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ABSTRACT
The New York Independent System Operator (NYISO) has developed a carbon-pricing proposal to reduce carbon intensive electricity generation in anticipation of future clean energy goals in the state. The proposed measure is a so-called ‘carbon adder’ on CO2 emissions from the power sector that targets the social cost of carbon amidst existing overlapping policies. The carbon adder is set as the difference between the targeted social cost of carbon and the prevailing RGGI price for CO2 emission allowances. We investigate the economic and environmental impacts from the imposition of a carbon adder on New York’s power sector. Our analysis indicates that the carbon adder gives the ‘right’ price signal for New York’s power generation to turn into a greener one and is shown to be more cost-effective than clean energy standards. Requirements for permit price floors in the RGGI market induces carbon permit retirements across RGGI states leading to small reductions in region- and country-wide emissions levels.The proposed border carbon adjustments on electricity trade are shown to further mitigate emission leakage.
Key policy insights
NYISO’s proposed carbon adder provides strong incentives for decarbonization and renewable power generation in the New York region.
The carbon adder is shown to be more cost-effective than New York's clean energy standard measures in achieving an equivalent level of emissions reductions. This is because the carbon adder targets energy carriers by their specific CO2 emissions while the clean energy standard implicitly targets energy carriers by their specific CO2 emissions while the clean energy standard implicitly provides a uniform subsidy to all clean energy carriers.
The existing RGGI price floor mechanism assures that NYISO’s unilateral use of carbon adder is environmentally effective by generating overall emission reductions in the RGGI region in order to maintain the existing price floor.
Border carbon adjustments on the embodied carbon of electricity imports to New York, and an emission containment reserve, which dynamically adjusts RGGI cap as a response to RGGI price change, will likely mitigate leakage further and improve competitiveness of the New York’s energy sector.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 As of 2016, the electricity sector accounted for more than 16% of New York State’s GHG emissions. See: https://www.eia.gov/environment/emissions/state/.
2 Estimates of the social cost of carbon are denoted in 2007 US dollars per metric ton of CO2. Over the past decade, the US Interagency Working Group has estimated the social cost of carbon that monetizes climate damages from carbon emissions, accounting for damages to human and ecosystem health, agricultural productivity, and property damages due to increased flood risk. Its 2016 report provides annual values for the social cost of carbon over the period from 2010 to 2050 using alternative discount rates of 2.5, 3 and 5 percent. The NYISO adopted the social cost of carbon as estimated with a 3 percent discount rate, starting at $42 per metric ton of CO2 in 2020, and increasing up to $69 per metric ton of CO2 by 2050.
3 The program covers electricity production in New York, Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, Rhode Island, and Vermont.
4 The short ton is a unit of weight (most commonly used in the US) equal to 2,000 pounds (907.18474 kg). If not explicitly labelled as short ton, we refer to a ton as a metric ton. Note that 1 short ton is approximately equal to 0.9 metric tons.
5 A mix of differentiated policy instruments may be justified if there are multiple policy objectives, such as social or technology-related criteria that may conflict with narrowly defined efficiency considerations (Tinbergen, Citation1952). The reasoning behind could stem from the pre-existence of multiple market imperfections such as asymmetric information, market power, initial tax distortions, external knowledge spillovers, transaction costs, etc. For example, sector-specific differences in transaction costs have served as an argument for limiting the EU ETS to large-scale stationary industrial combustion plants while applying efficiency standards for CO2 reduction to the building and traffic sectors. Varma (Citation2003) concludes that the simultaneous adoption of a climate change levy (CCL), which is a downstream tax charged on industrial and commercial uses of energy, and the EU ETS is necessary for the United Kingdom to fulfill its emission reduction commitments in a cost-effective and flexible way. The author proposes emission trading to cover production sectors, while additional taxes like CCL to focus more on smaller or mobile sources whose emissions are difficult to monitor.
6 See Shawhan et al. (Citation2019) for a similar quantitative partial equilibrium analysis using the Engineering, Economic, and Environmental Electricity Simulation Tool (E4ST) electricity model. The authors study this policy environment in a detailed bottom up model that explicitly captures margins at the electricity generating unit level. Our own quantitative analysis in Section 4 relies on a top-down aggregated economy-wide framework that allows us to capture welfare impacts due to income effects and changes in outside of directly affected markets.
7 Without loss of generality, we can assume that there is no markup between wholesale supply and retail demand prices.
8 Using data from the Energy Information Administration (EIA) and performing a literature review for extractive sectors, Marten et al. (Citation2019b) find that the return to the natural resource, relative to man-made capital is 25% for oil and natural gas extraction and 40% for coal mining.
9 Notably, the model does incorporate structural unemployment through an empirical relationship known as a wage curve (e.g. Blanchflower & Oswald, Citation1994). The wage curve is a reduced-form function which relates increases in the real wage rate to decreases in structural unemployment. In our model parametrization, we adopt an estimate of -0.1 for the elasticity of the real wage with respect to the unemployment rate.
10 In international trade, the US is assumed to be a price-taker.
11 Based on empirical evidence from complementary data sources such as GTAP (Aguiar et al., Citation2016) we assume that nationally and regionally produced goods are more substitutable relative to foreign imports.
12 This process is described in detail in Rutherford and Schreiber (Citation2019).
13 States are aggregated to model regions and assigned to electricity markets (CAISO, MISO, ISONE, NYISO, NW, PJM, SE, SW, SPP, TEXAS). Note that if multiple electricity markets span a given model region, more than one market is used to constrain the optimization routine. The routine also imposes a lower bound on regional electricity trades based on the level of aggregate supply in origin regions and demand in the destination region. Note that in our estimation of bilateral electricity trade, the level of national exports and imports is adjusted to account for aggregation bias.
14 Connecticut, Delaware, Massachusetts, Maryland, Maine, New Hampshire, Rhode Island, Vermont.
15 PJM (Pennsylvania-New Jersey-Maryland) interconnection includes District of Columbia, Illinois, Indiana, Kentucky, Michigan, North Carolina, New Jersey, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia.
16 We define energy/emission intensive sectors as those with high levels of embodied carbon (>0.5 kilograms per dollar) as reported in Rutherford and Schreiber (Citation2019).
17 These remaining sectors include wholesale and retail trade and public administration.
18 Note that these shares represent recalibrated measures which reconciles BEA input output data with SEDS electricity production estimates and therefore will be slightly different than what is reported in the gross state product measures.
19 See: https://www.eia.gov/todayinenergy/detail.php?id=30712. The country wide average in our constructed dataset is 294 tons per million dollars of GDP.
20 Power technologies are represented by fuel types and reflect the used fuel types as reported in SEDS. Small generation totals are filtered to zero (oil-based production in New York) to avoid numerical issues in the model. While we report no oil-based power generation in the state of New York for 2016, some power plants are dual fuel based. Given our model structure, we are unable to capture potential fuel switching to an unused fuel type for these types of plants.
21 Notably, the prior renewable portfolio standard in New York ended in 2015. Therefore, we do not explicitly model this requirement in the baseline calculation as it is already reflected in the composition of the electricity generation sector in the underlying data.
22 From here to the end of the analysis, any reference to RGGI states does not include New York. New York is treated as separated as described in the data section.
23 In reality, the adder proposal applies only to generators greater than 25 MW. We are unable to capture this distinction in our dataset and therefore the adder is applied to the entire electricity sector.
24 There are various ways to revenue recycling, such as returning revenue to households, load-serving entities, etc. Each of these will give rise to different price, resource utilization and leakage impacts. In our analysis, we use the recycling scheme where carbon adder revenue is transferred back to households in a lump-sum fashion.
25 To capture a prevailing price, or maintain a price floor, the quantity of emissions in the market must be adjusted to reflect changes in the demand for permits by the electricity generation sector in the model. If this constraint were not imposed in the model, then the RGGI price would fall to zero, and the constraint, or emissions cap, would be non-binding.
26 This does not include co-pollutants of electricity generation. Local levels of ambient air pollution could change because of this policy. This is beyond the scope of the current study.
27 Note, however, that given the limitations discussed in our modeling framework, we are unable to capture the welfare effects of a binding clean energy standard and a non-zero carbon adder. These simulations assess the costs of using a clean energy standard to achieve a given level of emissions reductions with the adder set to zero.
28 We operationalize this by endogenizing taxes/subsidies on dirty/clean electricity producers to achieve the emissions target set by the carbon adder scenarios. As noted above, we do not include nuclear in this standard setting process to prevent artificially inflating its influence on our policy outcomes given future expected retirements before the 2030 standard kicks in.
29 We only include border cost adjustments on intra-national trade of electricity into New York. We refrain from including foreign imports (e.g. from Canada) in this calculation due to a lack of information on transboundary electricity trade that is inclusive of embodied carbon estimates. However, this electricity is most likely generated from hydro technologies, so we expect that it would not impact the results to a large degree.
30 Letting η denote the assumed supply elasticity and θ be the fixed factor value share, we calibrate the substitution elasticity, σ, to be equal to: XXXX.