To inform and refine the parameters of the BeZero Ratings model, we have undertaken a number of deep dives into different offset project types. In the absence of a full dataset of carbon returns for projects, this process enables us to build a close substitute.
In this article, we take a look at a Chinese renewable energy project in detail, a bottom up process that follows on from our top down analysis of the market detailed in a previous blog post.
Project ID: GS10411/GS6753
Accreditor: Gold Standard
Sector: Energy industries (renewable — / non-renewable sources)
Project description: wind farm generating approximately 438,550 MWh annually to be sold to the North China Power Grid (NCPG).
Renewable infrastructure is cost-competitive and carbon credits make up a small fraction of the overall revenue (Sutter & Parreño 2007, Spalding-Fecher et al 2012, Cames et al 2016). There is further evidence of low additionality specifically for Chinese renewable energy offset projects (He & Morse (2010)). The penetration of renewables in China was 13% by 2010 (IRENA 2014), which would not meet the eligibility requirements set forth by the revised Gold Standard guidelines where the cut off for penetration percentage is 5%.
Inflated baselines lead to risks of over-crediting, depending on the methods used to determine the counterfactual (e.g. grid emissions factors, projected demand, growth, etc — Cames et al 2016).
There is evidence of increased energy consumption (and associated emissions) if funded in part by a levy on electricity consumption (Jarke & Perino 2015). In addition, for China more specifically, Zhang et al (2013) report that CO2 emission reductions due to increased renewables are offset annually by increased emissions in non-covered sectors through 2050. This is because the increased reliance on renewable energy can result in lower fossil fuel prices and consequently, lead to greater fuel demand in unregulated sectors. However, the project documentation indicates that consideration of leakage emissions have been omitted.
Permanence & Policy
Most countries have renewables targets and under current Chinese policies and investment patterns, the fraction of renewables in the country’s energy mix will rise to 16% by 2030 (IRENA 2014). The support of national policies and feed-in tariffs that support renewables is documented by Bogner & Schneider (2011), Zhang et al (2013), Liu (2014) and IRENA (2014). The World Bank Property Rights Index is used as a proxy for contract enforceability.
What Is Happening On The Ground?
- We estimate that 80% of wind energy in China did not need financing from offsets.
- For this project, offset revenue is likely to have contributed at most 7% of total revenue.
- We estimate that the fixed baseline grid intensity factor driving annual credit issuance is inflated by around 17% for the first crediting period.
A key differentiation between the BeZero Rating and the accreditation process is our grading of additionality. Whereas additionality is a binary test within most methodology processes, we assess the relative strength of a project’s additionality. There are two key factors in this assessment:
- What sources of revenue does the project generate or have available to it beyond carbon finance?
- To what extent can we find evidence of similar projects that went ahead without the need for carbon finance?
For renewable energy projects, the majority of revenue generated comes from selling energy to the grid. Typically, carbon finance makes up a small fraction of the revenue stream for these projects, making them less additional.
For this project in particular, we estimate the proportion of revenue generated by carbon finance by examining the data on electricity produced and number of credits issued (both available from the project documentation). To calculate the revenue from carbon finance, we based our calculation by applying a value of 2 USD per carbon credit. This assumption is generous given the price of renewable energy carbon credits was < 1.70 USD since 2018 (Ecosystem Marketplace ‘State of the Market’ report) and that a bulk of the credits issued (98.5%) were sold to the compliance market.
Based on the average cost of electricity for industrial consumers in China which is around 0.084 USD per kWh, it was estimated that the wind farm could sell its power to the regional grid at a price between 0.025 and 0.1 USD. For the Hebei region specifically, where the project is located, the price is at 0.095 per kWh (Yang 2019).
We estimated the contribution of carbon finance to the project’s revenue stream for the year 2019. We chose this year because 2019 was the only full year (365 days) for which energy data, credits issued and emission factor applied were all available within the project documentation and monitoring reports. The role of carbon finance was calculated for the three electricity price estimates — 0.025, 0.05 and 0.1 USD (Figure 3). The last of these three buckets covers a conservative estimate based on the regional price for the project. Regardless, even in the most conservative scenarios included in this study, the percentage of annual revenue from carbon credits is at most 7%.
Given that our initial assumption of 2 USD value per carbon credit is generous for renewable energy type carbon credits, we also calculated the contribution of carbon finance to the 2019 revenue stream of the project based on three assumptions of carbon credit price — 1, 1.5 and 2 USD per credit (Table 1).
We estimate that offsets financed at most 20% of wind energy generation in China between 2015 and 2018 (Figure 4). To do this, we acquire annual data for the wind sector from the Chinese Energy Statistical Yearbook (2019) and credit issuance data both from the Berkeley Voluntary Registry Offsets Database (accessed on May 11, 2021) and CDM (access on May 12, 2021). To calculate the electricity generation (MWh/yr) from Chinese wind carbon credit issuances, we apply a conservative emission factor of 0.555 based on the 2018 national data provided within the Climate Transparency Report (2019). Application of this emission factor in comparison to dynamic emission factor did not change the contribution of the VCM to Chinese wind energy generation by an order of magnitude. While each new project will have idiosyncratic factors, this analysis demonstrates the majority of wind energy generation did not need carbon credit financing.
Over the last two decades, the levelized cost of electricity (LCOE) for commissioned onshore wind projects in China decreased by approximately 73% (IRENA 2020). In fact, China achieved one of the most competitive LCOEs in 2019, with a weighted-average value of USD 0.054/kWh (IRENA 2020).
The falling costs of onshore wind infrastructure associated with technology improvements has led to the explosive growth in penetration of wind energy in China (Figure 5). These trends are accompanied by national policies and targets to increase the share of renewables in the energy mix and decrease reliance on fossil fuels. To accomplish this, China has spent 0.9% of its gross domestic product on the renewable energy sector (O’Meara 2020).
Baselines & Over-Crediting
The project uses a fixed grid intensity factor for the region from 2015, the most recent available at the time of validation and in line with the methodology requirements. Annual issuance is then calculated using this fixed emission factor multiplied by the amount of electricity generated by the plant.
However given the rising penetration of wind and broader renewables, a static grid intensity factor is unlikely to be reflective of reality. We estimate the relevant grid intensity factor over the crediting period in Figure 6, using data gathered from a range of sources (see footnote in figure). The application of a static emission factor for the entirety of a crediting period may not be appropriate because it doesn’t account for the dynamics of renewable energy infrastructure that are continually reducing grid emission factors.
Using the data in Figure 6, the trend in emission factors shows a 2% reduction every year. Extrapolating this from the 2015 emissions factor used to calculate the entire 2017–2023 crediting period would imply the emission factor is 15% over-estimated by the end of the crediting period.
The BeZero score listed (B-) did not consider the project specific data presented above as these were based on a series of conservative assumptions to illustrate the rationale behind the BeZero Rating for an active project. Our analysis on renewable energy projects could be improved through the application of dynamic grid emission factors, leakage accounting and consideration of national/local renewable installation trends.