Moving towards a green decoupling between economic development and environmental stress? A new comprehensive approach for Ecuador

1 Published Title: MOVING TOWARDS A GREEN DECOUPLING BETWEEN ECONOMIC DEVELOPMENT AND ENVIRONMENTAL STRESS? A NEW COMPREHENSIVE APPROACH FOR ECUADOR Preprint Title: MITIGATION AND ENERGY EFFICIENCY POLICIES IN ECUADOR José M. Cansino1,2, *, Antonio Sánchez-Braza3, Nereyda Espinoza4 1 Universidad de Sevilla Departamento de Análisis Económico y Economía Política Avda. Ramón y Cajal, 1 Postal Code 41018 Sevilla Spain E-mail: [email protected] Phone: + (34) 954 55 75 28. Fax: + (34) 954 55 76 29 2 Universidad Autónoma de Chile, Av. Pedro de Valdivia 425, Providencia Santiago 758-0150, Chile 3 Universidad de Sevilla Departamento de Análisis Económico y Economía Política Avda. Ramón y Cajal 1 Postal Code 41018 Sevilla Spain E-mail: [email protected] Tel.: + (34) 954 557 529. Fax: + (34) 954 557 629 4 Escuela Superior Politécnica del Litoral, ESPOL Facultad de Ciencias Sociales y Humanísticas Campus Gustavo Galindo Km. 30.5 Vía Perimetral P.O. Box 09-01-5863, Guayaquil, Ecuador E-mail: [email protected] Tel.: + (593) 2 269062 * Corresponding author 2 GREEN HOUSE GASES’ MITIGATION POLICIES IN ECUADOR Abstract Focused on Ecuatorian’s Economy this article analyzes for the 2000-2014 period. i) What have the drivers of CO2 emission in Ecuador been? ii) What are the determining factors to be focused on with regards to energy policy efforts and the mitigation of GHG emissions? And iii) Is the economy of Ecuador moving towards decoupling between economic growth and environmental stress? To respond to question i) a decomposition analysis of the change in CO2 emissions has been developed using the LMDI-I model. The decomposing factors or effects used include Carbon Intensity, Renewable Energy Sources penetration, Energy Intensity, Economic Structure, Economic Activity and Population. To answer question ii) a so-called Innovative Accounting Approach (IAA) has been implemented. Another innovative aspect of the methodology used has been to incorporate decoupling analysis between economic growth and environmental stress. This facilitates answering question iii). This is the first time that a comprehensive model is undertaken as described. Keywords: Greenhouse gas emissions, Paris’ Agreement, Ecuador, mitigation policies, LMDI-I model decomposition analysis, Innovative Accounting Approach. 3 MITIGATION AND ENERGY EFFICIENCY POLICIES IN ECUADOR 1. Introduction Despite its status as a Non-annex I country, an increased concern on Climate Change moved Ecuador (officially the Republic of Ecuador) to sign the Paris Agreement (2016). Ecuadorian authorities declared that it remains clear to Ecuador that the urgency of this phenomenon requires the widest possible global cooperation, in line with the norms, objective and principles of the United Nations Framework Convention on Climate Change (UNFCCC) (Gobierno Nacional de la República de Ecuador, 2015). Its National Contribution established that Ecuador intends to reduce its greenhouse gas (GHG) emissions in the energy sector to 20.4-25% below the Business as Usual (BAU) scenario. This scenario was defined through the Long-range Energy Alternatives Planning Systems software (LEAP). A potential for reducing emissions even further in the energy sector, to a level between 37.5 and 45.8% with respect to the BAU baseline, was also calculated. In addition to mitigation commitments in GHG emissions, Ecuador's commitment under the Paris Agreement also includes a greater penetration of renewable energies sources (RES) in its energy matrix. This commitment arose from two government actions prior to the Paris Agreement titled National Plan of Good Living 2009-2013 and 2013-2017 (PNBV for its abbreviation in Spanish). According to PNBV 2009-2013, Ecuador aims to reach a 90% of clean energy rate in its total electricity production in 2017 thanks to hydropower plants and increase the renewable energy percentage even more within the energy matrix by 2025. Together with mitigations and RES targets, a new constitution in 2008 launched “Good Living” as a crucial issue in its main legal text. “Good Living” is considered to be a societal paradigm based on the principle that economic growth on a planet with limited resources cannot be unlimited. Art. 283 of Ecuador’s constitution says that the National Development Regime is established under the framework of an economic system that seeks to guarantee production and reproduction of the material and immaterial conditions that will facilitate “Good Living”. This new vision references sustainable and harmonious management of nature to consider its limits and regeneration cycles. In other words, this implies decoupling environmental stress from economic growth. Despite its contribution to total world CO2 equivalent emissions (hereafter CO2), which only represents 0.5% (Ministry of Environment, 2016), interest in Ecuador’s case derives from the fact 4 that the consequences of Climate Change would be especially serious given its geographical location. Jiménez et al. (2012) estimated that an increase in temperature would imply high losses for the agriculture sector in terms of crops. In the coastal region, floods would affect rice crops, corn and sugarcane. In general, around 80,000 hectares of rice would be lost, which represents 19% of the cultivated surface nation-wide. Ecuador is highly concerned with Climate Change due to such consequences. This article analyzes for the 2000-2014 period. i) What have the drivers of CO2 emission in Ecuador been? ii) What are the determining factors to be focused on with regards to energy policy efforts and the mitigation of GHG emissions? And iii) Is the economy of Ecuador moving towards decoupling between economic growth and environmental stress? To answer questions i) and iii) an ex-post analysis was developed; to respond to question ii) an exante analysis was used. The analysis is a multi-sector type that focuses on energy transformation, industry, agriculture, transportation, construction, commerce and residential sectors. Ecuadorian authorities have considered all of these sectors as key, both for national actions as well as for their commitments with the Paris Agreement. The research develops a comprehensive and innovative model based on three analyses. To the best of our knowledge, scientific literature offers no similar type of analysis, so this paper contributes to filling this gap. More specifically, to respond to question i) a decomposition analysis of the change in CO2 emissions has been developed using the LMDI-I model (Ang, 2004). The decomposing factors or effects used include Carbon Intensity, Renewable Energy Sources penetration, Energy Intensity, Economic Structure, Economic Activity and Population. Another novelty of this paper refers to its multisectoral approach, as it includes the residential sector together with productive sectors. To do that, the domestic sector incorporated into the National Accounts as the last one sector was considered to proxy the residential energy consumption. To answer question ii) this research implemented the so-called Innovative Accounting Approach, which includes Impulse Response Functions (IRFs) and the forecast error variance decomposition applied to the previously defined decomposition factors, according to Robaina-Alves and Moutinho (2013), Moutinho et al. (2016), and Cansino et al. (2018). Another innovative aspect of the methodology used has been to incorporate decoupling analysis between economic growth and environmental stress, measured by the volume of CO2 emissions. This facilitates answering 5 question iii). For this, a decoupling index is calculated as of a second decomposition of the results obtained from the LMDI I analysis (Diakoulaki and Mandaraka, 2007; Jiang et al., 2016; Wang et al., 2016; Cansino and Moreno, 2017; Román-Collado et al., 2018a). The main methodological contribution lies in the fact that this is the first time that a comprehensive model is undertaken as described, which includes the LMDI, the IAA and the decoupling analyses. The study offers useful lessons for developing countries, and it could be used as a policy-making tool because it is easily transferable to any other time period or region. This paper is structured as follows. After the Introduction, Section 2 explains Ecuador’s main indicators and also offers a review of the literature. Section 3 describes the methods used. Data are presented in Section 4, with Section 5 showing and discussing the main findings. Conclusions and policy recommendations appear in Section 6. 2. Ecuadorian context and literature review Ecuador is a South American country with a population of 16.38 million (The World Bank, 2018). Its economy is the eighth largest in Latin American while its annual growth average rate of constant GDP per capita was 2.57% between 2000 and 2016. In terms of human development, it has a score of 0.739 (UNDP, 2016) with 25.6% of its population living below the Poverty line (Central Intelligence Agency, 2017). Ecuador’s concern for Climate Change problems had moved its authorities to sign international agreements. In 1990, Ecuador signed the Vienna Convention (1985) and the Montreal Protocol (1987) to create environmental and GEI mitigation policies, as well as care for the ozone layer and climate change. In 1994, the country subscribed to the UN Framework Convention on Climate Change; in 1999, it signed the Kyoto Protocol, in 2000, it signed the Millennium Developments Goals and the Bali Action Plan in 2007. The 2008 Constitution also include, among other articles, concern for the preservation of the environment (mainly Article 414). The most recent commitment acquire has been to sign the Paris Agreement, which will come into effect in 2020, once the effectiveness of the Kyoto Protocol concludes. In recent years, scholar’s interest in Ecuador’s CO2 emissions has produced emerging growing amount of scientific literature. Part of this literature shows controversy regarding the question of 6 Environmental Kuznets Curve (EKC). For a list of countries, including Ecuador, Azomahou et al. (2005) using panel data researched the EKC using a nonparametric approach to model the relationship between greenhouse gas emissions and economic development. They found that that the relationship between GHG emissions and GDP displays a complex pattern, despite its monotonous shape, which is different from the well-known Kuznets curve obtained from ad hoc parametric specifications. This was in line with Rentería et al. (2016) that focuses specifically on the Ecuador case. However, Almeida (2013), Robalino-López et al. (2014b), Zambrano- Monserrate et al. (2016), and Cuesta (2018) also focus on the Ecuador case; they gave support to the EKC hypothesis by finding that in the short-run, economic growth leads to more pollution, but in the long-run degradation begins to decrease. Other variables, such as energy consumption, help to explain the behavior of Pollutant emissions (in a similar sense see Román-Collado et al., 2018a). Based on the ECK approach Robalino-López et al. (2014a) conducted a model using a variation of the Kaya identity by taking 1980–2010 as the reference period to fix the parameters of the model. Energy efficiency has also been of concern in Ecuador, although from the mainly from an engineering perspective (Salazar and Panchi, 2014). Pollutant emissions from certain specific sectors also received attention from scholars, as was the cases of the electricity (Parra, 2015) and agriculture (Morocho, 2018) sectors. Finally, the constitutional concept of “Good Living” was analyzed in depth by Viteri (2015). 3. Methodology 3.1. LMDI Analysis The comprehensive model developed starts by decomposing CO2 emission changes to find drivers and the inhibitors that influence them. Various and different decomposition techniques are found in the literature. Two recent reviews of the literature are offered in Cansino and Moreno (2017) and Román-Collado et al. (2018a). Among the various IDA methods, the LMDI method seems to provide the most advantages (Ang, 2004; Timilsina and Shrestha, 2009; Zhang et al., 2009; Fernández-González et al., 2014; Guo et al., 2014; Chen and Yang, 2015; Shahiduzzaman and Layton, 2015; Sumabat et al., 2016). LMDI- I is a refined, non-parametric approach based on the IDA method, with a weighted logarithmic mean. 7 The analysis conducted considers seven sectors for the Ecuadorian economy: energy, transportation, industry, agriculture, construction, commercial and residential. Following Cansino et al. (2015) and Cansino and Moreno (2017), six factors are proposed to analise the main drivers of the changes for total energy-related CO2 emissions in Ecuador from 2000 to 2014. Decomposition factors include the Carbon Intensity effect (CI), Renewable Energy Sources share effect (RES), Energy Intensity effect (EI), Economic Structure effect (ES), Economic Activity (Yp) and Population effect (P). The total CO2 emissions can be decomposed as: 𝐶𝑂2=∑𝐶𝐼𝑖∙𝑅𝐸𝑆𝑖∙𝐸𝐼𝑖∙𝐸𝑆𝑖∙EA∙𝑃 7 𝑖=1 = ∑𝐶𝑂2𝑖 𝐹𝐹𝑖∙𝐹𝐹𝑖 𝐸𝑖∙𝐸𝑖 𝑌𝑖∙𝑌𝑖 𝑌∙𝑌 𝑃∙𝑃 7 𝑖=1 [1] The subscript i refers to a given sector from the seven ones considered. CO2i measures the energyrelated CO2 emissions of a given sector i. Similarly, FFi represents the total amount of fossil fuels use, Ei stands for the energy consumption, and Yi measures the output of a given sector. Otherwise, CO2 and Y variables without i subscript represent the total CO2 emissions and the total output for the whole economy. Variations in CO2 emissions may be evaluated by developing a multiplicative or additive decomposition methods. An additive LMDI-I approach is the one conducted in this paper. The overall ratio of variation in CO2 emissions between period 0 and t may be decomposed as Eq [2] shows: ∆𝐶𝑂2= 𝐶𝑂2𝑡−𝐶𝑂20= ∆𝐶𝐼+∆𝑅𝐸𝑆+∆𝐸𝐼+ ∆𝐸𝑆+ ∆𝐸𝐴+ ∆𝑃 [2] ΔCO2 measures changes in total CO2 emissions in the economy from one period to another, with the right-hand variables representing various contributing determinants as previously defined, but now being referred to as changes. By considering the additive decomposition identity, Eq [3] to [8] expose the LMDI formulas for each effect: ∆𝐶𝐼=∑𝑤𝑖(𝑡)∙ln(𝐶𝐼𝑖,𝑡 𝐶𝐼𝑖,0) 7 𝑖=1 [3] 8 ∆𝑅𝐸𝑆=∑𝑤𝑖(𝑡)∙ln(𝑅𝐸𝑆𝑖,𝑡 𝑅𝐸𝑆𝑖,0) 7 𝑖=1 [4] ∆𝐸𝐼=∑𝑤𝑖(𝑡)∙ln(𝐸𝐼𝑖,𝑡 𝐸𝐼𝑖,0) 7 𝑖=1 [5] ∆𝐸𝑆=∑𝑤𝑖(𝑡)∙ln(𝐸𝑆𝑖,𝑡 𝐸𝑆𝑖,0) 7 𝑖=1 [6] ∆𝐸𝐴=∑𝑤𝑖(𝑡)∙ln(𝐸𝐴𝑡 𝐸𝐴0) 7 𝑖=1 [7] ∆𝑃=∑𝑤𝑖(𝑡)∙ln(𝑃𝑡 𝑃0) 7 𝑖=1 [8] Using the mean value theorem the term wi (t) is the estimated weight to be applied from Eq [3] to Eq [8] and is defined by Ang (2005), 𝑤𝑖(𝑡)=𝐶𝑂2𝑖,𝑡 −𝐶𝑂2𝑖,0 ln𝐶𝑂2𝑖,𝑡 −ln𝐶𝑂2𝑖,0 [9] According to the nomenclature used, Eq [3] to Eq [8] captures changes in every given sector during periods t and 0. Eq [3] measures the Carbon Intensity factor (CI), and ΔCI captures changes in CO2 emissions from fossil fuel consumption (= CO2i / FFi). The CI factor describes the CO2 emission amount produced by each sector with respect to the consumption of fossil fuels. It serves as an energy mix factor allowing to analise the substitution between fossil fuel types. This is possible if the statistics show changes in the types of primary energy sources used (i.e., natural gas replacing coal or vice versa). It is assumed that the higher the quality of a fossil fuel, the less CO2 pollutant is. Eq [4] shows the Renewable Energy Source share factor (RES), and ΔRES indicates the share of fossil fuel consumption with respect to the total primary energy consumed (=FFi / Ei) (O’Mahony, 2013; Cansino et al., 2018). 9 Eq [5] measures the Energy Intensity factor (EI), and ΔEI captures the total primary energy required in comparison to the output of the considered sector (=Ei /Yi). The EI factor is used as a proxy of the energy efficiency of a country’s economy (Goldemberg and Johansson, 2004; Voigt et al., 2014). Eq [6] presents the Economic Structure factor (ES), and ΔES captures the effect of structural changes in Ecuador’s economy. It incorporates the relative impact of structural changes on Ecuador’s economy in terms of CO2 emissions for a given year under analysis. Eq [7] is the Economic Activity (EA), and ΔEA measures the output per capita between two periods. The EA factor captures the income factor in CO2 emission variations from energy consumption. Eq [8] shows the Population factor (P), and ΔP indicates the variation of total population between given periods. The P factor enables the effects of population growth to be analyzed as an explain factor for CO2 emissions. In order to accommodate cases of zero value, Ang and Liu (2001) and proposed that the best way to handle this situation is by substituting zeros for a δ value between 10-10 and 10-20 (see also Ang and Choi, 1997; and Ang et al., 1998). 3.2. IAA IAA is the second analysis in the comprehensive model implemented here. It facilitates the relationships between CO2 emission drivers and identifies how they could influence each other in the future (Robaina-Alves and Moutinho, 2013; Moutinho et al., 2016). Both short-run and longrun relationships are explored. IAA includes Impulse Response Functions (IRFs) and forecast error variance decomposition (Maghyereh, 2004; Cansino et al., 2018). One of the advantages of the IAA method over other approaches used in the literature is that it allows us to incorporate a forward period analysis, i.e. perform predictions of the behavioral relationships between time series from the selected sample period (Park and Ratti, 2008; Ozkan and Ozkan, 2012; Shahbaz et al., 2013). In this research, the generalized forecast variance decomposition approach estimates simultaneous shock effects, which are determined from the estimation of the VAR model to test the strength of casual relationships between the determining factors of total CO2 emissions. 16 Figure 2. Energy consumption of non-fossil source by economic sector 2000-2014. Source: Own elaboration. The use of renewable energies on behalf of the industrial and residential sectors has advanced at an accelerated rate. However, in 2009 and 2010 and due to a recession in the country both the residential and industrial sectors showed a decrease in the demand for renewable energy. The rapid penetration of RES in Ecuador’s energy matrix could benefit from the introduction of tender mechanisms for new generation plants. Other Latin American countries have been successful in this regard, but Ecuador has yet to use this option (International Renewable Energy Agency, 2015). The EI factor worked as an inhibitor of CO2 emissions, but without compensating the drivers. The greatest inhibiting effect was seen in 2005, 2006 and 2012. In 2005 and 2006 registered sharp increases in crude oil prices. Based on sectors, the exceptions appeared in the transportation sector for 2002-2004 period and for 2007, the industrial sector in 2006 and the commercial sector in 2007. For these sectors, one must analyze the existence of a possible knock-off effect associated with technological improvements. (Somuncu and Hannum, 2018). The contribution of the industrial sector in the mitigation of greenhouse gases coincided with technological improvements, in addition to the implementation of clean technology. These results recommend acting on the energy intensity of the transportation, industrial and commercial sectors. In the transportation sector, 17 together with standard recommendations to favor public transportation, efforts must be made to increase the use of electrified railways to transport goods, if and when the electricity matrix increases the participation of clean energy generation technologies (Wilmsmeier and Guidry, 2013). Together with this, the rapid develop of the e-commerce currently forces society to deal with the last mile problem of logistic (Gevaers et al., 2011). The industrial sector could receive fiscal benefits for company tax linked to the investment in technologies that improve efficiency or those focusing on the sequestration of polluting emissions (Román-Collado et al., 2018b). Likewise, the commercial sector could improve its efficiency through investments that improve the insulations of large malls. In this regard, a higher standard demand for Ecuador’s Building Code– NEC-11—would be recommendable (Comité Ejecutivo de la Norma Ecuatoriana de la Construcción, 2011). When it comes to the behavior of the ES factor, the reduced importance of the transportation sector in Ecuador’s economic structure in 2004 and 2005 influenced in the contribution of the ES factor in the reduction of GEI emissions. In 2009 and 2014, this sector saw an upturn in its contribution to the increase of GHG emissions. On the other hand, the greater weight of the energy sector as a driver of emissions from 2004 until 2008 is due to a constant increase in international oil prices. Also, the EA factor contributed positively to the variations of GEI emissions during the period studied. The exception was 2009. That year, Ecuador saw an economic slowdown due to the price of international oil prices. The transportation sector was determining sector in the behavior of EA, followed by the energy and industrial sectors. P effect drove GHG emissions as was expected, in line with Robalino-López et al. (2014a). These authors denoted that the extreme poverty rate in Ecuador declined significantly between 2000 and 2010. In 2000, the estimate was approximately 20.7% of the population; by 2010, this number had dropped to 4.6% of the total population. This is largely explained by emigration and the economic stability achieved after the dollarization of the economy. Transportation is the most important sector. Naciph et al. (2013) offered limited yet interesting information about CO2 emissions for transport in terms of student use. Contrary to industrialized countries where the residential sector is more important than transportation, in Ecuador, this sector has a greater specific weight in the country’s economy. In this context, new technologies facilitating car-sharing are a very costeffective way to optimize the use of passenger cars more efficiently, thus reducing its importance 18 as a driver of emissions (Schor, 2016). Along this same line would be the installation of selfsufficient energy systems in homes by incorporating small, clean technology devices. Chile’s experience with the Netbilling Act is a precedent to be taken into consideration (Walters et al., 2018). 5.2. IAA results To ensure robustness before estimating the VAR model, statistical properties of CI, EI, EA, ES, RES and P effects are analised. Table A.1 in the annex shows that the mean, variance, and autocorrelation of the time series for the VAR model all remained constant over time (Brooks, 2014). For CI, EI, EA, ES and RES effects means and variances did not show tendency. ADF’s test showed that was no autocorrelation between these effects, nor was there any autocorrelation (see supplemental material). Due to its nature, only the Population effects revealed a tendency, but the unit root test showed all effects were stationary in the first differences. Engel-Granger’s test determined that the series were not co-integrated (p value>0.05). 5.2.1. Results from variance decomposition Table 1 presents the results obtained for the generalized variance decomposition over the selected timespan. This research considered a ten-year period for the decomposition factor of CO2 emissions in Ecuador. The variance decomposition shows how much the predicted error variance of a variable is described by innovation generated from each independent variable in the system. (Table 1) 19 Table 1. Variance decomposition Period CI RES EI ES EA P Variance decomposition of CI: 1 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 2 57.38620 2.346447 3.029810 17.63667 19.50927 0.091596 3 29.49275 6.046937 33.41920 7.024960 23.95696 0.059191 4 30.20455 3.895387 45.27266 4.410487 16.18440 0.032515 5 33.70266 3.486255 44.08101 4.225269 14.47502 0.029789 10 33.14091 3.560160 44.51605 4.318444 14.43465 0.029787 Variance decomposition of RES: 1 64.02666 35.97334 0.000000 0.000000 0.000000 0.000000 2 53.11551 29.87786 14.95493 0.022541 2.020738 0.008417 3 48.75352 26.01929 19.25088 2.801488 3.133468 0.041351 4 41.88260 23.35987 25.59112 2.386764 6.744384 0.035263 5 40.53572 19.73968 30.85341 2.869476 5.969984 0.031734 10 39.44256 18.63762 32.43613 3.126156 6.302104 0.055429 Variance decomposition of EI: 1 11.93017 36.72795 51.34188 0.000000 0.000000 0.000000 2 16.62990 33.33257 46.52960 2.977543 0.529190 0.001197 3 17.43661 33.06219 45.38558 3.179245 0.928847 0.007524 4 17.77026 31.56806 45.17881 3.287832 2.187978 0.007056 5 18.97435 29.77726 45.63968 3.066387 2.531427 0.010895 10 20.23564 28.37164 45.44579 2.955266 2.968940 0.022718 Variance decomposition of ES: 1 18.17109 67.02400 0.884735 13.92018 0.000000 0.000000 2 13.75404 51.50229 20.49325 10.92481 3.313246 0.012364 3 16.72425 40.66796 31.38117 8.548324 2.661188 0.017114 4 18.10400 39.72336 30.66016 8.352205 3.143511 0.016772 5 17.89781 38.70524 31.64612 8.651017 3.082004 0.017800 10 18.10873 36.75547 32.97450 8.453027 3.673565 0.034709 Variance decomposition of EA: 1 23.78321 51.30177 12.41278 4.159627 8.342609 0.000000 2 23.35713 49.28957 12.29949 6.319887 8.701182 0.032742 3 22.68233 45.76119 12.88360 6.109912 12.53135 0.031612 4 23.82239 40.49266 17.77261 6.099312 11.76722 0.045810 5 24.57900 39.24837 18.55520 5.942549 11.61530 0.059575 10 24.94073 38.11436 18.90702 6.062296 11.84354 0.132051 Variance decomposition of P: 1 22.31505 4.400698 30.37099 1.024324 29.82865 12.06029 2 26.23396 5.887627 18.33133 9.054020 31.28317 9.209888 3 32.19301 4.045198 11.56641 13.62564 29.79309 8.776658 4 32.40513 3.339482 9.180209 15.75755 29.99985 9.317778 5 31.25042 2.937308 8.406543 16.47756 31.57339 9.354781 10 32.48718 1.935758 4.314652 19.76073 32.20638 9.295307 The results indicate that 33.14% of the CI effect is due to its own innovative shocks. The standard deviation shock in the EI factor is the variable that better explains carbon intensity with a 20 percentage of 44.51%. Other factors that explain the CI are the EA and ES effects with 14.43% and 4.31%, respectively. The RES factor only explains carbon intensity with a 3.56%. A portion of 18.63% of RES effect may be explained by its own innovative shock. However, a strong share of this effect is explained by CI (39.44%) and by EI effect (32.43%). The EA, ES, and P factors only explain RES with a 6.30%, 3.12% and a 0.05%, respectively. The empirical evidence indicates that 45.44% of the EI effect is due to its own innovative shocks. RES and CI effects also explain a strong percentage of this effect--28.37% and 23.23% respectively—while EA and ES explain it almost in the same portion (2.96%) and P only give explanation to 0.02%. The structural composition of Ecuador’s economy is mainly affected by the RES share effect (36.75%) and the EI effect (32.97%). The ES factor is explained solely by its own innovative shock with a small portion as 8.45%. This small portion of the EA factor is explained by its own shocks (11.84%), while the contribution of the RES factor is the main calculation (38.11%) is followed by CI (24.94%) and EI (18.90%) factors. The economic structure effect is 6.06% while the Population factor explained only 0.13%. Similar to the case of the EA factor is the population effect, which is explained by its own standard innovative shock with a portion of 9.29%. The standard deviation shock in the CI penetration effect and EA effect represented the higher proportion with a 32.48% and a 32.20% respectively. The value for ES was 19.76%. Finally, EI (4.31%) and RES (1.93) explained the lower portion. Taking 5% as a threshold, it could be inferred that there is bidirectional causality between the carbon intensity and the Energy Intensity effect. This means that technological advanced energy efficiency orientation represents an effective way to reduce CO2 emissions in Ecuador. Bidirectional causality also appears between RES and EI. Thus, by considering the reference of 5%, we may infer that there is unidirectional causality from CI to RES, EA and P, from RES to ES and EA, from EI to ES and from EA to P. Following Cansino et al. (2018), the results obtained as shown in Table 1 highlight the following major findings: If the Energy Intensity effect decreases, then the CI decreases too because the energy requirements to obtain an additional unit of output are lower; therefore, total energy related emissions decrease. 21 If the RES effect decreases, the EI also decreases, due to most RES use advanced techniques and resources such as the sun or wind, which are freely accessed. If the EA effect decreases, the Population effect also decreases. The main reason behind this is the income effect on family consumption patterns. The ES effect and EA effect cause the RES effect. Moreover, a unidirectional causality exists from P effect to CI effect. 5.2.2. Impulse-Response analysis Impulse-Response Functions (IRF) allows us to simulate the decomposition effects involved in the analysis over time. Figure 3 represents the IRFs for the six effects used in the LMDI decomposition analysis accomplished. This Figure shows that carbon intensity in the short term reacts more significantly to shocks in EA and the Energy Intensity effect, compared to shocks in other effects. The reaction to EA has an opposite behavior to shocks in EI. Nevertheless, both of reactions end up dissipating in the long-run. The RES share effect has a significant reaction to a shock in the CI factor, in the EA effect and to a shock in itself. The three reactions have the same behavior until they reach the 3rd time horizon; after this point, the EA effect turns to the opposite behavior. The three reactions dissipate after the 6th time horizon. The response to shocks decreases in the long-run. EI reacts more sharply to shocks in RES, EA and CI. The first two have the same behavior, but the last shows the opposite reaction. However, the response to shock disappears rapidly after the 2nd time horizon to dissipate in the long-run (after the 8th time horizon). The economy structure effect reacts to all effects except to Population. ES effect reacts less sharply after the 5th time horizon. Finally, the response to these shocks disappears in the long term in a similar way as EI. When analyzing the Economic Activity effect, the sharpest reactions come from shocks in RES, CI and itself (three reactions showed a similar behavior) and also in EI (but with an opposite behavior). The response to shocks dissipates after 6th time horizon. 22 Finally, the Population effect reacts to all effects except to itself. Reaction patterns are not dissimilar in trends and responses do not disappear in the long-run. Most of the factors have a significant reaction to a shock in the Economic Activity, RES and the Carbon Intensity effect. These results would seem to indicate coupling between CO2 emissions and economic growth. IRF results also show the importance of mix level resource efficiency to mitigate CO2 emissions without compromising economic growth. (Figure 3) 23 Figure 3. Impulse-Response functions. Source: Own elaboration. 24 5.3. Decomposition index results The decoupling index, calculated according to Eq [11] and [13], determines the degree to which the CO2 emission reduction effort outweighs the activity effect in Ecuador from 2000 to 2014. This gives a measure (finer than the Tapio index) of the real progress in decoupling economic growth from environmental stress measured as CO2 emissions. It should be borne in mind that this is a requirement included in Ecuador’s 2008 Constitution. Figure 4 shows the trend of the decoupling, broken down into the five considered effects, all of which have been included in the LMDI analysis. (Figure 4) Figure 4. Decoupling index’s trend. Source: Own elaboration. Table A.2 in annex gives detailed numerical values from which Figure 4 is built. When effects are considered all together, it can be seen that for most of the years under consideration, the decoupling index suggests that decoupling efforts did not overcome the role of the activity effect as a driver of 25 CO2 emissions in Ecuador. This ex-post analysis flows in line with the role played by the same effect in the ex-ante analysis based on IRFs. For most of the year, the Energy Intensity effect showed positive value, thus implying its role as an inhibitor of CO2 emissions. The economic structure effect shows a similar trend and this is relevant when considering how many of the effects react to a shock derived from this effect. Despite these optimistic results, when we observed CI and RES effect values, the optimistic level seemed to be damaged. However, drought as an uncontrollable climatic event ought to be considered when analyzing CI and RES effect values in the decoupling analysis, as it has a direct impact on hydroelectricity. These are the cases in 2005, 2009, 2010 and 2013 when electricity from hydropower plants decreased, thus pushing the Carbon Intensity effect to take negative values in the decoupling index when it is decomposed. Together with the other effects, the total value of the decoupling index is affected by the Population effect which acted against decoupling for all the years under consideration, as was expected. A sectoral perspective of the decoupling process in Ecuador is provided in Figure 5 where information appears disaggregated by sectors. (Figure 5) 32 Rentería, V., Toledo, E., Bravo-Benavides, D., Ochoa-Jiménez, D., 2016. 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Descriptive stats Sample: 2001 2014 CI EA EI ES RES P Mean 300.5169 934.3352 -139.1819 -178.8102 26.18849 553.9773 Median 361.3757 878.5109 -137.4860 -71.92590 -38.71667 555.8188 Maximum 2118.123 2319.538 1071.563 710.5548 1109.017 680.4099 Minimum -901.2356 -386.7628 -1199.867 -836.0961 -855.2257 450.6401 Std. Dev. 869.2653 707.4823 658.8014 403.9059 448.7121 80.49613 Skewness 0.336855 0.122708 0.284018 0.272257 0.580059 0.003780 Kurtosis 2.595737 2.669667 2.668669 2.934435 4.222401 1.622164 Jarque-Bera 0.360099 0.098787 0.252259 0.175463 1.656747 1.107452 Probability 0.835229 0.951807 0.881501 0.916007 0.436759 0.574804 Sum 4207.237 13080.69 -1948.546 -2503.342 366.6388 7755.682 Sum Sq. Dev. 9823089. 6506906. 5642251. 2120820. 2617454. 84235.15 Observations 14 14 14 14 14 14 35 Table A.2 Table A.2. Decomposition values 00/01 01/02 02/03 03/04 04/05 05/06 06/07 07/08 08/09 09/10 10/11 11/12 12/13 13/14 γCI -0.50 -0.66 0.87 0.13 -2.01 1.05 -4.90 0.61 -4.26 -1.72 0.33 -0.27 -0.40 -0.34 γRES -0.46 0.37 0.35 -0.06 0.27 0.27 0.29 0.14 -3.87 -0.37 0.37 -0.06 -0.43 0.04 γEI 0.37 -0.21 -0.02 0.11 1.04 -1.29 0.47 0.30 -1.70 0.91 -0.01 0.77 0.50 -1.08 γES 0.02 0.85 -0.02 -0.01 0.79 -0.08 4.27 0.25 -2.84 0.40 0.27 0.08 -0.06 -0.07 γP -0.81 -0.73 -1.59 -0.26 -0.47 -0.64 -3.54 -0.38 -2.51 -0.90 -0.27 -0.41 -0.54 -0.74 γ -1.38 -0.38 -0.41 -0.09 -0.38 -0.68 -3.41 0.91 -16.18 -1.68 0.70 0.12 -0.93 -2.20 36 Table A.3 Table A.3 Decomposition analysis in figures SECTOR 2000/2001 2001/2002 2002/2003 2003/2004 2004/2005 2005/2006 2006/2007 2007/2008 2008/2009 2009/2010 2010/2011 2011/2012 2012/2013 2013/2014 γCI Energy sector -0.92 -0.50 0.36 -0.04 -2.75 0.91 -6.25 0.89 -3.31 -2.03 0.40 -0.26 -0.79 -0.69 Industry sector 0.06 0.05 0.17 0.04 0.50 -0.03 1.15 -0.14 -0.10 0.26 -0.05 0.00 0.13 0.02 Transport sector 0.02 -0.07 -0.06 -0.01 0.01 0.01 -0.07 -0.01 -0.18 0.13 0.00 0.02 0.01 -0.01 Others 0.11 -0.20 0.16 0.06 0.14 0.02 0.52 -0.11 -1.08 -0.33 0.04 -0.06 0.10 0.25 Commercial and Public sectors 0.05 0.00 0.07 0.02 0.03 0.03 -0.21 -0.04 -0.05 0.04 -0.02 -0.01 0.06 0.02 Agriculture sector 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.20 0.00 0.00 0.00 0.00 0.00 Residential sector 0.17 0.06 0.18 0.06 0.06 0.10 -0.05 0.00 0.25 0.20 -0.03 0.04 0.10 0.06 TOTAL -0.50 -0.66 0.87 0.13 -2.01 1.05 -4.90 0.61 -4.26 -1.72 0.33 -0.27 -0.40 -0.34 γRES Energy sector -0.10 0.17 0.34 -0.07 0.20 0.32 -1.51 0.06 -1.72 -0.30 0.18 -0.06 -0.24 0.07 Industry sector -0.04 0.10 0.13 0.04 0.12 0.00 1.28 -0.01 -0.78 -0.04 0.09 -0.01 -0.11 -0.02 Transport sector 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.20 0.00 0.00 0.00 0.00 0.00 Others -0.16 0.09 -0.16 -0.01 -0.02 0.00 0.02 0.03 -0.58 0.06 0.02 0.01 0.00 -0.04 Commercial and Public sectors -0.06 -0.01 0.03 -0.01 -0.01 -0.02 0.15 0.01 -0.37 -0.05 0.03 -0.01 -0.03 0.01 Agriculture sector 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.20 0.00 0.00 0.00 0.00 0.00 Residential sector -0.10 0.02 0.02 -0.01 -0.02 -0.03 0.35 0.05 -0.43 -0.04 0.05 0.01 -0.05 0.02 TOTAL -0.46 0.37 0.35 -0.06 0.27 0.27 0.29 0.14 -3.87 -0.37 0.37 -0.06 -0.43 0.04 γEI Energy sector -0.03 0.09 1.42 0.63 0.60 -1.47 0.89 0.02 1.18 0.47 0.16 0.35 0.40 -0.10 Industry sector 1.10 -0.36 1.01 -0.15 0.94 -0.33 2.19 -0.04 -1.99 0.09 0.02 -0.18 0.19 -0.15 Transport sector 0.14 -1.71 -1.06 -0.24 -0.07 0.60 1.42 0.45 0.82 -2.33 -0.07 0.46 0.00 -0.11 Others -0.83 1.82 -1.46 -0.13 -0.18 -0.07 -3.44 -0.05 -2.54 2.55 -0.02 0.26 -0.08 -0.69 Commercial and Public sectors -0.03 -0.02 -0.14 -0.01 -0.02 -0.01 0.05 0.02 -0.18 0.01 0.00 -0.03 0.00 -0.02 Agriculture sector 0.01 0.00 0.00 0.00 -0.01 -0.02 -0.07 -0.02 -0.26 -0.04 0.00 -0.02 0.01 -0.02 Residential sector 0.01 -0.02 0.21 0.02 -0.22 0.03 -0.58 -0.08 0.86 0.16 -0.09 -0.08 -0.02 0.02 37 Figure A.1 Fig. A.1.1. CI effect Fig. A.1.2. RES effect Fig. A.1.3. EI effect