Virtual CO2 emission flows in the global electricity trade network

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Energy and the Environment

Virtual CO2 emission flows in the global electricity trade network Shen Qu, Yun Li, Sai Liang, Jia-Hai Yuan, and Ming Xu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b05191 • Publication Date (Web): 08 May 2018 Downloaded from http://pubs.acs.org on May 8, 2018

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Environmental Science & Technology

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Virtual CO2 Emission Flows in the Global Electricity Trade Network

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Shen Qu a, Yun Li b,a, Sai Liang c, Jiahai Yuan b, Ming Xu a,d,*

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a

5 6

b

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c

9 10

d

School for Environment and Sustainability, University of Michigan, Ann Arbor, Michigan 48109-1041, United States School of Economics and Management, North China Electric Power University, Beijing 102206, People’s Republic of China State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, People’s Republic of China Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan 48109-2125, United States

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*corresponding authors.

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Phone: +1-734-763-8644; fax: +1-734-936-2195; e-mail: [email protected] (Ming Xu).

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Abstract

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Quantifying greenhouse gas emissions due to electricity consumption is crucial for climate mitigation in the electric power sector. Current practices primarily use production-based emission factors to quantify emissions for electricity consumption, assuming production and consumption of electricity take place within the same region. The increasingly intensified crossborder electricity trade complicates the accounting for emissions of electricity consumption. This study employs a network approach to account for the flows in the whole electricity trade network to estimate CO2 emissions of electricity consumption for 137 major countries/regions in 2014. Results show that in some countries, especially those in Europe and Southern Africa, the impacts of electricity trade on the estimation of emission factors and embodied emissions are significant. The changes made to emission factors by considering inter-grid electricity trade can have significant implications for emission accounting and climate mitigation when multiplied by total electricity consumption of the corresponding countries/regions.

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TOC

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1. Introduction

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Electric power generation contributes significantly to global greenhouse gas (GHG) emissions. In 2014, over 40% of the global carbon dioxide (CO2) emissions were from the electric power sector 1. Mitigation initiatives, strategies, and policies related to the power sector have taken place at various scales, including the national, regional, organizational, and even individual scales. Underpinning such effects is the accurate and fair accounting for GHG emissions, for emissions from both electricity generation and consumption. Consumption-based accounting is particularly relevant to initiatives and policies at regional, organizational, and individual levels. Converting grid electricity consumption (or purchased electricity) into emissions from power generation requires the measurement and use of the emission factor, which is defined as the emission generated due to unitary electricity consumption.

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Current practices mostly use production-based emission factors for estimating emissions driven by electricity consumption. For example, the Greenhouse Gas Protocol requires organizations to report Scope 2 emissions due to grid electricity consumption by using emission factors based on national electricity generation 2, 3, except for US where emission factors for eGRID subregions should be used 4. However, as electricity is purchased and consumed from interconnected grids, production-based emission factors lead to inaccurate measurements of emissions due to electricity consumption. In particular, power grids are connected and interdependent at the regional and even the global level. Indeed, global electricity trade has been steadily increasing in past decades. For example, electricity exports and imports of OECD countries have been growing by 4.5% and 4.3% annually from 1974, and reached 511 TWh and 510 TWh in 2015, respectively 5. Electricity trade brings about economic benefit, since it opens the opportunities to exploit region variations in natural resources, climate and load timing, reducing the surplus generation capacity needed 5, 6. However, similar to the fact that globalized supply chains distance production and consumption and render environmental responsibilities more “invisible” 7, 8, cross-border electricity trade furthers the separation between electricity generation and consumption. Such separation subsequently complicates the accounting for emissions due to electricity consumption, since consumed electricity may be generated by multiple regional grids where electricity generation may rely on specific energy sources, use various technologies, and produce different amounts of emissions for unitary electricity generated.

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Given the complexity of interconnected grid and increasingly intensified electricity trade, a proper accounting framework for cross-border electricity trade is needed to correctly measure the emissions due to electricity consumption. Without such a framework, accounting exercises may lead to wrong estimations of GHG emissions by consuming entities, and even consequently undermine the promised or expected effects of mitigation efforts due to carbon leakage in the power sector 9-11. Data disclosure initiatives have begun to report emissions from power plants worldwide 12, 13. However, without controlling for emissions from electricity trade, countries/regions may well stabilize or even reduce direct emissions from their power sector 3 ACS Paragon Plus Environment


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while importing embodied emission from other nations, and thus compromise the effectiveness of initiatives directly targeting electricity production.

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In this study, we explore the impacts of cross-border electricity trade on country-/region- level average CO2 emission factors of electricity consumption. We include 137 countries/regions with complete data on electricity generation, cross-border electricity transfers, fuel mixes and emission factors for electricity generation. Over 90% of global electricity trade in 2014 occurred among these countries/regions. We employ and compare three different methods to estimate emission factors of electricity consumption (which will be detailed in the next section), including a network-based approach for estimating embodied emission flows in electricity trade networks 14-16 which, to our knowledge, has never been employed at the global scale before this study. Therefore, we are able to identify where and how electricity trade may change embodied CO2 emissions and fuel mix of electricity consumption. Even though for most world regions the effects of cross-border electricity trade on emission factors remain relatively marginal, the impacts can be significant when combined with the amounts of electricity consumed. In addition, we evaluate the virtual emission transfers across borders due to electricity trade and identify top net emission importers and exporters, which are hotspots for collective efforts among countries/regions to mitigate carbon leakage occurring in the power sector.

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2. Method

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Although the emission factor of electricity consumption is conceptually analogous to consumption-based emission accounting, there is a fundamental difference between exchanges in electricity and exchanges in other goods and services, which render the quantification of emission factors of electricity consumption unique and nontrivial 17. For example, at a particular moment, if grid A only imports electricity from grid B which in turn imports from grid C, in effect grid A is importing a blend of electricity (or electrical energy) from both B and C. In other words, there is a virtual emission flow generated in grid C due to electricity consumption in grid A, although there is no direct electricity transfer from grid C to A. The virtual flows of emissions should follow the pattern of inter-grid electricity transfers. Therefore, when n grids are generally interconnected and transfer electricity among themselves, to correctly estimate the emission factor of electricity consumption from a particular grid, ideally one should include all the n grids in the model, instead of only considering its direct trade partners. This becomes more crucial as inter-grid electricity trade intensifies.

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Table 1 summarizes the methodologies, resolutions, and scales of previous research on embodied emissions in electricity consumption. We exclude studies that simply base their estimates on electricity generation within the considered geographical boundary and neglect electricity trade/transfers. There are mainly two methods to account for electricity trade. The first method considers only a grid’s direct electricity trade partners to quantify its emission factor (i.e., direct trade-adjustment approach). Embodied emissions in electricity consumption 4 ACS Paragon Plus Environment

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in a grid is calculated as the emissions directly from electricity generation minus the emissions for producing exported electricity plus the emissions (generated outside the grid) for producing the imported electricity, with the (partly incorrect) assumption that the electricity a grid exports is entirely generated in this grid. The second method (i.e., network-based approach), improves upon the first one, enlarging the geographic boundary when evaluating every single grid’s embodied emissions to reflect all virtual emission flows in the electricity trade network. Recently we developed this method with extensive discussion on its relation to previous approaches 17, and applied it to China’s interprovincial electricity trade network 18.

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Table 1. Studies that report embodied emissions in electricity consumption and take account of inter-grid electricity trade. Study

Method to account for electricity trade

Geographic coverage and resolution

Boundary for calculating embodied emissions for a grid

Marriott and Matthews (2005)19

Optimization

US and US states

States relatively close to the considered state

Kang et al. (2012)14

Network-based

China and subnational grids

Entire China

Song et al. (2013)20

Direct tradeadjustment

China and subnational grids

Sub-nation grids with direct electricity transfers

Lindner et al. (2013)21


China and Provinces with direct Chinese provinces electricity transfers

Ji et al. (2015)16

Network-based, with only net electricity transfers

Eurasian continent and nations

Entire electricity trade network

Kodra et al. (2015)15

Network-based

US and US power control areas

Entire US

Zafirakis et al. (2015)22


Europe and nations

Nations with direct electricity transfers

Colett et al. (2016)23

Nested approach

US and US power control areas

NERC regions surrounding the power control area

Qu et al. (2017)18

Network-based

China and Entire China Chinese provinces

120 121 122

Besides the above two approach, other approaches in Table 1 are not directly relevant to our study. Marriott and Matthews (2005) use an optimization method to estimate interstate 5 ACS Paragon Plus Environment


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electricity transfers in the US without real-world data. Colett et al. (2016) adjusts for the electricity transfers between a power control area of the US and the surrounding North American Electric Reliability Corporation (NERC) regions to estimate GHG emission factors, considering two protocols based respectively on bilateral and net electricity trade which are essential based on the direct trade-adjustment method.

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It is also worth mentioning that the electricity market has been systematically modelled in sophisticated life cycle inventory (LCI) databases such as ecoinvent 24. In particular, the supply mix of electricity to consumers is modelled as domestic production plus imports, according to Itten et al. (2012) 25. Conceptually, electricity imports should have the same composition as the supply mixes of the exporting countries, but in current practice of LCI database compilation , production mixes of the exporting countries were used for simplification, to avoid “virtual feedback loops between the electricity supply mixes of different countries” 25. However, modeling such feedback loops is not only theoretically valid, but may prove necessary as electricity trade intensifies (e.g., see ref 15). Recent methodological contribution by us and coauthors 17 has simplified and standardized this procedure, and clarified both its similarity to and difference from the Economic Input-Output (EIO) model. In the Discussion section, we will calculate the fuel compositions of electricity supply in different countries with the methods mentioned above. Although this paper focuses on CO2 emissions, the inferred changes in fuel composition (e.g. more hydropower and less fossil fuels) could have important life cycle implications.

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The interconnected grid network. In this study, a national/regional grid is modelled as node. In the considered period (i.e., a year), we define total electricity inflow to the node and total electricity outflow from the node. The former equals to total electricity generation in the country/region plus total electricity import. The latter is total electricity consumption in the country/region plus total electricity export. These two terms are equal by conservation of energy, as in equation (1): n

n

j =1

j =1

xi = pi + ∑Tji =ci + ∑Tij

(1)

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where xi denotes the total electricity inflow or outflow of grid i, pi is electricity generation, ci is electricity consumption, and Tij is the annual electricity transfer from grid i to grid j.

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Based only on annual data on grid electricity generation, consumption, and inter-grid electricity transfers among the 137 countries, it is not possible to differentiate components of electricity inflow (i.e., imports from other grids and generation) by their destination (i.e., export to other grids or used for own consumption). We assume that, for each node, electricity generation and imports are first mixed, and then either consumed or exported to other grids.


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Next we use a matrix representation of the electricity supply chains in an n-grid electricity trade network to compute the emissions embodied in each grid’s electricity consumption. We define the direct outflow matrix, B, and total outflow matrix, G, in equations (2) and (3).

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B = xˆ −1T (2)

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G = ( I − B)−1 = I + B + B2 +L (3)

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where xˆ is the diagonal matrix with total electricity flow xi on the diagonal, and T is the inter-

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grid electricity transfer matrix with T ij as the (i,j)th element. The right-hand-side of equation (3)

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invokes the Taylor expansion of ( I − B)−1 , which is a commonly used technique in input-output economics 26.

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In this accounting framework, the (i,j)th element of total outflow matrix G is the amount in the total electricity inflow to grid j that comes from unitary electricity generation in grid i. This transfer is enabled through infinite electricity supply chains, including self-supply (represented by the term I), direct transfers (B), transfers through one intermediate node (B2), transfers through two intermediate nodes (B3), and so on.

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Note a known fraction of the total electricity inflow to a grid is consumed without further exporting. Based on the above results, we can immediately derive the generation-consumption matrix, H, where the (i,j)th element is the electricity consumed in grid j that corresponds to the unitary electricity generation in grid i:

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ˆ ˆ −1 (4) H = Gcx

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where cˆ is the diagonal matrix that contains electricity consumption of each grid. Since virtual emission flows are concurrent with electricity transfers, the (i,j)th element of H also represents the allocation of grid i’s production-based emissions to grid j’s electricity consumption.

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Virtual emission flows and emission factors. Now we are ready to link the embodied emissions of electricity consumption in a particular grid to direct emissions of each grid in the network, and therefore to compute the emission factors of electricity consumption. Given emission factors of electricity generation efi G for each national/regional grid i, the total direct emission

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from each grid is eiG = ef i G pi . We can now compute the matrix of inter-grid virtual emission

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flows E C , the row vector of embodied emissions in grid electricity consumption eC , and the corresponding vector of network-based emission factors of electricity consumption ef C , Network , respectively, with equations (5)-(7).

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EC = EG H (5)

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eC = [1,L,1] E C (6)

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ef C , Network = eC cˆ −1 (7)

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where E G is diagonal matrix with eiG on the diagonal, E C is an n by n matrix with the (i,j)th

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element being the direct and indirect emission flow from i to j, eC is the 1 by n vector with the ith element being the embodied emissions in country/region i’s electricity consumption, and

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ef C , Network is the associated 1 by n vector containing emission factors for electricity consumption.

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Adjustment for open systems. In practice, the n grids in the model may not constitute a closed system, because of incomplete data for all countries/regions in the global electricity trade network (see section 3.3). Some countries/regions, therefore, have to be left out, but they may still export electricity to or import electricity from the system in the real world. Therefore, the method presented in the previous section needs slight adjustments to reflect the open nature of the system in practice. In particular, we can keep all the procedures the same but simply rewrite equations (1) and (5) as follows.

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xi = pi + ∑Tji + ∑T%ji =ci + ∑Tij + ∑T%ij

n

mi

n

li

j =1

j =1

j =1

j =1

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E C = ( E G + E In ) H (5’)

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where

(1’)

mi

∑ T%ji denote the mi electricity transfers to grid i from outside of the system, j =1

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li

∑ T%

ij

j =1

denote the li electricity transfers from grid i to outside, and E In is the diagonalized matrix where each diagonal element is the emission generated from producing electricity that is imported into the corresponding country in the n-grid system from outside the system.

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Direct trade-adjustment approach. The following equation lays out the direct trade-adjustment approach, which we used to compute emission factors of purchase electricity for comparison.

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  ef i C , DirectAjust =  ef i G ⋅ pi + ∑ ef jG ⋅ T ji − ∑ ef i G ⋅ Tij  ci j ≠i j ≠i  

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As described previously, the estimation of ef i C , DirectAjust is only based on its own emission factors

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of generation, efi G , and values of ef jG of those direct electricity trade partners with grid i.

(8)

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3. Data

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Electricity trade. Global electricity trade data (in kWh) in 2014 are reported in the United Nations (UN) Comtrade Database 27. To address statistical discrepancies, we cleaned the data with the following procedures. First, if only export or import data exist between an exporterimporter pair, we simply use the existing data. Second, if both export and import data are reported but not equal, we use the quantity which is greater. This is because some might conceal trade volumes to evade regulations such as tariffs. Alternatively, one may argue that the import data should be used, since they are recorded with greater accuracy for regulation purposes. Therefore, we also calculate the results with import data if inconsistency occurs. The effect on emission factors are discussed in Section 4. Third, when the physical electricity trade volumes are unrealistically high, such as Ecuador importing 2.2×108MWh from Colombia, we adjusted these data and report the adjustment in Table S1. The principle is to rely on trade volumes reported in US dollars to correct data in kWh. Given the possibility of typos when physical data are entered, we multiply the unrealistic data by 10-n such that, after the adjustment, the electricity price falls in the reasonable range as reported by the International Energy Agency 5. Lastly, we delete electricity trade records that are not within the reasonable geographical ranges (Table S2). For example, it was reported that Japan sent 7,050 MWh electricity to the United Arab Emirates in 2014, which is possibly due to electricity purchased by cargo ships and evidently does not conform to the concept of electricity trade in our model. We simply delete such records. All the adjustments we made to the original UN Comtrade data are described in Table S1 and S2.

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Furthermore, all electricity not transferred out of a country, including the extra amount of electricity to enable the transmission to another country, is assumed to be consumed in the country. In this way, we implicitly assign the transmission losses to the generating country 15.

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Electricity generation and direct emissions. Average CO2 emission factors of electricity generation at the country level in 2014 are from IEA statistics 1. IEA has estimated these values according to the “Tier 1” approach in IPCC guidelines 28, using country-specific net calorific values for various types of fuels 1. These statistics provide the most comprehensive coverage, but could be substituted with more accurate data if available. National/regional electricity generation and fuel mixes divided into six categories (fossil fuels, nuclear, geothermal, hydro, solar/wind, and biofuels) for electricity generation are also from IEA 5.

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Countries/regions included in this study. There are 137 countries/regions (listed in Table S4) for which data are complete for this study. The electricity trade among them comprises about 91% of total global electricity trade recorded in the UN Comtrade database. We include these 137 countries/regions in our final analysis for emission factors and virtual emission flows.

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Some countries that are left out in this study may export electricity to and import electricity from the countries/regions included in this study, as reported by the UN Comtrade database. The quantities of these electricity transfers are not significant. For each of these outside countries, we made reasonable assumptions about its fuel mix and emission factor for 9 ACS Paragon Plus Environment


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electricity generation (Table S3). For example, Laos, which is not among the 137 countries/regions, is from the “other Asia” category in IEA statistics 5. We then use the IEA data for “other Asia” on fuel mix and emission factor of electricity generation in equation (5’).

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4. Results

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Global electricity trade network in 2014

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Figure 1 visualizes the global electricity trade network in 2014, where nodes represent electricity generation of the country/region and clockwise curves represent direct electricity transfers from origins to destinations. As previously revealed for the year 2011 29, there are four major communities in the global electricity trade network, namely, Eurasian, North and African, Central American, and South American. The insets of Figure 1A and 1B zoom in the African community and the European part of the Eurasian community, where electricity trade is more intensive and the network structure is more complex.

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Obviously, limitations of transmission technology dictate the community structure of the crossborder electricity trade. National/regional policies, resource endowments, and generation technologies are also important factors underlying the structure of the global electricity trade network. This structure leads to the differences of emission factor estimates and patterns of virtual emission flows among countries/regions.

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(B)

Figure 1. Global electricity trade network in 2014. Curves represent direct electricity transfers, which flow clockwise from origins to destinations. Curve widths indicate the amount electricity transfers, some of which are labeled in TWh. Node size represents electricity generation of countries/regions. Countries/regions are labeled with their ISO codes. The insets of (A) and (B) zoom in the African community and the European part of the Eurasian community respectively. Map images are from the GADM database of Global Administrative Areas 30.

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CO2 emission factors of electricity consumption in countries/regions

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Variation of CO2 emission factors of electricity consumption. At the global level, there is a wide range of CO2 emission factors of electricity consumption across countries/regions. Figure 2A illustrates the global distribution of emission factors of electricity consumption for each of the 137 countries/regions in our dataset with shades of color. For comparison, we also tabulate the specific estimates with different methods in Table S4. The places where consuming unitary electricity from the local grid is responsible for the most global CO2 emissions are found in East and Southeast Asia, Middle East, and Southern Africa. Emission factors of electricity consumption are highest in Iraq (1,169 g/kWh), Mongolia (1,148 g/kWh), South Africa (987 g/kWh), Turkmenistan (890 g/kWh), and India (813 g/kWh), due to the dominant uses of carbon-intensive fuels in their own electricity generation and/or their foreign electricity suppliers.

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On the other hand, some countries have very low emission factors of electricity consumption mainly because of the energy composition for their electricity supply. Here the focus is on CO2 emissions from fossil fuel consumption, and the entire life cycle emissions of the generation technologies are beyond the scope of this study. Although it only offers an incomplete picture of the life cycle environmental impacts of electricity, our method can help improve the accuracy of a full LCA study with improved estimation of life cycle emissions from electricity consumption. In addition, our method is also consistent with some widely used standards such as the Scope 2 emission of the Greenhouse Gas Protocol 2. Iceland has zero CO2 emission from fossil fuels since it solely relies on hydro and geothermal energy (Table S4, Figure S1). Emission factors are also low in Ethiopia (1 g/kWh), Tajikistan (7 g/kWh), DR Congo (7 g/kWh), Norway (12 g/kWh), and Sweden (18 g/kWh). These countries mainly depend on hydropower, except for Sweden which consumes electricity from a mix of nuclear, hydro, and wind power and biofuels. In Section 5, we discuss how modelling cross-border electricity trade differently may change the fuel mixes of countries’ electricity consumption, which could have implications for relevant life cycle assessments.

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Figure 2. (A) Emission factors of electricity consumption of countries/regions. Shades of color represent the values of emission factors ( efi C , Network for country/region i), while sizes of circles

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represent total electricity consumption. White areas are where data are incomplete. (B) Differences between various accounting methods for country/region-level emission factors of electricity consumption. Blue area indicates that emission factors for electricity consumption ( efi C , Network ) is smaller than those for generation ( efi G ); red area indicates the opposite. For

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hashed/meshed areas, enlarging the system boundary of accounting for electricity trade significantly changes estimates of emission factors, either downward (in hashed areas, where

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efi C ,Network efi C ,DirectAdjust 105%).

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Map images are from the GADM database of Global Administrative Areas 30.

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Impacts of electricity trade on emission factors. Figure 2B illustrates the impacts of crossborder electricity trade on emission factors of electricity consumption. Cross-border electricity trade has the greatest impacts in emission factors of Mongolia, Argentina, and clusters of countries in Europe and Southern Africa, where cross-border electricity transfers are intensive and fuel mix differences among countries/regions are significant. For most countries/regions, the effect of indirect electricity transfers on emission factors is less significant than the effect of direct electricity transfers. But our results also show that, in some countries in Europe and Northern Africa, the former effect is already an important factor to consider when estimating emissions from electricity consumption.

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Figure S2 plots specific values of emission factors of electricity consumption, for countries/regions where cross-border trade can change the results by at least 5%. We compare emission factors calculated based on three different methods, including the production-based approach, direct trade-adjustment approach, and network-based approach. In a couple of countries, cross-border electricity transfers already have important implications for emission factor estimates. Generally, if only adjusting for direct electricity trade raises (lowers) a country’s emission factor, further accounting for indirect electricity transfers with the networkbased approach tends to lower (raise) the estimate, although the change is usually smaller than the change from adjusting for direct trade. Take Estonia and Macedonia as examples. Their emission factors based on the network-based approach are respectively 81% and 86% of emission factors based on the production-based approach, and 120% and 118% of direct tradeadjustment emission factors. For Botswana and Zimbabwe, the emission factors from the network-based approach are respectively 50% and 92% of emission factors from the production-based approach, and 117% and 111% of direct trade-adjustment emission factors. Changes in the opposite direction occur for countries like Switzerland and Luxembourg, where the emission factors estimated with the network-based approach are 424% and 125% of the production-based emission factors, and 74% and 70% of direct trade-adjustment emission factors. For Mozambique and Namibia, emission factors of the network-based approach are respectively 920% and 4,441% of the production-based emission factors, and 61% and 3% of direct trade-adjustment emission factors. The drastic changes observed for Mozambique and Namibia are due to their pass-through roles in the cross-border electricity trade network: because the amounts of electricity imports and exports are much greater than local electricity generation and consumption, they serve as relaying nodes in the trade network. Therefore, it is virtually impractical to estimate their emission factors simply based on electricity generation or direct electricity trade.


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Inconsistencies between electricity export and import data may also have significant impacts on emission factors estimations. Table S5 illustrate the changes of such estimations if we use electricity import data when export and import do not match. In 7 of the 137 countries, the results change by more than 5%. Also, for these countries, no matter what electricity trade data is used, emission factors for electricity consumption diverge significantly from those for production. Thus, greater international efforts to collect electricity trade data are needed for more accurate estimation of carbon footprint of electricity consumption.

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Two general patterns in our results of different emission factor estimations are worth noting. First, more notable impacts of cross-border electricity trade occur in smaller countries. This is due to the fact that large countries are usually endowed with abundant and diverse energy sources and less reliant on electricity trade. However, if data on inter-grid electricity transfers in a large country are available with finer geographic resolutions, advanced methods that are able to properly account for direct and indirect electricity trade, such as the network-based approach used in this study, will be indispensable for understating the environmental impacts of electricity consumption (e.g., see Kodra et al.15 for inter-PCA emission flows in the US).

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Second, as noted above, incorporating the effects of indirect electricity transfers tends to pull the emission factors back to the levels implied by local electricity generation. In retrospect, this is due to the general pattern that if a country/region imports electricity from another one with very carbon-intensive technologies, the exporting country/region tends to import less carbonintensive electricity from other grids and indirectly transfer the electricity to the original importing country/region. This result is consistent with previous findings 19 that accounting for inter-state electricity trade in the US tends to push the state emission factors to the country average level. The selection of geographic boundaries for emission factor estimation thus has important implications for the evaluation of emission responsibility on the consumer side, which we will further discuss in section 5.

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Embodied CO2 emission flows in electricity trade. Cross-border electricity trade can lead to shifts in emission responsibilities or carbon leakages, whereby countries/regions may well decarbonize electricity generation inside their borders but at the same time import carbonintensive electricity. Among the countries included in this study, 177.9 Mt of CO2 emissions from electricity generation are embedded in cross-border electricity trade in 2014. This amount lies between the total CO2 emissions of the Netherlands (158 Mt) and Argentina (199 Mt) 31, and may increase in the future due to intensifying electricity trade.

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Figure 3 and Table S6 show net virtual CO2 imports/exports from electricity trade of the studied countries/regions. The clusters of net virtual emission transfers mainly reside in Europe, Southern Africa, and North America (from Canada to the US). These virtual emission transfers coincide with those of the global electricity trade network (Figure 1). However, as the traded



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electricity may be generated from different energy sources, the strengths of the specific virtual emission flows can be quite different from those simply implied by electricity trade flows.

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Figure 3. Net virtual CO2 emission imports in electricity trade. Lines and Arrows indicate net flows of embodied emissions. The largest net virtual CO2 emission flows are listed in Table S6. Countries/regions are indexed with their ISO codes. Map images are from the GADM database of Global Administrative Areas 30.

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When a country/region is a net virtual CO2 importer (or exporter) in the electricity trade network, the CO2 emission responsibility for its electricity consumption is greater (or less) than that for its electricity generation. In Europe, countries with the largest amounts of net virtual CO2 imports through electricity trade are Italy (11 Mt), Austria (7.7 Mt), Switzerland (5.5 Mt) and Hungary (5.1 Mt); and the most important net virtual CO2 exporters are Germany (32.4 Mt), Czech Republic (8.5 Mt) and Ukraine (5.4 Mt). In Africa, Mozambique and Botswana have the largest net virtual CO2 imports (5.5 Mt and 4.9 Mt, respectively), and South Africa is the most important net CO2 exporter (12.4 Mt).

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For most of the above-mentioned countries, the net virtual CO2 imports through electricity trade constitute significant shares of embodied CO2 emissions of electricity consumption. For example, Austria’s net virtual CO2 imports are 45% of the total CO2 emissions embodied in its electricity consumption, while that percentage is 89% for Mozambique. These results again attest to the importance of virtual emission flows in cross-border electricity trade and consequently for environmental policies relevant to the electric power sector.

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5. Discussion

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Impacts of cross-border electricity trade on embodied CO2 emissions. In 2014, the impacts of cross-border electricity trade on emission factors of electricity consumption still remain marginal in most regions, and this is especially true for the impacts of indirect electricity transfers. However, for the key countries/regions identified in Figure 2B, virtual emission flows have become more important for CO2 emissions of their electricity consumption. Without properly accounting for such virtual emissions from electricity trade, the environmental implications of the electricity consumption can be significantly misunderstood. Moreover, electricity trade has been increasing 5, and may have even greater effects on embodied emissions embodied in the future.

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For the shaded countries/regions in Figure 2B, we calculate how much CO2 emissions will be added to or deducted from their emission responsibilities due to electricity consumption, when we change the geographic boundary of estimation from the country/region itself or from the country/region with its direct electricity trade partners, to the entire electricity trade network, as shown in Figure S3. Red bars represent the effects of considering both direct and indirect trade, and green bars illustrate effects of accounting for indirect trade in addition to direct trade. Note that different methods use the same country-/region- level electricity consumption, but different emission factor estimates. Similar to the pattern we previously observed for emission factors, while directly adjusting for electricity trade can significantly change the country-/region- level embodied emissions, it may also overestimate the impact of electricity trade. Using the network-based approach generally leads to emission estimates closer to those based on country-/region- energy mixes for electricity generation.

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At the country/region level, cross-border electricity trade has important implications for emission responsibilities of electricity consumption. Using emission factors fully accounting for cross-border electricity trade, in almost every country/region, the total embodied CO2 emissions in electricity consumption increase or decrease by at least one million tons (Figure S3, red bars). Such variation amounts to CO2 emissions of almost 150,000 homes’ annual electricity use and more than 200,000 passenger vehicles driven in one year, based on some rough estimations 32. Accounting for indirect cross-border electricity transfers in addition to direct transfers also results in increases in total CO2 emissions relate to electricity consumption over one million tons in Germany, Italy, South Africa, and Botswana, and decreases in total CO2 emissions over one million tons in Austria, Switzerland, Latvia, Slovak Republic and Mozambique (Figure S3, green bars).

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Therefore, even with the current level of cross-border electricity trade, it is already important to properly account for emissions embodied in electricity trade when estimating emission factors of electricity consumption, especially for countries in Europe and Southern Africa. Moreover, global electricity trade has been intensifying over time, with the physical trade volume of OECD countries growing more than sixfold in the past four decades 5. In the future, the impacts of electricity trade on emissions accounting for the power sector can be much 17 ACS Paragon Plus Environment


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more significant, and the network-based approach used in this paper will become more useful for understanding environmental implications of electricity consumption.

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Fuel mix of electricity generation and consumption. With cross-border electricity trade, the energy sources virtually supporting a country/region’s electricity consumption may be quite different from those directly contributing to its electricity generation. As mentioned earlier, traditional LCI databases such as ecoinvent model the electricity supply to consumers in a country as coming from its generation plus imports, practically using the exporting countries’ generation mix to represent their electricity exports 24, 25. As electricity trade intensifies, even such adjustment may to a certain extent misestimate electricity fuel mix since it leaves out indirect electricity transfers. Figure S1 compares the fuel mixes of electricity generation, consumption and (production + import) (with the method in ref 25) for countries included in this study. In the following we discuss two notable patterns.

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First, the fuel mix of the electricity consumption differs much from that of the electricity generation in a lot of countries/regions. For example, from the consumption side, Austria and Croatia respectively rely 18% and 24% less on hydropower, 10% and 5% more on fossil fuels, and 8% and 18% more on nuclear energy than from the production side. Due to cross-border electricity trade, consumed electricity usually has more diversified fuel mixes. For example, the fuel mix of electricity consumed by Mongolia and Botswana, inferred with the network-based approach, contains sizable shares of hydropower (4% and 36%), although their generated electricity almost entirely comes from fossil fuels (97% and 100%). Denmark generates electricity using fossil fuels, wind, and geothermal energy, but also consumes hydro and nuclear power virtually from other countries/regions.

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Second, the (production + import) calculation 25 provides a good approximation for fuel mixes while for most countries/regions , in a few countries, the results are significantly different (Figure S1). The network approach reveals that Botswana consumes considerably more fossil fuels (from 51% to 62% of electricity consumption) and less hydropower (from 47% to 35%). Noticeable changes in fuel mixes also occur in Central and Eastern Europe, including Croatia, Hungary, Latvia, Montenegro and Slovenia.

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The above differences not only can drive the CO2 emission factors for electricity consumption away from those for electricity generation, but may also have other important environmental implications since generation technologies are not innocuous to the environment 33-35. These results attest the importance of cross-border electricity trade in the electric power sector which may have rendered the environmental impacts of electricity consumption more complicated than ever. Thus appropriately and timely accounting for its effects is critical for understanding the environmental implications of electricity consumption.

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It will be useful to integrate more detailed electricity data with the network method and its adaption. Note here the fuel mix for electricity consumption is derived using aggregate cross18 ACS Paragon Plus Environment

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border electricity trade data which per se does not differentiate various types of energy sources to produce transferred electricity. The results are inferred from the fuel mixes of each country/region’s electricity generation and the structure of aggregate electricity trade. In reality, different types of electricity can be mixed in the transmission and distribution system. Therefore it is impractical to separate them in cross-border trade, except when there are records of specific contracts between suppliers and users 3. Recent progress in building multiregional input-output (MRIO) database have enabled separate international trade records for specific types of electricity such as hydro or solar for as late as 2007 36, 37. However, assembling MRIO databases entails a lot of assumptions and the resulted data are meant for economic systems analysis rather than to precisely represent individual trade flows.

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Limitations and future research. The resolution of the results is at the country level. This is more satisfactory for small countries, where incorporating the impacts of electricity trade can be crucial for estimating emission factors of electricity consumption. However, for large countries such as the US and China 15, 18, regional heterogeneity within the country is relatively more important for understanding environmental impacts of electricity consumption. To better understand the environmental impacts of electricity consumption in large countries in the future research, data on electricity generation, consumption and transfers should be acquired at finer geographic resolutions within the country. These data, of course, need to be linked with cross-border electricity trade data. Furthermore, if specific contracts for purchased electricity are available, it is more appropriate to allocate emission responsibilities based on these contracts rather than on any type of grid-average emission factors. However, only a small portion of electricity can be traced in this way 2, 25.

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This study quantifies average emission factors of electricity consumption from specific grids. This type of emission factor serves to estimate emissions due to the electricity consumption of existing activities, and underpins the accounting of corporate emission responsibilities. Alternatively, marginal emission factors are used to measure impacts of incremental changes of electricity demand 38, for example, the large-scale deployments of new technologies 39. There is no estimation for marginal electricity factors that incorporate the impacts of electricity trade 38, despite the obvious importance of doing so. This is primarily because estimating marginal emission factors are more data intensive. However, if data on electricity generation, consumption and transfers are available at finer temporal and geographic resolutions, statistical approaches 40 can be used together with the matrix formulation in this study to estimate marginal emission factors from electricity consumption, taking account of the role of the ever-growing electricity trade.



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Supporting Information

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Tables and Figures supporting the main text.

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Competing financial interests

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Authors declare no competing financial interests.

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Reference

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Virtual CO2 emission flows in the global electricity trade network

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