets and water heaters; wind turbine blades and hubs; gas and hydraulic turbines; filtering or purifying machinery; and apparatus for liquids and gases.

  2. Recent research examining the factors determining EGs’ trade highlights the fact that, whereas lowering tariffs may increase trade, higher gains could be obtained by the removal of non-tariff barriers. Trade in EGs is found to be sensitive to the economic size of the country, national environmental performance indicators, technical assistance, foreign direct investments, etc. It should be noted, however, that the impacts of liberalisation of EGs vary across products and countries, depending on existing tariff levels and the import elasticity of demand.

  3. A distinction should be made between “moderation” (conditional effect) and “mediation” (indirect effect). The integration of an interaction term in the regression (e.g., between trade in EGs and income) would only control for a possible moderation of the environmental effect of trade in EGs by the levels of income. It would never reveal the indirect effect on pollution of trade in EGs via the latter’s impact on income.

  4. The literature linking trade and environment is very extensive and rich in lessons. See, for example, Brunnermeier and Levinson (2004), Cherniwchan et al. (2017), Copeland and Taylor (2004), Elliott and Zhou (2013), Levinson (2008), Millimet and Roy (2012) and Zugravu-Soilita (2017) for definitions and extensive reviews of the literature related to the pollution haven, race-to-the-bottom/top, and pollution halo hypotheses. Although focusing on trade in EGs, we control for the overall trade openness and discuss some of these phenomena (in particular, the race-to-the-bottom/top) when analysing the effects on pollution of trade intensity in EGs.

  5. Corden (1974) and Svedberg (1979), etc.

  6. To better capture the effects of EGs’ trade liberalisation, we prefer using EGs’ trade openness (or intensity); that is, (EGs exports + EGs imports)/GDP, because it allows encompassing all the possible channels (a country can encounter both effects, specific to imports and exports, for specific EGs) and, by controlling for GDP (country size), it measures the EGs’ availability/sufficiency on a country’s market. Indeed, the same amount of imported end-of-pipe products, for instance, should have a weaker impact on pollution in a big country compared to a small country. A minimum EGs availability would, in particular, be crucial for indirect, environmental regulation- and income-induced technique effects. We note, however, that specific channels are also tested for EGs imports and exports separately, in Sect. 6.3.

  7. Detailed classifications, at a higher level of disaggregation than the six-digit HS code level, differ highly between countries worldwide, but they are likely to be relatively harmonious inside an economic integrated zone, like the post-Soviet and post-communist countries.

  8. We compute instrumental variables for EGs’ trade intensity following the methodology proposed by Frankel and Rose (2005) and further employed in recent empirical researches on the effects of trade on pollution, water use, fisheries’ catch, etc. (e.g., Managi et al. 2009; Kagohashi et al. 2015; Abe et al. 2017), i.e., we predict trade for various categories of EGs using gravity model estimations.

  9. See Steenblik (2005) for more details about the genesis, description and comparison of the OECD and APEC lists, which were compiled in the late 1990s.

  10. United Nations Conference on Trade and Development.

  11. See Hamwey (2005) and de Melo and Vijil (2014) for more discussion on these issues.

  12. See Sect. 4.1 for more information about the categories of EGs considered in the empirical analysis.

  13. See definition in Sect. 4.1.

  14. With \(0 \le g(a) \le \theta\); \(g^{\prime}(a) > 0\), that is, abatement effort reduces pollution, and \(g^{\prime\prime}(a) < 0\), meaning decreasing returns to abatement.

  15. In the literature linking trade and environment, it is common to estimate the technique effect by assuming that anything raising per-capita income increases (through willingness-to-pay for environment) the stringency of the environmental standards [see Copeland and Taylor (2004) for more discussion of these issues].

  16. The pollution haven hypothesis predicts that, under free trade, stringent environmental regulations in one country lead to the relocation of pollution-intensive industries in countries with laxer regulations. For recent reviews of the literature on this hypothesis, see Brunel and Levinson (2016), Cole et al. (2017), and Zugravu-Soilita (2017).

  17. Gross domestic product for exporting and importing countries in trade variables’ instrumentation are examples of country-specific variables that we include in the analysis. Geographical distance, adjacency, and main language, amongst others, are examples of other characteristics that we consider for each pair of countries in the gravity model.

  18. See the list of countries in “Appendix A”.

  19. See “Appendix B” for data definition and sources.

  20. This index measures the extent to which governments fight corruption and takes values ranging between − 2.5 and + 2.5, the maximum values signifying less corruption. The change of sign that we make thus yields an indicator that varies directly with the degree of a country’s corruption.

  21. Countries are assessed as free, partly free, or unfree. The political rights and civil liberties categories contain numerical ratings between 1 and 7 for each country or territory, with 1 representing the most free and 7 the least free.

  22. See “Appendix C” for definitions.

  23. Using the estimated coefficients, we obtain the fitted values of bilateral trade. We then take the exponent of the fitted values and finally sum across bilateral trading partners. In this manner, we obtain instrumental variables for various EGs classifications’ trade flows, which appear to be exogenous in our system of simultaneous equations, as also reported by the Durbin–Wu–Hausman test. Moreover, the statistic (chi2 = 6.51; Prob > chi2 = 0.1642) of the Hausman specification test does not allow us to reject its null hypothesis, indicating that the model with the instrumented trade openness variable performs better than with its real value, i.e., in the first case, the coefficients are consistent and efficient.

  24. Three stages are necessary to obtain the 3SLS coefficients: we first regress the right-hand-side endogenous variables on all of the exogenous variables from the model; second, we regress the endogenous variables on the fitted values from the first stage and the exogenous variables of the model; and third, we apply the feasible generalised least squares to get structural parameters.

  25. Under the null hypothesis of no misspecification, the 3SLS results are both efficient and consistent, whereas the 2SLS coefficients are consistent but not efficient. We should note that if any equation from the structural model is misspecified, only this single equation is affected while estimating with the 2SLS technique; conversely, any single misspecification is transmitted to all equations under 3SLS estimation because of the use of an inconsistently estimated covariance matrix in the third stage.

  26. See Bollen (1987) for these different concepts.

  27. Only statistically significant elasticities are considered.

  28. In total we have 377 products: 161 are present in the current WTO408 list (of which 106 are from OA list) and 20 in the WTO26 list (with 14 codes from OA). With the exception of the Oth-TypeA-EGs and EPP-core lists, which generally contain unique products not present in the other lists (with a few exceptions, see codes in bold), the OA and CT lists share some common goods (see codes in italics underline, bold italics and bold underline values).

Abbreviations

APEC:

Asia-Pacific Economic Co-operation

BOD:

Biological oxygen demand (the most common measure of pollutant organic material in water, e.g., a low BOD is an indicator of good quality water)

CEE:

Central and Eastern Europe

CIS:

Commonwealth of Independent States

CTP:

Cleaner technologies and products (category)

EGs:

Environmental goods

EOP:

End-of-pipe products (category)

EPPs:

Environmentally preferable products

GHG:

Greenhouse gas

HS:

Harmonised system (reduced term of harmonised commodity description and coding system)

INGO:

International nongovernmental organization

MEA:

Multilateral environmental agreement

OA:

OECD + APEC (list)

OECD:

Organisation for Economic Co-operation and Development

PCA:

Principal component analysis

SEP:

Stringency of environmental policy (index)

SER:

Stringency of environmental regulation (index)

UNCTAD:

United Nations Conference on Trade and Development

WTO:

World Trade Organization

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Authors and Affiliations

  1. University of Versailles Saint-Quentin-en-Yvelines, UFR des Sciences Sociales/CEMOTEV, 47, Bd Vauban, 78280, Guyancourt, France

    Natalia Zugravu-Soilita

Authors
  1. Natalia Zugravu-Soilita
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Correspondence to Natalia Zugravu-Soilita.

Appendices

Appendix A: List of countries

Country

CO2 modelsa

SO2 modelsb

BOD modelsc

Albania

CEE

+

+

+

Armenia

CIS

+

+

+

Azerbaijan

CIS

+

+

+

Belarus

CIS

+

+

−

Bulgaria

CEE

+

+

+

Croatia

CEE

+

−

+

Czech Republic

CEE

+

+

+

Estonia

CEE

+

+

−

Georgia

CIS

+

+

−

Hungary

CEE

+

+

+

Kazakhstan

CIS

+

+

−

Kyrgyzstan

CIS

+

+

+

Latvia

CEE

+

+

+

Lithuania

CEE

+

+

+

Poland

CEE

+

+

+

Republic of Moldova

CIS

+

+

+

Romania

CEE

+

+

+

Russian Federation

CIS

+

+

+

Slovakia

CEE

+

+

+

Slovenia

CEE

+

+

+

Tajikistan

CIS

+

+

−

The former Yugoslav Rep.

CEE

+

−

+

Ukraine

CIS

+

+

+

Uzbekistan

CIS

+

+

−

Total

 

24

22

18

  1. a216 observations: 24 countries for 9 years (1995–2003)
  2. b148 observations: 22 countries for 8 years (1995–2002); some data are missing for 2001 and 2002 years
  3. c128 observations: 18 countries for 9 years (1995–2003) with many missing points

Appendix B: Data summary

Variables

Definition

Sources

CO2

Carbon dioxide emissions, in kT

International Energy Agency

SO2

Sulphur emissions, in TgS

Stern (2006)

BOD

Organic water pollutant (BOD) emissions (kg per day)

WDI 2007, World Bank

GHG

Greenhouse gas emissions (CO2, CH4, N2O, PFCs, HFCs, SF6)

CAIT (WRI)

GDP

GDP in constant 2000 US$

WDI 2007, World Bank

GNI/cap

GNI: Atlas method, current US$- Net per capita income

WDI 2007, World Bank

K

Capital stock calculated by using the following formula: creation of fixed assetst + 0.95 × Capital stock t-1

WDI 2007, World Bank + author’s calculation

L

Active population (the labour)

WDI 2007, World Bank

K/L

Capital stock to labour ratio

Author’s calculation

SEP

Stringency of Environmental Policy Index

Zugravu-Soilita et al. (2008)

SER

Stringency of Environmental Regulation Index

Author’s calculation

Corrup

Corruption index

Kaufmann et al. (2005)

Democ

The average of the two variables of Freedom House: «Political Rights» and «Civil Liberties»

Freedom House

Lat

Technically, latitude is an angular measurement in degrees ranging from 0° at the equator to 90° at the poles

CEPII’s database Distances

Trade

Bilateral trade (all products)

UN Comtrade database

TradeA_OA

Bilateral trade in class A EGs, aggregated OECD and APEC list (OA)

Author’s database (using UN Comtrade database and EGs lists)

TradeA_EOP

Bilateral trade in OA list’s end-of-pipe/pollution control products; involve different products while explaining air or water pollution

Author’s database (using UN Comtrade database and EGs lists)

TradeA_CTP

Bilateral trade in OA list’s cleaner technologies and products/beginning-of-the-pipe products (pollution prevention/resource management products)

Author’s database (using UN Comtrade database and EGs lists)

Open

Openness/total trade intensity: (Export + Import)/GDP

Author’s calculation

TradeIntEGs

Trade intensity in EGs (all classifications confused)

Author’s calculation

TradeIntA_OA

Trade intensity in class A EGs, OA list

Author’s calculation

TradeIntA_EOP

Trade intensity in OA list’s end-of-pipe/pollution control products; involve different products while explaining air or water pollution

Author’s calculation

TradeIntA_CTP

Trade intensity in OA list’s cleaner technologies and products/beginning-of-the-pipe products (pollution prevention/resource management products)

Author’s calculation

TradeIntA_OtherEGs

Trade intensity in other class A EGs not included in the OA list

Author’s calculation

TradeIntB_CT

Trade intensity in class B EGs: Clean Technologies (used for power generation)

Author’s calculation

TradeIntB_EPP

Trade intensity in class B EGs: Environmentally Preferable Products

Author’s calculation

Ex…/Im…

Exports and imports, respectively, for different EGs classifications

Author’s calculation

…_1

One year lagged variable

 

Appendix C: EGs classifications

UNCTAD has identified two types of environmental goods for analytical purposes

 Class A EGs, which include all chemicals and manufactured goods used directly in the provision of environmental services

 Class B EGs, which include all industrial and consumer goods not primarily used for environmental purposes but whose production, end-use and/or disposal have positive environmental characteristics relative to similar substitute goods

To analyse environmental good trade flows, these two broad sets of EGs have been further decomposed into 10 homogeneous groups of EGs

Class A EGs have been subdivided into two groups

 OA list comprised of the group of all EGs included on the OECD and APEC lists while avoiding double-counting of goods appearing on both lists. OA list covers three groups: (A) pollution management, (B) cleaner technologies and products, and (C) resources management group. The first group includes mainly end-of-pipe products, while the two last ones generally cover clean technologies and products used to prevent environmental degradation

 Oth-TypeA-EGs list comprised of several goods used to provide environmental services which have not been captured by the OECD and APEC lists. This list contains, for example, plastic gloves and protective eyewear which are used in environmental clean-up and remediation activities

Class B EGs that have been subdivided into eight groups

 CT list comprised of clean technologies used for power generation. This list includes energy efficient natural gas-based power generation and renewable energy technologies and their components

 EPP-core list comprised of consumer and industrial non-durable and semi-durable EPP goods. Goods on the EPP list have been selected based on environmentally superior end-use and disposal characteristics only (i.e., not based on PPMs). This list includes a wide variety of goods including natural fibres for industrial uses and in the form of textiles; natural rubber; natural vegetable derivatives, colourings and dyes

 CT-fuel list including fuels for CT, and some conventional (i.e., fuel-switching), power generation technology applications. This list includes natural gas, propane and butane, as well as ethanol and a range of agricultural feedstocks—bagasse and oilseeds—used, respectively, to produce ethanol and biodiesel fuels

 EPP-RCY list comprised of recoverable materials that are reintegrated into the production cycle. This list includes scrap and waste paper, wood, plastics, rubber and various scrap metals

 EPP-WOOD list comprised of wood and wood-based products including building supplies and furniture

 EPP-WSA list comprised of apparel manufactured from natural wool and silk fibres

 EPP-CM list comprised of raw cotton materials and cotton textiles.

 EPP-CA list comprised of apparel manufactured from natural cotton fibres

  1. Source: Hamwey (2005)

Appendix D: Composition of EGs group lists examined in this paper, by HS-96 6-digit code. Footnote 28

Class A, OECD + APEC list for ‘end-of-pipe products’:

230210, 252100, 252220, 281410, 281511, 281512, 281610, 281830, 282010, 282090, 282410, 283210, 283220, 283510, 283521, 283523, 283524, 283525, 283526, 283529, 283822, 380210, 392020, 392490, 392690, 560314, 580190, 591190, 681099, 690210, 690220, 690290, 690310, 690320, 690390, 690919, 701710, 701720, 701790, 730900, 731010, 731021, 731029, 732510, 780600, 840410, 840510, 840991, 841000, 841320, 841350, 841360, 841370, 841410, 841430, 841440, 841459, 841480, 841490, 841780, 841790, 841940, 841960, 841989, 842119, 842121, 842129, 842139, 842191, 842199, 842220, 842381, 842382, 842389, 842490, 842833, 846291, 847290, 847410, 847432, 847439, 847982, 847989, 847990, 848110, 848130, 848140, 848180, 850590, 851410, 851420, 851430, 851490, 851629, 870892, 890710, 890790, 901320, 901540, 901580, 901590, 902229, 902290, 902511, 902519, 902580, 902590, 902610, 902620, 902680, 902690, 902710, 902720, 902730, 902740, 902750, 902780, 902790, 902830, 902890, 903010, 903020, 903031, 903039, 903083, 903089, 903090, 903110, 903120, 903130, 903149, 903180, 903190, 903220, 903281, 903289, 903290, 903300, 960310, 960350, 980390—142 items

Class A, OECD + APEC list for ‘cleaner technologies and products’ (including resource management products):

220100, 220710, 280110, 284700, 285100, 290511, 320910, 320990, 381500, 391400, 460120, 700800, 701990, 840420, 840999, 841011, 841012, 841013, 841090, 841381, 841911, 841919, 841950, 841990, 843680, 850231, 853931, 854140, 854389, 902810, 902820, 903210—32 items

Other type Class A EGs (Oth-TypeA-EGs):

284700, 392321, 392329, 392620, 401519, 440130, 441700, 611610, 630533, 630611, 630612, 630619, 640110, 640191, 640192, 640199, 691010, 691090, 820110, 820120, 820130, 820140, 820150, 820160, 820190, 820210, 842820, 842832, 842833, 842839, 842890, 842959, 847490, 850530, 850590, 850810, 850820, 850880, 850890, 850910, 850930, 853949, 870490, 870892, 900490, 902000—46 items

Class B, Clean Technologies (CT):

392510, 731010, 731100, 732211, 732219, 732290, 761100, 761300, 830249, 840211, 840212, 840219, 840220, 840290, 840310, 840390, 840410, 840420, 840490, 840681, 840682, 840690, 840890, 841011, 841012, 841013, 841090, 841181, 841182, 841199, 841350, 841360, 841370, 841381, 841391, 841620, 841630, 841869, 841911, 841919, 841950, 841990, 842129, 842139, 842199, 847960, 848110, 848130, 848140, 848180, 848190, 848310, 848360, 848410, 848490, 850131, 850132, 850133, 850134, 850161, 850162, 850163, 850164, 850211, 850212, 850213, 850220, 850231, 850239, 850240, 850300, 850421, 850422, 850423, 850431, 850432, 850433, 850434, 850440, 850490, 851150, 851610, 851621, 854140, 900190, 900290—86 items

Class B, Environmentally Preferable Products (EPP-core):

050900, 121110, 121120, 121190, 130110, 130120, 130190, 130219, 140190, 140310, 140390, 140410, 150510, 150590, 152110, 152190, 230690, 230890, 310100, 320190, 320300, 320910, 321000, 400110, 400121, 400122, 400129, 400280, 450110, 450200, 450310, 450390, 460120, 460191, 460210, 480610, 500200, 500400, 500600, 500710, 500720, 500790, 510111, 510119, 510121, 510129, 510130, 510310, 510320, 510400, 510510, 510521, 510529, 510610, 510710, 510910, 510910, 511111, 511119, 511190, 511211, 511219, 511290, 511290, 530110, 530121, 530129, 530210, 530290, 530310, 530410, 530521, 530591, 530710, 530720, 530810, 530890, 531010, 531090, 531100, 531100, 560710, 560721, 560729, 560750, 560890, 570110, 570220, 570231, 570241, 570251, 570291, 570310, 580110, 581099, 600129, 600199, 600241, 600291, 630120, 630510, 670100, 680800, 850680, 850780, 960310—106 items

Appendix E: Alternative empirical estimations

See Tables 7, 8 and 9

Table 7 Robustness tests for environmental regulation variable
Full size table
Table 8 Environmental impact of trade intensity in EGs, pooled lists
Full size table
Table 9 Environmental impact of EPPs imports and exports
Full size table

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Zugravu-Soilita, N. The impact of trade in environmental goods on pollution: what are we learning from the transition economies’ experience?. Environ Econ Policy Stud 20, 785–827 (2018). https://doi.org/10.1007/s10018-018-0215-z

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Keywords

  • Trade liberalisation
  • Environmental goods
  • Environmental policy
  • Pollution
  • Transition countries

JEL Classification

  • F13
  • F14
  • F18
  • Q56