5 Product sophistication and the environment

In document Economic complexity and environmental performance: Evidence from a world sample (Page 25-44)

Using the ECI methodology, Hartmann et al. [54] recently introduced a measure that associates products with income inequality and showed how the development of sophisticated products is associated with changes in income inequality. Here, we introduce a measure that links a product to the average environmental performance and air pollution level of the countries that export it. In this way, we illustrate how environmental performance is being affected by the level of sophistication of exported products and we quantify the influence of countries’ level of economic complexity on their environmental performance.

Following the methodology in Hartmann et al. [54], we define the Product Environmental Per-formance Index (PEPI) and the Product Air Pollution Index (PAPI) as the average EPI and the average level of CO2 emissions, respectively, of the countries that export the focal product, normalized by the importance of this product to the total exports of the countries that export it. More precisely, we decompose the relationship between the ECI and both the EPI and CO2 emissions into individual economic sectors by creating product-level estimators of these measures for the countries exporting a given product.

5.1 Product environmental indexes

Assuming that we have trade data for l countries and k products, we can fill the (l × k) matrix M so that its matrix element Mcp= 1 if country c has a RCA for product p, and zero otherwise

−3 −2 −1 0 1

Figure 1: PEPI and PAPI against PCI. The solid lines represent the fit of a linear model and the dashed lines a 95% prediction interval based on the fitted linear model.

(see the Appendix A). Dataset 2 (see Section 2) contains information for the 88 developed and developing countries used in the above analysis and for 772 products from 2002 to 2012, classified according to the Standard International Trade Classification (SITC) at the 4-digit level.

Every product p generates some value for the country c that exports it. Therefore, for every product p, we can calculate the fraction scp:

scp= Xcp

P

p0Xcp0, (2)

where Xcp is the total export value of product p when exported by country c, whileP

p0Xcp0 is the value of all exports of country c. If EP Ic (resp. CO2c) is the EP I (resp. CO2 emissions) of country c, we can calculate the PEPIp and the PAPIp for every product as:

PEPIp= 1

cMcpscp is a normalization factor.

Table 6: List of the five products with the highest and lowest P EP I and P AP I values during the period of 2002-2012

SITC4 Product name Product section PEPI PAPI PCI

Highest PEPI

8851 Watches and clocks Miscellaneous manufactured articles 77.2 0.16 0.45

5415 Medicinal and pharmaceutical products Chemicals and related products, n.e.s. 76.9 0.19 1.20 7259 Paper mill and pulp mill machinery, paper-cutting machines Machinery and transport equipment 76.2 0.26 1.23 5416 Glycosides, glands, antisera, vaccines Chemicals and related products, n.e.s. 75.9 0.21 1.18

7913 Railway vehicles Machinery and transport equipment 75.3 0.23 1.01

Lowest PEPI

2654 Vegetable textile fibres, waste of these fibres Crude materials, inedible, except fuels 32.0 0.08 -0.96 2923 Vegetable materials of a kind used primarily for plaiting Crude materials, inedible, except fuels 30.9 0.12 -0.78 2631 Cotton (other than linters), not carded or combed Crude materials, inedible, except fuels 30.8 0.18 -2.82 2683 Fine animal hair, not carded or combed Crude materials, inedible, except fuels 29.4 0.16 -0.47 2922 Lac, natural gums, resins, gum resins, and balsams Crude materials, inedible, except fuels 26.0 0.11 -2.01

Highest PAPI

6812 Platinum metals Manufactured goods classified chiefly by material 54.0 0.60 0.73

7915 Railway vehicles Machinery and transport equipment 56.0 0.60 0.49

3353 Pitch and pitch coke Mineral fuels, lubricants 59.1 0.57 0.36

2814 Roasted iron pyrites Crude materials, inedible, except fuels 55.2 0.54 0.04

6712 Pig-iron and spiegeleisen Manufactured goods classified chiefly by material 49.7 0.53 0.07

Lowest PAPI

2922 Lac, natural gums, resins, gum resins, and balsams Crude materials, inedible, except fuels 26.0 0.11 -2.01

2225 Sesame seeds Crude materials, inedible, except fuels 34.0 0.10 -2.58

4314 Vegetable waxes Animal and vegetable oils, fats and waxes 39.7 0.10 -1.29

2876 Tin ores and concentrates Crude materials, inedible, except fuels 36.5 0.09 -1.44

2654 Vegetable textile fibres, waste of these fibres Crude materials, inedible, except fuels 32.0 0.08 -0.96

Notes: PEPI: Product Environmental Performance Index; PAPI: Product Air-Pollution Index. Average values for 2002-2012

For every year in the period of 2002-2012, we utilize the EPI and CO2 emissions for the 88 developed and developing countries in our sample and calculate the mean value of all the product-related indexes (PCI, PEPI and PAPI) for each product.

Table 6 lists the five products with the highest and lowest PEPI and PAPI values during the period of 2002-2012. It is evident that technologically moderate manufacturing industries and primary sectors such as textile fibres and their wastes (SITC Rev.4 division: 22) and crude animal and vegetable materials (SITC Rev.4 division: 29) appear to be associated with lower environmental performance. Regarding the index of air pollution (PAPI), the reader can easily verify that a country’s RCA in products such as non-ferrous metals (SITC Rev.4 division: 68) and railway vehicles (SITC Rev.4 group: 791) is associated with higher CO2 emissions. In Table 6, the SITC Rev.4 group 2654 of vegetable textile fibres and the waste of these fibres and the

SITC Rev.4 subgroup 2922 of lac, natural gums, resins, gum resins, and balsams appear to be environmentally detrimental but air pollution friendly.

5.2 Product environmental indexes and product complexity

We test the existence of a bivariate relationship between the PCI and the PEPI and PAPI indexes by calculating Pearson’s correlation coefficient for both pairs, PEPI against PCI and PAPI against PCI. If such a relation exists, it should allow us to derive expectations about whether product sophistication can be associated with the quality of the environment and the level of CO2 emissions. For the case of PEPI against PCI, the correlation coefficient is ρ = 0.74 with a p-value < 10−99, while for PAPI against PCI, it is ρ = 0.29 with a p-value < 10−15. In Figure 1, we present the scatter plots of the PEPI and PAPI indexes against the PCI index for all 772 products in our dataset, together with the fitted linear models. The slopes of the linear fits are the corresponding correlation coefficients.

The statistically significant positive correlations between (a) PEPI and PCI and (b) PAPI and PCI, indicate that more sophisticated products are associated with countries that demonstrate relatively better environmental performances as measured by the EPI, but higher air pollution as measured by CO2 emissions. This adds to our previous discussion about economic complexity at the country level, as it allows us to understand which sets of products are leading to a better overall environmental performance and less air pollution, based on their embodied sophistication.

6 Conclusions

Our analysis illustrates that the environmental performance of a country is highly correlated with the mix of products that it produces and exports. In a panel data setting, we have verified that there is a robust positive (resp. negative) relationship between environmental performance

(resp. air quality) and product sophistication. Moreover, the effect of economic complexity on environmental performance has been verified with fixed-effects instrumental variables estimation techniques. Thus, the evidence presented in the paper suggests that the sophistication of a country’s productive structure predicts its environmental performance.

More specifically, countries that produce more complex products are associated with improved environmental performance but also with inferior air quality (higher exposure to PM2.5 and CO2 emissions). Explicitly, it seems that when an economy accelerates from an agricultural productive structure to a more sophisticated one with industrial and technological sectors, the effect on overall environmental performance is positive but the particular effect on air quality is negative.

We build a Product Environmental Performance Index and a Product Air Pollution Index that associate exported products with the average level of countries’ environmental performance as measured by the EPI and air pollution as measured by CO2 emissions, respectively.

With these indexes, we show how the development of complex products is associated with changes in the environment. Hence, the two indexes could be a valuable tool for evaluating smart special-ization strategies and/or sectoral reallocation policies towards activities/sectors that are associ-ated with better environmental performance and lower air pollution. According to WHO [125], exposure to air pollution has been found to have both direct and indirect detrimental effects on human health. The direct effects include respiratory irritation, chronic respiratory symptoms, heart diseases, lung cancer, premature mortality and reduced life expectancy [37, 70, 94]. The indirect mechanisms relate to pulmonary oxidative stress and inflammatory responses. Hence, empirical evidence about the effect of economic complexity on air quality should be highly infor-mative for policy makers.

In sum, this study identifies economic complexity as an explanatory variable of the observed differences in environmental footprints across countries. However, we do not attempt to address

the question of why some countries have a higher level of economic complexity than others.

Trying to answer this question is beyond the scope of this paper, but an interesting way forward.

References

[1] Abdon, A.; Felipe, J. (2011). The product space: What does it say about the opportunities for growth and structural transformation of Sub-Saharan Africa? Levy Economics Institte Working Paper (670).

[2] Acaravci, A.; Ozturk, I. (2010). On the relationship between energy consumption, CO2 emissions and economic growth in Europe. Energy 35(12), 5412–5420.

[3] Agras, J.; Chapman, D. (1999). A dynamic approach to the Environmental Kuznets Curve hypothesis. Ecological Economics 28(2), 267–277.

[4] Aiken, L. S.; West, S. G.; Reno, R. R. (1991). Multiple regression: Testing and interpreting interactions. Sage.

[5] Akbostancı, E.; Türüt-Aşık, S.; Tunç, G. İ. (2009). The relationship between income and environment in Turkey: is there an environmental Kuznets curve? Energy policy 37(3), 861–867.

[6] Al-Mulali, U.; Ozturk, I. (2015). The effect of energy consumption, urbanization, trade openness, industrial output, and the political stability on the environmental degradation in the MENA (Middle East and North African) region. Energy 84, 382–389.

[7] Albeaik, S.; Kaltenberg, M.; Alsaleh, M.; Hidalgo, C. A. (2017). 729 new measures of economic complexity (Addendum to Improving the Economic Complexity Index). arXiv preprint arXiv:1708.04107 .

[8] Albeaik, S.; Kaltenberg, M.; Alsaleh, M.; Hidalgo, C. A. (2017). Measuring the Knowledge Intensity of Economies with an Improved Measure of Economic Complexity. arXiv preprint arXiv:1707.05826 .

[9] Ang, J. B. (2007). CO2 emissions, energy consumption, and output in France. Energy Policy 35(10), 4772–4778.

[10] Ang, J. B. (2008). Economic development, pollutant emissions and energy consumption in Malaysia. Journal of Policy Modeling 30(2), 271–278.

[11] Antoci, A.; Russu, P.; Sordi, S.; Ticci, E. (2014). Industrialization and environmental externalities in a Solow-type model. Journal of Economic Dynamics and Control 47, 211–

224.

[12] Antweiler, W.; Copeland, B. R.; Taylor, M. S. (2001). Is free trade good for the environ-ment? American Economic Review 91(4), 877–908.

[13] Asane-Otoo, E. (2015). Carbon footprint and emission determinants in Africa. Energy 82, 426–435.

[14] Balassa, B. (1965). Trade liberalisation and "revealed" comparative advantage. The Manch-ester School 33(2), 99–123.

[15] Bank, T. W. (2000). World Development Indicators 2000. Oxford University Press, USA.

[16] Bimonte, S. (2002). Information access, income distribution, and the Environmental Kuznets Curve. Ecological economics 41(1), 145–156.

[17] Brooks, N.; Sethi, R. (1997). The distribution of pollution: community characteristics and exposure to air toxics. Journal of environmental economics and management 32(2), 233–250.

[18] Bustos, S.; Gomez, C.; Hausmann, R.; Hidalgo, C. A. (2012). The dynamics of nestedness predicts the evolution of industrial ecosystems. PloS one 7(11), e49393.

[19] Caldarelli, G.; Cristelli, M.; Gabrielli, A.; Pietronero, L.; Scala, A.; Tacchella, A. (2012).

A network analysis of countries’ export flows: firm grounds for the building blocks of the economy. PloS one 7(10), e47278.

[20] Carpenter, S. R.; Caraco, N. F.; Correll, D. L.; Howarth, R. W.; Sharpley, A. N.; Smith, V. H. (1998). Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecological applications 8(3), 559–568.

[21] Chay, K. Y.; Greenstone, M. (2005). Does air quality matter? Evidence from the housing market. Journal of political Economy 113(2), 376–424.

[22] Chenery, H. B.; Taylor, L. (1968). Development patterns: among countries and over time.

The Review of Economics and Statistics , 391–416.

[23] Cherniwchan, J. (2012). Economic growth, industrialization, and the environment. Re-source and Energy Economics 34(4), 442–467.

[24] Clark, C. (1967). The conditions of economic progress. The conditions of economic progress.

.

[25] Cole, M. A. (2007). Corruption, income and the environment: an empirical analysis. Eco-logical Economics 62(3-4), 637–647.

[26] Cole, M. A.; Elliott, R. J. (2003). Determining the trade–environment composition ef-fect: the role of capital, labor and environmental regulations. Journal of Environmental Economics and Management 46(3), 363–383.

[27] Coondoo, D.; Dinda, S. (2002). Causality between income and emission: a country group-specific econometric analysis. Ecological Economics 40(3), 351–367.

[28] Cristelli, M.; Gabrielli, A.; Tacchella, A.; Caldarelli, G.; Pietronero, L. (2013). Measuring the intangibles: A metrics for the economic complexity of countries and products. PloS one 8(8), e70726.

[29] Cristelli, M.; Tacchella, A.; Pietronero, L. (2015). The heterogeneous dynamics of economic complexity. PloS one 10(2), e0117174.

[30] Damania, R.; Fredriksson, P. G.; List, J. A. (2003). Trade liberalization, corruption, and environmental policy formation: theory and evidence. Journal of environmental economics and management 46(3), 490–512.

[31] Dietrich, A. (2012). Does growth cause structural change, or is it the other way around?

A dynamic panel data analysis for seven OECD countries. Empirical economics 43(3), 915–944.

[32] Dinda, S. (2004). Environmental Kuznets curve hypothesis: a survey. Ecological economics 49(4), 431–455.

[33] Dinda, S.; Coondoo, D. (2006). Income and emission: a panel data-based cointegration analysis. Ecological Economics 57(2), 167–181.

[34] Dominici, F.; Greenstone, M.; Sunstein, C. R. (2014). Particulate matter matters. Science 344(6181), 257–259.

[35] Elgin, C.; Oztunali, O.; et al. (2012). Shadow economies around the world: model based estimates. Bogazici University Department of Economics Working Papers 5(2012), 1–48.

[36] European Commission (1999). The EU’s Eco-Industry’s Export Potential. Final report to DGXI of the European Commission .

[37] European Environment Agency (2015). Air quality in Europe-2015 re-port. Tech. Rep. 5. URL http://www.actu-environnement.com/media/pdf/

news-25756-rapport-air-aee.pdf.

[38] Farhani, S.; Mrizak, S.; Chaibi, A.; Rault, C. (2014). The environmental Kuznets curve and sustainability: A panel data analysis. Energy Policy 71, 189–198.

[39] Feenstra, R. C.; Lipsey, R. E.; Deng, H.; Ma, A. C.; Mo, H. (2005). World trade flows:

1962-2000. Tech. rep., National Bureau of Economic Research.

[40] Felipe, J. (2012). Inclusive Growth, Full Employment, and Structural Change: Implications and Policies for Developing Asia. Anthem Press.

[41] Foray, D. (2014). Smart specialisation: Opportunities and challenges for regional innovation policy. Routledge.

[42] Foray, D.; David, P. A.; Hall, B. (2009). Smart specialisation–the concept. Knowledge economists policy brief 9(85), 100.

[43] Foray, D.; Goenaga, X.; et al. (2013). The goals of smart specialisation. S3 policy brief series 1, 2013.

[44] Fourastié, J. (1963). Le grand espoir du XXe siècle. Revue Française de Sociologie 4(2), 224.

[45] Frankel, J. A.; Rose, A. K. (2005). Is trade good or bad for the environment? Sorting out the causality. Review of economics and statistics 87(1), 85–91.

[46] Galeotti, M.; Lanza, A.; Pauli, F. (2006). Reassessing the environmental Kuznets curve for CO2 emissions: A robustness exercise. Ecological economics 57(1), 152–163.

[47] Galeotti, M.; Manera, M.; Lanza, A. (2009). On the robustness of robustness checks of the environmental Kuznets curve hypothesis. Environmental and Resource Economics 42(4), 551.

[48] Goel, R. K.; Herrala, R.; Mazhar, U. (2013). Institutional quality and environmental pollution: MENA countries versus the rest of the world. Economic Systems 37(4), 508–

521.

[49] Grossman, G. M.; Krueger, A. B. (1995). Economic growth and the environment. The quarterly journal of economics 110(2), 353–377.

[50] Hafner, O. (1998). The role of corruption in the misappropriation of tropical forest resources and in tropical forest destruction. Transparency International Working Paper .

[51] Halicioglu, F. (2009). An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey. Energy Policy 37(3), 1156–1164.

[52] Hall, R. E.; Jones, C. I. (1999). Why do some countries produce so much more output per worker than others? The quarterly journal of economics 114(1), 83–116.

[53] Hamilton, C.; Turton, H. (2002). Determinants of emissions growth in OECD countries.

Energy Policy 30(1), 63–71.

[54] Hartmann, D.; Guevara, M. R.; Jara-Figueroa, C.; Aristarán, M.; Hidalgo, C. A. (2017).

Linking Economic Complexity, Institutions, and Income Inequality. World Development 93, 75–93.

[55] Hausmann, R.; Hidalgo, C. A. (2011). The network structure of economic output. Journal of Economic Growth 16(4), 309–342.

[56] Hausmann, R.; Hidalgo, C. A.; Bustos, S.; Coscia, M.; Simoes, A.; Yildirim, M. A. (2014).

The atlas of economic complexity: Mapping paths to prosperity. Mit Press.

[57] Hausmann, R.; Hwang, J.; Rodrik, D. (2007). What you export matters. Journal of economic growth 12(1), 1–25.

[58] Hidalgo, C. A.; Hausmann, R. (2009). The building blocks of economic complexity. pro-ceedings of the national academy of sciences 106(26), 10570–10575.

[59] Hidalgo, C. A.; Klinger, B.; Barabási, A.-L.; Hausmann, R. (2007). The product space conditions the development of nations. Science 317(5837), 482–487.

[60] Hilty, L. M.; Arnfalk, P.; Erdmann, L.; Goodman, J.; Lehmann, M.; Wäger, P. A.

(2006). The relevance of information and communication technologies for environmen-tal sustainability–a prospective simulation study. Environmenenvironmen-tal Modelling & Software 21(11), 1618–1629.

[61] Holdren, J. P. (1991). Population and the energy problem. Population and environment 12(3), 231–255.

[62] Holtz-Eakin, D.; Selden, T. M. (1995). Stoking the fires? CO2 emissions and economic growth. Journal of public economics 57(1), 85–101.

[63] Hossain, M. S. (2011). Panel estimation for CO2 emissions, energy consumption, economic growth, trade openness and urbanization of newly industrialized countries. Energy Policy 39(11), 6991–6999.

[64] Hsu, A.; Zomer, A. (2014). Environmental performance index. Wiley StatsRef: Statistics Reference Online , 1–5.

[65] International Trade Centre (2001). The Environmental Services Business: Big and Growing.

International Trade Forum 2, 6–9.

[66] Jalil, A.; Mahmud, S. F. (2009). Environment Kuznets curve for CO2 emissions: a cointe-gration analysis for China. Energy policy 37(12), 5167–5172.

[67] Jayanthakumaran, K.; Verma, R.; Liu, Y. (2012). CO2 emissions, energy consumption, trade and income: a comparative analysis of China and India. Energy Policy 42, 450–460.

[68] Jorgenson, A. K.; Clark, B. (2010). Assessing the temporal stability of the popula-tion/environment relationship in comparative perspective: a cross-national panel study of carbon dioxide emissions, 1960–2005. Population and Environment 32(1), 27–41.

[69] Kaldor, N.; et al. (1967). Strategic factors in economic development. New York State School of Industrial and Labor Relations, Cornell University.

[70] Kampa, M.; Castanas, E. (2008). Human health effects of air pollution. Environmental pollution 151(2), 362–367.

[71] Kasman, A.; Duman, Y. S. (2015). CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: a panel data analysis.

Economic Modelling 44, 97–103.

[72] Kaufmann, D.; Kraay, A.; Zoido-Lobaton, P. (1999). Governance Matters. World Bank Policy Research Department. Tech. rep., Working Paper.

[73] Kennett, M.; Steenblik, R. (2005). Environmental goods and services: A synthesis of country studies, vol. 3. OECD Publishing.

[74] Kuznets, S.; Murphy, J. T. (1966). Modern economic growth: Rate, structure, and spread, vol. 2. Yale University Press New Haven.

[75] Lall, S. (1992). Technological capabilities and industrialization. World development 20(2), 165–186.

[76] Lapatinas, A.; Litina, A.; Sartzetakis, E. S. (2018). Environmental projects in the presence of corruption. International Tax and Public Finance , 1–42.

[77] Lee, C.-C.; Lee, J.-D. (2009). Income and CO2 emissions: evidence from panel unit root and cointegration tests. Energy policy 37(2), 413–423.

[78] Lewis, W. A. (2013). Theory of economic growth, vol. 7. Routledge.

[79] Li, H.; Mu, H.; Zhang, M.; Gui, S. (2012). Analysis of regional difference on impact factors of China’s energy–Related CO2 emissions. Energy 39(1), 319–326.

[80] Li, K.; Lin, B. (2015). Impacts of urbanization and industrialization on energy consump-tion/CO2 emissions: does the level of development matter? Renewable and Sustainable Energy Reviews 52, 1107–1122.

[81] Liddle, B. (2004). Demographic dynamics and per capita environmental impact: Using panel regressions and household decompositions to examine population and transport.

Population and Environment 26(1), 23–39.

[82] Liddle, B.; Lung, S. (2010). Age-structure, urbanization, and climate change in devel-oped countries: revisiting STIRPAT for disaggregated population and consumption-related environmental impacts. Population and Environment 31(5), 317–343.

[83] Lin, C.-Y. C.; Liscow, Z. D. (2012). Endogeneity in the environmental Kuznets curve:

an instrumental variables approach. American Journal of Agricultural Economics 95(2), 268–274.

[84] Lin, S.; Zhao, D.; Marinova, D. (2009). Analysis of the environmental impact of China based on STIRPAT model. Environmental Impact Assessment Review 29(6), 341–347.

[85] Lippe, M. (1999). Corruption and environment at the local level. Transparency Interna-tional Working Paper. Retrieved March 31, 2014.

[86] Lisciandra, M.; Migliardo, C. (2017). An Empirical Study of the Impact of Corruption on Environmental Performance: Evidence from Panel Data. Environmental and Resource Economics 68(2), 297–318.

[87] Lopez, R.; Mitra, S. (2000). Corruption, pollution, and the Kuznets environment curve.

Journal of Environmental Economics and Management 40(2), 137–150.

[88] Madlener, R.; Sunak, Y. (2011). Impacts of urbanization on urban structures and energy demand: What can we learn for urban energy planning and urbanization management?

Sustainable Cities and Society 1(1), 45–53.

[89] Marsiglio, S.; Ansuategi, A.; Gallastegui, M. C. (2016). The environmental Kuznets curve and the structural change hypothesis. Environmental and resource economics 63(2), 265–

288.

[90] Martínez-Zarzoso, I.; Bengochea-Morancho, A.; Morales-Lage, R. (2007). The impact of population on CO 2 emissions: evidence from European countries. Environmental and Resource Economics 38(4), 497–512.

[91] Martínez-Zarzoso, I.; Maruotti, A. (2011). The impact of urbanization on CO2 emissions:

evidence from developing countries. Ecological Economics 70(7), 1344–1353.

[92] Mauro, P. (1995). Corruption and growth. The quarterly journal of economics 110(3), 681–712.

[93] Nejat, P.; Jomehzadeh, F.; Taheri, M. M.; Gohari, M.; Majid, M. Z. A. (2015). A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries). Renewable and sustainable energy reviews 43, 843–862.

[94] OECD (2016). The Economic Consequences of Outdoor Air Pollution. Tech. rep.

[95] Ollo-López, A.; Aramendía-Muneta, M. E. (2012). ICT impact on competitiveness, inno-vation and environment. Telematics and Informatics 29(2), 204–210.

[96] Ozcan, B. (2013). The nexus between carbon emissions, energy consumption and economic growth in Middle East countries: a panel data analysis. Energy Policy 62, 1138–1147.

[97] Panayotou, T. (2016). Economic growth and the environment. The environment in an-thropology , 140–148.

[98] Pellegrini, L.; Gerlagh, R. (2006). Corruption, democracy, and environmental policy: an empirical contribution to the debate. The Journal of Environment & Development 15(3), 332–354.

[99] Ranis, G.; Fei, J. C. (1961). A theory of economic development. The american economic review , 533–565.

[100] Raudenbush, S. W.; Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods, vol. 1. Sage.

[101] Rodrik, D. (2006). What’s so special about China’s exports? China & World Economy 14(5), 1–19.

[102] Romm, J. (2002). The internet and the new energy economy. Resources, conservation and recycling 36(3), 197–210.

[103] Samet, J. M.; Dominici, F.; Curriero, F. C.; Coursac, I.; Zeger, S. L. (2000). Fine particulate air pollution and mortality in 20 US cities, 1987–1994. New England journal of medicine 343(24), 1742–1749.

[104] Sari, R.; Soytas, U. (2009). Are global warming and economic growth compatible? Evidence from five OPEC countries? Applied Energy 86(10), 1887–1893.

[105] Savci, S. (2012). An agricultural pollutant: chemical fertilizer. International Journal of Environmental Science and Development 3(1), 73.

[106] Saviotti, P. P.; Frenken, K. (2008). Export variety and the economic performance of coun-tries. Journal of Evolutionary Economics 18(2), 201–218.

[107] Shafiei, S.; Salim, R. A. (2014). Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: A comparative analysis. Energy Policy 66, 547–556.

[108] Shafik, N. (1994). Economic development and environmental quality: an econometric anal-ysis. Oxford economic papers , 757–773.

[109] Sharma, S. S. (2011). Determinants of carbon dioxide emissions: empirical evidence from 69 countries. Applied Energy 88(1), 376–382.

[110] Sherbinin, A. d.; Carr, D.; Cassels, S.; Jiang, L. (2007). Population and environment.

Annu. Rev. Environ. Resour. 32, 345–373.

[111] Sinclair-Desgagné, B.; et al. (2008). The environmental goods and services industry. In-ternational Review of Environmental and Resource Economics 2(1), 69–99.

[112] Soytas, U.; Sari, R. (2009). Energy consumption, economic growth, and carbon emissions:

challenges faced by an EU candidate member. Ecological economics 68(6), 1667–1675.

[113] Soytas, U.; Sari, R.; Ewing, B. T. (2007). Energy consumption, income, and carbon emis-sions in the United States. Ecological Economics 62(3-4), 482–489.

[114] Stern, D. I. (2002). Explaining changes in global sulfur emissions: an econometric decom-position approach. Ecological Economics 42(1-2), 201–220.

[115] Stern, D. I. (2004). The rise and fall of the environmental Kuznets curve. World develop-ment 32(8), 1419–1439.

[116] Stock, J.; Yogo, M. (2005). Testing for Weak Instruments in Linear IV Regression, New York: Cambridge University Press. pp. 80–108. URL http://www.economics.harvard.

edu/faculty/stock/files/TestingWeakInstr_Stock%2BYogo.pdf.

[117] Tacchella, A.; Cristelli, M.; Caldarelli, G.; Gabrielli, A.; Pietronero, L. (2012). A new metrics for countries’ fitness and products’ complexity. Scientific reports 2, 723.

[118] Tacchella, A.; Cristelli, M.; Caldarelli, G.; Gabrielli, A.; Pietronero, L. (2013). Economic complexity: conceptual grounding of a new metrics for global competitiveness. Journal of Economic Dynamics and Control 37(8), 1683–1691.

[119] Torras, M.; Boyce, J. K. (1998). Income, inequality, and pollution: a reassessment of the environmental Kuznets curve. Ecological economics 25(2), 147–160.

[120] Tsurumi, T.; Managi, S. (2010). Decomposition of the environmental Kuznets curve: scale, technique, and composition effects. Environmental Economics and Policy Studies 11(1-4), 19–36.

[121] Wang, C.; Chen, J.; Zou, J. (2005). Decomposition of energy-related CO2 emission in China: 1957–2000. Energy 30(1), 73–83.

[122] Wang, S.; Fang, C.; Guan, X.; Pang, B.; Ma, H. (2014). Urbanisation, energy consumption, and carbon dioxide emissions in China: a panel data analysis of China’s provinces. Applied Energy 136, 738–749.

[123] Wang, S.; Zhou, D.; Zhou, P.; Wang, Q. (2011). CO2 emissions, energy consumption and economic growth in China: A panel data analysis. Energy Policy 39(9), 4870–4875.

[124] Welsch, H. (2004). Corruption, growth, and the environment: a cross-country analysis.

Environment and Development Economics 9(5), 663–693.

[125] WHO (2013). Review of evidence on health aspects of air pollution: REVIHAAP project:

final technical report.

[126] Xu, B.; Lin, B. (2015). How industrialization and urbanization process impacts on CO2 emissions in China: evidence from nonparametric additive regression models. Energy Eco-nomics 48, 188–202.

[127] York, R.; Rosa, E. A.; Dietz, T. (2003). STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecological economics 46(3), 351–365.

[128] Zhang, C.; Lin, Y. (2012). Panel estimation for urbanization, energy consumption and CO2 emissions: A regional analysis in China. Energy Policy 49, 488–498.

[128] Zhang, C.; Lin, Y. (2012). Panel estimation for urbanization, energy consumption and CO2 emissions: A regional analysis in China. Energy Policy 49, 488–498.

In document Economic complexity and environmental performance: Evidence from a world sample (Page 25-44)