CO2 Emissions Embodied in Interprovincial Electricity Transmissions

Subscriber access provided by Columbia University Libraries

Article

CO2 Emissions Embodied in Interprovincial Electricity Transmissions in China Shen Qu, Sai Liang, and Ming Xu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b01814 • Publication Date (Web): 09 Aug 2017 Downloaded from http://pubs.acs.org on August 9, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 23

Environmental Science & Technology

1

CO2 Emissions Embodied in Interprovincial Electricity Transmissions in China

2

Shen Qu a, Sai Liang b, Ming Xu a,c,*

3 4

a

5 6

b

7 8

c

School for Environment and Sustainability, University of Michigan, Ann Arbor, Michigan 48109-1041, United States 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

9 10

*corresponding author.

11

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

12

1 ACS Paragon Plus Environment


Page 2 of 23

13

Abstract

14 15 16 17 18 19 20 21 22 23 24 25 26

Existing studies on the evaluation of CO2 emissions due to electricity consumption in China are inaccurate and incomplete. This study uses a network approach to calculate CO2 emissions of purchased electricity in Chinese provinces. The CO2 emission factors of purchased electricity range from 265 g/kWh in Sichuan to 947 g/kWh in Inner Mongolia. We find that emission factors of purchased electricity in many provinces are quite different from the emission factors of electricity generation. This indicates the importance of the network approach in accurately reflecting embodied emissions. We also observe substantial variations of emissions factors of purchased electricity within sub-national grids: the provincial emission factors deviate from the corresponding sub-national-grid averages from -58% to 44%. This implies that using subnational-grid averages as required by Chinese government agencies can be quite inaccurate for reporting indirect CO2 emissions of enterprises’ purchased electricity. The network approach can improve the accuracy of the quantification of embodied emissions in purchased electricity and emission flows embodied in electricity transmission.

27 28

TOC

29


Page 3 of 23


30

1. Introduction

31 32 33 34 35

The power sector plays a critical role to global greenhouse gas (GHG) emissions and mitigation policies 1. In 2013, electricity generation contributes over 40% of carbon dioxide (CO2) emissions, both in the whole world and particularly in China 2. During 2000-2013, CO2 emissions from China’s electricity generation have increased by 2.6 Gt 3, which exceeds the annual CO2 emissions of any single country in 2013 except the US and China 2.

36 37 38 39 40 41 42 43 44 45 46 47 48 49

Significant efforts have been done or planned in China to mitigate GHG emissions from the power sector. For example, the 13th five-year plan has outlined targets for both reducing coal use in power generation and conserving energy in key sectors 4. These policies often involve the allocation of emission reduction responsibilities from the nation to provinces and from a province to municipalities. Such allocation needs be based on accounting emissions from both electricity generation and consumption. In particular, emissions from power generation are ultimately driven by the consumption of electricity. Considering the role of electricity consumption in policymaking can help leverage the consumption-side drivers for emission reductions, thus make mitigation policies for the power sector more effective. However, despite the increasingly precise accounting of direct emissions from electricity generation 5, 6, the accounting for emissions due to electricity consumption in China remains inconsistent and often inaccurate. The difficulty for the quantification of emission responsibilities from electricity consumption lies largely in understanding the fuel mix of purchased electricity from the grid, especially when regions transfers electricity among themselves intensively.

50 51 52 53 54 55 56 57 58 59 60

Inter-regional electricity transmissions 7, 8, which has been increasing worldwide due to the infrastructure development and market liberalization 9, lead to the separation of upstream emissions in power generation from downstream electricity consumption. The separation of electricity consumption from production and associated emissions are particularly important for nations in which the power sector consists of multiple regional grids trading electricity with each other. China has a spatial mismatch between electricity demands in more economically developed, east coastal areas and greater resource availability in less developed, inland regions. As a result, electricity is traded frequently between regional power grids. As highlighted in its State Council’s Action Plan for Energy Development Strategy towards 2020 10, the advancement and deployment of long-distance transmission technologies will only signify the role of interregional electricity trade in China’s power sector and its climate mitigation policies.

61 62 63 64 65 66 67 68

Few studies have examined the separation of electricity consumption from generation and associated emissions in China, which all suffer from low level of spatial resolution and methodology accuracy/completeness. At the coarser resolution of sub-national grids, each composed of several provinces, Kang et al. 11 estimated CO2 emissions from electricity generation and consumption in 2005 and 2020 with projection. Song et al. calculated average GHG emission factors for purchased electricity for sub-national grids by attributing all direct electricity imports to the consumption of the importing grid 12 . Lindner et al. 13 calculated embodied CO2 emission flows in China at the provincial level using the same method. However, 3 ACS Paragon Plus Environment


Page 4 of 23

69 70 71 72 73 74 75

the method used in these two studies neglects indirect imports of electricity passing through a third grid, leaving the accounting of emissions driven by electricity consumption inaccurate and incomplete. This lack of the understanding of the separation of electricity consumption from generation in China has led to GHG emission estimations for provinces and cities with inconsistent methods for treating electricity consumption 14-18. Table S1 shows that these studies use emission factors at different spatial scales with electricity trade accounted or unaccounted for.

76 77 78 79 80 81 82 83 84 85

This study investigates the impacts of interprovincial electricity transfers on the estimation of CO2 emissions due to electricity consumption in China, and explores to what extent such estimations could be improved and standardized. We used a network approach to account for both direct and indirect electricity trade in interconnected grid networks with theoretical accuracy and completeness, and compare the results with those derived from previous relatively simple methods. We conduct the analysis to examine the relationship between CO2 emissions from power generation and electricity consumption in 2013 at the provincial level. The results shed light on the impacts of interprovincial electricity trade on CO2 emissions in China and provide insights on the effectiveness of consumption-side measures for climate mitigation.

86 87

2. Method

88 89 90 91 92 93 94 95 96 97 98 99 100

The concept of accounting for the amount emissions from electricity production driven by specific electricity consumption is similar to the emission transfers embodied in the trade of goods and services, which have been extensively discussed 19-21. However, fundamental differences between these two types of transfers exist. Electricity produced in one region (e.g., province A) can be imported to another region (e.g., province B). The imported electricity can be either consumed by province B or re-exported to a third region (e.g., province C). If it is the latter, the electricity exported from province B to province C is effectively a blend of electricity produced in province B itself and electricity produced in province A imported to province B. As a result, electricity consumption in province C drives not only just electricity production and emissions from the production in province C itself, but also production and emissions in province B (direct effect) and province A (indirect effect). As the inter-regional electricity trade becomes more complex, the consumption of electricity becomes more separated from the production as well as emissions from the production.

101 102 103 104 105 106

Accounting for emissions driven by electricity demand in interconnected grid networks is not a trivial task, entailing a broad variety of methods 22. Some studies attribute all direct electricity imports to the consumption of the importing grid 7, 13, 18, 23, 24. This method (henceforth the direct trade-adjustment method) neglects the possibility that electricity can be passed through the grid. Alternatively, a nested approach 25 is employed to reflect electricity exchanges between a smaller region and a surrounding larger one, assuming the smaller region either 4 ACS Paragon Plus Environment

Page 5 of 23


107 108 109 110 111 112

imports electricity for local use or for exporting to the neighboring region. A more complete and accurate, network-based approach makes use of data on electricity exchanges among all of the interconnected grids during a certain time period for the accounting of embodied emissions in purchase electricity 26, 27. This method has been applied to study the energy and emissions flows among sub-national grids in China 11 and among production control areas (PCAs) in the US 28 .

113 114 115 116 117 118 119 120 121 122 123

In this study we use a matrix generalization of the network approach to estimate emission factors of purchased electricity and embodied emission flows 11, 27, 28. The method connects direct emissions from electricity generation of a grid to electricity consumption in another grid, based on statistically aggregated data over certain period (e.g., a year) of grid generation, direct emissions, and inter-grid transmission. In particular, sites of power generation and consumption are modeled as nodes, and inter-regional electricity transmissions are treated as linkages. Electrical energy flows in this network. The movement of embodied emissions is concurrent with the energy flows, forming a virtual emission network. If a grid imports electricity from another one which in turn imports from a third grid, the first grid may indirect import some electricity from the third one. The method is able to account for all such higherorder electricity transfers, which are of an infinite number.

124

2.1 Previous grid emission factor estimations

125 126

For comparison, we first lay out commonly used methods to calculate grid CO2 emission factors (i.e. emission factors of electricity generation and from the direct trade-adjustment method).

127

2.1.1 CO2 emission factors for electricity generation

128 129

This type of CO2 emission factor for grid i is simply the emissions from fuel combustion for electricity generation divided by total electricity generation of this grid, as in equation (1).

130

efi G = eiG pi

131

2.1.2 CO2 emission factors with the direct trade-adjustment approach

132 133 134 135 136

This method assumes all electricity imports are consumed, and all electricity exports come from generation of the exporting grid. Therefore, with emission factors for electricity generation of each grid, one can directly adjust for electricity imports and exports to calculate embodied emissions in electricity consumption and emission factors of each grid, as in equations (2) and (3).

137

eiC , DirectAjust = efi G ⋅ pi + ∑ ef jG ⋅ T ji − ∑ efi G ⋅ Tij

(1)

j ≠i

138

ef i C , DirectAjust = eiC , DirectAjust ci

(2)

j ≠i

(3)



139

where eiC , DirectAjust and ef i C , DirectAjust are respectively embodied emissions and emission factor for

140 141

grid i calculated with the direct trade-adjustment method, pi and ci denote electricity generation and consumption of grid i, and Tij denotes electricity transfer from grid i to grid j.

142

2.2 Modeling the interconnected grid network

143 144 145 146 147

In the network approach, a power grid is treated as a node, and we define total electricity inflow and outflow for each node. The total electricity inflow consists of 1) local electricity generation and 2) direct imports from other nodes; the total electricity outflow consists of 1) local electricity consumption and 2) direct exports to other nodes. These two must be equal due to the conservation of energy:

148

n

n

j =1

j =1

xi = pi + ∑Tji =ci + ∑Tij

(4)

149 150 151

where xi represents the total electricity inflow or outflow of grid i, pi stands for electricity generation, ci is electricity consumption, and Tij is the total amount of electricity transmitted from grid i grid j during a year.

152 153 154

Furthermore, given data on electricity generation, consumption, and inter-grid transmission during a period, one needs to assume that the imported electricity first mixed with the generated electricity in the grid, and then is either consumed or transmitted to other grids.

155 156

For simplicity xi is called total electricity flow henceforth. Let n by n matrix T contain inter-grid electricity transmissions (values of Tij). Then we define the direct outflow matrix B:

157

 0   T21  −1 B = xˆ T =  x2  M   Tn1  xn

T12 x1

L

0

O

O

O

L

Tn ( n −1) xn

Page 6 of 23

T1n  x1   T2 n  x2  M   0  

158 159

where the (i,j)th entry of B is the fraction of grid i’s total electricity inflow xi that is transmitted to grid j.

160

The total outflow coefficient matrix quantifies direct and indirect electricity transfers:

161

G = ( I − B)−1 = I + B + B2 +L (5)

162 163 164

where the element Gij is the total electricity inflow to grid j that corresponds to unitary electricity generation in grid i. Specifically, as is shown in the right-hand-side of equation (5), the inter-provincial electricity transfers can occur directly (represented by the term I), through 6 ACS Paragon Plus Environment

Page 7 of 23


165 166

one pass-through provincial grid (represented by the term B), through two pass-through provincial grids (represented by the term B2), or through more provincial grids.

167 168 169 170 171

Here we have borrowed from the concept of the supply-driven Ghosh model in Input-Output (IO) theory. Although formulation of this model is subject to debate when applied to predict economic output change 29, 30, it works perfectly well as an accounting model to calculate material 31 and electricity flows 27. However, the distinct feature of electricity trade prevents direct application of IO models (see our previous theoretical discussion 27).

172 173

Then we define the generation-consumption matrix, H, linking electricity generation and consumption in different grids:

174

ˆˆ −1 H = Gcx

175 176

where the hat operator diagonalizes the electricity consumption vector c and total electricity flow vector x. The (i, j)th entry H ij = Gij ⋅ ci / xi represents the fraction of electricity generated in

177 178

grid i that is consumed in grid j, through all possible inter-grid transfer paths laid out in equation (5).

179

2.3 Embodied inter-grid CO2 emission flows

180 181

The CO2 emission of each grid from thermal power generation is calculated from different types of fuel uses:

182

eiG = ∑ EFk ⋅ FCk ,i

(6)

k

183 184

where EFk is the CO2 emission from combustion of 1 ton (or 104 m3) of fuel type k, and FCk,i is the amount of fuel type k used for power generation in grid i.

185

Let E G be the diagonalized matrix where the ith diagonal element is eiG . Then the generation-

186 187

consumption matrix H links the emissions from power generation to consumption, resulting in the matrix of emission flows:

188

EC = EG H

189 190

In the above equation, EC is a matrix where the (i,j)th entry is the embodied emission from grid i to j.

191 192

Given EC, we can directly calculate the emissions embodied in electricity consumption for each grid, as well as the corresponding emission factor, using equation (8) and (9).

193

eC = [1,L,1] E C

(8)

194

ef C = eC cˆ −1

(9)

(7)



195 196

Page 8 of 23

where e C is an 1 by n vector containing emissions embedded in electricity consumption for each grid, and ef C is an 1 by n vector with provincial emission factors for purchased electricity.

197 198

3. Data

199

3.1 Inter-provincial electricity transfer in China

200 201 202 203 204 205 206 207

According to the provisions of the National Energy Administration of China 32, power dispatching agencies are required to report electricity exchanges that cross sub-national grids and provinces (including municipalities which are administrative units of the provincial level). Consequently, the finest spatial resolution at which complete data is public available is the provincial level, and this should be the proper spatial resolution of estimating emissions due to electricity consumption with electricity trade. Therefore, the spatial resolution of this study is at the provincial scale, and the system boundary covers 30 provinces in China, not including Tibet, Taiwan, Hong Kong, and Macao, due to data unavailability. The time period is set at 2013.

208 209 210 211 212

We collect inter-provincial electricity transmission data in 2013 from China Electricity Council 33. This data source contains electricity transmission data from the delivering side. Most of transmission data are directly at the provincial level. A small amount of transmission data are from provinces to subnational grid covering multiple provinces, and we disaggregate them to the provincial level based on actual electricity transmission situations 34.

213 214 215 216 217 218 219

Provincial electricity generation and consumption data are collected from China Electric Power Year Book 35. For each province, the sum of electricity generation and imports should theoretically equal to the sum of electricity consumption and exports. Due to statistical discrepancies, there are small differences which are less than 2% for each province (except for Guangxi where the difference is 4%). For consistency, this study re-calculates provincial electricity consumption as local electricity generation plus imports minus exports. We do not consider transmission loss in this study, mainly due to data unavailability.

220

3.2 Provincial CO2 emissions from electricity generation

221 222 223 224 225 226 227

Provincial CO2 emissions from electricity generation are calculated based on fuel combustion and corresponding emission factors. Data for the consumption of 22 types of fuels (listed in Table S2) by thermal power plants in each province in 2013 are from China Energy Statistical Yearbook 36. The emission factor for raw coal, the most important source of China’s CO2 emissions, is taken from Liu et al. 37 who revised the emission factor for raw coal based on China’s coal samples. Emission factors for other types of fuels are from China-specific emission factors published by the World Resource Institute 38.

228


Page 9 of 23


229

4. Results

230 231 232 233

In 2013, 14% of the generated electricity in China is transmitted across provinces (Figure 1A). The largest inter-provincial transmission flows are from Yunnan and Guizhou to Guangdong (60 TWh and 39 TWh), Inner Mongolia to Hebei (42 TWh), Liaoning (37 TWh) and Beijing (23 TWh), Sanxi to Hebei (37 TWh), and Sichuan to Jiangsu (23 TWh).

234 235 236 237 238 239 240 241 242 243 244 245 246

Based on these direct electricity transfers, we calculate the network-inferred origins and supply chain paths of provincial electricity consumption. Figure S1 shows provincial electricity consumption that is produced elsewhere and its composition. In particular, we allocate traded electricity to consumption through an infinite number of steps, similar to previous IO studies quantifying “feedback effects” for emissions embodied in traded goods 39, 40. Using equation (5), the imported electricity consumption in province A is divided into tier 1 consumption (i.e., electricity produced in B and transmitted to A for consumption), tier 2 consumption (i.e., electricity produced in C, passing through B, and transmitted to A for consumption), tie 3 consumption, and etc. Tier 1 consumption, which represent the only type of electricity transfer in the direct trade-adjustment approach, accounts for less than 75% of imported electricity consumption in Hainan, and less than 90% in 8 other provinces. Significant electricity transfers occurs with tier 2 electricity supply chains (Table S3), for example, Inner Mongolia -> Liaoning -> Beijing (3.0 TWh) and Heilongjiang -> Jilin -> Liaoning (2.8 TWh).

247 248 249 250 251 252 253 254 255 256 257

To reveal the composition of the electricity that could be consumed in each province, we calculate provincial total electricity inflow (i.e., electricity generation plus total electricity import). Figure S2 shows this variable as well as the shares of provincial electricity consumption and generation in it. Provinces with lower shares of electricity consumption in total inflow export higher portions of its available electricity, and provinces with lower shares of electricity generation import higher portions of its available electricity. If both shares for a province are low, it is highly likely that this province serves as an important “pass-through” role in the electricity trade network. We find Beijing, Shanghai, and Chongqing imported >30% of their available electricity, and Inner Mongolia, Hubei, Guizhou, Yunnan and Ningxia exported >30% of their available electricity. The most important “pass-through” nodes are Jilin, Heilongjiang, Shaanxi and Gansu.

258 259 260

Total CO2 emissions from electricity generation in China are 3.7 billion tonnes (Bt). About 0.48 Bt, contributing 13% of total CO2 emissions from electricity generation, are embodied in interprovincial electricity transmission (Figure 1B).

261 262 263 264 265 266

Furthermore, the embodied emission flows network (Figure 1B) is more densely interconnected than the electricity transmission network (Figure 1A), given that there are 95 links (i.e., direct electricity transmissions) in Figure 1A while 173 links (i.e., embedded emission flows larger than 104 tons) in Figure 1B. This indicates that substantial hidden emission flows between provinces without direct electricity transmissions are identified through at least one pass-through provincial grid. 9 ACS Paragon Plus Environment


267 268 269 270

Page 10 of 23

Figure 1. (A) Inter-provincial electricity transmission flows and (B) network-inferred interprovincial CO2 emission flows of China in 2013. Only CO2 emission flows greater than 104 ton are kept as network edges in (B) (Table S4). The width of arrows in (B) is proportional to the amount of embodied emission flows.

271


Page 11 of 23


272

4.1 Emission factors of purchased electricity

273 274 275 276 277 278 279 280

Figure 2A shows the spatial pattern of CO2 emission factors of purchased electricity at the provincial scale, calculated with equation (9) (with detailed results in Table S5). Emission factors are significantly different across provinces. As a reference, China’s overall CO2 emission factor of electricity generation in 2013 is 7.1 tCO2/10MWh 2. This value is most close to the emission factor of purchased electricity in Jiangsu (7.11 tCO2/10MWh) in the east coast and Jiangxi in the south (6.96 tCO2/10MWh). However, emission factors of purchased electricity in other provinces are quite different from the IEA value. This indicates the importance of the networkinferred emission factors in accurately reflecting embodied emissions.

281 282 283 284 285 286

Our results also reveal that, even within the same sub-national grid, there are significant differences in emission factors of provinces. For example, in the Northwest China Grid comprising five provincial grids of Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang, emission factors of purchased electricity range from 2.77 tCO2/10MWh in Qinghai where most electricity is generated from nuclear power, to 8.18 tCO2/10MWh in Ningxia where thermal power (other than nuclear power) dominates 35, 36.

287 288 289 290 291 292 293

Moreover, we compare the network-inferred emission factor of purchased electricity (EF_Consumption, discussed in the preceding paragraph) with the emission factor based on the fuel mix of local power plants within each province (EF_Generation, calculated with equation (1)). We observe large differences between these two emission factors in some provinces (Figure 2A). For example, Beijing’s EF_Consumption is 41% higher than its EF_Generation, because Beijing purchases electricity from inland provinces of Inner Mogolia, Shanxi, and Liaoning where coal is the dominant source for power generation.

294 295 296 297 298 299 300 301

In Table S6 we report the sensitivity of the network-inferred emission factors to uncertainties related to emission factors of electricity generation of each province. When the generation emission factor of a province changes by 10%, the effect generally spreads over a few provinces, each of which has a change in the network-inferred emission factor by less than 10%. In absence of systematic errors in measuring provincial generation emission factors, the networkinferred emission factor should be therefore more robust that the generation emission factor, since the various errors in measuring the latter tend to cancel out when calculating the former 27 .



Page 12 of 23

302

303 304 305 306 307 308 309

Figure 2. (A) Provincial emission factors of purchased electricity in China. Shades of the color indicate the amounts of CO2 emissions embodied in unitary electricity use. Areas with mesh/horizontal lines are provinces where emission factors of electricity consumption are significantly lower/higher than emission factors of local electricity generation. For Tibet and Taiwan, data are unavailable. (B) Net CO2 emissions embodied in inter-provincial electricity transmission in China in 2013.

310


Page 13 of 23


311

4.2 Embodied emissions of purchased electricity

312 313 314 315 316 317

Figure S3 compares each province’s direct CO2 emissions from electricity generation and embodied CO2 emissions in electricity consumption. The electricity consumption in Jiangsu (353 million tons), Shandong (320 million tons), Hebei (305 million tons), and Guangdong (274 million tons) induces the largest CO2 emissions. These amounts are comparable to the total CO2 emissions from fuel combustion in some major countries such as France (316 million tons) and Italy (338 million tons) 2.

318 319 320 321 322 323 324 325 326 327

The largest direct CO2 emitter is the Inner Mongolia grid, discharging nearly 350 million tons of CO2 (Figure S3). However, domestic electricity consumption in Inner Mongolia is only responsible for 60% of its direct CO2 emissions. A large part of direct CO2 emissions of the Inner Mongolia grid are embodied in electricity transmissions to Hebei and Liaoning (two industrial centers) and Beijing and Tianjin (China’s economic and political centers) (Table S4). The CO2 emissions embodied in electricity outflows of the Inner Mongolia grid are 137 million tons, comparable to the total CO2 emission from fuel combustion of Netherlands in 2013 (156 million tons) 2. The Shanxi grid is also a big exporter of embodied CO2 emissions by sending out 70 million tons of embodied CO2 emissions, an amount greater than the total CO2 emissions from Austria and Greece 2.

328 329 330 331 332 333 334 335 336 337 338

Figure 2B illustrates the net inflows/outflows of CO2 emissions embodied in inter-provincial electricity transmissions. We observe large differences between CO2 emissions from electricity generation and CO2 emissions caused by electricity consumption in northern regions of China, which is also illustrated in Figure 2A for East China Grid, North China Grid, and Northeast China Grid. Although large amounts of electricity are transferred in both the north and south of China (Figure 1A), the south has much smaller embodied emission flows (Figure 1B). This is mainly due to the structure of exported electricity: thermal power exports in the northern regions such as Shanxi, Inner Mongolia, and Liaoning, and hydropower exports in the southern regions such as Sichuan, Hubei, and Yunnan. For Guangdong, CO2 emissions embodied in its purchased electricity is 55 million tons which are higher than its direct CO2 emissions from power generation, due to the large volume of electricity imports to this province (131 TWh).

339 340 341 342 343

China’s inland provinces are usually net CO2 exporters, while east coastal regions are usually net CO2 importers (Figure 2B). The CO2 emissions embodied in electricity consumption of Beijing are about four times as CO2 emissions from its electricity generation. The embodied CO2 emissions from Inner Mongolia to Beijing are 25 million tons, comparable to the total CO2 emissions of Iceland 2.

344 345 346 347 348

Figure 3 illustrates the spatial pattern of CO2 emissions caused by unitary electricity consumption in Beijing, Shanghai, Zhejiang, and Guangdong. These four selected provinces/municipalities feature economic prosperity and lifestyle affluence. Unitary electricity consumption in these regions is responsible for CO2 emissions in broad inland areas. The hashed areas represent provinces that do not have direct electricity transmission to the target 13 ACS Paragon Plus Environment


349 350 351

Page 14 of 23

province, and embodied inter-provincial CO2 emissions come from indirect electricity transfers passing through (at least) a third province. Such a situation will become more important with increasing inter-provincial electricity transfers.

352

(A) Per kWh consumption in Beijing

(B) Per kWh consumption in Shanghai

353 354

(C) Per kWh consumption in Zhejiang (D) Per kWh consumption in Guangdong Figure 3. Spatial pattern of CO2 emissions due to unitary electricity consumption in Beijing (A), Shanghai (B), Zhejiang (C), and Guangdong (D).5. Discussion

355

5.1 The importance of the network approach

356 357 358 359 360

The results of this study reveal that in some provinces of China, interprovincial electricity transfers significantly change the estimations of emissions due to electricity consumption. Furthermore, the estimations may also depend on the approaches accounting for electricity transfers. The network approach take into account the indirect imports and exports of electricity, and hence is more suitable for quantifying average emission factors of purchased 14 ACS Paragon Plus Environment

Page 15 of 23


361 362 363 364 365

electricity than the direct trade-adjustment approach. Figure 4 compares results of the network approach with those of the direct trade-adjustment approach. Positive (negative) changes due to using the network approach indicate that the provincial grid may indirectly import electricity with high (low) CO2 emission intensity. Overall, the difference is modest, but it may become substantial with increasing inter-grid transfers.

366 367 368 369 370 371

Beijing has the largest increase in both emission factor (51 g/kWh) and embodied emissions (4.7 million tons). It already directly purchases electricity from CO2-intensive provinces such as Inner Mongolia and Shanxi (Figure 1B and Figure 2). The network approach reveals that Beijing consumes even more electricity from the most CO2-intensive province – Inner Mongolia (with 947 g/kWh for electricity generation) – than implied by direct electricity import data. Thus, Beijing needs to take more emission responsibility from the network perspective.

372 373 374 375 376 377

On the contrary, some provinces may be pass-through grids of parts of electricity transmissions, such as Hubei (of the Central China Grid), Jilin, Heilongjiang (both of the Northeast China Grid), Gansu (of the Northwest China Grid), and Gongdong (of the Southern Power Grid). For example, there is no direct electricity transmission from Sichuan to Guandong but embodied emission flows between them, due to the indirect electricity transmission passing through the provincial grids of Hubei and Hunan.

378 379 380 381 382

The accuracy of emission factors of purchased electricity can influence the effectiveness and equity of policy choices 8. The pilot emission trading scheme (ETS) in Beijing has taken into account the embodied emissions of purchased electricity, and other pilots in China intend to follow this action 41. The network approach in this study gives more accurate emission factors than the direct trade-adjustment approach, and hence can better support China’s ETS.



Page 16 of 23

(A) Differences in emission factors of purchased electricity

383 384 385

(B) Differences in embodied emissions in electricity consumption Figure 4. Changes from the direct trade-adjustment approach to the network approach, with respect to (A) provincial emission factors of purchased electricity and (B) embodied CO2 emissions in electricity consumption.


Page 17 of 23


386

5.2 The importance of spatial resolution

387 388 389 390 391 392 393 394 395 396 397 398 399 400

Currently, the Chinese government requires industrial enterprises to report CO2 emissions related to their purchased electricity using the latest average sub-national-grid emission factors 42 . Figure 5 compares the average emission factors of sub-national grids with the provincial emission factors. To be consistent, both types of emission factors are calculated by the network approach. Results show that, for many provinces, using the average emission factors at the subnational-grid level can significantly distort the estimates of embodied emissions in purchased electricity. The most pronounced differences are observed in Central China Grid, Northwest China Grid, and Southern Power Grid. For example, in Henan province, shifting to its provincial emission factor will increase embodied CO2 emissions in unitary purchased electricity by 251 g/kWh, 44% higher than using the sub-national-grid average. In contrast, the estimates of embodied CO2 emissions using the provincial emission factor of Qinghai are 58% less than that using the sub-national-grid average. In addition, we observe >100 g/kWh increases in emission factors for Anhui, Jiangxi, Shanxi, Ningxia, and Hainan, while >100 g/kWh decreases for Hubei, Sichuan, Gansu and Yunan, when provincial emission factors are used.

401 402 403 404 405 406 407 408 409 410 411 412

The latest average sub-national-grid emission factors published by China’s National Development and Reform Commission (NDRC) 43 are the ones most commonly used by enterprises to report emissions due to electricity consumption. However, they are only for the year 2012, hindering direct comparison of our results at the sub-national-grid level with previous statistics. Nonetheless, in Table S7, we list emission factors published by NDRC for 2011 and 2012 43, those by WRI for 2011 12, and our estimates in 2013. Our results are generally consistent with the NDRC estimates, and combined, they indicate moderate reduction in emission intensity of electricity at the sub-national-grid level. The only exception is the Northeast China Grid where the emission factor estimated in this study is high than the 2011 and 2012 NDRC estimates, but smaller than the 2011 WRI estimate. The discrepancies can come from various sources such as differences in fuel composition and electricity trade across years and different emission factors for the fuels used in the these estimations.

413 414 415 416 417

Compared to the more precise provincial emission factors, using the average sub-national-grid emission factors by industrial enterprises in the provinces identified in Figure 5 can substantially over- or under-estimate their environmental impacts. China should improve its electricity statistics to provide higher spatial resolution data for the network approach, which can provide more accurate emission factors for the ETS.



Page 18 of 23

418 419 420

Figure 5. Changes from sub-national-grid average to provincial emission factors of purchased electricity in 2013.

421 422

5.3 Carbon leakage through inter-provincial electricity transmission

423 424 425 426 427 428 429 430 431 432 433 434 435 436 437

China has put forward the goal of reducing CO2 emissions by 60%-65% below its 2005 level by 2030 in the “intended nationally determined contribution” (INDC) for the UNFCCC conference in December 2015 44. Efforts in the power sector will be an important component, and understanding emissions from both power generation and consumption would be helpful for policies and initiatives to decarbonize the economy. China is planning its national ETS, which will place restrictions on emissions from the power sector 45. Regional pilots has been carried out in five municipalities (Beijing, Tianjin, Shanghai, Shenzhen and Chongqing) and two provinces (Guangdong and Hubei), preceded by pilots of smaller scales around the country 46. The large amounts of inter-provincial transfers of emissions embodied in electricity transmission underscores the necessity of nationwide ETS to prevent carbon leakage related to the power sector. Here the concept of carbon leakage denotes that a province may well decarbonize its power sector while importing carbon-intensive electricity from other provinces. The phenomenon differs from carbon leakage through trade in goods and services, in that electricity “consumption” often occurs in industrial production while consumption of goods and services refers to the final demand of a society. Therefore, quantifying footprints of electricity 18 ACS Paragon Plus Environment

Page 19 of 23


438 439 440

consumption underlies different levers to decarbonize the economy. Similar to analysis for interprovincial carbon leakage associated with goods and services 47, emission caps on the power sector can only have the intended effects with a nationwide coverage.

441 442 443 444 445 446 447 448 449 450

Currently, in preparation for the national ETS, companies report their GHG emissions from electricity based on sub-national-grid average emission factors, as required by NDRC 48. Given the significant variance of carbon emissions embodied in unitary electricity consumption across provinces (Figure 2(A) and 5), more accurate emission factors nationwide will be indispensable for delineating companies’ emission responsibilities. With better quantification of carbon footprints of electricity which properly accounts for electricity trade, China’s national ETS would be more effective at curbing carbon leakage through electricity transfers. Moreover, the national ETS still does not cover all sectors, only including several emission-intensive sectors 48. For other sectors, voluntary emission mitigation efforts still need to be supported by proper emission accounting methods especially for upstream emissions from purchased electricity.

451 452 453 454 455 456 457 458 459 460

When assigning electricity emission responsibilities, it is critical to recognize issues related to equity 8. Consumers of electricity often have little leverage in choosing what type of electricity they consume, which largely depends on past infrastructural development. As a result, when performing emission calculation with a particular set of emission factors (even more accurate ones), “winners” and “losers” would inevitably emerge 8. An in-depth discussion about equitable policies is beyond the scope of this paper, but there are two points worth mentioning. First, such complex issues should not discourage the efforts for tackling sustainability challenges. Second, quantification of carbon footprint of electricity consumption serves as a basis for related policy design. For example, the NDRC has been collecting data on companies’ historical emissions in preparation of the national ETS 45, 48.

461 462

5.4 Limitation and future research

463 464 465 466 467 468 469 470 471 472

This study has several limitations with respect to the temporal and spatial resolutions of data. Inter-provincial electricity transmission data are only published annually, necessitating the assumption that if the generated electricity in a province is exported, it must have the average emission factor over the year. (Although this may not be an inaccurate assumption in reality 23.) Moreover, the fuel mix data of electricity generation are at the provincial level, making it impossible to identify the variance in emission factors within a province. If more disaggregated data for fuel mix and electricity transmission were available at the power-plant level, more precise estimates of emissions of electricity consumption would be possible. Such precise evaluations would be crucial for effective and fair climate and environmental policies related to electricity consumption.

473 474 475

It would also be interesting to use the network approach to examine emissions embodied in electricity consumption at the international or global scale. In particular, in the past decades, energy intensity for electricity production decreased only marginally at the global scale despite 19 ACS Paragon Plus Environment


476 477 478 479

Page 20 of 23

significant reduction in many regions 49. The reason is that electricity production had shifted to countries with higher emission factors of electricity generation 49. Therefore, properly accounting for electricity trade and emission transfers can enhance the understanding of environmental impacts of electricity consumption at the global scale.

480 481

Supporting Information

482

Additional tables, data and figures.

483 484

Acknowledgements

485

Shen Qu thanks the support of the Dow Sustainability Fellows Program.

486 487

Reference

488 489 490 491 492 493 494

1. Williams, J. H.; DeBenedictis, A.; Ghanadan, R.; Mahone, A.; Moore, J.; Morrow, W. R.; Price, S.; Torn, M. S., The Technology Path to Deep Greenhouse Gas Emissions Cuts by 2050: The Pivotal Role of Electricity. Science 2012, 335, (6064), 53-59. 2. IEA, CO2 Emissions from Fuel Combustion 2016. IEA: 2016. 3. IEA, CO2 Emissions from Fuel Combustion. Organization for Economic Cooperation & Development, Stationery Office, The [Distributor]: Paris : London, 2015. 4. China’s State Council, Energy Conservation and Emissions Reduction Comprehensive

495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512

Work Plan for the 13th Five-Year Plan (2016–2020) Period. In China’s State Council, Beijing, 2016. 5. Liu, F.; Zhang, Q.; Tong, D.; Zheng, B.; Li, M.; Huo, H.; He, K. B., High-resolution inventory of technologies, activities, and emissions of coal-fired power plants in China from 1990 to 2010. Atmospheric Chemistry and Physics 2015, 15, (23), 13299-13317. 6. Wang, J.; Cai, B.; Zhang, L.; Cao, D.; Liu, L.; Zhou, Y.; Zhang, Z.; Xue, W., High resolution carbon dioxide emission gridded data for China derived from point sources. Environ Sci Technol 2014, 48, (12), 7085-7093. 7. Marriott, J.; Matthews, H. S., Environmental Effects of Interstate Power Trading on Electricity Consumption Mixes. Environ Sci Technol 2005, 39, (22), 8584-8590. 8. Weber, C. L.; Jaramillo, P.; Marriott, J.; Samaras, C., Life Cycle Assessment and Grid Electricity: What Do We Know and What Can We Know? Environ Sci Technol 2010, 44, (6), 18951901. 9. IEA, Electricity Information 2016. https://www.iea.org/statistics/relateddatabases/electricityinformation/, 2016. 10. China State Council Action Plan for Energy Development Strategy (2014-2020); 2014. 11. Kang, C.; Zhou, T.; Chen, Q.; Xu, Q.; Xia, Q.; Ji, Z., Carbon emission flow in networks. Sci Rep-Uk 2012, 2, 479. 20 ACS Paragon Plus Environment

Page 21 of 23

513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555


12. Song, R.; Zhu, J.; Hou, P.; Wang, H., Getting every ton of emissions right: An analysis of emission factors for purchased electricity in China. World Resources Institute, Working Paper. 2013. 13. Lindner, S.; Liu, Z.; Guan, D.; Geng, Y.; Li, X., CO2 emissions from China’s power sector at the provincial level: Consumption versus production perspectives. Renewable and Sustainable Energy Reviews 2013, 19, 164-172. 14. Bi, J.; Zhang, R.; Wang, H.; Liu, M.; Wu, Y., The benchmarks of carbon emissions and policy implications for China's cities: Case of Nanjing. Energ Policy 2011, 39, (9), 4785-4794. 15. Wang, H.; Zhang, R.; Liu, M.; Bi, J., The carbon emissions of Chinese cities. Atmos. Chem. Phys. 2012, 12, (14), 6197-6206. 16. Lin, J.; Liu, Y.; Meng, F.; Cui, S.; Xu, L., Using hybrid method to evaluate carbon footprint of Xiamen City, China. Energ Policy 2013, 58, 220-227. 17. Dhakal, S., Urban energy use and carbon emissions from cities in China and policy implications. Energ Policy 2009, 37, (11), 4208-4219. 18. Bai, H.; Zhang, Y.; Wang, H.; Huang, Y.; Xu, H., A Hybrid Method for Provincial Scale Energy-related Carbon Emission Allocation in China. Environ Sci Technol 2014, 48, (5), 25412550. 19. Peters, G. P.; Minx, J. C.; Weber, C. L.; Edenhofer, O., Growth in emission transfers via international trade from 1990 to 2008. Proceedings of the National Academy of Sciences 2011, 108, (21), 8903-8908. 20. Peters, G. P.; Hertwich, E. G., CO2 embodied in international trade with implications for global climate policy. Environ Sci Technol 2008, 42, (5), 1401-1407. 21. Malik, A.; Lan, J.; Lenzen, M., Trends in global greenhouse gas emissions from 1990 to 2010. Environ Sci Technol 2016, 50, (9), 4722-4730. 22. Ryan, N. A.; Johnson, J. X.; Keoleian, G. A., Comparative Assessment of Models and Methods To Calculate Grid Electricity Emissions. Environ Sci Technol 2016, 50, (17), 8937-8953. 23. Soimakallio, S.; Saikku, L., CO 2 emissions attributed to annual average electricity consumption in OECD (the Organisation for Economic Co-operation and Development) countries. Energy 2012, 38, (1), 13-20. 24. Zafirakis, D.; Chalvatzis, K. J.; Baiocchi, G., Embodied CO2 emissions and cross-border electricity trade in Europe: Rebalancing burden sharing with energy storage. Appl Energ 2015, 143, 283-300. 25. Colett, J. S.; Kelly, J. C.; Keoleian, G. A., Using Nested Average Electricity Allocation Protocols to Characterize Electrical Grids in Life Cycle Assessment. J Ind Ecol 2016, 20, (1), 29-41. 26. Ji, L.; Liang, S.; Qu, S.; Zhang, Y.; Xu, M.; Jia, X.; Jia, Y.; Niu, D.; Yuan, J.; Hou, Y.; Wang, H.; Chiu, A. S. F.; Hu, X., Greenhouse gas emission factors of purchased electricity from interconnected grids. Appl Energ 2015, 184, 751-758. 27. Qu, S.; Wang, H.; Liang, S.; Shapiro, A. M.; Suh, S.; Sheldon, S.; Zik, O.; Fang, H.; Xu, M., A Quasi-Input-Output model to improve the estimation of emission factors for purchased electricity from interconnected grids. Appl Energ 2017, 200, 249-259. 28. Kodra, E.; Sheldon, S.; Dolen, R.; Zik, O., The North American Electric Grid as an Exchange Network: An Approach for Evaluating Energy Resource Composition and Greenhouse Gas Mitigation. Environ Sci Technol 2015, 49, 13692-13698. 21 ACS Paragon Plus Environment


556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599

Page 22 of 23

29. Dietzenbacher, E., In Vindication of the Ghosh Model: A Reinterpretation as a Price Model. Journal of Regional Science 1997, 37, (4), 629-651. 30. Oosterhaven, J., Leontief versus Ghoshian Price and Quantity Models. Southern Economic Journal 1996, 62, (3), 750-759. 31. Suh, S., Theory of materials and energy flow analysis in ecology and economics. Ecological Modelling 2005, 189, (3–4), 251-269. 32. National Energy Administration, Provisions on Information Disclosure of Electric Power Enterprises. In 2011. 33. Chinese Electricity Council, Annual Compilation of Power Industry Statistics. 2014. 34. Zhang, C.; Zhong, L.; Liang, S.; Sanders, K. T.; Wang, J.; Xu, M., Virtual scarce water embodied in inter-provincial electricity transmission in China. Appl Energ 2017, 187, 438-448. 35. China Electric Power Committee, China Electric Power Yearbook 2014. China Electric Power Publishing House: Beijing, China 2014. 36. National Bureau of Statistics of China China Energy Statistical Yearbook; 2014. 37. Liu, Z.; Guan, D.; Wei, W.; Davis, S. J.; Ciais, P.; Bai, J.; Peng, S.; Zhang, Q.; Hubacek, K.; Marland, G.; Andres, R. J.; Crawford-Brown, D.; Lin, J.; Zhao, H.; Hong, C.; Boden, T. A.; Feng, K.; Peters, G. P.; Xi, F.; Liu, J.; Li, Y.; Zhao, Y.; Zeng, N.; He, K., Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature 2015, 524, (7565), 335-338. 38. Song, R.; Yang, S.; Sun, M., GHG protocol tool for energy consumption in china (Version 2.1). World Resources Institute (WRI) 2013. 39. Su, B.; Ang, B., Multi-region input–output analysis of CO 2 emissions embodied in trade: the feedback effects. Ecological Economics 2011, 71, 42-53. 40. Round, J. I., Feedback effects in interregional input-output models: What have we learned? 2001. 41. ZHU, J.; WANG, Y.; SONG, R., CLIMATE POLICY GREENHOUSE GAS IMPACT ASSESSMENT: A CASE STUDY OF BEIJING EMISSION TRADING SCHEME. 42. Standardization Administration of the People's Republic of China, General guidelines of the greenhouse gas emissions accounting and reporting for industrial enterprises. In 2015. 43. National Development and Reform Commission (NDRC), Average CO2 emission factors of China's sub-national grids, 2011-2012. In http://www.tanpaifang.com/tanjiliang/2014/0928/38588.html, 2013. 44. Fransen, T.; Song, R.; Stolle, F.; Henderson, G., A Closer Look at China's New Climate Plan (INDC). World Resources Institute, July 2015, 201, (5). 45. Swartz, J., China’s National Emissions Trading System. 2016. 46. Lo, A. Y., Carbon emissions trading in China. Nature Climate Change 2012, 2, (11), 765766. 47. Feng, K.; Davis, S. J.; Sun, L.; Li, X.; Guan, D.; Liu, W.; Liu, Z.; Hubacek, K., Outsourcing CO2 within China. Proceedings of the National Academy of Sciences 2013, 110, (28), 1165411659. 48. National development and Reform Commission (NDRC), Notice on Key Work for Launching National Carbon Emissions Trading Market. In http://www.ndrc.gov.cn/zcfb/zcfbtz/201601/t20160122_772123.html, 2016. 49. Ang, B. W.; Su, B., Carbon emission intensity in electricity production: A global analysis. Energ Policy 2016, 94, 56-63. 22 ACS Paragon Plus Environment

Page 23 of 23


600


CO2 Emissions Embodied in Interprovincial Electricity Transmissions

Recommend Documents