California and Guangdong: a Tale of Two Cap-And-Trade Programs

California and Guangdong: A tale of two cap-and-trade programs

Pu Wang Cheng-Kuan Lin Yi-Hua Wu Guangdong: California: • Largest province in population and GDP • Largest state in population and GDP • Pop (2015): 108.49 million • Pop (2015): 39,14 million • GDP (2015): $1.07 trillion • GDP (2015): $2.46 trillion • Area: 69,410 sq mi • Area: 163,696 sq mi • Most progressive province in China • Most progressive state in the U.S. • Hub of high tech industries in China: • Hub of IT industries in the U.S.: Tencent (WeChat), Huawei, Foxconn… Google, Apple, Tesla… • Leader in low-carbon policy: • Leader in low-carbon policy: Guangzhou and Shenzhen to peak CO2 emission reduction goals in 2020 emission by 2020 and 2022. and 2050 • Launched pilot emission trading • Launched cap-and-trade program in system(ETS) in 2013 2013 Research ideas:

• A comparison between the design and implementation of Guangdong and California’s cap-and-trade programs; • Using model simulations to examine how emissions in different sectors were affected by the C/Ts; • How the different socioeconomic settings and policy designs influence their effectiveness; • California’s program is considered as the best designed of its kind; find the gap between Guangdong and CA could help improve C/T in China. Differences in economic structure

372Mton CO2 (2014) Wholesale, Retail CA GD 444Mton CO2 (2015) Wholesale, Trade and Agriculture Agriculture Retail Trade Catering Services 1% 1% and Catering 1% Services Buildings 3% Buildings 7% 8% Transportation Electricity and and heat imports communication 10% 14% Transportation and Petroleum Primary metals communication Processing 8% 44% 1% Electricity and heat in State 17% Glass and Electricity and cement heat in State 11% 50% Petroleum Glass and cement Processing 1% 8% Oil and Gas Primary metals Extraction 0% 5% Legislation

California Guangdong • In June 2005, Governor • The National Development and Schwarzenegger signed Executive Reform Commission (NDRC) Order S305, requiring the state published “The notice of to reduce its greenhouse gas developing carbon emission (GHG) emissions levels to 2000 trading pilot programs” in 2011. levels by 2010, to 1990 levels by • ETS in Guangdong is one of the 2020, and to a level 80% below seven pilot programs in order to 1990 levels by 2050. provide lessons to future national • AB 32, or the California Global program. Warming Solutions Act of 2006, gave authority to California Air • Subsequently, Guangdong DRC Resources Board (ARB) to published “Carbon emissions develop regulations and market management interim measures mechanisms to reduce GHG to for Guangdong Province”. 1990 levels by 2020. • Lack of detailed scrutiny, policy certainty, and support for enforcement. Seven ETS pilot programs in China

Beijing

Tianjin

Shanghai

Chongqing

Hubei Guangdong Shenzhen CA Complementary policies GD Complementary policies

• From 2011 to 2015, China implemented the “ten thousand firms energy saving and low-carbon action” program. • The overall goal was to reduce energy consumption per unit of GDP by 16%, and reduce CO2 emission per unit of GDP by 17%. • A command-and-control approach: the participating firms were required to improve their energy efficiency through upgrading technologies, optimizing production processes, and shutting down inefficient facilities. Cap setting

California Guangdong • Returning to 1990 level by 2020 • No explicit cap or target. (431 million ton) • A bottom-up approach: cap is • The 2014 level was 441 million calculated from the sum of ton. facility-level data. • No specific future trajectories; • Cap decreases 2% annually from likely to adjust the cap according 2012-14, 3% annually thereafter. to economic conditions. • Energy intensity target: reduce energy consumption per GDP unit by 18% between 2010-15. • Carbon intensity target: reduce CO2 emission per GDP unit by 19.5% between 2010-15. CA emission reduction targets Coverage

California Guangdong • Covers 7 types of GHG • Only CO2 is covered • Program covers about 450 • Program covers 193 entities entities (2014) • Starts in 2013 for electricity generators and large industrial • Covered 4 industries: facilities emitting 25,000 electricity generation, MTCO2e or more annually petrochemical(refinery), • Starts in 2015 for distributors cement, Steel; The threshold of transportation, natural gas, is 20,000 ton annually. and other fuels • Transportation not covered. • Imported electricity is • Imported electricity not covered. covered. Allowance allocation

CA GD • Free allocation early in the • A combination of benchmarking program, transitions to more (primary), grandfathering, and auction later in program auctioning • Allocation of allowances for most • Auction: 5% in power sector, 3% industrial sectors is set at about in other sectors; the share of 90 percent of average emissions, auctioning should increase based on benchmarks that overtime. reward efficient facilities • For most industrial sectors, distribution of allowances is • Allowances are updated annually. updated annually according to the production at each facility • Offset credits: Allowed for up to • Offset credits: Allowed for up to 10 percent of a facility’s 8 percent of a facility’s compliance obligation compliance obligation Data

California Guangdong and Jiangsu

GDP 2000-2014, 9 sectors 2000-2015, 9 sectors

Energy consumption 2000-2014, 29 sectors 2000-2015, 29 sectors

2000-2014, 29 sectors, published 2000-2015, 29 sectors, calculated CO2 emissions by ARB from energy consumption data

Guangdong: List of the names of Emissions from each covered Facility level data the firms covered by cap-and- facility trade

Energy balance sheets Imports data available 2000-2014 （imports and exports） Trends in CO2 emissions

MT CO2 800

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500

400

300

200

100

0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

CA GD JS Generalized mixed-effects model

• estimate the carbon emission and carbon intensity before and after implementing cap-and trade, stratified by different industrial sectors. • We performed the PROC MIXED procedure using SAS version 9.4 (SAS Institute, Cary, NC, US) to estimate the effect of selected factors on carbon dioxide emission.

• E[푌푖푗|푋푖푗] = 훽0 + 훽1 ∗ 퐶푎푝&푇푟푎푑푒푖푗 + 훽2 ∗ 푠푡푎푡푒푖 + 훽3 ∗ 푦푒푎푟푖 + 훽4 ∗ 푠푡푎푡푒푖 ∗ 푦푒푎푟푖 + +훽5 ∗ 퐼(푆푒푐푡표푟)푖 + 훽6 ∗ 퐺퐷푃푖 + 훽7 ∗ 푠푡푎푡푒푖 ∗ 퐼(푆푒푐푡표푟)푖 ∗ 퐶푎푝&푇푟푎푑푒푖푗

• i: industry sectors; j: time in years since the baseline year (from 1 to 6); • E[Y_ij |X_ij]: the expected carbon dioxide emission conditioned on covariates X;

• β0: the intercept for the effect; β1 to β6 are estimated coefficients for the marginal effects on the emission derived from maximum likelihood method. • Cap&Trade are dummy variable, state are categorical variable for the three states/provinces, and I(Sector) is indicator variable for the six sectors. • Similar model with outcome as carbon intensity (KgCO2/GDP) is expressed as following:

• E[휆푖푗|푋푖푗] = 훽0 + 훽1 ∗ 퐶푎푝&푇푟푎푑푒푖푗 + 훽2 ∗ 푠푡푎푡푒푖 + 훽3 ∗ 푦푒푎푟푖 + 훽4 ∗ 푠푡푎푡푒푖 ∗ 푦푒푎푟푖 + +훽5 ∗ 퐼(푆푒푐푡표푟)푖 + 훽6 ∗ 푠푡푎푡푒푖 ∗ 퐼(푆푒푐푡표푟)푖 ∗ 퐶푎푝&푇푟푎푑푒푖푗

• Where E[λ_ij |X_ij] denotes the expected carbon intensity conditioned on covariates X. Solution for Fixed Effects Effect state ct sectorn Estimate Standard t Value Pr > |t| Error Intercept -48.7684 12.5163 -3.90 0.0002 ct 1 -6.6692 13.5532 -0.49 0.6241 ct 0 0 . . . GDP_mil 0.000471 0.000107 4.43 <.0001 yrp 0.08729 1.7698 0.05 0.9608 yrp*state CA -5.4866 2.6336 -2.08 0.0407 yrp*state GD -4.9747 2.6330 -1.89 0.0628 yrp*state JS 0 . . . state*ct*sectorn CA 1 1 152.75 45.4345 3.36 0.0012 state*ct*sectorn CA 1 2 -292.31 45.1333 -6.48 <.0001 state*ct*sectorn CA 1 3 137.67 41.8532 3.29 0.0015 state*ct*sectorn CA 1 4 181.20 27.3039 6.64 <.0001 state*ct*sectorn CA 1 5 -100.71 27.0329 -3.73 0.0004 state*ct*sectorn CA 1 9 1.1793 18.9168 0.06 0.9505 state*ct*sectorn CA 0 1 132.65 37.7246 3.52 0.0008 state*ct*sectorn CA 0 2 -294.32 40.3577 -7.29 <.0001 state*ct*sectorn CA 0 3 120.07 34.3428 3.50 0.0008 state*ct*sectorn CA 0 4 177.91 20.2524 8.78 <.0001 state*ct*sectorn CA 0 5 -85.3285 19.1354 -4.46 <.0001 state*ct*sectorn CA 0 9 0 . . . state*ct*sectorn GD 1 1 2.8302 16.2562 0.17 0.8623 state*ct*sectorn GD 1 2 -284.71 15.4725 -18.40 <.0001 state*ct*sectorn GD 1 3 12.1169 16.7617 0.72 0.4720 state*ct*sectorn GD 1 4 28.1017 15.9431 1.76 0.0821 state*ct*sectorn GD 1 5 0.1707 15.3621 0.01 0.9912 state*ct*sectorn GD 1 9 0 . . . state*ct*sectorn GD 0 1 -14.6268 12.0679 -1.21 0.2294 state*ct*sectorn GD 0 2 -235.68 12.0611 -19.54 <.0001 state*ct*sectorn GD 0 3 -5.5244 12.2152 -0.45 0.6524 state*ct*sectorn GD 0 4 5.8596 12.0332 0.49 0.6277 state*ct*sectorn GD 0 5 -10.3111 12.0573 -0.86 0.3952 state*ct*sectorn GD 0 9 0 . . . Solution for Fixed Effects Effect state ct sectorn Estimate Standard t Value Pr > |t| Error Intercept 0.1725 0.02520 6.84 <.0001 ct 1 0.07518 0.04788 1.57 0.1206 ct 0 0 . . . yrp -0.01316 0.004620 -2.85 0.0057 yrp*state CA 0.003477 0.009449 0.37 0.7139 yrp*state GD -0.02786 0.009449 -2.95 0.0043 yrp*state JS 0 . . . state*ct*sectorn CA 1 1 -0.04683 0.07302 -0.64 0.5232 state*ct*sectorn CA 1 2 -1.2686 0.07302 -17.37 <.0001 state*ct*sectorn CA 1 3 -0.01771 0.07302 -0.24 0.8090 state*ct*sectorn CA 1 4 -0.3167 0.07302 -4.34 <.0001 state*ct*sectorn CA 1 5 -0.01113 0.07302 -0.15 0.8793 state*ct*sectorn CA 1 9 -0.05934 0.06771 -0.88 0.3836 state*ct*sectorn CA 0 1 -0.00478 0.04322 -0.11 0.9122 state*ct*sectorn CA 0 2 -1.1539 0.04322 -26.70 <.0001 state*ct*sectorn CA 0 3 0.02318 0.04322 0.54 0.5934 state*ct*sectorn CA 0 4 -0.1323 0.04322 -3.06 0.0031 state*ct*sectorn CA 0 5 0.03568 0.04322 0.83 0.4117 state*ct*sectorn CA 0 9 0 . . . state*ct*sectorn GD 1 1 -0.1178 0.05467 -2.16 0.0344 state*ct*sectorn GD 1 2 -0.9720 0.05467 -17.78 <.0001 state*ct*sectorn GD 1 3 -0.03150 0.05467 -0.58 0.5662 state*ct*sectorn GD 1 4 0.4100 0.05467 7.50 <.0001 state*ct*sectorn GD 1 5 0.01420 0.05467 0.26 0.7957 state*ct*sectorn GD 1 9 0 . . . state*ct*sectorn GD 0 1 -0.1525 0.04322 -3.53 0.0007 state*ct*sectorn GD 0 2 -0.7454 0.04322 -17.25 <.0001 state*ct*sectorn GD 0 3 -0.06158 0.04322 -1.42 0.1583 state*ct*sectorn GD 0 4 0.6494 0.04322 15.02 <.0001 state*ct*sectorn GD 0 5 -0.02937 0.04322 -0.68 0.4988 state*ct*sectorn GD 0 9 0 . . . Industry

Emission reduction after 2 years of cap-and-trade

Emissions Carbon intensity Actual BAU Actual BAU CA -3.30% 0.47% -11.93% -2.65% GD -17.05% 6.61% -26.27% -3.40% JS 9.22% 0.49% -0.52% -0.17% Transportation

Emission reduction after 2 years of cap-and-trade

Emissions Carbon intensity Actual BAU Actual BAU CA 0.17% -0.83% -12.60% -11.94% GD 14.42% 5.68% -4.27% -6.60% JS 14.17% 4.82% 2.35% -2.05% 372Mton CO2 (2014) Wholesale, Retail CA GD 444Mton CO2 (2015) Wholesale, Trade and Agriculture Agriculture Retail Trade Catering Services 1% 1% and Catering 1% Services Buildings 3% Buildings 7% 8% Transportation Electricity and and heat imports communication 10% 14% Transportation and Petroleum Primary metals communication Processing 8% 44% 1% Electricity and heat in State 17% Glass and Electricity and cement heat in State 11% 50% Petroleum Glass and cement Processing 1% 8% Oil and Gas Primary metals Extraction 0% 5% Other sectors

Emission reduction after 2 years of cap-and-trade

Emissions Carbon intensity Actual BAU Actual BAU CA -11.46% -15.79% GD 21.27% -3.00% JS 11.29% -18.75% Principal component analysis

• We adopt principal component analysis to forecast CO2 emissions for 29 sectors in CA, GD, and JS, respectively. • We divide data sets into two categories: the first contains economic variables, and the second includes CO2 emissions of detail industrial sectors. • The economic variables include value added output of each industrial sector in the i region – where i denotes California, JS, and GD. The CO2 emissions variables include emissions from each of the 29 sectors. • Let X_(i,t)^ECON denote a vector of value added output in region i at period t, and Y_(i,t)^CO2 denote a vector of CO2 emissions in region i at period t: 퐸퐶푂푁 • 푋푖,푡 = 푥푖,1푡, 푥푖,2푡, … , 푥푖,푛푡 • And 퐶푂2 • 푌푖,푡 = [푦푖,1푡, 푦푖,2푡, … , 푦푖,푚푡] • where 푛 is the number of value added output in region 푖, and 푚 is the number of emission sources, and 푡 is the sample period (푡 = 1, … , 푇). 1. Use principal component analysis to estimate factors from economic variables and CO2 emissions, respectively. 퐸퐶푂푁 • We follow the principal component analysis of Stock and Watson (2002). Let 퐹푡 퐸퐶푂푁 퐶푂2 퐶푂2 denote the factor for 푋푖,푡 , and 퐹푡 denote the factor for 푌푖,푡 . The factor model is

퐸푐표푛 퐸푐표푛 푥 • 푥푖,푗푡 = 퐹푡 휆푗 + 푒푗푡 퐶푂2 퐶푂2 푦 • 푦푖,푗푡 = 퐹푡 휆푗 + 푒푗푡

푥 푦 where 푗 = 1, … , 푛, and 푖 = 1, … , 푚. 푒푗푡 and 푒푗푡 are idiosyncratic errors for 푥푖,푗푡 and 퐸푐표푛 퐸푐표푛 푦푖,푗푡,respectively. The objective function for the estimation of 퐹푡 and 휆푗 is

1 2 • min 푛 푇 푥 − 퐹퐸푐표푛휆퐸푐표푛 퐸푐표푛 퐸푐표푛 푁푇 푗=1 푖=1 푖,푗푡 푡 푗 퐹푡 ,휆푗

퐶푂2 퐶푂2 and that for 퐹푡 and 휆푗 is

1 2 • min 푚 푇 푦 − 퐹퐶푂2휆퐶푂2 퐶푂2 퐶푂2 푁푇 푗=1 푖=1 푖,푗푡 푡 푗 퐹푡 ,휆푗

퐸푐표푛 퐸푐표푛 퐶푂2 퐶푂2 • The estimation strategy for 퐹푡 and 휆푗 as well as 퐹푡 and 휆푗 can be found in Bai and Ng (2002). 2. Forecast the future trend of factors using the vector autoregressive (VAR) model.

• Write the VAR model as:

• 퐹푡 = 푐 + 퐹푡−1퐵 + 푈푡

퐶푂2 퐸퐶푂푁 • where 퐹푡 ≡ 퐹푡 , 퐹푡 – the vector of estimated factors, 푐 is a constant term, and 퐵 is a parameter matrix. The lag order of VAR is set to be one, due to the short sample period. We can forecast the future trend of factors using the equation above for periods 푡 = 푇 + 1, … , 푇 + ℎ. 3. Use the future trend of factors to forecast the CO2 emissions of detail industrial sector • We estimate the relationship between emission of each sector and factors using:

• 푦푖,푗푡 = c + 퐹푡퐴 + ut

• where 퐴 is a parameter matrix and ut is measurement error. Once we have estimated c and 퐴 , we can estimate the future movements of 푦푖,푗푡 for period 푡 = 푇 + 1, … , 푇 + ℎ, given the forecast of 퐹푡. 31 30 29 28 27 Two years after Cap-and-trade 26 compared to BAU: 25 CA: -3.31% 24 GD: -13.5% 2000 2002 2004 2006 2008 2010 2012 2014

CA_Petroleum Processing Mean CI CI

8 7 6 5 4 3 2 1 0 2000 2002 2004 2006 2008 2010 2012 2014

GD_Petroleum Processing Mean CI CI 6

2 Two years after Cap-and-trade

1 compared to BAU:

0 CA: 14.6% 2000 2002 2004 2006 2008 2010 2012 2014 GD: -10.28%

CA_Glass and cement Mean CI CI

70 60 50 40 30 20 10 0 2000 2002 2004 2006 2008 2010 2012 2014

GD_Glass and cement Mean CI CI 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 Two years after Cap-and-trade 0.1 compared to BAU: 0.0 2000 2002 2004 2006 2008 2010 2012 2014 CA: 12.11% GD: -21.84% CA_Primary metals Mean CI CI

0 2000 2002 2004 2006 2008 2010 2012 2014

GD_Primary metals Mean CI CI 80 70 60 50 40 30 20 Two years after Cap-and-trade 10 compared to BAU: 0 2000 2002 2004 2006 2008 2010 2012 2014 CA: -2.2% GD: -21.51% CA_Electricity and heat in State Mean CI CI

350 300 250 200 150 100 50 0 2000 2002 2004 2006 2008 2010 2012 2014

GD_Electricity and heat Mean CI CI 70 60 50 40 30 20 Two years after Cap-and-trade 10 compared to BAU: 0 CA: -4.32% 2000 2002 2004 2006 2008 2010 2012 2014 GD: N/A

CA_Electricity and heat imports Mean CI CI

2013 2014 2015 GD import 1175 1709 1426 185 180 175 170 165 160 155 150 Two years after Cap-and-trade 145 compared to BAU: 140 2000 2002 2004 2006 2008 2010 2012 2014 CA: 1.16% GD: 11.16% CA_Transportation and communication Mean CI CI

70 60 50 40 30 20 10 0 2000 2002 2004 2006 2008 2010 2012 2014

GD_Transportation and communication Mean CI CI 34 33 32 31 30 29 28 Two years after Cap-and-trade 27 26 compared to BAU: 25 CA: -18.58% 2000 2002 2004 2006 2008 2010 2012 2014 GD: 16.05%

CA_Buildings Mean CI CI

40 35 30 25 20 15 10 5 0 2000 2002 2004 2006 2008 2010 2012 2014

GD_Buildings Mean CI CI Summary of PCA results

• All GD’s covered heavy industrial sectors had dramatic decrease in emissions: 10-20%. This would make the cap unbinding, so it was more likely to be caused by economic slowdown. • CA’s heavy industries were relatively small, and some of them had noticeable increase in emissions. But overall the C/T achieved its reductions, mostly from imported electricity. • Emissions from transportation were not affected in either CA or GD—covering refinery is not sufficient; • GD imported electricity increased, while CA’s decreased: the importance to address leakage in electricity grids. • Emissions from buildings: CA decreased, while GD increased: indicating GD’s trend to transition from manufacturing economy to service economy. Conclusions • Two years after implementation of C/T, both CA and GD experienced significant emission reduction, but the patterns are different; • Emissions in all heavy industries in GD dropped dramatically, but not true for heavy industries in CA; • CA emission reduction mostly comes from electricity, particularly imported electricity • CA and GD face different types of challenges in emission reduction; • CA is a relatively stable economy  smooth emissions trend, easier for cap setting. • GD is a transitioning economy; its heavy industries are susceptible to economic fluctuations  cap setting is difficult; • GD: emissions in heavy industries are declining overall, but emissions from transportation, residential, and commercial activities are increasing: C/T is not sufficient to address these sources. Conclusions (2)

• The importance to tailor C/T system for the specific socioeconomic conditions • GD’s current system almost exactly copied the designing rules of EU and CA • Other pilot programs, most importantly, the national C/T program, are likely to copy the western-style program design • Lessons China should learn: emission dataset, transparent rules, legislation support, enforcement… • Changes China needs to make: coverage, allowance allocation (equity concerns, declining industries), trading rules (State owned companies), complementary polices (transportation, buildings, electricity system) • It is still too soon to evaluate the effectiveness of the two programs • CA: transportation started to be covered in 2015 • GD: economic recovery in 2016 would change the emissions trend; • a longer time series data would allow separation of the effects of economic change and C/T.