Do CO₂ emissions trading schemes deliver co-benefits? Evidence from Shanghai

Figures & data

Table 1. Policy Design Features of the Shanghai ETS.

Design features	Details
Identifying potential participants	Firms are capped if they meet the following thresholds: Emissions >20,000 tons CO2 in 2010 or 2011 for major industrial sectors; the threshold is >10,000 tons CO2 for non-industrial sectors. Total 197 firms were capped during 2013–2015 and 368 firms were capped in 2016.
Cap coverage	Firms with mandatory targets cover industrial sectors such as the electricity generation sector, steel and iron, non-ferrous metals, paper, rubber, ferrous metals, chemicals, chemical fibre, petrochemicals, textiles, building materials, etc. Some entities are from non-industrial sectors such as aviation, ports, railway, large commercial shops and hotels, etc.
Scale	Share of the cap coverage in the total emissions: about 57%.
Covered emissions	Direct CO2 emissions from industrial processes and energy consumption; indirect CO2 emissions from heat and electricity consumption.
Cap setting and allowance allocation	Intensity-based emission caps are assigned to participating firms, which can then buy more emission allowance from the ETS market. Cap-setting is based on the historical emissions, or the sectoral benchmark and activity level.
	Emission allowance allocation: auctioning + free allocation.
Restriction on offset credit usage	≤5% of initial allowances.
Enforcing compliance	Non-compliance incurs a fine of 50,000–100,000 yuan; failure to submit the emission report incurs a fine of 10,000–30,000 yuan; failure to accept verification as required incurs a fine of 30,000–50,000 yuan.
Calendar	At the beginning of year T, the regulated firms receive their emission allowances for year T. The regulated firms must ask a third party to verify their reports and then submit the verified emission reports of year (T-1) by 30th April. At the end of June in year T, Shanghai ETS-regulated firms must submit the allowances valid during year (T-1) to comply with their targets of year (T-1).

Note: information collected from Swartz (2013), Wu et al. (2014), Wu et al. (2016), Zhang et al. (2017), and relevant Chinese policy documents (see Table S2).

Table 2. Main regression model of CO₂ prices on SO₂ emissions.

	(1)	(2)	(3)
VARIABLES	Fixed effects	+time trend	+weather
ln(CO2 price)	−0.213***	−0.124**	−0.126**
	(0.064)	(0.059)	(0.060)
Smokestack fixed effects	Yes	Yes	Yes
Year and month fixed effects	Yes	Yes	Yes
Week-year polynomial	No	Yes	Yes
Weather controls	No	No	Yes

Notes: N=6,738. Dependent variable is log-transformed weekly SO₂ emissions. Week-year polynomial are time trends included until the quadratic term. Weather controls are temperature, wind speed, sea-level pressure, windspeed, and precipitation. All weather controls are included until the quadratic term. Standard errors are clustered at smokestack level and shown in parentheses;

*** p<0.01, ** p<0.05, * p<0.1.

Table 3. Heterogeneity analyses by sectors and time periods.

	(1)	(2)	(3)	(4)	(5)	(6)
VARIABLES	Power utilities	non-Power utilities	Ferrous metals	non-Ferrous metals	Q1 & Q2	Q3 & Q4
ln(CO2 price)	−0.074	−0.206***	−0.293**	−0.135*	−0.289***	−0.070
	(0.084)	(0.067)	(0.116)	(0.073)	(0.105)	(0.074)
Observations	4,354	2,384	1,039	1,345	3,209	3,525

Notes: Dependent variable is log-transformed weekly SO₂ emissions. The non-Ferrous metals sector in Column (4) does not include power utilities firms. Q1 in Column (5) refers to the first quarter of the year, and so on. All models are estimated with smokestack-fixed effects, year- and month-fixed effects, week-year polynomial, and weather controls. Week-year polynomials are time trends included until the quadratic term. Weather controls are temperature, wind speed, sea-level pressure, windspeed, and precipitation. All weather controls are included until the quadratic term. Standard errors are clustered at smokestack level and shown in parentheses;

*** p<0.01, ** p<0.05, * p<0.1.

Figure 1. Association between current Shanghai CO₂ prices and ETS firms’ SO₂ emissions at various lagged week.

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