Everywhere we look, companies are announcing public sustainability goals or what actions they’re taking to go green. Corporate sustainability reporting has gone mainstream over the last few decades, and we need look no further than CDP’s disclosures database from over 8,400 companies. Taking a peek beyond public disclosures, how are companies effectively selecting and implementing their strategies for decarbonization?
Introducing the Marginal Abatement Cost (MAC) curve. This financial tool has been used since the mid-1990s as an effective means of illustrating the relative cost effectiveness and scale of impact of each measure for reducing carbon emissions. The abatement cost curve was made famous in the last 15 years when McKinsey published their original MAC curve in 2007 (similar to the graph below). This tool helps regulators, companies and industries to have a practical conversation about macro-level carbon emissions and realistic order-of-magnitude costs associated with realistic abatement levels.
Looking at the MAC curve shown below, the vertical axis shows the cost of abatement for each opportunity (Dollars per ton of carbon dioxide equivalent) while the horizontal axis shows the abatement potential for each mitigation option (in GtCO2e). Each rectangle represents a different mitigation option with a different marginal abatement cost, ranked from the least to the most costly ones. The measures on the left side of the graph provide net dollars back to the company (aka life-time savings), while measures towards the right side require net capital investment. But beyond the financial analysis, this tool allows users to combine both abatement and net cost concepts that are essential to meaningful decision-making around the cost of reducing emissions.
Challenges and benefits of the MAC Curve
Individual companies can benefit greatly from building and refining their own MAC curves. Firstly, MAC curves can help a company understand its internal emissions abatement potential. For companies that have yet to set an ambitious public goal, a MAC curve provides a high level understanding of where abatement opportunities exist in their operations and their net impact.
Conversely, companies that have big, ambitious goals benefit from using MAC curves to understand the portfolio of measures for achieving the ambitious goal as well as help companies understand where gaps may exist in their decarbonization journey. Companies need to focus on the triple bottom line of doing well for the planet while maintaining (and ideally improving) profitability. The most cost effective measures are easy to identify on a MAC curve. However, MAC curves can be cumbersome to build, since it requires updated financial and environmental data. If there are constant updates to the data, the models need to be constantly updated to reflect new investments. These data collection and updates can be very siloed in large organizations and laborious to be done in excel spreadsheets.
Finally, MAC curves allow for multiple future scenario planning depending on an individual company’s time sensitivity for achieving its multi-year climate goal. Usually, MAC curves should be updated every calendar year, to reflect the company’s new investments and budgeting allocation. Also, projects need to be measured and verified as they are implemented. Having a dashboard for data consolidation on new and ongoing projects is essential to managing medium- and long-term climate goals.
Getting started and underlining opportunities
To get started building a MAC curve, companies can use sector level data. Scenario planning software or consulting teams exist to support this work. Once an organization has a high level understanding of the opportunities in its sector, the company can take steps to incorporate its own operational and market level data, so that the MAC curve can be as realistic as possible in representing abatement and implementation costs.
The biggest challenges to MAC curves include input data and evolving external conditions that change the cost for implementation. Companies building MAC curves need to mind the precision of input data and understand the margin of error. A concrete example is inputting projected implementation costs upfront to build a MAC curve. These are precise values that may not represent a high level of confidence that they will be the actual implementation costs given that material, regulatory, labor or other costs may change. Related to ongoing challenges to collecting data to measure a company’s current emissions and/or factoring in future emissions, the accuracy and ability to understand this data will impact the potential abatement opportunity of projects. Furthermore, external market conditions can have a drastic impact on estimated project cost effectiveness.
To make the case for MAC curve projects, it is important to help a company’s finance department and other decision makers understand the overarching emissions reduction goals that the company is also committed to achieving. The MAC curve will show projects with a positive net present value (NPV) alongside the opportunities that may have a negative NPV, and depending on the established abatement level and deadline to achieve the goal, decision makers may need to get comfortable with approving both types of projects to achieve the decarbonization goal. Furthermore, a company’s chosen discount rate will affect the cost-effectiveness of different abatement measures. Companies with high discount rates will be particularly punitive towards projects with high initial capital costs and long-term accrual of benefits.
Instead of building a MAC curve manually and then realizing the difficulty of updating it to reflect the latest evolving conditions, reach out to us at Sinai Technologies to support your dynamic MAC curves, sector-specific mitigation options, and climate-related forecasting needs. Our platform helps companies to mitigate climate change by enabling more intelligent carbon emission measurement, monitoring and risk management. Our goal is to provide the tools you need to make the best decisions around emissions possible as well as simplifying the process to incorporate your latest data into actionable insights. Schedule a demo with us today via https://www.sinaitechnologies.com/request-a-demo.
1. CDP. “What we do”. Retrieved from https://www.cdp.net/en/info/about-us/what-we-do.
2. Grubb, Michael et al. (January 1993). “The Costs of Limiting Fossil-Fuel CO2 Emissions: A Survey and Analysis”. Annual Review of Energy and the Environment, 18(1): 397 – 478. Retrieved from https://www.researchgate.net/publication/275069761_The_Costs_of_Limiting_Fossil-Fuel_CO2_Emissions_A_Survey_and_Analysis.
3. McKinsey & Company. (February 1, 2007). “A cost curve for greenhouse gas reduction”. Retrieved from https://www.mckinsey.com/business-functions/sustainability/our-insights/a-cost-curve-for-greenhouse-gas-reduction.